From 8591a4fd030a246ff1cb880c404962f7b7eb9804 Mon Sep 17 00:00:00 2001
From: dplynn <122953195+dplynn@users.noreply.github.com>
Date: Thu, 12 Dec 2024 22:55:36 -0600
Subject: [PATCH 1/6] Graduate anal??

---
 Lab7Cook.ipynb | 28 ++++++++++++++++++++++++++++
 1 file changed, 28 insertions(+)

diff --git a/Lab7Cook.ipynb b/Lab7Cook.ipynb
index 90f9839..63d13d4 100644
--- a/Lab7Cook.ipynb
+++ b/Lab7Cook.ipynb
@@ -2672,6 +2672,16 @@
     "plot_history(history1, title='Model with Glove embeddings')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "da7249c3",
+   "metadata": {},
+   "source": [
+    "As can be seen below, the Glove Model does an overall excellent job classifying the tweets correctly, with 2204 correct positives and 4475 correct negatives.  However, this benchmark still falls below the Transformer model with 2 multi-heads, as the Glove model has nearly twice as many false positives and false negatives (323 and 464 compared to 247 and 247 respectively).  As can be seen in the graphs, the model converges around 13 epochs, as the validation accuracy and validation loss appear to plateau.  However, the training accuracy and training loss continue to increase marginally to peak at an accuracy of about 0.98 based on the graph.\n",
+    "\n",
+    "As can be seen in the classification report below, the precision for positive tweets is slightly less than that of negative tweets, with a score of 0.87 compared to 0.91.  This is consistent with the rate of false positives shown in the confusion matrix, along with the fact that there are more negative tweets than positive tweets.  The relatively high scores of accuracy, macro average, and weighted average further contribute to the fact that this is a good classification model.  However, it still falls short of the marks achieved by the Transformer model.\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 43,
@@ -2905,6 +2915,16 @@
     "plot_history(history2, title='Model with ConceptNet Numberbatch embeddings')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "93a9e95c",
+   "metadata": {},
+   "source": [
+    "Similarly to the Glove Model, the ConceptNet Model performs very well, albeit still below the mark of the Transformer model previously discussed.  2258 tweets were correctly identified as positive and 4400 as negative, with the positive rate slightly higher than Glove, but the negative rate slightly lower.  Again, the model falls short of the 247 false positives and negatives from the Transformer model, and has 398 false positives and 410 false negatives.  The false positive rate is slightly higher than that of the Glove model, a sign that the Glove model may be the better model.  The graphs demonstrate a convergence around 8 epochs for the validation accuracy and validation loss, but the training accuracy and training loss continue to increase as the epochs increase.  These values never plateau to the degree that the Glove model does.\n",
+    "\n",
+    "As can be seen in the classification report below, the ConceptNet Model shows very similar results to the Glove model, with only marginal differences in the precision, recall, and F1 scores of all of the values.  This makes sense, given that the confusion matrix was extremely similar to that of the Glove model.\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 47,
@@ -2930,6 +2950,14 @@
    "source": [
     "print(classification_report(test_df['labels'], y_pred_binary, target_names=['Positive', 'Negative']))"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b8fb694",
+   "metadata": {},
+   "source": [
+    "There isn't much difference between the pre-trained ConceptNet Numberbatch embedding and the pre-trained GloVe models for our specific application, as they performed extremely similarly.  I would argue that the pre-trained GloVe model is likely better for this purpose, though, as it tends to capture statistical information about word co-occurrences better, a useful tool for sentiment analysis.  With that being said, the inability of GloVe to take into account contextual semantic understandings of words makes it difficult for some classifications.  There doesn't appear to be much of a difference between the models for our purpose, though, and both models fall below the previously used models, particularly the Transformer model with 2 multi-heads."
+   ]
   }
  ],
  "metadata": {

From ee56b2bb7b2036d2d75cf0d6a844683f568ce2a7 Mon Sep 17 00:00:00 2001
From: dplynn <122953195+dplynn@users.noreply.github.com>
Date: Thu, 12 Dec 2024 22:58:49 -0600
Subject: [PATCH 2/6] Perhaps

---
 Lab7Cook.ipynb | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/Lab7Cook.ipynb b/Lab7Cook.ipynb
index 82be704..cb1e16e 100644
--- a/Lab7Cook.ipynb
+++ b/Lab7Cook.ipynb
@@ -2951,6 +2951,14 @@
     "print(classification_report(test_df['labels'], y_pred_binary, target_names=['Positive', 'Negative']))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "883a5cd3",
+   "metadata": {},
+   "source": [
+    "There isn't much difference between the pre-trained ConceptNet Numberbatch embedding and the pre-trained GloVe models for our specific application, as they performed extremely similarly.  I would argue that the pre-trained GloVe model is likely better for this purpose, though, as it tends to capture statistical information about word co-occurrences better, a useful tool for sentiment analysis.  With that being said, the inability of GloVe to take into account contextual semantic understandings of words makes it difficult for some classifications.  There doesn't appear to be much of a difference between the models for our purpose, though, and both models fall below the previously used models, particularly the Transformer model with 2 multi-heads."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "b6725e16",

From 0b99a292099ae2bec2ee06e61af7aedd842bf433 Mon Sep 17 00:00:00 2001
From: ccook7bit <123575369+ccook7bit@users.noreply.github.com>
Date: Thu, 12 Dec 2024 23:09:31 -0600
Subject: [PATCH 3/6] New changes

Cited glove and numberbatch, took away stupid plots that were bad
---
 Lab7CookNew.ipynb | 2918 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 2918 insertions(+)
 create mode 100644 Lab7CookNew.ipynb

diff --git a/Lab7CookNew.ipynb b/Lab7CookNew.ipynb
new file mode 100644
index 0000000..11208f6
--- /dev/null
+++ b/Lab7CookNew.ipynb
@@ -0,0 +1,2918 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "55a17141-e102-4eb4-9d10-c6ebc36eaf06",
+   "metadata": {},
+   "source": [
+    "### Lab 7 ###\n",
+    "*Christopher Cook, Davis Lynn, Anekah Kelley, Bonita Davis*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8ce2ce3-95d7-4f75-b16e-7933d79765c1",
+   "metadata": {},
+   "source": [
+    "__Business Summary__\n",
+    "\n",
+    "For Lab 7, we are using the ChatGPT Sentiment Analysis dataset. This dataset that consists of tweets that mention the AI platform ChatGPT. Each tweet is labeled as having a good, neutral, or bad sentiment. The tweets in the dataset were collected over the span of one month and then sentiments were labeled through Natural Language Processing (NLP) techniques. The dataset is available for free on Kaggle through the link below.\n",
+    "\n",
+    "Data Source: https://www.kaggle.com/datasets/charunisa/chatgpt-sentiment-analysis\n",
+    "\n",
+    "The prediction task we have selected is to classify each tweet by its sentiment using a sequential neural network architecture. We are performing a binary classification as we remove the neutral label, leaving the dataset with tweets of good or bad sentiment. The selected dataset provides a medium-sized text dataset, which is ideal for this prediction task.\n",
+    "\n",
+    "The primary business case of this task is to provide marketing feedback to ChatGPT's developers so that they can quickly gain feedback on their marketing campaigns, product announcements, and general perception about their product. By categorizing each tweet by sentiment, ChatGPT can efficiently analyze which features users like, feel neutral about, or dislike. This will allow the developers to highlight features that users love and identify areas of improvement. Additionally, developers can analyze trends and track negative spikes in public sentiment so that they can prepare for potential issues when announcing product updates and manage their brand image.\n",
+    "\n",
+    "This sentiment analysis model is applicable for companies across any industry as all companies can benefit from public feedback. For example, retail companies can use sentiment analysis with product reviews to determine popular features in products, or universities can use sentiment analysis with student surveys to improve courses and degree programs. Through analyzing sentiment data, companies can make data-driven decisions to improve their products and enhance customer satisfaction."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "820738de-4190-4389-b356-8b9a69b0d8cf",
+   "metadata": {},
+   "source": [
+    "### Preparation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "237f843e-bfce-4230-96e7-4346d7e884f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-12 19:12:16.232351: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
+      "2024-12-12 19:12:16.812966: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
+      "2024-12-12 19:12:17.260816: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
+      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
+      "E0000 00:00:1734052337.496225 2912121 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1734052337.590338 2912121 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "2024-12-12 19:12:18.079992: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+      "[nltk_data] Downloading package vader_lexicon to\n",
+      "[nltk_data]     /users/cdcook/nltk_data...\n",
+      "[nltk_data]   Package vader_lexicon is already up-to-date!\n",
+      "[nltk_data] Downloading package punkt to /users/cdcook/nltk_data...\n",
+      "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
+      "[nltk_data] Downloading package stopwords to\n",
+      "[nltk_data]     /users/cdcook/nltk_data...\n",
+      "[nltk_data]   Package stopwords is already up-to-date!\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib as mpl\n",
+    "import seaborn as sns\n",
+    "import re\n",
+    "import itertools\n",
+    "import tensorflow as tf\n",
+    "import nltk\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "from tensorflow.keras.models import Sequential \n",
+    "from tensorflow.keras.layers import Embedding, SimpleRNN, LSTM, GRU, Dropout, Layer, TextVectorization, Dense, GlobalMaxPooling1D, MultiHeadAttention, LayerNormalization # type: ignore\n",
+    "from tensorflow.keras.regularizers import l1_l2 \n",
+    "from nltk.corpus import stopwords\n",
+    "from sklearn.metrics import recall_score, f1_score, roc_auc_score, confusion_matrix, classification_report\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from keras.layers import Embedding # type: ignore\n",
+    "\n",
+    "mpl.style.use(\"seaborn-v0_8-deep\")\n",
+    "mpl.rcParams[\"figure.figsize\"] = (20, 5)\n",
+    "mpl.rcParams[\"figure.dpi\"] = 100\n",
+    "plt.rcParams[\"figure.dpi\"] = 100\n",
+    "plt.rcParams[\"lines.linewidth\"] = 2\n",
+    "\n",
+    "nltk.download(\"vader_lexicon\")\n",
+    "nltk.download(\"punkt\")\n",
+    "nltk.download(\"stopwords\")\n",
+    "\n",
+    "nltk.data.path.append('/users/cdcook/nltk_data/tokenizers/punkt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "92adc200-539f-4c87-86a8-34054e802081",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(50000, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load dataset\n",
+    "df = pd.read_csv(\"./file.csv\")\n",
+    "df = df.head(50000)\n",
+    "og_shape = df.shape\n",
+    "print(og_shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2708e5e2-d426-44d3-ad3c-d55c94c8a038",
+   "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>Unnamed: 0</th>\n",
+       "      <th>tweets</th>\n",
+       "      <th>labels</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>ChatGPT: Optimizing Language Models for Dialog...</td>\n",
+       "      <td>neutral</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Try talking with ChatGPT, our new AI system wh...</td>\n",
+       "      <td>good</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>ChatGPT: Optimizing Language Models for Dialog...</td>\n",
+       "      <td>neutral</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>THRILLED to share that ChatGPT, our new model ...</td>\n",
+       "      <td>good</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>As of 2 minutes ago, @OpenAI released their ne...</td>\n",
+       "      <td>bad</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49995</th>\n",
+       "      <td>49995</td>\n",
+       "      <td>The ChatGPT hyperbolic hype train is in overdr...</td>\n",
+       "      <td>bad</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49996</th>\n",
+       "      <td>49996</td>\n",
+       "      <td>This instance of artificial intelligence has a...</td>\n",
+       "      <td>good</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49997</th>\n",
+       "      <td>49997</td>\n",
+       "      <td>Ripple CTO shuts down ChatGPT’s XRP conspiracy...</td>\n",
+       "      <td>bad</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49998</th>\n",
+       "      <td>49998</td>\n",
+       "      <td>The Brilliance and Weirdness of ChatGPT https:...</td>\n",
+       "      <td>neutral</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49999</th>\n",
+       "      <td>49999</td>\n",
+       "      <td>Game, set and match #chatGPT \\n\\nWell done @Op...</td>\n",
+       "      <td>bad</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>50000 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Unnamed: 0                                             tweets   labels\n",
+       "0               0  ChatGPT: Optimizing Language Models for Dialog...  neutral\n",
+       "1               1  Try talking with ChatGPT, our new AI system wh...     good\n",
+       "2               2  ChatGPT: Optimizing Language Models for Dialog...  neutral\n",
+       "3               3  THRILLED to share that ChatGPT, our new model ...     good\n",
+       "4               4  As of 2 minutes ago, @OpenAI released their ne...      bad\n",
+       "...           ...                                                ...      ...\n",
+       "49995       49995  The ChatGPT hyperbolic hype train is in overdr...      bad\n",
+       "49996       49996  This instance of artificial intelligence has a...     good\n",
+       "49997       49997  Ripple CTO shuts down ChatGPT’s XRP conspiracy...      bad\n",
+       "49998       49998  The Brilliance and Weirdness of ChatGPT https:...  neutral\n",
+       "49999       49999  Game, set and match #chatGPT \\n\\nWell done @Op...      bad\n",
+       "\n",
+       "[50000 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4d0e3d64-d6ad-4910-a204-4da958b2c115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cleanText(txt):\n",
+    "      \n",
+    "    #Creates list of possible stopwords from nltk library\n",
+    "    stop = stopwords.words('english')\n",
+    "    \n",
+    "    # Lowercase\n",
+    "    txt = txt.lower()\n",
+    "    \n",
+    "    # Remove stopwords\n",
+    "    txt = ' '.join([word for word in txt.split() if word not in (stop)])\n",
+    "    \n",
+    "    # Remove non-alphabetic characters\n",
+    "    txt = re.sub('[^a-z]',' ',txt)\n",
+    "    return txt  \n",
+    "\n",
+    "def cleanNumbers(x):\n",
+    "    digit_map = {\n",
+    "        '0': 'zero',\n",
+    "        '1': 'one',\n",
+    "        '2': 'two',\n",
+    "        '3': 'three',\n",
+    "        '4': 'four',\n",
+    "        '5': 'five',\n",
+    "        '6': 'six',\n",
+    "        '7': 'seven',\n",
+    "        '8': 'eight',\n",
+    "        '9': 'nine'\n",
+    "    }\n",
+    "    \n",
+    "    x = re.sub('[0-9]{7,}', 'millions', x)\n",
+    "    x = re.sub('[0-9]{4,6}', 'thousand', x)\n",
+    "    x = re.sub('[0-9]{3}', 'hundred', x)\n",
+    "    x = re.sub('[0-9]{2}', 'tens', x)\n",
+    "    x = re.sub('[0-9]{1}', 'tens', x)\n",
+    "    x = re.sub(r'\\b\\d\\b', lambda match: digit_map[match.group()], x)\n",
+    "\n",
+    "    return x\n",
+    "contraction_dict = {\"ain't\": \"is not\", \"aren't\": \"are not\",\"can't\": \"cannot\", \"'cause\": \"because\", \"could've\": \"could have\", \"couldn't\": \"could not\", \"didn't\": \"did not\",  \"doesn't\": \"does not\", \"don't\": \"do not\", \"hadn't\": \"had not\", \"hasn't\": \"has not\", \"haven't\": \"have not\", \"he'd\": \"he would\",\"he'll\": \"he will\", \"he's\": \"he is\", \"how'd\": \"how did\", \"how'd'y\": \"how do you\", \"how'll\": \"how will\", \"how's\": \"how is\",  \"I'd\": \"I would\", \"I'd've\": \"I would have\", \"I'll\": \"I will\", \"I'll've\": \"I will have\",\"I'm\": \"I am\", \"I've\": \"I have\", \"i'd\": \"i would\", \"i'd've\": \"i would have\", \"i'll\": \"i will\",  \"i'll've\": \"i will have\",\"i'm\": \"i am\", \"i've\": \"i have\", \"isn't\": \"is not\", \"it'd\": \"it would\", \"it'd've\": \"it would have\", \"it'll\": \"it will\", \"it'll've\": \"it will have\",\"it's\": \"it is\", \"let's\": \"let us\", \"ma'am\": \"madam\", \"mayn't\": \"may not\", \"might've\": \"might have\",\"mightn't\": \"might not\",\"mightn't've\": \"might not have\", \"must've\": \"must have\", \"mustn't\": \"must not\", \"mustn't've\": \"must not have\", \"needn't\": \"need not\", \"needn't've\": \"need not have\",\"o'clock\": \"of the clock\", \"oughtn't\": \"ought not\", \"oughtn't've\": \"ought not have\", \"shan't\": \"shall not\", \"sha'n't\": \"shall not\", \"shan't've\": \"shall not have\", \"she'd\": \"she would\", \"she'd've\": \"she would have\", \"she'll\": \"she will\", \"she'll've\": \"she will have\", \"she's\": \"she is\", \"should've\": \"should have\", \"shouldn't\": \"should not\", \"shouldn't've\": \"should not have\", \"so've\": \"so have\",\"so's\": \"so as\", \"this's\": \"this is\",\"that'd\": \"that would\", \"that'd've\": \"that would have\", \"that's\": \"that is\", \"there'd\": \"there would\", \"there'd've\": \"there would have\", \"there's\": \"there is\", \"here's\": \"here is\",\"they'd\": \"they would\", \"they'd've\": \"they would have\", \"they'll\": \"they will\", \"they'll've\": \"they will have\", \"they're\": \"they are\", \"they've\": \"they have\", \"to've\": \"to have\", \"wasn't\": \"was not\", \"we'd\": \"we would\", \"we'd've\": \"we would have\", \"we'll\": \"we will\", \"we'll've\": \"we will have\", \"we're\": \"we are\", \"we've\": \"we have\", \"weren't\": \"were not\", \"what'll\": \"what will\", \"what'll've\": \"what will have\", \"what're\": \"what are\",  \"what's\": \"what is\", \"what've\": \"what have\", \"when's\": \"when is\", \"when've\": \"when have\", \"where'd\": \"where did\", \"where's\": \"where is\", \"where've\": \"where have\", \"who'll\": \"who will\", \"who'll've\": \"who will have\", \"who's\": \"who is\", \"who've\": \"who have\", \"why's\": \"why is\", \"why've\": \"why have\", \"will've\": \"will have\", \"won't\": \"will not\", \"won't've\": \"will not have\", \"would've\": \"would have\", \"wouldn't\": \"would not\", \"wouldn't've\": \"would not have\", \"y'all\": \"you all\", \"y'all'd\": \"you all would\",\"y'all'd've\": \"you all would have\",\"y'all're\": \"you all are\",\"y'all've\": \"you all have\",\"you'd\": \"you would\", \"you'd've\": \"you would have\", \"you'll\": \"you will\", \"you'll've\": \"you will have\", \"you're\": \"you are\", \"you've\": \"you have\"}\n",
+    "\n",
+    "def _get_contractions(contraction_dict):\n",
+    "    contraction_re = re.compile('(%s)' % '|'.join(contraction_dict.keys()))\n",
+    "    return contraction_dict, contraction_re\n",
+    "\n",
+    "contractions, contractions_re = _get_contractions(contraction_dict)\n",
+    "\n",
+    "def replaceContractions(text):\n",
+    "    def replace(match):\n",
+    "        return contractions[match.group(0)]\n",
+    "    return contractions_re.sub(replace, text)\n",
+    "\n",
+    "\n",
+    "size_mini = 1000\n",
+    "\n",
+    "def pre_process(df):\n",
+    "    \"\"\"\n",
+    "    Parameters:\n",
+    "    - df (pd.Dataframe): The DataFrame to be preprocessed\n",
+    "\n",
+    "    Returns: \n",
+    "    - df (pd.DataFrame): The DataFrame after preprocessed\n",
+    "\n",
+    "    \"\"\"\n",
+    "    # Remove the 'Unnamed: 0' column, if it exists, as it's usually an artifact\n",
+    "    df = df.drop([\"Unnamed: 0\"], axis=1)\n",
+    "    \n",
+    "    # Map 'labels' values from 'bad'/'good' to 1/0, and set 'neutral' to NaN\n",
+    "    df[\"labels\"] = df[\"labels\"].map({\"bad\": 1, \"good\": 0, \"neutral\": None})\n",
+    "    \n",
+    "    # Drop rows with NaN values in 'labels'\n",
+    "    df = df.dropna(subset=[\"labels\"])\n",
+    "\n",
+    "    print(\"We have kept\", (df.shape[0]/og_shape[0])*100, \"% of the data\")\n",
+    "\n",
+    "    # Cleaning the text, numbers, and replacing contractions\n",
+    "    df['tweets'] = df['tweets'].apply(cleanText)\n",
+    "    df['tweets'] = df[\"tweets\"].apply(cleanNumbers)\n",
+    "    df['tweets'] = df[\"tweets\"].apply(replaceContractions)\n",
+    "\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "4075ec80-d1a5-4839-8024-6d6953c2cbc7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def num_characters(df, col_name):\n",
+    "    \"\"\"\n",
+    "    Adds a new column to the DataFrame containing the number of characters in each message.\n",
+    "\n",
+    "    Parameters:\n",
+    "    - df: DataFrame. The DataFrame to which the new column will be added.\n",
+    "    - col_name: str. The name of the column containing the messages.\n",
+    "\n",
+    "    Returns:\n",
+    "    - DataFrame. The original DataFrame with a new column added.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    df[\"Characters\"] = df[col_name].apply(len)\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "def num_sentences(df, col_name):\n",
+    "    \"\"\"\n",
+    "    Adds a new column to the DataFrame containing the number of sentences in each message.\n",
+    "\n",
+    "    Parameters:\n",
+    "    - df: DataFrame. The DataFrame to which the new column will be added.\n",
+    "    - col_name: str. The name of the column containing the messages.\n",
+    "\n",
+    "    Returns:\n",
+    "    - DataFrame. The original DataFrame with a new column added.\n",
+    "    \"\"\"\n",
+    "    df[\"Sentences\"] = df[col_name].apply(lambda x: len(nltk.sent_tokenize(x)))\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "def num_words(df, col_name):\n",
+    "    \"\"\"\n",
+    "    Adds a new column to the DataFrame containing the number of words in each message.\n",
+    "\n",
+    "    Parameters:\n",
+    "    - df: DataFrame. The DataFrame to which the new column will be added.\n",
+    "    - col_name: str. The name of the column containing the messages.\n",
+    "\n",
+    "    Returns:\n",
+    "    - DataFrame. The original DataFrame with a new column added.\n",
+    "    \"\"\"\n",
+    "    df[\"Words\"] = df[col_name].apply(lambda x: len(nltk.word_tokenize(x)))\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9fd01920-9d12-467d-b500-3bbb17c2c254",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "We have kept 74.652 % of the data\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pre_process(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a92e66b6-9eb6-4df9-bd30-1b27c62ae0ac",
+   "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>tweets</th>\n",
+       "      <th>labels</th>\n",
+       "      <th>Characters</th>\n",
+       "      <th>Sentences</th>\n",
+       "      <th>Words</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>try talking chatgpt  new ai system optimized d...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>107</td>\n",
+       "      <td>1</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>thrilled share chatgpt  new model optimized di...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>158</td>\n",
+       "      <td>1</td>\n",
+       "      <td>24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>minutes ago   openai released new chatgpt   ...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>112</td>\n",
+       "      <td>1</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>launched chatgpt  new ai system optimized dial...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>113</td>\n",
+       "      <td>1</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>minutes ago   openai released new chatgpt   ...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>144</td>\n",
+       "      <td>1</td>\n",
+       "      <td>31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49994</th>\n",
+       "      <td>can t even use regular gpt  chatgpt usage  bummer</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>49</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49995</th>\n",
+       "      <td>chatgpt hyperbolic hype train overdrive    one...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>210</td>\n",
+       "      <td>1</td>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49996</th>\n",
+       "      <td>instance artificial intelligence fantastic ans...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>117</td>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49997</th>\n",
+       "      <td>ripple cto shuts chatgpt s xrp conspiracy theo...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>72</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49999</th>\n",
+       "      <td>game  set match  chatgpt  n nwell done  openai</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>49</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>37326 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                  tweets  labels  Characters  \\\n",
+       "1      try talking chatgpt  new ai system optimized d...     0.0         107   \n",
+       "3      thrilled share chatgpt  new model optimized di...     0.0         158   \n",
+       "4        minutes ago   openai released new chatgpt   ...     1.0         112   \n",
+       "5      launched chatgpt  new ai system optimized dial...     0.0         113   \n",
+       "6        minutes ago   openai released new chatgpt   ...     1.0         144   \n",
+       "...                                                  ...     ...         ...   \n",
+       "49994  can t even use regular gpt  chatgpt usage  bummer     1.0          49   \n",
+       "49995  chatgpt hyperbolic hype train overdrive    one...     1.0         210   \n",
+       "49996  instance artificial intelligence fantastic ans...     0.0         117   \n",
+       "49997  ripple cto shuts chatgpt s xrp conspiracy theo...     1.0          72   \n",
+       "49999  game  set match  chatgpt  n nwell done  openai        1.0          49   \n",
+       "\n",
+       "       Sentences  Words  \n",
+       "1              1     19  \n",
+       "3              1     24  \n",
+       "4              1     21  \n",
+       "5              1     22  \n",
+       "6              1     31  \n",
+       "...          ...    ...  \n",
+       "49994          1      9  \n",
+       "49995          1     36  \n",
+       "49996          1     18  \n",
+       "49997          1     13  \n",
+       "49999          1      8  \n",
+       "\n",
+       "[37326 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = num_characters(df, \"tweets\")\n",
+    "df = num_sentences(df, \"tweets\")\n",
+    "df = num_words(df, \"tweets\")\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "29ea2b82-d11b-4f9b-a423-bbaf69b61c01",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_distribution_features(df, features, col_name):\n",
+    "    \"\"\"\n",
+    "    Plot distributions of specified features in a DataFrame, comparing positive and negative tweets.\n",
+    "\n",
+    "    Parameters:\n",
+    "    - df: DataFrame containing the data.\n",
+    "    - features: List of strings, names of the columns for which to plot the distributions.\n",
+    "\n",
+    "    Each feature's distribution is plotted in a separate row, with 'good' tweets in blue and 'bad' tweets in pink.\n",
+    "    \"\"\"\n",
+    "    # Create a figure with subplots arranged in 3 rows and 1 column\n",
+    "    fig, axes = plt.subplots(\n",
+    "        nrows=len(features), ncols=1, figsize=(15, 6 * len(features))\n",
+    "    )\n",
+    "\n",
+    "    for i, feature in enumerate(features):\n",
+    "        # Plot distribution for 'good' tweets\n",
+    "        sns.histplot(\n",
+    "            df[df[\"labels\"] == 0][feature],\n",
+    "            kde=True,\n",
+    "            color=\"#66b3ff\",\n",
+    "            label=\"Positive\",\n",
+    "            ax=axes[i],\n",
+    "        )\n",
+    "        # Plot distribution for 'bad' tweets\n",
+    "        sns.histplot(\n",
+    "            df[df[\"labels\"] == 1][feature],\n",
+    "            kde=True,\n",
+    "            color=\"#ff9999\",\n",
+    "            label=\"Negative\",\n",
+    "            ax=axes[i],\n",
+    "        )\n",
+    "        # Setting the title for each subplot\n",
+    "        axes[i].set_title(f\"{feature} Distribution for {col_name} dataset\")\n",
+    "        # Setting the labels for each subplot\n",
+    "        axes[i].set_xlabel(feature)\n",
+    "        axes[i].set_ylabel(\"Count\")\n",
+    "        # Adding legend to each subplot\n",
+    "        axes[i].legend()\n",
+    "\n",
+    "    # Adjust layout\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a3f11a7a-e191-49ca-98ee-e7190238dd65",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1800 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# List of features to plot\n",
+    "features = [\"Characters\", \"Words\", \"Sentences\"]\n",
+    "\n",
+    "# Call the function\n",
+    "plot_distribution_features(df, features, \"Tweet\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dbe17d2d-363b-4501-b506-0f97f6003476",
+   "metadata": {},
+   "source": [
+    "These visualizations show the distribution of characters, words, and sentences in each tweet after preprocessing steps have been applied. According to these plots, negative tweets generally have a lower number of characters and words than positive tweets. Additionally, the sentence distribution plot shows that we have more negative tweets than positive tweets in this dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "45181805-83f1-4710-87d2-173177b1ffed",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Length covering at least 95% of the Tweets: 34\n",
+      "The kept sequence is: 34 words\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['text_length'] = df['tweets'].apply(lambda x: len(x.split()))\n",
+    "\n",
+    "# Determine the 95th percentile of these lengths\n",
+    "sequence_length_95 = np.percentile(df['text_length'], 95)\n",
+    "\n",
+    "sequence_length = int(np.ceil(sequence_length_95))\n",
+    "\n",
+    "print(f\"Length covering at least 95% of the Tweets: {sequence_length}\")\n",
+    "df = df.drop(columns=[\"text_length\"])\n",
+    "print(\"The kept sequence is:\", sequence_length, \"words\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3333a53c-1f0a-470f-8ba8-bcf95523e13e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-12 19:12:56.999715: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:152] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)\n"
+     ]
+    }
+   ],
+   "source": [
+    "max_features = 10000\n",
+    "\n",
+    "vectorization = TextVectorization(standardize=\"lower_and_strip_punctuation\", \n",
+    "                                  max_tokens=max_features, \n",
+    "                                  output_mode='int', \n",
+    "                                  output_sequence_length=sequence_length)\n",
+    "vectorization.adapt(df[\"tweets\"])\n",
+    "\n",
+    "words_database = vectorization.get_vocabulary()\n",
+    "database_index = {word: index for index, word in enumerate(words_database)}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "8b9689c7-855f-42c5-8f07-5abf62abfdab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 63531/63531 [00:00<00:00, 1716680.69it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "We found 9116 words\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "/lustre/work/client/users/cdcook/.conda/envs/cobaya_2024/lib/python3.10/site-packages/keras/src/layers/core/embedding.py:90: UserWarning: Argument `input_length` is deprecated. Just remove it.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
+    "from tensorflow.keras.layers import Embedding\n",
+    "import numpy as np\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "# Define max_features and sequence length\n",
+    "max_features = 20000\n",
+    "sequence_length = 100\n",
+    "\n",
+    "# Tokenizer to generate word index\n",
+    "tokenizer = Tokenizer(num_words=max_features)\n",
+    "tokenizer.fit_on_texts(df['tweets'])\n",
+    "database_index = tokenizer.word_index\n",
+    "\n",
+    "# Load GloVe embeddings\n",
+    "def load_glove_index():\n",
+    "    EMBEDDING_FILE = 'glove.twitter.27B.200d.txt'\n",
+    "    def get_coefs(word, *arr): return word, np.asarray(arr, dtype='float32')\n",
+    "    embeddings_index = dict(get_coefs(*o.split(\" \")) for o in open(EMBEDDING_FILE, 'r', encoding='utf-8'))\n",
+    "    return embeddings_index\n",
+    "\n",
+    "glove_embedding_index_twitter = load_glove_index()\n",
+    "\n",
+    "# Create embedding matrix\n",
+    "def create_glove(word_index, embeddings_index):\n",
+    "    all_embs = list(embeddings_index.values())\n",
+    "    embed_size = all_embs[0].shape[0]\n",
+    "    nb_words = min(max_features, len(word_index))\n",
+    "    embedding_matrix = np.random.normal(0, 1, (nb_words, embed_size))\n",
+    "\n",
+    "    count_found = nb_words\n",
+    "    for word, i in tqdm(word_index.items()):\n",
+    "        if i >= max_features: continue\n",
+    "        embedding_vector = embeddings_index.get(word)\n",
+    "        if embedding_vector is not None: \n",
+    "            embedding_matrix[i] = embedding_vector\n",
+    "        else:\n",
+    "            if word.islower():\n",
+    "                embedding_vector = embeddings_index.get(word.capitalize())\n",
+    "                if embedding_vector is not None: \n",
+    "                    embedding_matrix[i] = embedding_vector\n",
+    "                else:\n",
+    "                    count_found -= 1\n",
+    "            else:\n",
+    "                count_found -= 1\n",
+    "    print(\"We found\", count_found, \"words\")\n",
+    "    return embedding_matrix\n",
+    "\n",
+    "adapted_glove_twitter = create_glove(database_index, glove_embedding_index_twitter)\n",
+    "\n",
+    "# Define embedding layer\n",
+    "embedding_dim = 200\n",
+    "embedding_layer = Embedding(\n",
+    "    input_dim=max_features, \n",
+    "    output_dim=embedding_dim, \n",
+    "    weights=[adapted_glove_twitter], \n",
+    "    input_length=sequence_length, \n",
+    "    trainable=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6f68a74-0184-4029-bbce-d8fe6d419fba",
+   "metadata": {},
+   "source": [
+    "__Describe the final dataset that is used for classification/regression (include a description of any newly formed variables you created). Discuss methods of tokenization in your dataset as well as any decisions to force a specific length of sequence.__\n",
+    "\n",
+    "The final dataset used for our classification task has undergone several preprocessing and tokenization steps to prepare it for machine learning. Our first preprocessing steps are removing the column \"Unnamed: 0\" and mapping labels to binary values (\"bad\": 1, \"good\": 0, \"neutral\": None). We then drop rows with NaN labels, which removes all rows with the neutral label. We also use the nltk library to lowercase words and remove stop words. Additionally, we convert numbers into words and expand contractions (eg. ain't -> is not).\n",
+    "\n",
+    "To tokenize the tweets in the dataset, we are using the Tokenizer and TextVectorization classes from Keras. We use the Tokenizer class to generate a word index that maps each unique word to a unique integer ID. The TextVectorization class then allows us to split each tweet into individual tokens (words), while also removing punctuation.\n",
+    "\n",
+    "To determine the length of sequence, we found the number of tokens in each tweet and found that 95% of the tweets in the dataset have less than 35 tokens, so we adapted the dataset for each tweet to have 34 tokens by either adding tokens to bring the tweets up to 34 tokens or truncating tokens down to 34. Bringing the values to the same number of tokens allows the values to be passed into our model consistently, ensuring good performance.\n",
+    "\n",
+    "We have also decided to use Globe embedding made for Twitter, here is the dataset from Kaggle: https://www.kaggle.com/datasets/fullmetal26/glovetwitter27b100dtxt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "524ab632-9ba0-465e-b7a2-f564edd92cdb",
+   "metadata": {},
+   "source": [
+    "__Choose and explain what metric(s) you will use to evaluate your algorithm’s performance.__\n",
+    "\n",
+    "We have decided to use recall, F1, specificity, and AUC-ROC to evaluate the performance of our sentiment classification model. These metrics provide a comprehensive assessment of the model's ability to classify tweets as having \"good\" or \"bad\" sentiments.\n",
+    "\n",
+    "Recall measures the proportion of true positives out of all positive instances. In this case, true positives are the number of \"bad\" sentiment tweets that were correctly classified as \"bad\", and a false negatives are the number of \"bad\" sentiment tweets that were incorrectly classified as \"good\". For our prediction task, we want to identify as many negative sentiment tweets as possible so that companies can effectively address negative feedback and improve their product. A good recall score is greater than 0.7, and an excellent recall score is greater than 0.85.\n",
+    "\n",
+    "F1 score calculates a harmonic mean of precision and recall, which is helpful as it will ensure that we minimize both false negatives and false positives. Furthermore, our dataset is imbalanced, with only about a third of the dataset being comprised of \"good\" tweets, so F1 score will provide a balanced evaluation of model performance for both classes and ensure that the model effectively identifies \"bad\" sentiment while not misclassifying \"good\" sentiment too often. An F1 score above 0.7 is generally considered to be a good score.\n",
+    "\n",
+    "Specificity measures the proportion of true negatives out of all negative instances. In this case, true negatives are the number of \"good\" sentiment tweets correctly classified as \"good\", and false positives are the number of \"good\" sentiment tweets incorrectly classified as \"bad\". Measuring specificity is important because we want to ensure that good feedback is not misclassified as bad. This misclassification can cause the company to waste time and resources addressing features that are well-received by customers.\n",
+    "\n",
+    "AUC-ROC provides a more nuanced performance evaluation, particularly in distinguishing between classes at various decision thresholds. While accuracy measures the overall correctness, AUC-ROC focuses on the model's ability to discriminate between classes, making it an ideal metric for understanding how well the model performs across different thresholds. This metric will give us insight into how the model performs across the classes despite the class imbalance in our dataset. An AUC-ROC score above 0.9 is considered to have great performance.\n",
+    "\n",
+    "These four metrics provide a well-rounded evaluation of our model's performance. Recall ensures we correctly classify negative feedback, and F1 score balances the model's performance between the two sentiment classes. Specificity ensures we do not misclassify positive feedback, and AUC-ROC provides a robust assessment of the model's ability to distinguish between the two classes. Together, these performance metrics will allow us to effectively evaluate our model's performance and ensure we meet the requirements of our business case.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39f7ed27-9c7e-4a19-a4d5-8f849162ef10",
+   "metadata": {},
+   "source": [
+    "__Choose the method you will use for dividing your data into training and testing__\n",
+    "\n",
+    "We chose to use the 80/20 Test-Train split for our data. This allows our data to be split between training and testing efficently. Given the large size of our dataset and limited processing power of our computers, we elected to use this method instead of something more computationally expensive such as 10 Stratified K-folds. Furthermore, given the size of our dataset after preprocessing, we still have over 30,000 entries to train and test the network on. This is enough to not worry about some of the balancing issues a smaller dataset might suffer from if not split using a stratified method.\n",
+    "\n",
+    "If given more processing power and a greater amount of time, we would implement a different method such as 10 Stratified K-Folds."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74aeb46e-7dbb-4e8f-9b91-ea626b0dc23d",
+   "metadata": {},
+   "source": [
+    "### Modeling\n",
+    "\n",
+    "In this section, we will investigate four different sequential network architectures: GRU, LSTM, Simple RNN, and Transformer. We will use a pre-trained GloVe embedding of 200 dimensions to initialize the embedding layer. Using a pre-trained embedding will help our model learn semantic relationships between words without training from scratch, which will improve generalization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "7dedad10-3797-485a-a15b-322b5bd8d0b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.layers import TextVectorization\n",
+    "\n",
+    "# Parameters for vectorization\n",
+    "max_tokens = 20000  # Maximum number of unique tokens\n",
+    "sequence_length = 100  # Length of sequences to pad or truncate to\n",
+    "\n",
+    "# Define the TextVectorization layer\n",
+    "vectorization = TextVectorization(\n",
+    "    max_tokens=max_tokens,\n",
+    "    output_mode='int',\n",
+    "    output_sequence_length=sequence_length\n",
+    ")\n",
+    "\n",
+    "# Adapt the vectorization layer to your dataset\n",
+    "# Assuming `text_dataset` is a tf.data.Dataset of raw text\n",
+    "vectorization.adapt(df['tweets'])\n",
+    "\n",
+    "def gru_model():\n",
+    "    model = Sequential()\n",
+    "    model.add(tf.keras.Input(shape=(1,), dtype=tf.string))  # Input layer for raw strings\n",
+    "    model.add(vectorization)\n",
+    "    model.add(embedding_layer)\n",
+    "    model.add(GRU(128, return_sequences=True))\n",
+    "    model.add(GlobalMaxPooling1D())\n",
+    "    model.add(Dense(64, activation='relu', kernel_regularizer=l1_l2(0.01)))\n",
+    "    model.add(Dense(1, activation='sigmoid'))\n",
+    "\n",
+    "    return model\n",
+    "\n",
+    "def lstm_model():\n",
+    "    model = Sequential()\n",
+    "    model.add(tf.keras.Input(shape=(1,), dtype=tf.string))  # Input layer for raw strings\n",
+    "    model.add(vectorization)\n",
+    "    model.add(embedding_layer)\n",
+    "    model.add(LSTM(128, return_sequences=True))\n",
+    "    model.add(GlobalMaxPooling1D())\n",
+    "    model.add(Dense(64, activation='relu', kernel_regularizer=l1_l2(0.01)))\n",
+    "    model.add(Dense(1, activation='sigmoid'))\n",
+    "\n",
+    "    return model\n",
+    "\n",
+    "def rnn_model():\n",
+    "    model = Sequential()\n",
+    "    model.add(tf.keras.Input(shape=(1,), dtype=tf.string))  # Input layer for raw strings\n",
+    "    model.add(vectorization)\n",
+    "    model.add(embedding_layer)\n",
+    "    model.add(SimpleRNN(128, return_sequences=True))\n",
+    "    model.add(GlobalMaxPooling1D())\n",
+    "    model.add(Dense(64, activation='relu', kernel_regularizer=l1_l2(0.01)))\n",
+    "    model.add(Dense(1, activation='sigmoid'))\n",
+    "\n",
+    "    return model\n",
+    "\n",
+    "class TransformerBlock(Layer): # inherit from Keras Layer\n",
+    "    def __init__(self, embed_dim, num_heads, ff_dim, rate=0.2):\n",
+    "        super().__init__()\n",
+    "        # setup the model heads and feedforward network\n",
+    "        self.att = MultiHeadAttention(num_heads=num_heads, \n",
+    "                                      key_dim=embed_dim)\n",
+    "        \n",
+    "        # make a two layer network that processes the attention\n",
+    "        self.ffn = Sequential()\n",
+    "        self.ffn.add( Dense(ff_dim, activation='relu') )\n",
+    "        self.ffn.add( Dense(embed_dim) )\n",
+    "        \n",
+    "        self.layernorm1 = LayerNormalization(epsilon=1e-6)\n",
+    "        self.layernorm2 = LayerNormalization(epsilon=1e-6)\n",
+    "        self.dropout1 = Dropout(rate)\n",
+    "        self.dropout2 = Dropout(rate)\n",
+    "\n",
+    "    def call(self, inputs, training=True):\n",
+    "        # apply the layers as needed (similar to PyTorch)\n",
+    "        \n",
+    "        # get the attention output from multi heads\n",
+    "        # Using same inpout here is self-attention\n",
+    "        # call inputs are (query, value, key) \n",
+    "        # if only two inputs given, value and key are assumed the same\n",
+    "        attn_output = self.att(inputs, inputs)\n",
+    "        \n",
+    "        # create residual output, with attention\n",
+    "        out1 = self.layernorm1(inputs + attn_output)\n",
+    "        \n",
+    "        # apply dropout if training\n",
+    "        out1 = self.dropout1(out1, training=training)\n",
+    "        \n",
+    "        # place through feed forward after layer norm\n",
+    "        ffn_output = self.ffn(out1)\n",
+    "        out2 = self.layernorm2(out1 + ffn_output)\n",
+    "        \n",
+    "        # apply dropout if training\n",
+    "        out2 = self.dropout2(out2, training=training)\n",
+    "        #return the residual from Dense layer\n",
+    "        return out2\n",
+    "    \n",
+    "    \n",
+    "class TokenAndPositionEmbedding(Layer):\n",
+    "    def __init__(self, maxlen, vocab_size, embed_dim):\n",
+    "        super().__init__()\n",
+    "        # create two embeddings \n",
+    "        # one for processing the tokens (words)\n",
+    "        self.token_emb = Embedding(input_dim=vocab_size, \n",
+    "                                   output_dim=embed_dim)\n",
+    "        # another embedding for processing the position\n",
+    "        self.pos_emb = Embedding(input_dim=maxlen, \n",
+    "                                 output_dim=embed_dim)\n",
+    "\n",
+    "    def call(self, x):\n",
+    "        # create a static position measure (input)\n",
+    "        maxlen = tf.shape(x)[-1]\n",
+    "        positions = tf.range(start=0, limit=maxlen, delta=1)\n",
+    "        # positions now goes from 0 to 500 (for IMdB) by 1\n",
+    "        positions = self.pos_emb(positions)# embed these positions\n",
+    "        x = self.token_emb(x) # embed the tokens\n",
+    "        return x + positions # add embeddngs to get final embedding\n",
+    "\n",
+    "def transformer_model():\n",
+    "    embed_dim = embedding_dim  # Assuming you are using GloVe embeddings with dimension 200\n",
+    "    num_heads = 1\n",
+    "    ff_dim = 32\n",
+    "\n",
+    "    inputs = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)  # Use dtype=tf.string for token sequences\n",
+    "    x = inputs\n",
+    "    x = TokenAndPositionEmbedding(sequence_length, max_features, embed_dim)(x)\n",
+    "    x = TransformerBlock(embed_dim, num_heads, ff_dim)(x)\n",
+    "    x = tf.keras.layers.GlobalAveragePooling1D()(x)\n",
+    "    x = tf.keras.layers.Dense(20, activation=\"relu\")(x)\n",
+    "    x = tf.keras.layers.Dropout(0.2)(x)\n",
+    "    outputs = tf.keras.layers.Dense(1, activation='sigmoid', kernel_initializer='glorot_uniform')(x)\n",
+    "    \n",
+    "    model = tf.keras.Model(inputs=inputs, outputs=outputs)\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "d7e96d24-775f-4a24-afbe-ee1ca1aca613",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def train_and_evaluate_model(model, train_df, test_df, epochs=20):\n",
+    "\n",
+    "    model.summary()\n",
+    "    # Compile the model\n",
+    "    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
+    "\n",
+    "    # Train the model and store the training history\n",
+    "    history = model.fit(train_df['tweets'], train_df['labels'], epochs=epochs, validation_data=(test_df['tweets'], test_df['labels']))\n",
+    "\n",
+    "    # Return the training history and the trained model\n",
+    "    return history, model\n",
+    "\n",
+    "# Function to plot the training and validation accuracy and loss\n",
+    "def plot_history(history, title):\n",
+    "    plt.figure(figsize=(12, 4))\n",
+    "    plt.suptitle(title)\n",
+    "\n",
+    "    plt.subplot(1, 2, 1)\n",
+    "    plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
+    "    plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
+    "    plt.xlabel('Epoch')\n",
+    "    plt.ylabel('Accuracy')\n",
+    "    plt.legend()\n",
+    "\n",
+    "    plt.subplot(1, 2, 2)\n",
+    "    plt.plot(history.history['loss'], label='Training Loss')\n",
+    "    plt.plot(history.history['val_loss'], label='Validation Loss')\n",
+    "    plt.xlabel('Epoch')\n",
+    "    plt.ylabel('Loss')\n",
+    "    plt.legend()\n",
+    "\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "791e9a7d-c820-4891-a90d-d735e402483a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ text_vectorization_1            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TextVectorization</span>)             │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ embedding (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">200</span>)       │     <span style=\"color: #00af00; text-decoration-color: #00af00\">4,000,000</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ simple_rnn (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SimpleRNN</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │        <span style=\"color: #00af00; text-decoration-color: #00af00\">42,112</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ global_max_pooling1d            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalMaxPooling1D</span>)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">65</span> │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ text_vectorization_1            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
+       "│ (\u001b[38;5;33mTextVectorization\u001b[0m)             │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ embedding (\u001b[38;5;33mEmbedding\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m200\u001b[0m)       │     \u001b[38;5;34m4,000,000\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ simple_rnn (\u001b[38;5;33mSimpleRNN\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │        \u001b[38;5;34m42,112\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ global_max_pooling1d            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
+       "│ (\u001b[38;5;33mGlobalMaxPooling1D\u001b[0m)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │         \u001b[38;5;34m8,256\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m65\u001b[0m │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">4,050,433</span> (15.45 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m4,050,433\u001b[0m (15.45 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">50,433</span> (197.00 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m50,433\u001b[0m (197.00 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">4,000,000</span> (15.26 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m4,000,000\u001b[0m (15.26 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 26ms/step - accuracy: 0.6744 - loss: 2.5699 - val_accuracy: 0.7412 - val_loss: 0.5907\n",
+      "Epoch 2/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 25ms/step - accuracy: 0.7452 - loss: 0.5691 - val_accuracy: 0.7999 - val_loss: 0.4954\n",
+      "Epoch 3/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 26ms/step - accuracy: 0.8169 - loss: 0.4712 - val_accuracy: 0.8221 - val_loss: 0.4505\n",
+      "Epoch 4/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 25ms/step - accuracy: 0.8587 - loss: 0.3968 - val_accuracy: 0.8587 - val_loss: 0.3947\n",
+      "Epoch 5/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 26ms/step - accuracy: 0.8911 - loss: 0.3382 - val_accuracy: 0.8588 - val_loss: 0.3893\n",
+      "Epoch 6/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 25ms/step - accuracy: 0.9054 - loss: 0.2986 - val_accuracy: 0.8657 - val_loss: 0.3855\n",
+      "Epoch 7/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 25ms/step - accuracy: 0.9207 - loss: 0.2636 - val_accuracy: 0.8693 - val_loss: 0.3695\n",
+      "Epoch 8/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 25ms/step - accuracy: 0.9322 - loss: 0.2390 - val_accuracy: 0.8706 - val_loss: 0.3826\n",
+      "Epoch 9/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 26ms/step - accuracy: 0.9396 - loss: 0.2179 - val_accuracy: 0.8677 - val_loss: 0.3845\n",
+      "Epoch 10/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 26ms/step - accuracy: 0.9491 - loss: 0.1968 - val_accuracy: 0.8678 - val_loss: 0.3851\n",
+      "Epoch 11/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 25ms/step - accuracy: 0.9566 - loss: 0.1778 - val_accuracy: 0.8654 - val_loss: 0.3894\n",
+      "Epoch 12/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 25ms/step - accuracy: 0.9625 - loss: 0.1639 - val_accuracy: 0.8607 - val_loss: 0.4155\n",
+      "Epoch 13/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 26ms/step - accuracy: 0.9643 - loss: 0.1531 - val_accuracy: 0.8655 - val_loss: 0.4120\n",
+      "Epoch 14/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 27ms/step - accuracy: 0.9663 - loss: 0.1484 - val_accuracy: 0.8643 - val_loss: 0.4265\n",
+      "Epoch 15/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 27ms/step - accuracy: 0.9654 - loss: 0.1498 - val_accuracy: 0.8635 - val_loss: 0.4705\n",
+      "Epoch 16/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 27ms/step - accuracy: 0.9718 - loss: 0.1344 - val_accuracy: 0.8641 - val_loss: 0.4588\n",
+      "Epoch 17/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 27ms/step - accuracy: 0.9779 - loss: 0.1163 - val_accuracy: 0.8517 - val_loss: 0.5346\n",
+      "Epoch 18/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 27ms/step - accuracy: 0.9695 - loss: 0.1364 - val_accuracy: 0.8678 - val_loss: 0.4296\n",
+      "Epoch 19/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 27ms/step - accuracy: 0.9763 - loss: 0.1170 - val_accuracy: 0.8667 - val_loss: 0.4584\n",
+      "Epoch 20/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 27ms/step - accuracy: 0.9733 - loss: 0.1225 - val_accuracy: 0.8634 - val_loss: 0.4627\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ text_vectorization_1            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TextVectorization</span>)             │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ embedding (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">200</span>)       │     <span style=\"color: #00af00; text-decoration-color: #00af00\">4,000,000</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │       <span style=\"color: #00af00; text-decoration-color: #00af00\">168,448</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ global_max_pooling1d_1          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalMaxPooling1D</span>)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">65</span> │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ text_vectorization_1            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
+       "│ (\u001b[38;5;33mTextVectorization\u001b[0m)             │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ embedding (\u001b[38;5;33mEmbedding\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m200\u001b[0m)       │     \u001b[38;5;34m4,000,000\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │       \u001b[38;5;34m168,448\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ global_max_pooling1d_1          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
+       "│ (\u001b[38;5;33mGlobalMaxPooling1D\u001b[0m)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │         \u001b[38;5;34m8,256\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m65\u001b[0m │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">4,176,769</span> (15.93 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m4,176,769\u001b[0m (15.93 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">176,769</span> (690.50 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m176,769\u001b[0m (690.50 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">4,000,000</span> (15.26 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m4,000,000\u001b[0m (15.26 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m64s\u001b[0m 67ms/step - accuracy: 0.6991 - loss: 2.3295 - val_accuracy: 0.7799 - val_loss: 0.5578\n",
+      "Epoch 2/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 65ms/step - accuracy: 0.7973 - loss: 0.5150 - val_accuracy: 0.8405 - val_loss: 0.4440\n",
+      "Epoch 3/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 65ms/step - accuracy: 0.8743 - loss: 0.3852 - val_accuracy: 0.8742 - val_loss: 0.3713\n",
+      "Epoch 4/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m63s\u001b[0m 68ms/step - accuracy: 0.9082 - loss: 0.3116 - val_accuracy: 0.8823 - val_loss: 0.3474\n",
+      "Epoch 5/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 66ms/step - accuracy: 0.9277 - loss: 0.2644 - val_accuracy: 0.8823 - val_loss: 0.3450\n",
+      "Epoch 6/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 65ms/step - accuracy: 0.9423 - loss: 0.2304 - val_accuracy: 0.8926 - val_loss: 0.3242\n",
+      "Epoch 7/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 65ms/step - accuracy: 0.9553 - loss: 0.1978 - val_accuracy: 0.8920 - val_loss: 0.3593\n",
+      "Epoch 8/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 65ms/step - accuracy: 0.9636 - loss: 0.1694 - val_accuracy: 0.8934 - val_loss: 0.3330\n",
+      "Epoch 9/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 65ms/step - accuracy: 0.9714 - loss: 0.1471 - val_accuracy: 0.9005 - val_loss: 0.3318\n",
+      "Epoch 10/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 65ms/step - accuracy: 0.9719 - loss: 0.1395 - val_accuracy: 0.8950 - val_loss: 0.3749\n",
+      "Epoch 11/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 66ms/step - accuracy: 0.9765 - loss: 0.1286 - val_accuracy: 0.8997 - val_loss: 0.3506\n",
+      "Epoch 12/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m64s\u001b[0m 69ms/step - accuracy: 0.9827 - loss: 0.1055 - val_accuracy: 0.8971 - val_loss: 0.3910\n",
+      "Epoch 13/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m63s\u001b[0m 68ms/step - accuracy: 0.9818 - loss: 0.1068 - val_accuracy: 0.8982 - val_loss: 0.3564\n",
+      "Epoch 14/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 65ms/step - accuracy: 0.9824 - loss: 0.1037 - val_accuracy: 0.8995 - val_loss: 0.3577\n",
+      "Epoch 15/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 65ms/step - accuracy: 0.9854 - loss: 0.0958 - val_accuracy: 0.8946 - val_loss: 0.3910\n",
+      "Epoch 16/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 65ms/step - accuracy: 0.9859 - loss: 0.0935 - val_accuracy: 0.8971 - val_loss: 0.3803\n",
+      "Epoch 17/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 65ms/step - accuracy: 0.9872 - loss: 0.0870 - val_accuracy: 0.9018 - val_loss: 0.3634\n",
+      "Epoch 18/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 65ms/step - accuracy: 0.9881 - loss: 0.0839 - val_accuracy: 0.8999 - val_loss: 0.4023\n",
+      "Epoch 19/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 65ms/step - accuracy: 0.9867 - loss: 0.0896 - val_accuracy: 0.8986 - val_loss: 0.3987\n",
+      "Epoch 20/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 65ms/step - accuracy: 0.9874 - loss: 0.0856 - val_accuracy: 0.9006 - val_loss: 0.4002\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_2\"</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ text_vectorization_1            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TextVectorization</span>)             │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ embedding (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">200</span>)       │     <span style=\"color: #00af00; text-decoration-color: #00af00\">4,000,000</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ gru (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GRU</span>)                       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │       <span style=\"color: #00af00; text-decoration-color: #00af00\">126,720</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ global_max_pooling1d_2          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalMaxPooling1D</span>)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">65</span> │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ text_vectorization_1            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
+       "│ (\u001b[38;5;33mTextVectorization\u001b[0m)             │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ embedding (\u001b[38;5;33mEmbedding\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m200\u001b[0m)       │     \u001b[38;5;34m4,000,000\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ gru (\u001b[38;5;33mGRU\u001b[0m)                       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │       \u001b[38;5;34m126,720\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ global_max_pooling1d_2          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
+       "│ (\u001b[38;5;33mGlobalMaxPooling1D\u001b[0m)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │         \u001b[38;5;34m8,256\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m65\u001b[0m │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">4,135,041</span> (15.77 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m4,135,041\u001b[0m (15.77 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">135,041</span> (527.50 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m135,041\u001b[0m (527.50 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">4,000,000</span> (15.26 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m4,000,000\u001b[0m (15.26 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 59ms/step - accuracy: 0.7017 - loss: 2.4430 - val_accuracy: 0.8201 - val_loss: 0.4999\n",
+      "Epoch 2/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 58ms/step - accuracy: 0.8406 - loss: 0.4599 - val_accuracy: 0.8705 - val_loss: 0.4068\n",
+      "Epoch 3/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 57ms/step - accuracy: 0.8873 - loss: 0.3619 - val_accuracy: 0.8829 - val_loss: 0.3504\n",
+      "Epoch 4/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 57ms/step - accuracy: 0.9142 - loss: 0.2892 - val_accuracy: 0.8862 - val_loss: 0.3372\n",
+      "Epoch 5/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 57ms/step - accuracy: 0.9391 - loss: 0.2337 - val_accuracy: 0.8953 - val_loss: 0.3259\n",
+      "Epoch 6/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 57ms/step - accuracy: 0.9551 - loss: 0.1899 - val_accuracy: 0.9048 - val_loss: 0.3041\n",
+      "Epoch 7/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 57ms/step - accuracy: 0.9645 - loss: 0.1636 - val_accuracy: 0.9030 - val_loss: 0.3132\n",
+      "Epoch 8/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 57ms/step - accuracy: 0.9715 - loss: 0.1392 - val_accuracy: 0.9033 - val_loss: 0.3263\n",
+      "Epoch 9/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 57ms/step - accuracy: 0.9771 - loss: 0.1219 - val_accuracy: 0.9001 - val_loss: 0.3438\n",
+      "Epoch 10/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 57ms/step - accuracy: 0.9778 - loss: 0.1173 - val_accuracy: 0.9040 - val_loss: 0.3335\n",
+      "Epoch 11/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 57ms/step - accuracy: 0.9821 - loss: 0.1038 - val_accuracy: 0.9045 - val_loss: 0.3545\n",
+      "Epoch 12/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 57ms/step - accuracy: 0.9835 - loss: 0.0979 - val_accuracy: 0.9081 - val_loss: 0.3537\n",
+      "Epoch 13/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 57ms/step - accuracy: 0.9848 - loss: 0.0914 - val_accuracy: 0.9021 - val_loss: 0.3622\n",
+      "Epoch 14/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 58ms/step - accuracy: 0.9847 - loss: 0.0919 - val_accuracy: 0.9064 - val_loss: 0.3560\n",
+      "Epoch 15/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 57ms/step - accuracy: 0.9875 - loss: 0.0832 - val_accuracy: 0.9092 - val_loss: 0.3576\n",
+      "Epoch 16/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 57ms/step - accuracy: 0.9880 - loss: 0.0782 - val_accuracy: 0.9005 - val_loss: 0.3947\n",
+      "Epoch 17/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 58ms/step - accuracy: 0.9886 - loss: 0.0767 - val_accuracy: 0.9017 - val_loss: 0.3763\n",
+      "Epoch 18/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 58ms/step - accuracy: 0.9890 - loss: 0.0741 - val_accuracy: 0.9064 - val_loss: 0.3722\n",
+      "Epoch 19/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 57ms/step - accuracy: 0.9918 - loss: 0.0648 - val_accuracy: 0.8973 - val_loss: 0.4163\n",
+      "Epoch 20/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 57ms/step - accuracy: 0.9889 - loss: 0.0722 - val_accuracy: 0.9061 - val_loss: 0.3871\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_19\"</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mModel: \"functional_19\"\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ input_layer_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ token_and_position_embedding    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">200</span>)       │     <span style=\"color: #00af00; text-decoration-color: #00af00\">4,020,000</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TokenAndPositionEmbedding</span>)     │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ transformer_block               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">200</span>)       │       <span style=\"color: #00af00; text-decoration-color: #00af00\">174,632</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TransformerBlock</span>)              │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ global_average_pooling1d        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">200</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePooling1D</span>)        │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">4,020</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span> │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ input_layer_3 (\u001b[38;5;33mInputLayer\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ token_and_position_embedding    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m200\u001b[0m)       │     \u001b[38;5;34m4,020,000\u001b[0m │\n",
+       "│ (\u001b[38;5;33mTokenAndPositionEmbedding\u001b[0m)     │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ transformer_block               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m200\u001b[0m)       │       \u001b[38;5;34m174,632\u001b[0m │\n",
+       "│ (\u001b[38;5;33mTransformerBlock\u001b[0m)              │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ global_average_pooling1d        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m200\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
+       "│ (\u001b[38;5;33mGlobalAveragePooling1D\u001b[0m)        │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_8 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m)             │         \u001b[38;5;34m4,020\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_9 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m21\u001b[0m │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">4,198,673</span> (16.02 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m4,198,673\u001b[0m (16.02 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">4,198,673</span> (16.02 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m4,198,673\u001b[0m (16.02 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 47ms/step - accuracy: 0.7660 - loss: 0.4645 - val_accuracy: 0.9445 - val_loss: 0.1610\n",
+      "Epoch 2/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 47ms/step - accuracy: 0.9605 - loss: 0.1262 - val_accuracy: 0.9466 - val_loss: 0.1914\n",
+      "Epoch 3/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 47ms/step - accuracy: 0.9816 - loss: 0.0623 - val_accuracy: 0.9478 - val_loss: 0.1674\n",
+      "Epoch 4/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 49ms/step - accuracy: 0.9871 - loss: 0.0422 - val_accuracy: 0.9425 - val_loss: 0.1818\n",
+      "Epoch 5/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 50ms/step - accuracy: 0.9888 - loss: 0.0383 - val_accuracy: 0.9423 - val_loss: 0.2419\n",
+      "Epoch 6/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 47ms/step - accuracy: 0.9934 - loss: 0.0250 - val_accuracy: 0.9429 - val_loss: 0.2336\n",
+      "Epoch 7/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 47ms/step - accuracy: 0.9952 - loss: 0.0181 - val_accuracy: 0.9348 - val_loss: 0.2591\n",
+      "Epoch 8/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 47ms/step - accuracy: 0.9911 - loss: 0.0288 - val_accuracy: 0.9329 - val_loss: 0.2966\n",
+      "Epoch 9/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m49s\u001b[0m 52ms/step - accuracy: 0.9941 - loss: 0.0224 - val_accuracy: 0.9433 - val_loss: 0.2902\n",
+      "Epoch 10/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 48ms/step - accuracy: 0.9955 - loss: 0.0153 - val_accuracy: 0.9294 - val_loss: 0.3957\n",
+      "Epoch 11/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 47ms/step - accuracy: 0.9964 - loss: 0.0133 - val_accuracy: 0.9340 - val_loss: 0.4706\n",
+      "Epoch 12/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 47ms/step - accuracy: 0.9963 - loss: 0.0120 - val_accuracy: 0.9206 - val_loss: 0.4804\n",
+      "Epoch 13/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 47ms/step - accuracy: 0.9949 - loss: 0.0165 - val_accuracy: 0.9353 - val_loss: 0.3438\n",
+      "Epoch 14/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 47ms/step - accuracy: 0.9979 - loss: 0.0073 - val_accuracy: 0.9286 - val_loss: 0.2950\n",
+      "Epoch 15/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 47ms/step - accuracy: 0.9965 - loss: 0.0113 - val_accuracy: 0.9297 - val_loss: 0.3862\n",
+      "Epoch 16/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 47ms/step - accuracy: 0.9978 - loss: 0.0067 - val_accuracy: 0.9341 - val_loss: 0.5645\n",
+      "Epoch 17/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 47ms/step - accuracy: 0.9969 - loss: 0.0098 - val_accuracy: 0.9361 - val_loss: 0.4557\n",
+      "Epoch 18/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 47ms/step - accuracy: 0.9976 - loss: 0.0067 - val_accuracy: 0.9321 - val_loss: 0.4835\n",
+      "Epoch 19/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 47ms/step - accuracy: 0.9983 - loss: 0.0064 - val_accuracy: 0.9376 - val_loss: 0.4607\n",
+      "Epoch 20/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 47ms/step - accuracy: 0.9983 - loss: 0.0058 - val_accuracy: 0.9302 - val_loss: 0.6734\n"
+     ]
+    }
+   ],
+   "source": [
+    "model_variations = [\n",
+    "    (rnn_model),\n",
+    "    (lstm_model),\n",
+    "    (gru_model),\n",
+    "    # (transformer_model) - Added below b/c different architecture\n",
+    "]\n",
+    "epochs = 20\n",
+    "\n",
+    "histories = []\n",
+    "models = []\n",
+    "\n",
+    "train_df, test_df = train_test_split(df, test_size=0.2, random_state=10)\n",
+    "\n",
+    "for model_func in model_variations:\n",
+    "    _model = model_func()\n",
+    "    history, model = train_and_evaluate_model(model=_model, train_df=train_df, test_df=test_df, epochs=epochs)\n",
+    "    histories.append(history)\n",
+    "    models.append(model)\n",
+    "\n",
+    "# Add the transformer model to the list of models\n",
+    "train_sequences = vectorization(train_df['tweets'])\n",
+    "test_sequences = vectorization(test_df['tweets'])\n",
+    "\n",
+    "trans_model = transformer_model()\n",
+    "\n",
+    "trans_model.summary()\n",
+    "trans_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
+    "trans_history = trans_model.fit(train_sequences, train_df['labels'], epochs=epochs, validation_data=(test_sequences, test_df['labels']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "11508b86-5822-4f9d-b72c-d1cf69a33fa0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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33303YP9y4OWXX+bnn3+uMZF/5plnuPDCCwF49NFHGTNmDCUlJXh4ePDaa68xZcoUbr31VgCeeOIJli5dSkFBwVm9zsLCQubPn8/ChQsZPXo0AP/6179YtmwZ77zzDo888giJiYn07duX/v37A/aZBiclJibSsWNHLrjgAgwGA7GxsWcVR2NQIi8iImTkFLNtfwbb92ewbX86aWdIRtzdTHSICqBjdACdYwPpFB1IaKBnnda5R4Z4M3Z4e8YOb09BcTmbdqeybmcqG3anUlhcDtinxe84kMmOA5ks+O9OWof6ONbVd20ThMlkJC27iFWbk1i5OYmDx3OrPI+LyUC/zuEM79uaQd0j8HDXW5/Ur8pT65XIi7Q02fml1c7sak5OJq0nWSwWnn32WRYtWkRSUhKlpaWUlpbi7V3zHh+9evVy3D45hT8tLa3Wx0RGRgKQlpZGTEwMe/bscXwxcNLAgQNZsWJFrV7XHx04cIDy8nKGDh3qaHN1dWXgwIHs2rULgLvuuotrr72WTZs2MXLkSMaNG0d8fDwAkydP5rLLLqNz585cfvnlXHnllYwcOfKsYmlo+jQjInIeys4vYcf+TLYdyGDbvvQaN24zGiAmwq/SaHtMuC+meizD5uPpyvC+UQzvG4XZYmXXoSzHaH3F2JLSC1j8834W/7wfXy9XwoO92X80p8r5DAbo2T6E4X1bE9+r1VlNzxepLT9vN9xcjJSZrWQokRdpcQJ9nbM8qj6f948J+pw5c3j55ZeZO3cuPXv2xNvbm+nTp1NWVlbjef64SZ7BYMBqrXlvm4rHnPzCv+IxfxwEsNlqngFYk5PHVnfOk22jR4/myJEjfPvttyxfvpxLLrmEe+65hxdffJF+/fpx6NAhvvvuO5YvX84NN9zApZdeyueff37WMTUUJfIiIueB/KIydhzIYNu+DLYdyCAxJf+0fV1djHRtE0TPDiF0bxdMh6gAPBtxFNvFZKRnhxB6dghhytU9OJaW71hXv+tQJidn+OcXlZNflFPp2E4xAQzvG8UFvVtpyrs0GoPBQEiAJ8czCknPLqr0gVFEmr/6mt7elKxatYqxY8fy5z//GbAn1vv27aNr166NGkfnzp1Zt24dEydOdLRt2LDhrM/XoUMH3Nzc+PXXX7npppsA+y79GzZsqLRxX2hoKJMnT2by5MkMGzaMRx55hBdffBEAPz8/JkyYwIQJE7juuuu4/PLLycrKqvMu/g1NibyISAtUVFLOzoOZbNufwbb9GRw6nsvpvuA2GQ10igmkV8cQenUIoUtsEG6upsYNuAZRYb5EhfkyfkRH8grL2LArlXUJKWzanUZxqZnocF8u7NuaYX1b0yqk5jX00jStXLmSF154gY0bN5KcnMzixYsZN27caft/+eWXzJ8/ny1btlBaWkr37t158sknGTVqVOMF/QehgfZEvrjUQmGJGR/P2tdeFhFpbB06dOCLL75gzZo1BAYG8tJLL5GSktLoifx9993H7bffTv/+/YmPj2fRokVs27aNdu3anfHYP+5+D9CtWzfuuusuHnnkEYKCgoiJieH555+nqKiIKVOmAPZ1+HFxcXTv3p3S0lL+97//OV73yy+/TGRkJH369MFoNPLZZ58RERFBQEBAvb7u+qBEXkSkmbNYrKTnFJOUXsCOA5ls25/O/mO5p92czmiADtEB9GwfQq8OoXRtG9SoI+7nws/bjYv7R3Nx/2jKzVaKSsrx83bT6GczV1hYSO/evbn11lu59tprz9h/5cqVXHbZZfzzn/8kICCAd999l6uuuorff/+dvn37NkLEVf2xlrwSeRFpyh5//HEOHTrEqFGj8PLy4o477mDcuHHk5lbda6Yh3XzzzRw8eJCHH36YkpISbrjhBiZPnsy6devOeOyNN95Ype3QoUM8++yzWK1WJk6cSH5+Pv379+eHH34gMDAQADc3N2bOnMnhw4fx9PRk2LBhfPrppwD4+Pjw3HPPsW/fPkwmEwMGDGDJkiUYjU2varvBdi6LEFqovLw8/P39yc3Nxc/Pz9nhiMh5zmazkVdYRmpWESmZhSeui0jNKiQls4j0nOIad5Q3GKBtpD+9Otqnq3dvG4y3koxm53x5bzIYDGccka9O9+7dmTBhAk888UStj6nPn+lH3+/m02X20aEnpgxiQLeIczqfiDhHSUkJhw4dom3btnh4eDg7nPPSZZddRkREBB988IGzQ2kQNf2O1eV9qXkMwYiItHAlZWbSsopIySoiNbOIlKxCUjOLSM2yJ+zFpZY6nS8mwpde7e2Je4/2Ifh5a7M3abmsViv5+flnXL94clfmk/Ly8uothj+OyIuIyJkVFRXx5ptvMmrUKEwmE5988gnLly9n2bJlzg6tyVMiLyLSQGw2G4UlZvIKSskrLCO3oJTcE9d5hWXk5Jc6RtnPtj6tt4cL4cHehAd5ER7kRcfoAHp2CCHQV6MIcv6YM2cOhYWF3HDDDTX2mz17NrNmzWqQGFSCTkSk7gwGA0uWLOHpp5+mtLSUzp0788UXX3DppZc6O7QmT4m8iEgtWaw28gvLyC0sJa+gzJ6cF5aSW1BG3okkPe/k/UJ7sm62nNvqJReTgdBALyKCvAgP9iYiyIuIE4l7RLAXPiqrJue5Tz75hCeffJKvv/6asLCwGvvOnDmTGTNmOO7n5eURHR1dL3GEVhiRT89WIi8iUhuenp4sX77c2WE0S0rkRUROsFhtZOYUO6azp2SdmNp+Yop7dn7JaXd+PxeBvu4nEnNvwoNPJe3hQV4E+3tiMmojN5HqLFq0iClTpvDZZ5/VavTG3d0dd/eGqQddKZHXiLyIiDQwJfIict6w2WzkFpSRmlV4IlmvnKinZRdhqWHTuNpyMRnx93HDz9sNf293/Hzc8PdxP3HfDT8fd/y97W2hgZ54uOlPsUhdffLJJ9x222188sknjBkzxtnh4OHugq+XK/lF5UrkRUSkwenTo4i0OOVmKzsPZnAkJd+RqKdkFZKWVURJWd02jTspwNed0ABP/H3cTyTpJ5PxU4m5n7f9MU93F5VDE6mDgoIC9u/f77h/6NAhtmzZ4qgBPHPmTJKSknj//fcBexI/adIkXnnlFQYPHkxKSgpgn6Lp7+/vlNcA9g3v8ovKycotxmK1aTaNiIg0GCXyItIiFJWUs3F3Gr/tSGbDrlSKSsx1Ot7Lw8WxYVx40InN405Mcw8L8tKouUgD2rBhAyNGjHDcP7mO/ZZbbmHhwoUkJyeTmJjoePytt97CbDZzzz33cM899zjaT/Z3ltAALw4dz8NssZGTX0Kwv+eZDxIRETkL+mQqIs1Wdn4J63am8NuOFLbsTcdssZ62r4vJSHiQ56kk3ZGo29el+3i6ahRdxEkuuugibDVsQPHH5Pznn39u2IDO0h93rlciLyIiDUWJvIg0K8czCvhtewq/7Uhm95Gsajef8/Z0ZUC3cHp3CCUyxJuIYC8CfT0wapqriDSgKrXkY50YjIiItGhK5EWkSbPZbBw4lstvO5L5bUcyR1Lyq+0X7O/B4B6RDO4RQY/2IbiYjI0cqYic71SCTkSas4suuog+ffowd+5cANq0acP06dOZPn36aY8xGAwsXryYcePGndNz19d5zidO/6Q7b9482rZti4eHB3FxcaxatarG/m+88QZdu3bF09OTzp07Oza+OWnhwoUYDIYql5KSkoZ8GSJSj8wWK1v3pfPW4m3c9vQyHpz7C4uW762SxEeH+3L9JR2Z88Bw3n18JNPG96JPpzAl8SLiFBWn1mdo53oRaSRXXXXVaUtwrl27FoPBwKZNm+p83vXr13PHHXeca3iVPPnkk/Tp06dKe3JyMqNHj67X5/qjhQsXEhAQ0KDP0ZicOiK/aNEipk+fzrx58xg6dChvvfUWo0ePJiEhgZiYmCr958+fz8yZM/nXv/7FgAEDWLduHbfffjuBgYFcddVVjn5+fn7s2bOn0rEeHh4N/npE5OyVlJrZvDeN33aksG5nCgXF5VX6GAzQOSaQIT0jGdQjktahPk6IVESkeiGqJS8iTjBlyhTGjx/PkSNHiI2tvKZnwYIF9OnTh379+tX5vKGhofUV4hlFREQ02nO1FE4dtnrppZeYMmUKU6dOpWvXrsydO5fo6Gjmz59fbf8PPviAO++8kwkTJtCuXTtuvPFGpkyZwnPPPVepn8FgICIiotJFRJqWguJyNuxK5f0lCTz6xq/c9MR3/HPhelZsOFopiXcxGejXJYx7ruvNwidG8cL9wxk/oqOSeBFpcoL9PDi5FUd6dpFzgxGR88aVV15JWFhYlY1Bi4qKWLRoEVOmTCEzM5M//elPREVF4eXlRc+ePfnkk09qPG+bNm0c0+wB9u3bx/Dhw/Hw8KBbt24sW7asyjF//etf6dSpE15eXrRr147HH3+c8nL757qFCxcya9Ystm7d6pg1fTJmg8HAV1995TjP9u3bufjii/H09CQ4OJg77riDgoICx+OTJ09m3LhxvPjii0RGRhIcHMw999zjeK6zkZiYyNixY/Hx8cHPz48bbriB1NRUx+Nbt25lxIgR+Pr64ufnR1xcHBs2bADgyJEjXHXVVQQGBuLt7U337t1ZsmTJWcdSG04bkS8rK2Pjxo08+uijldpHjhzJmjVrqj2mtLS0ysi6p6cn69ato7y8HFdXV8BejzY2NhaLxUKfPn34v//7P/r27XvaWEpLSyktLXXcz8vLO9uXJSKnkZFTzM6DmSQcyiThUBZHUvKq3agOwNPdhQFdwxncI5K4rmF4ebg2brAiImfBZDIS5OdBRm4JGTla0icijcPFxYVJkyaxcOFCnnjiCUcVns8++4yysjJuvvlmioqKiIuL469//St+fn58++23TJw4kXbt2jFo0KAzPofVamX8+PGEhITw22+/kZeXV+3aeV9fXxYuXEirVq3Yvn07t99+O76+vvzlL39hwoQJ7Nixg++//57ly5cD4O/vX+UcRUVFXH755QwePJj169eTlpbG1KlTuffeeyt9WfHTTz8RGRnJTz/9xP79+5kwYQJ9+vTh9ttvr/PP0GazMW7cOLy9vfnll18wm83cfffdTJgwwVEp5eabb6Zv377Mnz8fk8nEli1bHPnnPffcQ1lZGStXrsTb25uEhAR8fBp20MlpiXxGRgYWi4Xw8PBK7eHh4aSkpFR7zKhRo/j3v//NuHHj6NevHxs3bmTBggWUl5eTkZFBZGQkXbp0YeHChfTs2ZO8vDxeeeUVhg4dytatW+nYsWO15509ezazZs2q99cocr6yWm0cTcsn4aA9aU84lEnaGTZ+igz2plfHEIb0jKRXhxBcXUyNFK2ISP0JDfQiI7eEnIJSysotuLnqb5lIc/fo0tnklDT+QF+Ahx/PjpxZq7633XYbL7zwAj///DMjRowA7NPqx48fT2BgIIGBgTz88MOO/vfddx/ff/89n332Wa0S+eXLl7Nr1y4OHz5MVFQUAP/85z+rrGv/+9//7rjdpk0bHnroIRYtWsRf/vIXPD098fHxwcXFpcYZ0x999BHFxcW8//77eHt7A/D6669z1VVX8dxzzznyx8DAQF5//XVMJhNdunRhzJgx/Pjjj2eVyC9fvpxt27Zx6NAhoqOjAfts8O7du7N+/XoGDBhAYmIijzzyCF26dAGolFsmJiZy7bXX0rNnTwDatWtX5xjqyum71v+xbrPNZjttLefHH3+clJQUBg8ejM1mIzw8nMmTJ/P8889jMtnfKAcPHszgwYMdxwwdOpR+/frx2muv8eqrr1Z73pkzZzJjxgzH/by8PMc/oIicWbnZwv6juSQcymTnoUx2Hcqqdo37SUYDtG3tT7e2wXRrG0S3tsEE+WkfCxFp/kIDPNl14nZGbjGtQrQMSKS5yynJI6s4x9lh1KhLly7Ex8ezYMECRowYwYEDB1i1ahVLly4FwGKx8Oyzz7Jo0SKSkpIcM5JPJspnsmvXLmJiYhxJPMCQIUOq9Pv888+ZO3cu+/fvp6CgALPZjJ+fX51ey65du+jdu3el2IYOHYrVamXPnj2ORL579+6OHBAgMjKS7du31+m5Kj5ndHR0pRywW7duBAQEsGvXLgYMGMCMGTOYOnUqH3zwAZdeeinXX3897du3B+D+++/nrrvuYunSpVx66aVce+219OrV66xiqS2nJfIhISGYTKYqo+9paWlVRulP8vT0ZMGCBbz11lukpqYSGRnJ22+/ja+vLyEhIdUeYzQaGTBgAPv27TttLO7u7ri7u5/9ixE5z5SbrWzbn35iqnwWexOzKTdbT9vfzdVEl9hAup5I2rvEBmq6vIi0SCF/KEGnRF6k+QvwqFsi6qznnTJlCvfeey9vvPEG7777LrGxsVxyySUAzJkzh5dffpm5c+fSs2dPvL29mT59OmVlZbU6t62a9ZB/HHz97bffuPHGG5k1axajRo3C39+fTz/9lDlz5tTpddQ0sFux/eS09oqPWa2n/zx6Ns9Zsf3JJ5/kpptu4ttvv+W7777jH//4B59++inXXHMNU6dOZdSoUXz77bcsXbqU2bNnM2fOHO67776ziqc2nJbIu7m5ERcXx7Jly7jmmmsc7cuWLWPs2LE1Huvq6ur4NujTTz/lyiuvxGisft8+m83Gli1bHNMcROTsWaw2Vm4+xoff7yYt6/QbOfl6udGtbRDd29lH3Nu1DsDVRSXhRKTlq1iCTrXkRVqG2k5vd7YbbriBBx54gI8//pj33nuP22+/3ZGErlq1irFjx/LnP/8ZsK9537dvH127dq3Vubt160ZiYiLHjx+nVatWgL20XUWrV68mNjaWxx57zNF25MiRSn3c3NywWCxnfK733nuPwsJCx6j86tWrMRqNdOrUqVbx1tXJ13f06FHHqHxCQgK5ubmVfkadOnWiU6dOPPjgg/zpT3/i3XffdeSy0dHRTJs2jWnTpjkqrbXIRB5gxowZTJw4kf79+zNkyBDefvttEhMTmTZtGmCf8p6UlOSoFb93717WrVvHoEGDyM7O5qWXXmLHjh289957jnPOmjWLwYMH07FjR/Ly8nj11VfZsmULb7zxhlNeo0hLYLPZWL8rlQ+W7OJwctU1YhHBXiemydsT96gwn9N+kyoi0pJVHJHPyFUiLyKNx8fHhwkTJvC3v/2N3NxcJk+e7HisQ4cOfPHFF6xZs4bAwEBeeuklUlJSap3IX3rppXTu3JlJkyYxZ84c8vLyKiXsJ58jMTGRTz/9lAEDBvDtt9+yePHiSn3atGnDoUOH2LJlC1FRUfj6+laZGX3zzTfzj3/8g1tuuYUnn3yS9PR07rvvPiZOnHjamdu1ZbFY2LJlS6U2Nzc3Lr30Unr16sXNN9/M3LlzHZvdXXjhhfTv35/i4mIeeeQRrrvuOtq2bcuxY8dYv3491157LQDTp09n9OjRdOrUiezsbFasWFHrn+3ZcmoiP2HCBDIzM3nqqadITk6mR48eLFmyxFH/MDk5mcTEREd/i8XCnDlz2LNnD66urowYMYI1a9bQpk0bR5+cnBzuuOMOUlJS8Pf3p2/fvqxcuZKBAwc29ssTaRF2HszkvW8T2HU4q1J7306hXDYolm5tgwj29zzN0SIi55fQAI3Ii4jzTJkyhXfeeYeRI0cSExPjaH/88cc5dOgQo0aNwsvLizvuuINx48aRm5tbq/MajUYWL17MlClTGDhwIG3atOHVV1/l8ssvd/QZO3YsDz74IPfeey+lpaWMGTOGxx9/nCeffNLR59prr+XLL79kxIgR5OTk8O6771b6wgHAy8uLH374gQceeIABAwbg5eXFtddey0svvXROPxuwVzf7YzWz2NhYDh8+zFdffcV9993H8OHDMRqNXH755bz22msAmEwmMjMzmTRpEqmpqYSEhDB+/HjHhukWi4V77rmHY8eO4efnx+WXX87LL798zvHWxGCrbsHDeS4vLw9/f39yc3PrvDmDSEtx6Hgu7y/ZxYZdqZXaO8UEMOmKbvTuGOqkyETOT3pvqn8N8TPNKyzj5ie+A6Bf5zBm3VF1MygRabpKSko4dOgQbdu2rVL2WqQ+1PQ7Vpf3JafvWi8iTUtyRiEffb+bXzYfq9QeHe7DxNFdGdwjUtPmRUROw9fLFTdXE2XlFtJzTr+XiIiIyLlQIi8iAGTllfDpsj0s/e0IFuupiTohAZ7cPKoLI/pHYzIqgRcRqYnBYCA0wJOk9ALSs4tr3H1ZRETkbCmRFznPFRSX8+VP+/h65UHKyk/tIurn7cYNl3Zi9JA2uLmaajiDiIhUdDKRLymzUFhcjo+Xm7NDEhGRFkaJvMh5qqTMzLe/HuLzFfsoKC53tHu6mxh3YQfGXdhetd5FRM5CpRJ0OcVK5EVEpN4pkRc5z5gtVpavS+STpXvIyitxtLuYjFwR34brL+lEgK97DWcQEZGa/HHn+rat/J0YjYiItERK5EXOE1arjdVbj/Ph97s4nlHoaDcaYET/aG4a2YWwIC8nRigi0jJUrCWfnqMSdCLNkQp7SUOpr98tJfIiLVxBcTmbdqfy5c/7OXCscq3QwT0i+PPorsRGqJSViEh9qTS1Pls714s0J66u9mWFRUVFeHp6nqG3SN2VlZUB9tr050KJvEgLdDy9gHUJKaxPSGXnwcxKu9AD9GgfzC1jutElNshJEYqItFwVR+Qzckpq6CkiTY3JZCIgIIC0tDQAvLy8VHlC6o3VaiU9PR0vLy9cXM4tFVciL9ICmC1Wdh3KOpG8p5CUXlhtv3at/bnlim707RyqNyURkQZSeWq9RuRFmpuIiAgARzIvUp+MRiMxMTHn/FlcibxIM5VfVMbGXamsT0hl4540CivsPF9RZLA3A7qHM7BbBD3bh2BULXgRkQbl4eaCn7cbeYVlWiMv0gwZDAYiIyMJCwujvLz6z1ciZ8vNzQ2j0XjO51EiL9JM2Gw2jqUVsD4hhXUJqew6lIm1mr0yjEYDXdsEMbBbOAO6RRAV5qPRdxGRRhYS4EleYRmZuSVYrDZM+hJVpNkxmUznvI5ZpKEokRdpwsrNVnYezGB9gn3kPTmz+inz3p6uxHUJY0C3COK6hOGrmsUiIk4VGuDJwaRcrFYb2Xkllabbi4iInCsl8iJNjM1mY832ZFZtTmLTnjSKS83V9msd6sOAbuEM7B5B1zZBuJjOfYqOiIjUj9BKG94VK5EXEZF6pURepAk5mJTLm19uY9fhrCqPmYwGurcLZkC3CAZ2C6dVqI8TIhQRkdqoXIKumC5tnBeLiIi0PErkRZqAgqIyPvx+N9+tOVRp3buvlytxXcMZ2DWCvl3C8PF0dV6QIiJSa6EBXo7b2rleRETqmxJ5ESeyWm0sX5/Ie98mkFdY5mhvHerNbVf3IK5zGCZNmRcRaXYql6DTzvUiIlK/lMiLOMm+o9m8+eU29ibmONo83ExMuKwzY4e3x9VFCbyISHP1x6n1IiIi9UmJvEgjyy0o5YPvdrH09yPYKkyjH9anNbdd1V0bIomItACBfh4YjQasVhsZuUrkRUSkfimRF2kkFquNH347zAdLdlFQXO5ojw73Zdr4nvTqEOrE6EREpD6ZjAaC/T1Izy7WiLyIiNQ7JfIijWD34Szmf7mNg0m5jjZPdxduGtWFKy9oq9JxIiItUGiAJ+nZxeQVllFSZsbDTR+7RESkfugdRaQBZeeXsPB/CazYcLRS+4i4KG69sjuBfh5OikxERBpaxaVSmbkltFbZUBERqSdK5EUagMVi5dvVh/joh90UlZgd7W1b+XHnNb3o3i7YidGJiEhjCK24c312kRJ5ERGpN0rkRerZ9gMZvPXlNo6k5DvavD1dmXh5Fy4f0kbl5EREzhOhgadqyWeoBJ2IiNQjJfIi9SQzt5gF/93Jys1JldovGxjDLWO64e/j7qTIRETEGSqPyCuRFxGR+qNEXuQcWSxWvl55gE+X7aG41OJo7xAdwF3je9EpJtCJ0YmIiLNUqiWvEXkREalHSuRFzkFeYRkvfLCBLfvSHW2+Xm7cMqYrlw6MxWQ0ODE6ERFxpoqb3SmRFxGR+qTFuiJn6XByHjPm/uJI4g0GGB3fhrdmXsKowW2UxIuI1NLKlSu56qqraNWqFQaDga+++uqMx/zyyy/ExcXh4eFBu3btePPNNxs+0Dry8XTFw80EaGq9iIjULyXyImdh9bbjPPLqSlKzigAI8HFn9t0XcPe1vfH1cnNydCIizUthYSG9e/fm9ddfr1X/Q4cOccUVVzBs2DA2b97M3/72N+6//36++OKLBo60bgwGg2N6fUZuMTabzckRiYhIS6Gp9SJ1YLXa+OiH3fxn+V5HW4cof/42eVCltZAiIlJ7o0ePZvTo0bXu/+abbxITE8PcuXMB6Nq1Kxs2bODFF1/k2muvbaAoz06IvydHUwsoLbOQX1SOn7e+7BURkXOnRF6klgqLy5nz8UbWJ6Q62i6Ki+Le6/vg7mpyYmQiIueXtWvXMnLkyEpto0aN4p133qG8vBxXV9dqjystLaW0tNRxPy8vr0HjhMol6NKzi5TIi4hIvdDUepFaOJaWz0OvrHQk8UYDTLm6OzP+1E9JvIhII0tJSSE8PLxSW3h4OGazmYyMjNMeN3v2bPz9/R2X6Ojohg610oZ3qiUvIiL1RYm8yBls2JXKQ6+sJCm9ALBvXjTrjiGMu7ADBoM2tBMRcYY//v09uf68pr/LM2fOJDc313E5evRog8YIf6glr0ReRETqiabWi5yGzWbj8xX7+OC7XZzcnyg2wpe/3zaIiGBv5wYnInIei4iIICUlpVJbWloaLi4uBAcHn/Y4d3d33N3dGzq8Sirun6IReRERqS9K5EWqUVJqZu6izazeetzRFt8rkuk39sPTXf9tREScaciQIfz3v/+t1LZ06VL69+9/2vXxzlJpRF4l6EREpJ4oIxH5g5TMQp55dx2Hk+2bIBkMcPPlXbjhkk6aSi8i0gAKCgrYv3+/4/6hQ4fYsmULQUFBxMTEMHPmTJKSknj//fcBmDZtGq+//jozZszg9ttvZ+3atbzzzjt88sknznoJpxWsqfUiItIAlMiLVLB1XzrPvb+B/KIyADzdXXj45jgGdo9wcmQiIi3Xhg0bGDFihOP+jBkzALjllltYuHAhycnJJCYmOh5v27YtS5Ys4cEHH+SNN96gVatWvPrqq02u9ByAu6sJfx83cgvKlMiLiEi9USIvgn09/DerDrLgvzuxWu0L4luFePP32wYRHe7r5OhERFq2iy66yLFZXXUWLlxYpe3CCy9k06ZNDRhV/QkN8CS3oIys3GIsFismk/YaFhGRc6NEXs57ZeUW3vh8Kys2nNq9uH/XcB66OQ4fz6a11lJERJqf0EAv9h/LxWqDzLwSwirUlhcRETkbSuTlvJaRU8w/F65j39EcR9v1l3Tk5su7YjJqPbyIiJy7P9aSVyIvIiLnyulzu+bNm0fbtm3x8PAgLi6OVatW1dj/jTfeoGvXrnh6etK5c2fHxjcVffHFF3Tr1g13d3e6devG4sWLGyp8acYSDmXy4NxfHEm8u5uJv0zsz6QruimJFxGReqOd60VEpL45NZFftGgR06dP57HHHmPz5s0MGzaM0aNHV9rQpqL58+czc+ZMnnzySXbu3MmsWbO45557KpWgWbt2LRMmTGDixIls3bqViRMncsMNN/D777831suSZuD7tYd5bP5qcvJLAQgL8uKF+4YxrE9rJ0cmIiItzR9H5EVERM6VwVbT7jINbNCgQfTr14/58+c72rp27cq4ceOYPXt2lf7x8fEMHTqUF154wdE2ffp0NmzYwK+//grAhAkTyMvL47vvvnP0ufzyywkMDKx1WZq8vDz8/f3Jzc3Fz8/vbF+eNEGFxeW8/dX2Suvhe3UI4S8T++Pv4+7EyEREaqb3pvrXWD/T3UeyeORV+4zDMUPbMm18rwZ7LhERab7q8r7ktBH5srIyNm7cyMiRIyu1jxw5kjVr1lR7TGlpKR4eHpXaPD09WbduHeXl5YB9RP6P5xw1atRpz3nyvHl5eZUu0vJs25/OfXN+qpTEXz2sHU/dMURJvIiINBhNrRcRkfrmtEQ+IyMDi8VCeHh4pfbw8HBSUlKqPWbUqFH8+9//ZuPGjdhsNjZs2MCCBQsoLy8nIyMDgJSUlDqdE2D27Nn4+/s7LtHR0ef46qQpKS238O+vd/DY/DWOD1Ce7i7MuKkft4/rqTJAIiLSoAJ8PRx7r2hqvYiI1AenZzAGQ+VNxWw2W5W2kx5//HFGjx7N4MGDcXV1ZezYsUyePBkAk8l0VucEmDlzJrm5uY7L0aNHT9tXmpf9R3N48OWf+XrlAUdbz/YhvP7wCEbE6QsbERFpeCajgeATo/LpOUVOjkZERFoCpyXyISEhmEymKiPlaWlpVUbUT/L09GTBggUUFRVx+PBhEhMTadOmDb6+voSEhAAQERFRp3MCuLu74+fnV+kizZvZYuWTpXt4+NWVHE0tAMDVxcjUsT14elo8YUEq/SMiIo3n5PT6/KJySkrNTo5GRESaO6cl8m5ubsTFxbFs2bJK7cuWLSM+Pr7GY11dXYmKisJkMvHpp59y5ZVXYjTaX8qQIUOqnHPp0qVnPKe0HMfS8vnLa6v4+IfdWKz2vRzbR/kz98ELGTu8PUaVlhMRkUZWaZ28pteLiMg5cnHmk8+YMYOJEyfSv39/hgwZwttvv01iYiLTpk0D7FPek5KSHLXi9+7dy7p16xg0aBDZ2dm89NJL7Nixg/fee89xzgceeIDhw4fz3HPPMXbsWL7++muWL1/u2NVeWi6r1ca3qw+x8H87KTNbATAaDVx/SUduvKwzLloLLyIiThIaWDmRjw73dWI0IiLS3Dk1kZ8wYQKZmZk89dRTJCcn06NHD5YsWUJsbCwAycnJlWrKWywW5syZw549e3B1dWXEiBGsWbOGNm3aOPrEx8fz6aef8ve//53HH3+c9u3bs2jRIgYNGtTYL08aUXp2Ma8s2sTWfRmOttah3jz4p350jg1yYmQiIiKqJS8iIvXLqXXkmyrV6m0+bDYbP208xtuLt1FYcmrN4ZUXtOWWMd3wcHPqd1UiIvVG7031rzF/pusTUnjqnd8BuPGyztx8eZcGfT4REWl+6vK+pCxHmq3cglLe+Hwra7cnO9pC/D144Ma+9OkU5sTIREREKtOIvIiI1Ccl8tIsrUtI4bX/bCEnv9TRdlFcFHde0wsfT1cnRiYiIlJVaOCpaikqQSciIudKibw0K0Ul5bzzzU6W/n7E0ebr5cY91/VmaO9WToxMRETk9Lw9XPB0d6G41Ex6tkbkRUTk3CiRl2Zjx4EMXv50M2lZp0YyBnQL577r+xDo5+HEyERERGpmMBgICfDkaGo+GTnF2Gw2DAaVQxURkbOjRF6avLJyCx9+v5uvftnPya0ZPd1NTB3bk8sGxuiDkIiINAuhgfZEvsxsJa+wDH8fd2eHJCIizZQSeWnSSsrMPPHWWnYdznK0dW8XzPQb+xIR7O3EyEREROomNKByLXkl8iIicraUyEuTZbXamPvJZkcS72IyMnF0V8Ze2B6TUaPwIiLSvFRK5LOL6RAV4LxgRESkWVMiL03Wh9/vYvW244B9Kv0zdw2lY3Sgk6MSERE5O6GBFUfktXO9iIicPaOzAxCpzvJ1iXz24z4AjAZ45M/9lcSLiEizVrmWfIkTIxERkeZOibw0Odv3Z/DG51sc96eO7cmAbhHOC0hERKQehAZUqCWfrRF5ERE5e0rkpUlJSi/gnwvXYbbYt6e/cmhbrhrWzslRiYiInLuQgFOlUjNyVEteRETOnhJ5aTLyCsuY9e/fKCguByCuSxhTx/ZwclQiIiL1w9XFRICvfaf6dCXyIiJyDpTIS5NQbrbwz4XrSM4oBKBNpB9/mdgfk0m/oiIi0nKc3Lk+K68Es8Xq5GhERKS5UpYkTmez2Xj9s63sPJgJQICvO49PGYSXh6uTIxMREalfJze8s9kgK1cb3omIyNlRIi9O958f97Jiw1EA3FxNPH7bIMICvc5wlIiISPNTuQSdpteLiMjZUSIvTrVqcxIffrfbcX/GTf3oFKMycyIi0jJp53oREakPSuTFaXYfzuLlTzc57t8yphtDe7VyYkQiIiINKzRAI/IiInLulMiLU6RkFvL0u79TbrZv9HPZwBiuHdHByVGJiIg0LE2tFxGR+qBEXhpdYXE5T73zO7kFZQD06hDCXdf2xmAwODkyERGRhhVSYUReteRFRORsKZGXRmW2WHn2/fUcTc0HoHWoDzNvGYCri34VRUSk5QvwccfFZP/iOj1bibyIiJwdZU/SaGw2G28t3s6WvekA+Hq58cTUQfh4uTk5MhERkcZhNBoco/KaWi8iImfLxdkByPnj65UH+X7tYQBcTEYeu3UgrUJ8nBtUA7PZbBSXl1BQXkRhWRGFZYUUlJ24XV506nY1901GExE+oUT4htHKN5wIn1AifcOI8AnD3UVffoiINFchAZ6kZBZRWFxOUUk5Xh6uzg5JRESaGSXy0ih+35HMgv/ucNy/f0IfurcLdmJE567EXMqx3GSO5BwjMfc4OSV5jiTckbiXF2Gz2c76OXJK8tidcaBKe7BnoD2p9w0j0ieMSF/7Jdw7BBeT/luLiDRloX9YJx8ToUReRETqRp/4pcHtP5bDCx9t5GQ+e+NlnRkRF+3coOrAarOSVphJYk4SR3KOcSQ3iaM5x0kpSMfG2Sfp1TFgwMvVA283L8os5eSU5FXbL7M4m8zibHak7al8vMFAmFewPcH/Q5If4hWEAQM2bNhsNqzY4MS1zWZztFe8XaVPhccwGAjw8MPNpA+g4jzZxbm4GE14unjoSyxpNipveFdCTISfE6MREZHmSJ96pEFl5hbzf+/8TmmZBYDhfVtz06jOTo7q9ArKCknMOU5ibhJHcpJIzE0iMfc4pebSWp/DgAEvN098XL3wdjt1qXjf52S7a4Xbbl54uXpiNJzauqK4vITk/DRSCtJIzj9xOXG7oKywynPbbDZSCzNILcxga0pCvfxMzsTfw48Qr0BCvYIJ8QokxDuIEK8gQr3t933cvFWRQOpdakE6c9e+w4GsI442V6MLnq4eeLp42K9P3PZw9cDrD21Vbp+49nL1xMvVU7+z0qBCA70ct9NzipwYiYiINFdK5KXBFJeaeeqd38nKKwGga5sgHpjQt0l8QDZbLSTnpzqS9ZPXmUXZtTrezeRKtH8rYv1bExPQmtiAKEK9gvB288LT1aNSMn4uPF09aBcUQ7ugmCqPFZQWOpJ6+yWV5II0UvLTKTaX1Mvz10ZuSR65JXmVEqqK3F3cCfUKOpHkB59K+r3t14Ge/piMpkaLV5q/LckJvPLbOxSWVU6Ayq1myksLyCstOKfz+7h5E+0fSWu/SKL9Ionyt1/7e/g1ib9f0vxVnFqvnetFRORsKJGXBmGx2pjz0UYOJuUCEB7kxWO3DsTN1XkJW3F5CRuPb2fN0Y1sTUmg3FJeq+PCvUNOJOutiTmRuEd4h2I0Orfog4+7Nx3d29IxuG2ldpvNRm5JXuUkvyCNnOI8DNin3xsMRsdto8EA2K8NVH6sUj+MYAAjBgwGAxableyiHNKLssguzj3tMoNScynH8pI5lpdc7eNGg5EgzwBCvAKJCWhNl5D2dAnpQIh3UH3+uM5KmbmM/VmH2ZW+n90Z+8ksyqk0auvt6omXm6fjfsWLd4X2+vxy53xms9n4evdSPtn2teP3Lcw7mNZ+kRSXF1NsLrVfl5dQZC7BYrWc1fMUlBWyK30/u9L3V2r3dvOyJ/Ynkvsov0ii/VsRoAS/3sybN48XXniB5ORkunfvzty5cxk2bNhp+3/00Uc8//zz7Nu3D39/fy6//HJefPFFgoOb9h4slRJ57VwvIiJnQYm8NIiF/9vJ7ztTAPD2cOEfUwfj7+Pe6HGUmEvZnLyDNYkb2ZS8o8bk3cvV05Gsn7yO9m+Fp6tHI0Z87gwGAwGe/gR4+tM1tGOjPKfZaiGrKJuMoizSC7NIL8oioyiLjEL7dXpR1ml/9lab1d63KIvdGQdYun8lYN/Qr3Noe7qEtKdzSHti/Vs3+JcnhWVF7Mk4YE/c0/ezP/vIWSeDf1TlC4ATF3cXd9xNrri5uOFucsPdxQ03kxseLu64mVxxP9HuduKxk/1PttXmZ2Kz2bBYLfYRa6sZs8WM+cTtcsftcsxWS+X7FgttAqOIDYiql5/BuSgpL2Heug/47dgmR1v/Vr24d9BkvNw8qz2m3FJeIbkvpdhc8bqkctuJLwIKygpIykslqzinyvkKy4rYnXGgygaU3q6eJ5L7VkT5RRDt34oov0gCPf2V4NfBokWLmD59OvPmzWPo0KG89dZbjB49moSEBGJiqs5K+vXXX5k0aRIvv/wyV111FUlJSUybNo2pU6eyePFiJ7yC2gsNrLzZnYiISF0ZbOeypXYLlZeXh7+/P7m5ufj5aQOauvpuzSHmfbENAJPRwKzbh9C7U2ijPX+ZuYzNKTtZm7iRjce3U2opq9InwMOPbmGdTk2N929NsFegPnQ3EJvNRl5pPhlF2aQXZpJRlE1GYWalhD+/mjX/FXm6eNAxuC1dQu2JfcegNnic45cs2cW5jqR9V8Z+EnOSatzA0NXkWuuZHI3F1eiCm4sbHiZ74m+xWewJ+YmEvdxqT8zPxfA2g5jYezz+Hs75e5icn8aLv77J0QqzOm7ocSXju41usJkORWXFjpkkR3Pt18dyk8ksrt3yG7B/OXhy9P6KjiOICWh9TjG19PemQYMG0a9fP+bPn+9o69q1K+PGjWP27NlV+r/44ovMnz+fAwdOfbHy2muv8fzzz3P06NFaPaczf6YTHvuWohIzkSHevD3z0kZ9bhERaZrq8r6kEXmpV0eS83hz8XbH/buu7dUoSXy5pZytKQmsObqJDUlbKalmczo/dx8GR/UjPiaOLiEdnD41/nxiMBjw9/DD38OP9kGx1fYpKitmf9ZhdmfsZ0/GAfZmHq60yWCxuYRtqbvYlroLsE/JbxMQReeQ9o7kPsgz4LQx2Gw2UgrSKyXuqQXpNcYd6RNGl9AOdD1xCfMOwWazUWQupqi8hKKyIorKi09cSiiscL/wxHVxeTFFZafuF5UXU1aPXwaUW82Ul5kppOE2zFp5+Hc2Jm3jT73Gcmm7YY36f2fT8e28+tu7FJXbRy09XT24f/BtxLXq2aDP6+XmSaeQdnQKaVepvai8mGO5yRzLS+FY7nF7op+XXO3+GkXlxezNPMjezIOMaDukQeNt7srKyti4cSOPPvpopfaRI0eyZs2aao+Jj4/nscceY8mSJYwePZq0tDQ+//xzxowZc9rnKS0tpbT01N+VvLzqK4M0htAAT46k5JORU4zVasNo1BfJIiJSe3VO5Nu0acNtt93G5MmTq53qJue3Bf/didVqH9Ecd2F7Rg1u02DPZbaY2Za6m7VHN7I+aavjg35Fvm7eDIzqS3xMHN1CO2pTtSbMy82TXhFd6RXRFQCL1cKRnGOOqcx7Mg6QXZzr6G+1WTmYncjB7ES+2/cTYF8v3fnEGvvOIe2w2qz2tc4Z9uT9dOX8wP5lQxv/KEfi3iWkPQGe/tX283HzxsfNG7zPbh2u2WK2J/XmEsrMZZRayiitcF1W4X6ZpYwSc9mpfpZTt8vMZZRYKj9mMhhxNbriYjThYnLB1Wi/uJhccKnmdpX7J267GF0oMZfw7Z4fKTzxxcS/N37KT4fWcnvcn2h3mi9k6ovVZuXLhO/5bMf/HLMkovwiefiCO2nlG96gz10TL9fTJ/jH81I5eiK5PzmCn16UBdhjl9PLyMjAYrEQHl753zY8PJyUlJRqj4mPj+ejjz5iwoQJlJSUYDabufrqq3nttddO+zyzZ89m1qxZ9Rr72QoN9OJISj7lZiu5haUE+javZVwiIuJcdU7kH3roIRYuXMhTTz3FiBEjmDJlCtdccw3u7o2//lmalk2709i0Jw2AsEBPJo7uWu/PYbFa2JG2hzWJG1mXtKXKrtVgX686IKoP8dH96RHeGRcl782SyWiiXVAs7YJiuaLTxdhsNtILMx1J/e6MAxzLTa40FT6tMJO0wkxWHVl3xvO7GF3oEBTrSNw7B7c/7Vrr+uZicsHP5Isfvo3yfOfisvbD+HDrYn45/BsAB7KOMHPZc4zsMJwbe16Nt5vXGc5Qd0Xlxbz++3tsSNrqaBsY1Yd7Bt7SZPes8HL1pENwGzoEt6nUbi8hmdogP6eW6I/Lm2w222mXPCUkJHD//ffzxBNPMGrUKJKTk3nkkUeYNm0a77zzTrXHzJw5kxkzZjju5+XlER0dXX8voA4q15IvViIvIiJ1ctZr5Ldu3cqCBQv45JNPMJvN3HTTTdx2223069evvmNsdC19HWJDsFhtPDDnJ46k5AMw46a+DOvbGpvNig37h7GKt61Y7W1gb3fctj+GDazYHI+lF2WxNnEjvydtIb+a0lKerh4MaN2b+Og4eoV3xcWkVSPng4KyQvZmHGLPieR+X9bh065h93TxoHNIO0fi3j6oDW4m10aOuPlKSNvHvzd+Uqn6gL+HH5N6X8sFsQPqbX+JpLwUXvz1LZLy7aOwBgzc2PNqxnUddd7vYdGS35vKysrw8vLis88+45prrnG0P/DAA2zZsoVffvmlyjETJ06kpKSEzz77zNH266+/MmzYMI4fP05k5JlnQTjzZ/qf5Xv54Dv7UqGZtwwgvlerRn1+ERFpehpljXzv3r155ZVXePHFF5k3bx5//etfmT9/Pj169OCBBx7g1ltvPe8/dJ1Plq87Yk/ijRYCem5l/v7vmb//zMedCw8Xd/q36sWQmDh6R3RTUnYe8nHzpl+rHvRr1QOwT1k/lHOU3ekH2Jd5CAw4ytm1CYjSvgjnoFtYR54f9RhL9v7IZzu+pdRSRm5JHq/9/i4rDq1mStyN5zx9fH3SVl7/bSHF5hLAPrvmgSFT6BPZvT5egjRhbm5uxMXFsWzZskqJ/LJlyxg7dmy1xxQVFeHiUvljjMlkn4HVHPbx/eOIvIiISF2cdSJfXl7O4sWLeffdd1m2bBmDBw9mypQpHD9+nMcee4zly5fz8ccf12es0kQVlZTz4fe7AXCJOEype1qDPZebyZW4Vr0YEt2PfpE9cHNxa7DnkubHxeRCx+C2dAxu6+xQWiQXo4mru4wkPro/Czd/xrqkLQDsTNvLIz88w1WdL2V8t9F4uNRtqZXVZuWzHd/yRcISR1uMf2sevuBOInwar+KFONeMGTOYOHEi/fv3Z8iQIbz99tskJiYybdo0wD4tPikpiffffx+Aq666ittvv5358+c7ptZPnz6dgQMH0qpV0x/drliCTrXkRUSkruqcyG/atIl3332XTz75BJPJxMSJE3n55Zfp0qWLo8/IkSMZPnx4vQYqTdcXP+0nJ78UXEpxb30IK/YdxTuHtMOAAYPBUOXaaDDAydsY4MR1pb4GAwbAYDDiZnKlV3gX+rXqWeckQUTqV4h3EA9fcCebjm9nwaZFpBVmYrFa+GrXD6w+sp5b+91A/9a9a3WuwrIiXvvtXTYl73C0xUfHMW3gRP1fP89MmDCBzMxMnnrqKZKTk+nRowdLliwhNta+sWJycjKJiYmO/pMnTyY/P5/XX3+dhx56iICAAC6++GKee+45Z72EOgmtMCKfnq1EXkRE6qbOa+RNJhOXXXYZU6ZMYdy4cbi6Vp3OXFhYyL333su7775bb4E2ppa8DrG+pWcXM+3Z5ZSZrbi13Ykp1F67d2SH4UyN+5OToxORhlZmLmPxrh/4evfSSvXq41r15NZ+EwirYWf/o7nHefHXt0gusM/iMRgM3NzrGq7qfKmWZlVD7031z5k/03KzlWsf/S82G3SOCeTFBzQAIiJyvmvQNfIHDx50fDt+Ot7e3s02iZe6+eC7BMrMVgweBbiEHsOGfVOx67ufvo6viLQcbi5uTOh5FcNiB/DOpkVsT7Uvs9l4fDvbU3dzbbcruKrzpVU2oPzt6CbeWPc+pWZ7TW9fN28eGDLFUX5QpKVzdTES6OtOVl4p6TlVK7CIiIjUpM47P6WlpfH7779Xaf/999/ZsGFDvQQlzcP+ozn8tPEYAJ5t9jnKgI3tOhJ/D40WiZxPWvlF8PcL72f6kCkEevgDUGYp55PtX/PID8+w40SCb7Va+XjbV7y05l+OJL5NQBSzR85UEi/nndAAe1nC7PxSys1WJ0cjIiLNSZ0T+XvuuYejR49WaU9KSuKee+6pcwDz5s2jbdu2eHh4EBcXx6pVq2rs/9FHH9G7d2+8vLyIjIzk1ltvJTMz0/H4woULT62vrnApKSmpc2xyejabjXf+a1/TavTNwuaXCkCQZwBjOl3izNBExEkMBgPxMf15+Yp/cEWnix3T45PyU3jq51d4de0CZq96g692/eA45oLYgfzfJY/UOAVfpKU6uXO9zQaZuVonLyIitVfnRD4hIaHaWvF9+/YlISGhTudatGgR06dP57HHHmPz5s0MGzaM0aNHV9rMpqJff/2VSZMmMWXKFHbu3Mlnn33G+vXrmTp1aqV+fn5+JCcnV7p4eHjUKTap2W87UthxIBOw4dV2n6P9xp5X466d5EXOa16unkzuez3PXTazUgWBXxPXszXF/j5hNBiZ3Pd67hs0WX8z5LylnetFRORs1TmRd3d3JzU1tUp7cnJylXquZ/LSSy8xZcoUpk6dSteuXZk7dy7R0dHMnz+/2v6//fYbbdq04f7776dt27ZccMEF3HnnnVWm9BsMBiIiIipdpP6Um60s/N9OAExByVg8sgGI9W/N8NhBzgxNRJqQNoHR/N8lD3Nn/5vxcfN2tPu5+/D4RQ9UGrUXOR+plryIiJytOifyl112GTNnziQ3N9fRlpOTw9/+9jcuu+yyWp+nrKyMjRs3MnLkyErtI0eOZM2aNdUeEx8fz7Fjx1iyZAk2m43U1FQ+//xzxoypvLFaQUEBsbGxREVFceWVV7J58+YaYyktLSUvL6/SRU7vu7WHOJ5RCAYLXm0PONon9rkWo7HOv1Ii0oIZDUYuaX8Bc694kqs6X8pFbYfw7GUz6R7WydmhiTidStCJiMjZqvOu9XPmzGH48OHExsbSt29fALZs2UJ4eDgffPBBrc+TkZGBxWIhPDy8Unt4eDgpKSnVHhMfH89HH33EhAkTKCkpwWw2c/XVV/Paa685+nTp0oWFCxfSs2dP8vLyeOWVVxg6dChbt26lY8eO1Z539uzZzJo1q9axn88Kisr4dOkeAFzCEzGbCgHoHdFNG1WJyGn5ufswsc+1zg5DpEmpOLVeI/IiIlIXdR4+bd26Ndu2beP555+nW7duxMXF8corr7B9+3aio6PrHMAfp1XabLbTTrVMSEjg/vvv54knnmDjxo18//33HDp0iGnTpjn6DB48mD//+c/07t2bYcOG8Z///IdOnTpVSvb/6OQMg5OX6jbzE7tFy/eSX1QOLmV4RB8CwICBP/e+xsmRiYiINC8Vp9ZrjbyIiNRFnUfkwV4n/o477jinJw4JCcFkMlUZfU9LS6sySn/S7NmzGTp0KI888ggAvXr1wtvbm2HDhvH0008TGRlZ5Rij0ciAAQPYt29flcdOcnd3x93d/RxezfkhOaOQ//1qT97dWx/EYigD4KK2Q4gNiHJmaCIiIs2Ov7c7ri5Gys1W0rNVS15ERGrvrBJ5sI+OJyYmUlZWVqn96quvrtXxbm5uxMXFsWzZMq655tRo7rJlyxg7dmy1xxQVFVXZUM9kMgH2kfzq2Gw2tmzZQs+ePWsVl5zee98mYLZYMbgXYQpPxAa4mVyZ0OMqZ4cmIiLS7BiNBkL8PUnOLNTUehERqZM6J/IHDx7kmmuuYfv27RgMBkcCfXI6vMViqfW5ZsyYwcSJE+nfvz9Dhgzh7bffJjEx0TFVfubMmSQlJfH+++8DcNVVV3H77bczf/58Ro0aRXJyMtOnT2fgwIG0atUKgFmzZjF48GA6duxIXl4er776Klu2bOGNN96o60uVChIOZbJ623EAvNrux4oVgCs7X0qQV4ATIxMREWc6evQoBoOBqCj7zKx169bx8ccf061bt3OevXc+CA20J/KFJWYKi8vx9nR1dkgiItIM1HmN/AMPPEDbtm1JTU3Fy8uLnTt3snLlSvr378/PP/9cp3NNmDCBuXPn8tRTT9GnTx9WrlzJkiVLiI2NBewl7SrWlJ88eTIvvfQSr7/+Oj169OD666+nc+fOfPnll44+OTk53HHHHXTt2pWRI0eSlJTEypUrGThwYF1fqpxgs9lY8I293JzBOwernz2h93f3ZWyXkTUdKiIiLdxNN93ETz/9BEBKSgqXXXYZ69at429/+xtPPfWUk6Nr+lSCTkREzobBdro56acREhLCihUr6NWrF/7+/qxbt47OnTuzYsUKHnrooTOWemsO8vLy8Pf3Jzc3Fz8/P2eH43QrNx/jhQ83AjZ8e23A7JEJwNS4PzGyw3DnBicicp5oqu9NgYGB/Pbbb3Tu3JlXX32VRYsWsXr1apYuXcq0adM4ePCgs0M8rabwM/3wu10sWr4XgH9MHUz/rtXvEyQiIi1fXd6X6jwib7FY8PHxAexJ/fHj9tHZ2NhY9uzZcxbhSlNWVm7hvW8TADAGpjmS+Na+EVzSbqgzQxMRkSagvLzcsWHs8uXLHXvldOnSheTkZGeG1ixULEGnnetFRKS26pzI9+jRg23btgEwaNAgnn/+eVavXs1TTz1Fu3bt6j1Aca5vVh0kLbsYDFZ82+13tN/c+xpMRpMTIxMRkaage/fuvPnmm6xatYply5Zx+eWXA3D8+HGCg4OdHF3Tp6n1IiJyNuqcyP/973/HarVvdPb0009z5MgRhg0bxpIlS3j11VfrPUBxntyCUj770T7dzyXsKGWmfAC6hXYkrpWqAIiICDz33HO89dZbXHTRRfzpT3+id+/eAHzzzTfan6YWQivWklcJOhERqaU671o/atQox+127dqRkJBAVlYWgYGBjp3rpWX4+IfdFJWYwVSOV+whyk+0T+xzrf6tRUQEgIsuuoiMjAzy8vIIDAx0tN9xxx14eXk5MbLmoeKIvKbWi4hIbdVpRN5sNuPi4sKOHTsqtQcFBSmxa2GOpubz/W9HAPCMOkw5JQBcEDOA9kGxzgxNRESakOLiYkpLSx1J/JEjR5g7dy579uwhLCzMydE1fV4ero6Sc5paLyIitVWnRN7FxYXY2Ng61YqX5und/+3EarVhcCvGGH4YABejCzf2GuvcwEREpEkZO3Ys77//PmAvATto0CDmzJnDuHHjmD9/vpOjax5OTq/PyCnGaq1TMSERETlPndUa+ZkzZ5KVldUQ8UgTsHVvOusTUgHwbnsQK/YvbkZ3vIgwb21cJCIip2zatIlhw4YB8PnnnxMeHs6RI0d4//33tXdOLZ2cXm+22MgtKHVyNCIi0hzUeY38q6++yv79+2nVqhWxsbF4e3tXenzTpk31Fpw0PovVxjv/tS+dMHjlYfE/CoCPmzfXdLvcmaGJiEgTVFRUhK+vLwBLly5l/PjxGI1GBg8ezJEjR5wcXfPwxxJ0gX4eToxGRESagzon8uPGjWuAMKSp+GlDIoeO5wE2/Dvu5+S4wLXdRuPj5l3ToSIich7q0KEDX331Fddccw0//PADDz74IABpaWn4+fk5ObrmofLO9cV0igmsobeIiMhZJPL/+Mc/GiIOaQJKSs188N0uAIz+GZS6pwEQ7h3CqA4XOjM0ERFpop544gluuukmHnzwQS6++GKGDBkC2Efn+/bt6+TomodQ7VwvIiJ1VOdEXlquxT/vJyuvFLDi1/GAYzT+pt7jcDHpV0VERKq67rrruOCCC0hOTnbUkAe45JJLuOaaa5wYWfMRGniqTF96jmrJi4jImdU5OzMajTWWmtOO9s1TZm4xX/y8HwDXsOOUGnMA6BjclsFR/ZwYmYiINHURERFERERw7NgxDAYDrVu3ZuDAgc4Oq9moWEteJehERKQ26pzIL168uNL98vJyNm/ezHvvvcesWbPqLTBpXB9+t5vSMgsYzXi1OUjZifaJva+t8YsbERE5v1mtVp5++mnmzJlDQUEBAL6+vjz00EM89thjGI11LpBz3gn298BgAJvNvkZeRETkTOqcyI8dW7WO+HXXXUf37t1ZtGgRU6ZMqZfApPEcTMrlxw2JAHhFJ1KGfVrfwKg+dAlt78zQRESkiXvsscd45513ePbZZxk6dCg2m43Vq1fz5JNPUlJSwjPPPOPsEJs8F5ORID8PMnNLtEZeRERqpd4WPg8aNIjbb7+9vk4njcRms7Hgvzuw2QDXEkwRhzDbwGQwcnMvrW0UEZGavffee/z73//m6quvdrT17t2b1q1bc/fddyuRr6WQAE8yc0vIyS+l3GzB1cXk7JBERKQJq5f5bsXFxbz22mtERUXVx+mkEW3Zm87WfRkA+LU/jNlWDsBl7YcT6RvmzNBERKQZyMrKokuXLlXau3TpQlZWlhMiap5CK62TL3FiJCIi0hzUeUQ+MDCw0pppm81Gfn4+Xl5efPjhh/UanDS8FRuPAmDwzMfsdwQAT1cPrut+hTPDEhGRZqJ37968/vrrvPrqq5XaX3/9dXr16uWkqJqfP254Fxni7cRoRESkqatzIv/yyy9XSuSNRiOhoaEMGjSIwMDAeg1OGla52cL6nSkAeMTuw4YNgGu6Xo6fh68zQxMRkWbi+eefZ8yYMSxfvpwhQ4ZgMBhYs2YNR48eZcmSJc4Or9kIDaxYS14l6EREpGZ1TuQnT57cAGGIM2zdl0FhiRmjbyb4pQEQ7BXIFR1HODkyERFpLi688EL27t3LG2+8we7du7HZbIwfP5477riDJ598kmHDhjk7xGYhNKBCLXntXC8iImdQ50T+3XffxcfHh+uvv75S+2effUZRURG33HJLvQUnDWvNtuOADdeYPY62P/Uci5uLm/OCEhGRZqdVq1ZVNrXbunUr7733HgsWLHBSVM1LxTXy2rleRETOpM6b3T377LOEhIRUaQ8LC+Of//xnvQQlDc9ssfLbjmRcIg9i9M4DoG1ANBfEDnByZCIiIuefylPrlciLiEjN6pzIHzlyhLZt21Zpj42NJTExsV6Ckoa340AGRV6HcI3e52ib2Gc8RkO9FDIQERGROvDzdsPNxf4enKFEXkREzqDOWVtYWBjbtm2r0r5161aCg4PrJShpeF9vXYtr252O+zf1GkeP8Krlg0RERKThGQwGx8716dlF2Gw2J0ckIiJNWZ3XyN94443cf//9+Pr6Mnz4cAB++eUXHnjgAW688cZ6D1DqX0Lafnaal2Iw2j8kjGp/EWO7jHRyVCIi0pyMHz++xsdzcnIaJ5AWJDTQk+MZhRSXWigsMePj6erskEREpImqcyL/9NNPc+TIES655BJcXOyHW61WJk2apDXyzUBiThLPrnwDjFYAAs3tuDXu+kolBUVERM7E39//jI9PmjSpkaJpGf5YS16JvIiInE6dE3k3NzcWLVrE008/zZYtW/D09KRnz57ExsY2RHxSj9ILM3lm5WuUWEoAsOQGc0PcDVoXLyIidfbuu+/W+znnzZvHCy+8QHJyMt27d2fu3Lk1lq8rLS3lqaee4sMPPyQlJYWoqCgee+wxbrvttnqPrTFULkFXRJtIPydGIyIiTVmdE/mTOnbsSMeOHeszFmlAeaUFPPPLa2QX5wJgLfDHejCOIZNaOTkyERERWLRoEdOnT2fevHkMHTqUt956i9GjR5OQkEBMTEy1x9xwww2kpqbyzjvv0KFDB9LS0jCbzY0cef2puHO9NrwTEZGa1Hko9rrrruPZZ5+t0v7CCy9UqS0vTUOJuZRnV77B8fxUAKzFXpTujaNvx0i8PDRtT0REnO+ll15iypQpTJ06la5duzJ37lyio6OZP39+tf2///57fvnlF5YsWcKll15KmzZtGDhwIPHx8Y0cef0JUS15ERGppTon8r/88gtjxoyp0n755ZezcuXKeglK6o/ZauGl1W+zP+swAO54U7anP5jdGNpLo/EiIuJ8ZWVlbNy4kZEjK2+8OnLkSNasWVPtMd988w39+/fn+eefp3Xr1nTq1ImHH36Y4uLmmwCHVkzks5vv6xARkYZX56n1BQUFuLm5VWl3dXUlLy+vXoKS+mG1WZm/7n22pCQA4OXqCfsHYStzw2Q0MKh7hJMjFBERgYyMDCwWC+Hh4ZXaw8PDSUlJqfaYgwcP8uuvv+Lh4cHixYvJyMjg7rvvJisriwULFlR7TGlpKaWlpY779fm5xWaznfPGsaEakRcRkVqq84h8jx49WLRoUZX2Tz/9lG7dutVLUFI/Pty6mFVH1gHganTh5s5/JjPV/iVM746h+HhV/UJGRETEWf6YCNeUHFutVgwGAx999BEDBw7kiiuu4KWXXmLhwoWnHZWfPXs2/v7+jkt0dHS9xF1mKefV3xaw6vC6czqPh7sLvl72JW9K5EVEpCZ1HpF//PHHufbaazlw4AAXX3wxAD/++CMff/wxn3/+eb0HKGfnm91L+d+e5YD9g9H0+KkkbDm1Hj6+V6SzQhMREakkJCQEk8lUZfQ9LS2tyij9SZGRkbRu3bpSGbyuXbtis9k4duxYtRvyzpw5kxkzZjju5+XlnXMyX2ou4+mfX2FP5kF+P7aFEO9Auoae/WbAoQFe5BflkplTjMVqw2RUeVgREamqziPyV199NV999RX79+/n7rvv5qGHHiIpKYkVK1bQpk2bBghR6urnQ2v5cOtix/074m6if6terN52HACjAQb3UCIvIiJNg5ubG3FxcSxbtqxS+7Jly067ed3QoUM5fvw4BQUFjra9e/diNBqJioqq9hh3d3f8/PwqXc45dpMr0QGtATBbzbz461uk5Ked9flObnhnsdrIyS855/hERKRlOqsC4mPGjGH16tUUFhayf/9+xo8fz/Tp04mLi6vv+KSONh3fzpvrP3Tcv7Hn1VzS/gIOJ+eRnFEIQI/2Ifj7uDsrRBERkSpmzJjBv//9bxYsWMCuXbt48MEHSUxMZNq0aYB9NH3SpEmO/jfddBPBwcHceuutJCQksHLlSh555BFuu+02PD09T/c09c5gMHBbvwn0Cu8KQH5ZIbNXvUFBWeFZna9iCTpNrxcRkdM5q0QeYMWKFfz5z3+mVatWvP7661xxxRVs2LChPmOTOtqbcZCX1vwLq80KwOUdL+KarpcDOEbjAeJ7ajReRESalgkTJjB37lyeeuop+vTpw8qVK1myZAmxsbEAJCcnk5iY6Ojv4+PDsmXLyMnJoX///tx8881cddVVvPrqq40eu4vRxIz424nys7+/JuenMWf125gtda9pX7EEnWrJi4jI6dRpjfyxY8dYuHAhCxYsoLCwkBtuuIHy8nK++OILbXTnZMdyk5m96g3KLOUAxEfHMbnv9Y5NgtZsSwbAYIDBSuRFRKQJuvvuu7n77rurfWzhwoVV2rp06VJlOr6zeLl58ujwe3hs2XPkluazM20vb2/8mLsGTKzTbvYqQSciIrVR6xH5K664gm7dupGQkMBrr73G8ePHee211xoyNqmljKIsnvnlNQrLigDoGd6FewbdgtFg/+c9mprP0dR8ALrEBhHs33hTDkVERM4XYd7BPHLBNFyN9nGSnw+t5evdS+t0Dk2tFxGR2qh1Ir906VKmTp3KrFmzGDNmDCaTqSHjklrKLy3gmV9eI7M4G4B2gTE8PPROXE2ndqhfU2Fa/dDerRo9RhERkfNFp5B23DNosuP+x9u+4rejm2p9vKbWi4hIbdQ6kV+1ahX5+fn079+fQYMG8frrr5Oent6QsckZlJhLeXbVPJLy7OV6InxCmTn8HjxdPSr1OzmtHmCIptWLiIg0qPiYOG7sebXj/mu/L2R/5uFaHRvs58HJinPp2UUNEJ2IiLQEtU7khwwZwr/+9S+Sk5O58847+fTTT2ndujVWq5Vly5aRn59/VgHMmzePtm3b4uHhQVxcHKtWraqx/0cffUTv3r3x8vIiMjKSW2+9lczMzEp9Tq7Zd3d3p1u3bixevPg0Z2u+zFYLL6/5N/syDwEQ4OHH3y+8H3+PyqV0kjMKOXg8F4BOMQGEBXo1eqwiIiLnm2u6Xs6FbQYDUG4p57lf55NemHmGo8BkMhLkZ/9CPiNH5edERKR6dd613svLi9tuu41ff/2V7du389BDD/Hss88SFhbG1VdffeYTVLBo0SKmT5/OY489xubNmxk2bBijR4+utCttRb/++iuTJk1iypQp7Ny5k88++4z169czdepUR5+1a9cyYcIEJk6cyNatW5k4cSI33HADv//+e11fapNls9l4c/0HbE7eAYCnqwd/G34fYT4hVfquqbRbvabVi4iINAaDwcCd/W+mW2hHAHJL8nh25RsUlZ15unzoiS/dcwpKKS23NGicIiLSPJ11+TmAzp078/zzz3Ps2DE++eSTOh//0ksvMWXKFKZOnUrXrl2ZO3cu0dHRzJ8/v9r+v/32G23atOH++++nbdu2XHDBBdx5552Vyt7NnTuXyy67jJkzZ9KlSxdmzpzJJZdcwty5c8/2ZTY5H21bzMrD9i8mXI0u/PWCu2gTGFVt30pl53opkRcREWksLiYXHh56J5E+YQAczUvm5bX/xmKtOTmvuHN9ptbJi4hINc4pkT/JZDIxbtw4vvnmm1ofU1ZWxsaNGxk5cmSl9pEjR7JmzZpqj4mPj+fYsWMsWbIEm81Gamoqn3/+OWPGjHH0Wbt2bZVzjho16rTnBCgtLSUvL6/Span6+dBavtltL7VjMBi4f8htdAvrVG3ftKwi9h3NAaBdK38iQ7wbK0wREREBfNy9eXT4Pfi42d+Dt6Yk8O6m/2Cz2U57TMUN77RzvYiIVKdeEvmzkZGRgcViITw8vFJ7eHg4KSkp1R4THx/PRx99xIQJE3BzcyMiIoKAgIBKZfBSUlLqdE6A2bNn4+/v77hER0efwytrWP/ds9xx+/a4PzEoqu9p+67ZfmqTu/je2uRORETEGSJ9w3h46J2YjPaKP0sPrGTJ3hWn7R8WdGo/m89/3Ee52drgMYqISPPitET+JIPBUOm+zWar0nZSQkIC999/P0888QQbN27k+++/59ChQ0ybNu2szwkwc+ZMcnNzHZejR4+e5atpWIk5SRzNtU+V7xzcjkvbD6uxv9bHi4iINA3dwjoyrf+fHfff3/IFG5K2Vds3vmckPp72MrJb9qUz99NNWK2nH8EXEWkqkvJS+H7fzxSUFTo7lBbPaYl8SEgIJpOpykh5WlpalRH1k2bPns3QoUN55JFH6NWrF6NGjWLevHksWLCA5GT76HNERESdzgng7u6On59fpUtTtOboqb0AhsYOqLFvZm4xuw5nARAT4Ut0uG+DxiYiIiI1u7DtYK7tdgUANmy88tsCDmVXHTwI9PPgiSmDcXO1j+Cv3JzEO//dUeN0fBERZ9uWsotHl85mwaZF/N/Pr1BmKXd2SC2a0xJ5Nzc34uLiWLZsWaX2ZcuWER8fX+0xRUVFGI2VQzaZ7G9yJ9/chgwZUuWcS5cuPe05mwubzcbqxI2AfcbB4Bqm1AP8VnFavUbjRUREmoQbelxJfEx/AErNpTy76g2yinKq9OvaNoi/TuyP8URR+W9WHuTLn/Y3ZqgiIrX229FNzF71BqWWMgAOZR/l3U3/cXJULZtTp9bPmDGDf//73yxYsIBdu3bx4IMPkpiY6JgqP3PmTCZNmuTof9VVV/Hll18yf/58Dh48yOrVq7n//vsZOHAgrVrZk9UHHniApUuX8txzz7F7926ee+45li9fzvTp053xEuvNwexEUgvSAegR1okAT/8a+6/eViGR76X18SIiIk2BwWDg7oGT6BTcDoDs4lyeWzWPkvKqNeMHdo/gnut6O+4v/DaBH9dXX6JXRMRZVhxcU21Fjh8P/spPB0+/4bicG6cm8hMmTGDu3Lk89dRT9OnTh5UrV7JkyRJiY2MBSE5OrlRTfvLkybz00ku8/vrr9OjRg+uvv57OnTvz5ZdfOvrEx8fz6aef8u6779KrVy8WLlzIokWLGDRoUKO/vvq0OvHUtPr46P419s3JL2XnwQwAWoV40yayaS4VEBEROR+5mVz5ywXTCPMOBuBQzlFe/e1drNaqm9qNHBTLxNFdHfdf/c8WNuxKbbRYRURq8r89y3lz/QeO2dEXtRnCXQMmOh7/96ZPOVzNEiI5dwabFlxVkZeXh7+/P7m5uU1ivbzVZuWe//6dzOJsTAYj/xr7PD7upy8l9/3aw7zx+VYArru4I7eM6dZYoYqISANpau9NLYGzf6bHcpP5+48vUFRuLzF3ZadLmNT3uir9bDYbby/ezv9WHwLA3c3EM9Pi6Rwb1KjxioicZLPZWLTjv3yZ8J2j7YpOFzOpz7UYDUb+teFjlh1YBUC4dwjPjpyJt5vX6U4nJ9Tlfcnpu9bLme3NOEhmcTYAvSO61ZjEQ+Xd6of20vp4ERGRpijKP5IZ8bdjMtg/jv1v748s3b+ySj+DwcDUcT0Z2tv+nl5aZmHWv3/naGp+o8YrIgL2QcYFmxZVSuJv6HElt/S5DuOJv2eT+15P+0D7LOvUwgxe/30hVptKadYnJfLNQKVp9TE1T6vPLypj2377tPqwIC/aR9W8ll5EREScp1dEV6bE/clxf8GmRWxJTqjSz2Q08NBN/ejVIQSwv9//419rycwtbrRYRUTMVgtv/P4eP+z/xdE2ue/1XNd9TKVy364mV2YMvR0fN/sA5Mbj2/lm97Iq55Ozp0S+ibNYLfx2dBNg/w8xoHXvGvv/viMFy4las/E9Iyv9hxIREZGm59L2F3B1l8sA+0jXy2v/RWJOUpV+ri4mHrt1IO1a2b+kT88u5h9vr6WgWCWeRKThlZnLmLP6LVYdWQeA0WDknoG3cEWni6vtH+odzP2Db8WAPR/5ZPvX7Ejd3WjxtnRK5Ju4nWl7yS21T52Li+yJp6tHjf1Xa1q9iIhIs3NTr3GOL+uLy0t4btU8ckryqvTz8nDlydsHEx5kX2t6JCWfpxf8Tmm5pUpfEZH6UlRezD9Xvs7G49sBcDW68NDQO7iw7eAaj+sT2Z3rul8B2NfVz137TrUlN6XulMg3cZWn1cfV2LeopJwte+0l6oL9PegUE9igsYmIiEj9MBqM3Df4VtoFxgCQXpTFc6vmkVGYVaVvoJ8HT90xBH8fNwB2HsxkzkcbHTPyRETqU15pAf/30yskpO8DwMPFnZnD7z3jTOGTru1+BX0iujnO9dKaf2G2mBss3vOFEvkmrNxSzrpjmwH7f5h+kT1q7L8uIRWzxb6JxJCekRiNmlYvIiLSXHi4uPPXYXcT7Gn/Iv5A1hEeWPIPPtq6mKKyymvhW4X68I+pg/FwMwGwdnsyb365DRUjEpH6lFmUzT9WzOFA9hEAfNy8eeKi6fQI71zrc5z8ojLEy15pY2/mQT7c+uUZjpIzUSLfhG1N2UXhiZI0A1r3xs3Frcb+2q1eRESkeQv09OfR4Xfj7+4LQLnVzNe7l3Lfkif4bu9PmK2nptB3jA5k5uSBmE58cf/92sN8unSPU+IWkZYnJT+NJ358kaS8FMD+92nWxTPoENymzufydfdhRvztuBhdAFiy7yfWVJh5LHWnRL4Jq/jLPfQMu9UXl5rZuCsVgAAfd7q2DW7Q2ERERKRhxAZEMfeKJ7m6y2WOD735pQW8u/k/PPTdU6w7tsUx8t6vcxjTb+zrOPbjpXv4bs0hp8QtIi3H4exjPL5iDulF9uU94d4h/N/FDxPtf/aDhR2C2zC57/WO+/PXf8ixvORzjvV8pUS+iSo1l7H++DYAvN286BXetcb+G3enUmY+Na3epGn1IiIizZa3mxd/7j2euVc8yQUxAxztyQVpvLj6LZ5YMYe9GQcBuCgumilXn1p+9+aX21i7/XiVc4qI1MaejAPM+uklck9suBnj35qnLnmYMJ+Qcz73Ze2HMTx2EACl5lLmrH6bkvKScz7v+UiJfBO1KXk7peZSAAZF9cXF5FJj/zXbTn2bFd8rskFjExERkcYR5h3M/UNuY/Zlj9IttKOjfU/GAf7+4wu8vObfpBakM+7C9oy/qAMAVhu88OFGdhzIcFbYItJMbUlO4OmfX3Us7+0Y3JYnRzxIoKd/vZzfYDBwe/+biPFvDUBSXgpvbvhI+3ucBSXyTdTqOkyrLy23sD7BvnbF18uNHu3P/dsyERERaTraB8XyjxEP8pcLptHKN9zRvvboRqZ/N4v3Nn/OtZfFMiIuCoBys5WnF/zO4eSqJexEpHkqNZfx08E1/Hf3ctYnbeVo7nHKLOX1dv7fjm7iuV/nUWopA6BneBcev/B+fNy96+05ANxd3Hho6B2OstprEjfw/b6f6/U5zgc1D/OKUxSVF7P5+A4A/D386B7aqcb+m/ekUVJm3/xmcI8IXEz6fkZERKSlMRgM9G/dmz6RPVhx8Fc+2/EtuaX5WKwWvt37Iz8fWsO4vpeTUxjM5t2ZFJaY+cfba3nhvmGEnag7LyLNj8Vq4adDa/lsx//ILsmt9JgBA8FegUT6hhLuE0akTxiRvqFE+IYR7h2Cq8m1Vs+x4uBq3qowMj4wqg8PDL6t1sfXVaRvGPcMvIUXV78FwPtbPqd9UCydQto1yPO1RErkm6D1x7ZSbrXXVhwS1Q+jsebEfHWF3erjtVu9iIhIi+ZiNDGyw4UMix3E17uX8r89yymzlFNYXsxH2xYTEhFElLUzx/b6kZVXwhNvr+W5ey/A38fd2aGLSB3YbDbWJ23lk21fk5SfUn0fbGQUZZFRlMX21MpVKwwYCPEKJMLXnuBH+IYS4RNG5Ikk/+TS3f/uXs4HW79wHHdR2yHc2f9mTEZTw7047F8WXN3lMr7ZvQyLzcpLa/7FcyNn4u/h16DPW1tmq4Xi8mKKzaX26/ISispLKDHbr4vLSyg2n7guL+aC2IF1Kst3rpTIN0FrjlaYVh9b87T6crOF9Tvt/7G9PVzo3TG0QWMTERGRpsHT1YMbe17NZe2HsWjHf/nl0G+OD/UErMWndyBFBzuSlA5PvfMbz0wbioe7PvqJNAe70w/w0bbF7Mk4UKl9QOveDGjdm7TCTFLy00gpSCe5II3CsqIq57BhI70oi/SiLLan7q70mMFgIMQriAAPP/Zlnqp0MabTJUzsMx6joXFm+P6p51j2ZR5mV/o+sopzeGXtAv5+4f1nHMg8WxmFWaxL2kJWcc6JBLyEohPJeInjtj15L6/jsoWYgNZK5M9neaUFbEvZBUCIVxAdg9vW2H/rvgwKS+yj9wO6R+Dqomn1IiIi55Ngr0DuHjiJKzpezIdbv2Rbqv1zhMU9G/eu67Bkh7HvaCeefX89f79tkJbgiTRhx/KS+Xjb12xI2lqpvXNwO27uPZ4uoe2rPS6/tIDkE4l9SkGa/Xa+PckvOrFxXUU2m430wkzSCzMdbRN6XMX4bqMxGBqv+pXJaOLBIVP4y9J/klOSx460PSza8V/+1GtsvT1HfmkBa49uYnXienal76+38/5RUSPvvq9EvolZd2wzFpu9jFx8TNwZvw1bU2Fa/VBNqxcRETlvtQmM4u8X3c+W5AQ+2PoFR3PtnxFMgWkYA9LZlpbI3/9VzKM3DyXAV9PsRZqSrOIcPtvxLSsOra60g3tr3whu6j2O/q161Zhg+7r74OvuU2WNuc1mI7+s8NTofX4ayQVppJy4Li4vwWQwckvf67m840UN9fJqFODpz4PxU5n101ysNiuLd31Px+C29G/d66zPWWouY+Pxbaw6so4tKQlYrJYzHmMyGPFy9cTD1QMvFw88XU9cXDzwdPU8cdsdT1dPvFw98HDxwKtCnyDPgLOO92wokW9iKu5WHx9d87R6s8XKbzvsZec83Ez07RzWoLGJiIhI09cnshu9wrvw8+HfWLT9G7JLcjEYbLiEJ3KgLJX73kri79ePoXNskLNDFTnvFZUV8/XupXy798dKO9AHevpzQ/cruajtkHNaq24wGPBz98HvdEl+aQGc6ONMXUM78ufe1/D+Fvta/dd/X8hzI2cS7lP7ZcMWq4XtqXv49cg61iVtoeREKe+KWvtFMCx2IF1C2juScy8XDzxcPXA1ujTqbIRzpUS+CckuziUhbR8AkT5htA2MrrH/jgMZ5BfZ/8MP6BaBu2vDbkghIiIizYPRaOTidvHEx8Txvz0/sjjhB8qtZRjcSimNXsPfvjnGLf2u5ar4js3qg6tIS1FuKWfp/pV8mfAd+WWFjnZPVw/GdhnJmE6X4O7i1qAxGAwG/Dx8G/Q56mJMp0vYk3GQ349tpqi8mDmr3+bpSx7BrYafg81mY3/WYVYdWcfaxI3kluZX6RPo6c/QmAEMix1Im4CoFvM3T4l8E7L26EZs2KfSxMf0P+Mv2eptyY7b8b0iGzQ2ERERaX48XNy5rvsVjGg7hFfWLGR35l4ATGGJvL//LbYevZS/XDtSgwEijcRqs7L6yAY+3fFNpfXpJqOJUR0uZHy30U4fHXcWg8HAXQMnkpibRHJ+GodzjvHOpkXcNXBilb7H81JYdWQ9vyauJ7UgvcrjXq6eDI7qywWxA+kW2rHBNs9zJiXyTciaxI2O20Njap5Wb7Ha+G27PZF3czUR1yW8QWMTERGR5ivYK5AnL3mA7/f+wvtbvsSKGaNHEdts3zDtvX3839hbiAr1d3aYIi3a1pQEPtq6mMM5xyq1XxA7kBt7XEWYT4iTIms6vFw9eSj+Dh5b/jylljJ+OrSGziHtuLjdULKKc1iTuJFfj6zjYHZilWNdjS70a9WTYbED6RvZHVeTqxNeQeNRIt9EpBVmsjfzIAAx/q2J8q95hD3hUCY5BfZ1H3FdwvBUORkRERGpgdFg5IrOI+jTqhuzV/yL1JIkDAYo9N3FjG//yW29b+byvme/uZSIVO9gViIfbVtcpQRc74iu3NTrmjMupz3fxAS05o7+N/Pa7+8C8M7GT1l1ZB0J6fsqbQQIYMBAj/BOXBAzkEFRffFy83RGyE6h7K+JWFNxk7uYuDP3r7Bbfbx2qxcREZFaauUbztyrZvLe+v/y/aGlYLCBZx7v7H6TdUeH8rcxN+Ji0lR7kbNVZinnWG4yiblJbEneyZqjGys93jYgmpt7X0OviK5OirDpG9ZmIHsyD7B0/0rKrWZ2pu2t9Hi7wBguiB1IfExco+8W31QokW8iKifyNU+rt1ptrDmxPt7FZGRgN02rFxERkdozGU3cNmgcg2N78exP/6LEmIPBaGNH8a9MXbSXJ0beSbsQDRSI1MRqs5JRmMWR3CQSc5I4kpvE0ZzjHC9IrTJyDBDmHcyNPcfWqsS0wC19ruNQViL7sg4DEO4TygUxA7ggdgCt/SKcG1wToES+CUjKS3GslWkfFEvEGcos7E3MJiuvBIC+nUPx8mjZ6z9ERESkYXSLaMfb183ime/eZ3fRRgwGKDKl8eiyfzK+09VM6HdZi9nhWeRcFJYVkZibxJGcJBJzj5OYk8TR3OMUm0vOeKyvmzfXdr+Cy9oPa/HrtuuTq8mVxy96gN+PbaGVXzgdgtro71EFSuSbgIqj8UNjBpyx/+qK0+p76ttyEREROXserm7839VT+WZTbz7c+Sm4FYHRwpf7F7Ph+Db+dunU83bqqjRNReXFbDq+g3XHtpCUl4ybyQ13FzfcXdzxcHHH3cUND5N7hTY33E/c96jQx9104rEKx1ixcTwvxZ6sVxhpzyzKrlVsrkYXovwiiQloTYx/a2ICWtE5pD0eLu4N/FNpmTxcPbiw7WBnh9EkKZF3MpvN5tit3oCB+Oia18fbbDbH+niT0cCgHppWIiIiLcO8efN44YUXSE5Opnv37sydO5dhw4ad8bjVq1dz4YUX0qNHD7Zs2dLwgbZQV/cbQJ/o9jzx3wUUeR8AILHoAPd+8yTTBt7M8LZnHmwQaSh5pQVsSNrGumOb2Za6G7PV3CDPY8DgKAd9JqHewcT4tyLWkbS3JtInDJNRe0xIw1Mi72RHcpJIyk8BoEtoB4K8Amrsf+BYLmnZxQD06hCCr5dbQ4coIiLS4BYtWsT06dOZN28eQ4cO5a233mL06NEkJCQQExNz2uNyc3OZNGkSl1xyCampqY0YccsUExrE2xMf5NnF37K9dAUGt1LMlPL6ugX8lriZuwffjI+7t7PDlPNEVlEO65K2sO7YFhLS92G1Wav0cTG6YLFaap18n0l15/F09SDW/1SyHhvQmmj/Vni5nj87pEvTo0TeydYcrTit/sy71VecVj+0t6bVi4hIy/DSSy8xZcoUpk6dCsDcuXP54YcfmD9/PrNnzz7tcXfeeSc33XQTJpOJr776qpGibdncXE08ccPVfLOmE+9v+w/GQPsGuxtSNnP/t/u5f8gt9Ins7uQopaVKLUjn92P25P1kaeY/CvIMYGBUHwZH9aVLSAcMBgPllnJKLGWUmkspNZdRYi6l1HLi2lxKifnEY442+/2SCn1KzWWYrWYifMNOjLRHEePfihCvIK3NliZHibwT2Ww2Vp9YH280GBkU1feMx6xPsI/eGwwwqHvNteZFRESag7KyMjZu3Mijjz5aqX3kyJGsWbPmtMe9++67HDhwgA8//JCnn376jM9TWlpKaWmp435eXt7ZB30euDq+C12j7+OpLxZTEroFg0s5BeX5/HPl61za7gIm9bkWD1cPZ4cpLcCx3GR+P7aZ349tdmwA/UfhPqEMiurL4Ki+tAuKqbLru5uLG24ubuDu0xghizidEnkn2p91mPTCTAB6hnfG38Ovxv7p2cUcSckHoFN0IAG+2jRDRESav4yMDCwWC+HhlcuphoeHk5KSUu0x+/bt49FHH2XVqlW4uNTu48zs2bOZNWvWOcd7PukYHcjrU29m9scx7LP9gikgA4DlB39la8pu+kZ2w9vNy35x9cTbzQsfNy+83bztt1298HB1V6ktqcRms3EoO9Ex8n5ymekfRfu3YlBUXwZF9SHGv7VGxUUqUCLvRKuPrHfcjo+uuXY8wMbdp9b+xXVV7XgREWlZ/vgh3WazVfvB3WKxcNNNNzFr1iw6depU6/PPnDmTGTNmOO7n5eURHR199gGfJ/x93Hlm6iV89H0rvtz2I64xezCYLKQXZbD0wMozHm8wGPBy9cTH1etU0n8iyfc6mfifeMzL1cOxq3jFi5uLm74MaEZsNpt9Cnt5CUXmEvt1eQlF5cXsSt/PumObSS/KqvbY9kGxDIrqy8CoPrTy1eddkdNRIu8kVquVtUc3AWAymhgY1eeMx1RK5LuENVRoIiIijSokJASTyVRl9D0tLa3KKD1Afn4+GzZsYPPmzdx7772A/X3VZrPh4uLC0qVLufjii6sc5+7ujru7ZrOdDZPRwKQrutE5JpA5X6zC0noLJt/aleOy2WwUlhVRWFYEhWcfg3s1Cf7Ji6fLiS8AXCu3h3gF0SYwGr8WON3aZrNhtVkxWy2YrWYsVgtmqwUr9v8LJx+3Yqt038bJ27YKt6trt2I9kZAXl5fYL+ZT1yXlpRSZiykpL6W4vJhic+mpPuYSbLbabT5nwECX0Pb25L11H0K8gxr4JyfSMiiRd5JdGfvJLskFoG9Ed7zdvGrsX262snVfOgD+Pm50iApo6BBFREQahZubG3FxcSxbtoxrrrnG0b5s2TLGjh1bpb+fnx/bt2+v1DZv3jxWrFjB559/Ttu2bRs85vPVoB6RzA2/gmcWBnP0QDq4lGFwKcfbG4b0CSGqlQdF5UUUlhc7kvfCsiIKyk/cLi+qdYL3R6UnNiTLPYtjQ7yCaBcYQ9vAaNoFxdA2MIaAMyxpbAjF5SUk5aWQlJfCsbxkMoqyHIn4H69PJuZ/fMxS4XZ97dTe2EwGIz3COzMoqi/9W/d2yr+FSHOnRN5JTm5yBxAfc+Zp9QmHMikutQDQr3MYRqPWCImISMsxY8YMJk6cSP/+/RkyZAhvv/02iYmJTJs2DbBPi09KSuL999/HaDTSo0ePSseHhYXh4eFRpV3qX6tQH16efhGLf97Pf37cR1mRhfw8WJoMnWM8ueOagXSKCaz2WJvNRrG55FSCfyK5t98vto/0mksrXUpPjP6WmEsprtBelzriGUVZZBRlsS5pi6MtyDPAntgH2hP7dkExBHr418s67PzSAkeyfiwvhaQT15lFtZvF0By5u7jj5eKBh6s7Xi6eeLjaZ0p4unqcunb1INQrmL6tuuPjpjKGIudCibwTmK0Wfj8xrd7N5Er/Vj3PeMzG3WmO23FdtF5IRERalgkTJpCZmclTTz1FcnIyPXr0YMmSJcTGxgKQnJxMYmKik6OUk9xcTUy4rDMj+kez4JudjvK4exKzefjVlVw6IIZJV3SrsjHvyfXyXq6ehHoHn1MMZouZEkuFpL/85G37lwFF5SUcz0vhUM5RDmUfpcRcWun4rOIcsopz2Hj81OwOfw+/UyP3gTG0C4wh2Cuw2uTeZrORXZLLsdxkR9J+crQ9tzT/nF6bAQMuRhMuRpdK16YztJmMJowGI0YMGAwGDJVuGzBiwGgwVrhvrPSY4xiDAQMGDAZwM7lVSsSr3D6xrMFo1B4GIo3JYDvb+U0tWF5eHv7+/uTm5uLnV/9TfbYk7+SfK18HID46junxU894zN3Pr+Boaj5GA3wwazR+3m71HpeIiDRdDf3edD7Sz7T+bN2bzltfbedo6qkE1tvDhZsu78KY+LaYTM5N8qw2Kyn5aRzMTuRg9lEOZSdyMDuR4vKSMx7r6+5Du8Bo2gbG4OPmdWKE3Z641+b4k7xdPYnyi6S1XwSt/SKJ8o8gwicMd5NblaRdSbHI+aku70sakXeCuk6rT8sqcrwxdooJVBIvIiIiTUrvTqG8+tBFfLv6EB//sJuiEjOFJWb+9dUOlv52hDuv6UXPDiFOi89oMNLKL4JWfhFcEDsQsCf3aQUZHMw+ysHsREdyX1hWVOnY/NICtqbsYmvKrlo9V4CH34lkPYIov0iiTlz7e/ipfJqI1Bsl8o2szFLuWJ/l6epBn8juZzxGZedERESkqXMxGRk7vD3D+7bm/W93sXy9fSnEkZR8/jZ/NRf0bsVtV/UgNNDTyZHaGQ1GInzDiPANIz4mDrBPl08vyrIn9Vmnkvu80oIqx4d6BRHlH0lr3xNJu799tF1rv0WkMSiRb2Rbknc6pmENbN0HN5PrGY+puD6+v9bHi4iISBMW6OvBAzf2ZdSQWN5avJ39R3MA+HXrcdbvSuX6SzpyzYUdcHM1OTfQahgMBsK8gwnzDmZQVF/AntxnFedwMDuRUnMprXzDaeUXgYeLShmKiPM4fQHOvHnzaNu2LR4eHsTFxbFq1arT9p08ebJjQ46Kl+7dT41qL1y4sNo+JSW1X8PUkCpOqx9ai2n15WaLo+xcgI877Vr7N1hsIiIiIvWlS2wQc+4fzr3X93EsCywts/Dhd7u594WfWJeQ4uQIa8dgMBDsFciA1r25IHYg7YJilcSLiNM5NZFftGgR06dP57HHHmPz5s0MGzaM0aNHn3ZX2ldeeYXk5GTH5ejRowQFBXH99ddX6ufn51epX3JyMh4eHo3xkmpUUl7CxuPbAPB186ZHeJczHrPzYCYlZSfKznVR2TkRERFpPoxGA6MGx/LWo5dw5QVtOfkxJjmzkP9753dm/fs3jqdXnbYuIiI1c2oi/9JLLzFlyhSmTp1K165dmTt3LtHR0cyfP7/a/v7+/kRERDguGzZsIDs7m1tvvbVSP4PBUKlfREREY7ycM9pwfDtllnIABkX3w8V45illmlYvIiIizZ2Plxt3XtOLVx4aQY/2p8rObdiVyj0v/MR73yZQXFr7uvAiIuc7pyXyZWVlbNy4kZEj/7+9O4+Lqtz/AP4ZBhgYGPZdVkXBfQFUNHdFcUmvlthVxNT8WenVrFwqU7uWlrm1aNffD9TKa2ouebNScL+ouQRmhrihIIvDIvs+nN8fIxPDjgKz+Hm/XvNi5pznnHmeeYBnvuc8S5Da9qCgIJw7d65R5wgPD8fw4cNVa8xWys/Ph4eHB1xdXTF27FjExMTUe56SkhLk5uaqPVrCuSZ2qwf+mujOQAT08LFvkXwRERERtQZPZwt89Gp/LJ7mD1tLZW/JckUFvj9xC69+fByRv95HWblCw7kkItJ+GgvkMzIyoFAo4OiofpfZ0dERaWkNj5lKTU3Fzz//jNmz1ddg9/X1xY4dO3D48GHs3r0bJiYm6N+/P27dulXnudasWQNLS0vVw83N7ckKVY/80gLEpF0HAFibWKKjnXeDxzzMKkTSQ2V3Mx8PG8ikXHaOiIiIdJtIJMKAnm2wdckwvDisPQwfrzGfmVOMz/bGYuY/I7H76A1k55VoOKdERNpL45PdVV9PUxCERq2xuWPHDlhZWWHChAlq2/v27Ytp06ahe/fuGDBgAPbu3YsOHTrg888/r/Ncy5YtQ05OjuqRlJT0RGWpz6UHV6GoUF5hDnTrBQODhj969WXnHJo9T0RERESaYioxxPTRnfDl4iHwr7K8bnZ+Cf59LB4v//MYNn8Xg3upLdNTkohIl2ls+Tk7OzuIxeIad9/lcnmNu/TVCYKAiIgIhIaGwti4/rvUBgYGCAgIqPeOvEQigUTSsrOPnkuq0q3eI6BRx1yOqxLIc3w8ERER6SEXO3OsmN0XN+5l4dCZOzj/ewoqBGWX+6hLiYi6lIju7e0wfmA7+Pk6cuJfIiJoMJA3NjaGn58fIiMj8be//U21PTIyEuPHj6/32NOnT+P27duYNWtWg+8jCAJiY2PRtWvXp87zk8opzsW1h/EAAHszW3jbeDZ4TGmZAr/fzgAAWMskaOvCZeeIiIhIf/l62mCppw3kWYX4MToBxy7cQ0GxcgK8q7cycPVWBtrYm2HcgHYY5u8GE4nGvsYSEWmcRv8DLlq0CKGhofD390dgYCC2bduGxMREzJ07F4Cyy3tycjK+/vprtePCw8PRp08fdOnSpcY5V61ahb59+6J9+/bIzc3FZ599htjYWHz55ZetUqbaXEiKQYVQAUA5yV1jhg5cv5uJEi47R0RERM8YBxspZo7rjCkjOuD4pST85+xdpGYWAACS0wvw1YHf8e3PcRjZ1wNjn2sLOytTDeeYiKj1aTSQDwkJQWZmJj744AOkpqaiS5cu+Omnn1Sz0KemptZYUz4nJwf79+/H5s2baz1ndnY25syZg7S0NFhaWqJnz544c+YMevfu3eLlqUvVbvX93Bo3W/3lG+xWT0RERM8uqYkRxg1oi9H9vXD5zzT8cOYurt1R9lbMLyrD/pO3cfD0HTzXzQXjB7VDB3drDeeYiKj1iARBEDSdCW2Tm5sLS0tL5OTkwMLC4qnOlVn4CK/+5x0AQBuZEzYEv9+oO/Jz1x5Hcno+DAxE2LVqFMw5Yz0R0TOtOdsmUuJnqnvuPMjG4bN3cSbmAcoV6l9hO3raYPzAdujbxQliscbncyYiarKmtEscXNTCziddUT3v5+7XqCA+LbMAyenKZed8PawZxBMREREBaOdqhTde6oUZYzrhyLkE/HzuHnILSgEAcfeyEHcvCw7Wphj7XFsE9fGAmamRhnNMRNQyGMi3sOjEKt3q3RvXrf5Kldnqqy7HQkRERESAtYUJpo3qiBeHdcDp3x7ghzN3kJiWBwCQPypCxH+uY/exGxjq746gPh5o24aTBhORfmEg34LkBZm4k3UfAOBp5Yo2Fk6NOu7yDbnqOcfHExEREdVOYiRGUB8PjOjtjtib6fjhzB1cefw9qqhEgSPRCTgSnYC2bSwxorc7BvVyhYw9HYlIDzCQb0H2UhusG/kuohMvw8ncvlHHVF12zsZCAi8XjtkjIiIiqo9IJEJPHwf09HFA0sM8/OfsXRy/nITSMuUKQHeTc/Cvg9cQfvg6Ars6Y3hvd3Rvbw8xVwUiIh3FQL4FiUQieFi5wsPKtdHH/HEnU9Xo+Pk6NmpMPREREREpuTnK8NoL3TF9TCecjXmAyIuJuJWUDQAoV1TgbGwyzsYmw87KFMMC3DA8wB1OtmaazTQRURMxkNcyXHaOiIiI6OmZmxohuJ8Xgvt54X5qLqIuJeLE5STV5HgZ2UXYE3kTeyJvopu3HYb3dkdgV2eYGPPrMRFpP/6n0jKVE90ZGIjQo0PjuuMTERERUd08nC0w6/kumD66Ey7HpSHyYiKuxD1ExeMV7H6/nYHfb2dAamKIgT1dMaK3O9q7WbFnJBFpLQbyWiQlIx8pGQUAlGuhcskUIiIiouZjZGiAwK4uCOzqgsycIpy88gBRF+8jOV35/auwuBy/nL+HX87fg5ujDCN6u2OInxusZBIN55yISB0DeS1yJa7qbPUOGswJERERkX6ztTTFC0PbY9IQb8Tdy0LUxUScjU1GcalyrqKkh3mI+M917DzyJwI6OWJEbw/4+TpALDbQcM6JiBjIa5UrN7h+PFFDFAoFysrKNJ0NomZnZGQEsVis6WwQPXNEIhE6edmik5ctXpnQFdFXkxF1KQnX72YCABQVAi78kYYLf6TBWibBED83DO/tDjdHmYZzTkTPMgbyWqKkTIFrj5eds7U0gaczl50jqkoQBKSlpSE7O1vTWSFqMVZWVnBycuK4XCINMZUYYnhvDwzv7YHk9Hwcv5SI45cSkZVbAgB4lFeCA6du48Cp2+jgboXhAe4Y0NMV5hwOSUStjIG8lrh2OwOl5RUAgF4+DvwSR1RNZRDv4OAAqVTKvxHSK4IgoLCwEHK5coiVs7OzhnNERG3szTF9dCdMHemLmJvpOPbrfVz6Mw3lCuUMeTcTs3EzMRv/98Mf6NvVGcMD3NGNa9MTUSthIK8l2K2eqG4KhUIVxNva2mo6O0QtwtTUFAAgl8vh4ODAbvZEWkIsNoB/R0f4d3RETn4JTv/2AFGXEpGQkgsAKC2vwJmYZJyJebw2vb8bhga4wcXOXMM5JyJ9xkBeS1ROdCfmsnNENVSOiZdKpRrOCVHLqvwdLysrYyBPpIUszSV4fmA7PD+wHe4m5yDqUiJOXXmAvMIqa9NH3cSeqJvo3NYWwwPc0L97G5hK+JWbiJoX/6togZT0fKRmKpc96eRlC6kJx1kR1Ybd6Unf8XecSHe0bWOJOW264uWxnXDxz4eIupiI3278tTb99buZuH43E/86eA39u7tgeIA7Ore15d85ETULBvJa4HLcX93quewcERERke4wMhSjfzcX9O/mgqzcYpy8nISoS4l4IM8HABSXKnD8UhKOX0qCk60UwwLcMdTfDQ7W7GVGRE+OC2FqgSs3qqwfz/HxRNSAwYMHY+HChY1Of+/ePYhEIsTGxrZYnoiICLCxMMGkoe2xZfFQfPqPARgV6AmpyV/3zdIyC7HrlxuY/WEkln91DqeuJOFRXrEGc0xEuop35DWsuLQc1+4ol52zszSBhxPXJCXSFw11nwwLC8OOHTuafN4DBw7AyKjxQ3Dc3NyQmpoKOzu7Jr/XkwoKCsLx48cRHR2Nvn37ttr7EhFpA5FIBB8PG/h42GD2+C44fy0Vxy8m4urtdAgCIAhA7K10xN5KBwA425rB19MaHT1t4OtpA3cnC85+T0T1YiCvYdduZ6Ds8bJzfh0dOW6KSI+kpqaqnu/Zswfvv/8+4uPjVdsqZymvVFZW1qgA3cbGpkn5EIvFcHJyatIxTyMxMRHnz5/HvHnzEB4ervFAvrGfKxFRS5AYiTG4lysG93KF/FEhTlxOwvFLiUjLLFSlSc0sQGpmAU5eeQBAuZ69j8dfgb2PuzXMuFY9EVXBrvUaptat3pfd6on0iZOTk+phaWkJkUikel1cXAwrKyvs3bsXgwcPhomJCb799ltkZmbipZdegqurK6RSKbp27Yrdu3ernbd613pPT0989NFHmDlzJmQyGdzd3bFt2zbV/upd60+dOgWRSITjx4/D398fUqkU/fr1U7vIAACrV6+Gg4MDZDIZZs+ejaVLl6JHjx4Nlnv79u0YO3YsXn31VezZswcFBQVq+7OzszFnzhw4OjrCxMQEXbp0wY8//qjaHx0djUGDBkEqlcLa2hojR47Eo0ePVGXdtGmT2vl69OiBlStXql6LRCJ89dVXGD9+PMzMzLB69WooFArMmjULXl5eMDU1hY+PDzZv3lwj7xEREejcuTMkEgmcnZ0xb948AMDMmTMxduxYtbTl5eVwcnJCREREg58JEREAOFhLMWWED/61dDg+eq0/Jg72RicvGxgZqn8lLyopR+zNdOw+Fo8V287jpeU/Yf6nJ/Hl91dx4nIiUjLyIQiChkpBRNqAd+Q1SBAE1UR3hmIRurdvvW6vRPrgjY2n8CivpNXf11omwcY3BjfLuZYsWYL169dj+/btkEgkKC4uhp+fH5YsWQILCwscOXIEoaGhaNu2Lfr06VPnedavX49//vOfeOedd/D999/j1VdfxcCBA+Hr61vnMe+++y7Wr18Pe3t7zJ07FzNnzkR0dDQAYNeuXfjwww+xZcsW9O/fH9999x3Wr18PLy+vessjCAK2b9+OL7/8Er6+vujQoQP27t2Ll19+GQBQUVGB4OBg5OXl4dtvv0W7du3w559/qpZai42NxbBhwzBz5kx89tlnMDQ0xMmTJ6FQKJr0ua5YsQJr1qzBxo0bIRaLUVFRAVdXV+zduxd2dnY4d+4c5syZA2dnZ0yePBkAsHXrVixatAhr165FcHAwcnJyVJ/H7NmzMXDgQKSmpsLZ2RkA8NNPPyE/P191PBFRYxkYiNC1nR26tlN+9ysrr8Dd5GzE3XuEG/eyEHcvE1m5f7VvggDcS83FvdRc/HL+HgDA0twYvh42qrv23m5WkBhx2UqiZwUDeQ1KTs/HwyxltyouO0fUdI/ySpCZo9uTBC1cuBATJ05U2/bWW2+pns+fPx+//PIL9u3bV28gP3r0aLz22msAlBcHNm7ciFOnTtUbyH/44YcYNGgQAGDp0qUYM2YMiouLYWJigs8//xyzZs1SBeDvv/8+jh07hvz8/HrLExUVhcLCQowcORIAMG3aNISHh6vOExUVhYsXLyIuLg4dOnQAALRt21Z1/CeffAJ/f39s2bJFta1z5871vmdt/v73v2PmzJlq21atWqV67uXlhXPnzmHv3r2qQHz16tV48803sWDBAlW6gIAAAEC/fv3g4+ODb775BosXLwag7Hnw4osvwtzcvMn5IyKqysjQQDWmHoPaQRAEpD8qQty9LGVgfz8LCSm5qKj46y58Tn4pfr2ehl+vpwFQ3hRq18YK3drbwc/XEb4e1hCL2fmWSF8xkNcgdqsnejrWMonOv6+/v7/aa4VCgbVr12LPnj1ITk5GSUkJSkpKYGZmVu95unXrpnpe2YVfLpfXc4T6MZV3meVyOdzd3REfH6+6MFCpd+/eOHHiRL3nDA8PR0hICAwNlc3LSy+9hLfffhvx8fHw8fFBbGwsXF1dVUF8dbGxsXjxxRfrfY/GqP65AsBXX32F//u//8P9+/dRVFSE0tJS1VABuVyOlJQUDBs2rM5zzp49G9u2bcPixYshl8tx5MgRHD9+/KnzSkRUnUgkgoONFA42Ugzq5QpA2d3+VtKjx8G98mdBUZnqmHKFgPjER4hPfIR9x2/BzNQIPTvYw8/XEX6+DrC2MNFUcYioBTCQ1yC19eM7cv14oqZqru7tmlQ9QF+/fj02btyITZs2oWvXrjAzM8PChQtRWlpa73mqT+YmEolQUVHR6GMqJ9qsekz1yTcbGo+ZlZWFQ4cOoaysDFu3blVtVygUiIiIwMcff1xjgr/qGtpvYGBQIx9lZWU10lX/XPfu3Ys33ngD69evR2BgIGQyGdatW4dff/21Ue8LANOnT8fSpUtx/vx5nD9/Hp6enhgwYECDxxERNQdTiSG6edujm7c9AKCiQkByev5fd+3vZanWrgeAgqIy/PdqCv57NQUA0M7VUhXU+7jzbj2RrmMgryHFJeX4404mAMDOyhTujlx2joiAs2fPYvz48Zg2bRoAZWB969YtdOzYsVXz4ePjg4sXLyI0NFS17fLly/Ues2vXLri6uuLQoUNq248fP441a9bgww8/RLdu3fDgwQPcvHmz1rvy3bp1w/Hjx9W6wVdlb2+vthpAbm4uEhISGizP2bNn0a9fP7VeBnfu3FE9l8lk8PT0xPHjxzFkyJBaz2Fra4sJEyZg+/btOH/+vGq4ABGRJhgYiODmKIObowxBfTwAAI9yi/FbvBxXbsjxW7xc7Y79nQc5uPMgB3ujbsLc1Ag9fRzg5+uAXr4OsJbxbj2RrmEgryG/38lAuUJ558ufy84R0WPe3t7Yv38/zp07B2tra2zYsAFpaWmtHsjPnz8fr7zyCvz9/dGvXz/s2bMHv//+u9p49urCw8PxwgsvoEuXLmrbPTw8sGTJEhw5cgTjx4/HwIEDMWnSJGzYsAHe3t64ceMGRCIRRo0ahWXLlqFr16547bXXMHfuXBgbG+PkyZN48cUXYWdnh6FDh2LHjh0YN24crK2tsXz5ctVEefXx9vbG119/jaNHj8LLywvffPMNLl26pDZ538qVKzF37lw4ODioJuSLjo7G/PnzVWlmz56NsWPHQqFQICws7Ak+WarPli1bsG7dOqSmpqJz587YtGlTnb0eDhw4gK1btyI2NhYlJSXo3LkzVq5cqZqfgehZZG1hgmEB7hgW4A6FogLxiY9w5YYcl+Me4m5yjipdflEZzsYm42xsMgDAW3W33hEdPKy5hj2RDmCfGg1R61bvy271RKS0fPly9OrVCyNHjsTgwYPh5OSECRMmtHo+pk6dimXLluGtt95Cr169kJCQgBkzZsDEpPa7NleuXMHVq1cxadKkGvtkMhmCgoIQHh4OANi/fz8CAgLw0ksvoVOnTli8eLFqVvoOHTrg2LFjuHr1Knr37o3AwED88MMPqjH3y5Ytw8CBAzF27FiMHj0aEyZMQLt27Rosz9y5czFx4kSEhISgT58+yMzMrDEHQFhYGDZt2oQtW7agc+fOGDt2LG7duqWWZvjw4XB2dsbIkSPh4uLS8AdJjbZnzx4sXLgQ7777LmJiYjBgwAAEBwcjMTGx1vRnzpzBiBEj8NNPP+HKlSsYMmQIxo0bh5iYmFbOOZF2EosN0MnLFqHBHbF50WDsXDESC0J6on93F5iZqN/Lu/0gB3uibmLxF2cRuuJnrPvmMk5cTkS2BlaGIaLGEQlchLKG3NxcWFpaIicnBxYWFs1+fkEQMPujKMizCmEoFuHf/xwNUwk7RxDVpbi4GAkJCfDy8qozkKSWN2LECDg5OeGbb77RdFY0prCwEC4uLoiIiKix2kBzqO93vaXbJk3r06cPevXqpTa/QseOHTFhwgSsWbOmUefo3LkzQkJC8P777zcqvb5/pkR1USgqcOP+I1y58RBX4uS4m5JTZ1oXOzO42JvDxd4MLnbmcLEzQxt7c9hZmcKAd+6JmlVT2iVGjxrwQJ4P+eNl5zq3tWUQT0Rap7CwEF999RVGjhwJsViM3bt3IyoqCpGRkZrOmkZUVFQgLS0N69evh6WlJZ5//nlNZ0mvlJaW4sqVK1i6dKna9qCgIJw7d65R56ioqEBeXh5sbGzqTFO5CkSl3NzcJ8swkY4Tiw3Qua0tOre1xfTRnZCVW4zfbjzE5Tg5Ym/KUVBcrkqbklGAlIwCIE79HEaGBnCyNUObygC/yk8bCxMOGyVqYYwgNeDKjard6rnsHBFpH5FIhJ9++gmrV69GSUkJfHx8sH//fgwfPlzTWdOIxMREeHl5wdXVFTt27FB19afmkZGRAYVCAUdH9TbR0dERaWlpjTrH+vXrUVBQgMmTJ9eZZs2aNXVOpEj0LLOxMMHw3h4Y3tsD5YoKxN9/hMtxDxF7U44keT5KShU1jikrr0DSwzwkPcyrsc/EWAxnu5oBfht7c1iYGTPIJ2oG/CaiAVfi/lrb2b8jA3ki0j6mpqaIiorSdDa0hqenZ4PL79HTq23Jw8Z84d+9ezdWrlyJH374AQ4Odc87s2zZMixatEj1Ojc3F25ubk+eYSI9ZFjlbn3YmE4QBAFZucVISS9ASkY+UtILkJyej5SMAqRlFqCsvOZSp8WlCiSk5CIhpWavF5nUGO3drODtZoX2jx+2lg0vAUpE6hjIt7KiknL8cVe57JyDtSlcHcw1nCMiIiLNsrOzg1gsrnH3XS6X17hLX92ePXswa9Ys7Nu3r8EeIxKJBBKJ5KnzS/QsEYlEsLU0ha2lKbp626ntU1QIyMwuUgX2lYF+Sno+HmYVQlFR8wJoXmEpfotXLo9XycZCAm9Xa7Xg3tKcf6tE9WEg38p+v5WuWnbOj8vOERERwdjYGH5+foiMjMTf/vY31fbIyEiMHz++zuN2796NmTNnYvfu3RgzZkxrZJWIqhAbiOBgI4WDjRQ9fdT3lSsqIH9UqArsUzKUd/ITUnKQk1+qljYrtwQX/0zDxT//upjnYG0KbzcreLtaoYObNdq5WcHc1Kg1ikWkExjIt7LLN6p0q+f4eCIiIgDAokWLEBoaCn9/fwQGBmLbtm1ITEzE3LlzASi7xScnJ+Prr78GoAzip0+fjs2bN6Nv376qu/mmpqawtLTUWDmISMlQbPB4lntzoMpQUkEQkJ5dhNtJ2bj9IBu3ErNx60E2CorK1I6XPyqC/FERzv2eqtrmYmdW5a69Ndq2seSk0fTM4m9+KxIEQTXRnaHYAN2qdU8iIiJ6VoWEhCAzMxMffPABUlNT0aVLF/z000/w8PAAAKSmpqqtKf+vf/0L5eXleP311/H666+rtoeFhWHHjh2tnX0iaiSRSAQHaykcrKXo180FgPI7cmpmAW4nZePW48edB9korjbJXuUM+mdikgEABiLAxd4cTrZmsLc2haO1sneAo40U9tamsDKXsPcr6S0G8q0o6WEe0h8VAQC6tLOFCa8gEhERqbz22mt47bXXat1XPTg/depUy2eIiFqFSCRS3b0f2NMVgHL8fbI8T+2ufUJyDkqrTK5XISiXdX4gz6/1vMZGYjhYmyovHNhIVc8dHw8HsDKXwMCAgT7pJkaSrehyldnquewcEREREVHtxAYiuDtZwN3JAkP93QEox90npuU9vmv/CLcfZCMxLa/WmfMBoLRMUW+gb2RoAHsrU7W7+PZWUsikRpCaGEFqYgizxz9NTYwgZtBPWoSBfCtSXz++7uVxiIiqGjx4MHr06IFNmzYBUC6FtnDhQixcuLDOY0QiEQ4ePIgJEyY81Xs313mIiIielqHYAG3bWKJtG0uM7KscdiMIArLzSyDPKoQ8qwjyR4V4+KhQ+fqR8nVJtS76lcrKK1Td9RvDxFgMqYmhKsivGuybVgn6q+83NzWCTGoMc6kxLwZQs2Eg30oKi8vwZ4Jy2TlHGymXnSN6BowbNw5FRUW1rsd+/vx59OvXD1euXEGvXr2adN5Lly7BzMysubIJAFi5ciUOHTqE2NhYte2pqamwtrZu1veqS1FREVxcXCASiZCcnAxTU64rTERE9ROJRLCWmcBaZgIfj5r7BUFAbkEp5I+Ugf7DrELlc1WwX4iiktoD/eqKSxUoLlUgK7fkifNrZmoEC6kxzKVGkJkZq55bSI0hM1MG+8rnyuBfJjWG1MSQY/2pBo0H8lu2bMG6deuQmpqKzp07Y9OmTRgwYECtaWfMmIGdO3fW2N6pUydcv35d9Xr//v1Yvnw57ty5g3bt2uHDDz9UW85GE67eykC5QrmWpp+vA/8YiZ4Bs2bNwsSJE3H//n3VhF2VIiIi0KNHjyYH8QBgb2/fXFlskJOTU6u91/79+9GlSxcIgoADBw5g6tSprfbe1QmCAIVCAUNDjTeTRET0FEQiESzNJbA0l6C9W80L04IgIL+oTBngZxUiI6cIhcXlKCgqQ1GJ8mdhSTkKq/8sLn+i/BQUlSln6M9s/DEGBiLIpH8F9nZWpnB3ksHdUQZ3Jxmcbc0gFhs8UX5Id2m0xvfs2YOFCxfi3XffRUxMDAYMGIDg4GC1WWmr2rx5M1JTU1WPpKQk2NjY4MUXX1SlOX/+PEJCQhAaGoqrV68iNDQUkydPxq+//tpaxapV1W71/h05Pp7oWTB27Fg4ODjUmKSrsLAQe/bswaxZs5CZmYmXXnoJrq6ukEql6Nq1K3bv3l3veT09PVXd7AHg1q1bGDhwIExMTNCpUydERkbWOGbJkiXo0KEDpFIp2rZti+XLl6OsTLnUz44dO7Bq1SpcvXoVIpEIIpFIlWeRSIRDhw6pznPt2jUMHToUpqamsLW1xZw5c5Cf/9fYwxkzZmDChAn49NNP4ezsDFtbW7z++uuq96pPeHg4pk2bhmnTpiE8PLzG/uvXr2PMmDGwsLCATCbDgAEDcOfOHdX+iIgIdO7cGRKJBM7Ozpg3bx4A4N69exCJRGq9DbKzsyESiVQTpp06dQoikQhHjx6Fv78/JBIJzp49izt37mD8+PFwdHSEubk5AgICavSwKCkpweLFi+Hm5gaJRIL27dsjPDwcgiDA29sbn376qVr6P/74AwYGBmp5JyIizRCJRJBJjeHtaoV+3Vzw/IB2mDLCB7Oe74J5L/bAkukBWPVKINb9YyC+fHsotr8/Ens+HIMf1j2PPR+ORsR7QfjirSH4ZN4ArJjdF4un+eP1F7rj5bGdETK8A8Y+54XBfq7w83WAj7s1nO3MYG5qhKbc06uoEJCTX4oH8nzE3cvC2dhk7PrlBtbsvIRXPz6BF985gn+sP4lPv72CvVE3ceGPVKRmFKCiQmi5D440TqO3GjZs2IBZs2Zh9uzZAIBNmzbh6NGj2Lp1K9asWVMjvaWlpdrasIcOHcKjR4/w8ssvq7Zt2rQJI0aMwLJlywAo1509ffo0Nm3a1OCX45YiCAKuxCkDeSNDA3TlsnNEzWLpsTXILs5t9fe1MrHA2qBlDaYzNDTE9OnTsWPHDrz//vuqnjj79u1DaWkppk6disLCQvj5+WHJkiWwsLDAkSNHEBoairZt26JPnz4NvkdFRQUmTpwIOzs7XLhwAbm5ubWOnZfJZNixYwdcXFxw7do1vPLKK5DJZFi8eDFCQkLwxx9/4JdfflEFqbWtw11YWIhRo0ahb9++uHTpEuRyOWbPno158+apXaw4efIknJ2dcfLkSdy+fRshISHo0aMHXnnllTrLcefOHZw/fx4HDhyAIAhYuHAh7t69i7Zt2wIAkpOTMXDgQAwePBgnTpyAhYUFoqOjUV6uvCOydetWLFq0CGvXrkVwcDBycnIQHR3d4OdX3eLFi/Hpp5+ibdu2sLKywoMHDzB69GisXr0aJiYm2LlzJ8aNG4f4+Hi4uysnX5o+fTrOnz+Pzz77DN27d0dCQgIyMjIgEokwc+ZMbN++HW+99ZbqPSIiIjBgwAC0a9euyfkjIiLtYGAgejwG3ghA04eCKSoEFBSVIa+wVPkoKEVeYVmV51VeV9lfVFKzJ0BZeQUSUnKRkKL+ncjYSAw3R/PHd+4t4OGk/GlvZcrZ+vWAxgL50tJSXLlyBUuXLlXbHhQUhHPnzjXqHOHh4Rg+fLhal9Xz58/jjTfeUEs3cuRItbtX1ZWUlKCk5K+xLrm5zRsYJKblISOnGADQpa0tTIzZVZOoOWQX5yKrKFvT2ajXzJkzsW7dOpw6dQpDhgwBoAzkJk6cCGtra1hbW6sFefPnz8cvv/yCffv2NSqQj4qKQlxcHO7duwdXV+WSPR999BGCg4PV0r333nuq556ennjzzTexZ88eLF68GKampjA3N4ehoWG9Xel37dqFoqIifP3116ox+l988QXGjRuHjz/+GI6Oyt5G1tbW+OKLLyAWi+Hr64sxY8bg+PHj9QbyERERCA4OVo3HHzVqFCIiIrB69WoAwJdffglLS0t89913MDIyAgB06NBBdfzq1avx5ptvYsGCBaptAQEBDX5+1X3wwQcYMWKE6rWtrS26d++u9j4HDx7E4cOHMW/ePNy8eRN79+5FZGQkhg8fDgCqiw8A8PLLL+P999/HxYsX0bt3b5SVleHbb7/FunXrmpw3IiLSH2IDESzMjGFhZtyk48rKFUjNKEDiwzwkpj1+PMxFcnrNO/ClZQrceZCDOw9y1LabGIvh9rhbvrujBdydZHCxN4ODtRSGWtZFXxAEFJcqYGIs5tDkajQWUWZkZEChUKi++FVydHREWlpag8enpqbi559/xr///W+17WlpaU0+55o1a7Bq1aom5L5pLsexWz1RS7AysdD69/X19UW/fv0QERGBIUOG4M6dOzh79iyOHTsGAFAoFFi7di327NmD5ORk1YXFxk5mFxcXB3d3d1UQDwCBgYE10n3//ffYtGkTbt++jfz8fJSXl8PCommfX1xcHLp3766Wt/79+6OiogLx8fGq/72dO3eGWCxWpXF2dsa1a9fqPK9CocDOnTuxefNm1bZp06bhjTfewKpVqyAWixEbG4sBAwaogviq5HI5UlJSMGzYsCaVpzb+/v5qrwsKCrBq1Sr8+OOPSElJQXl5OYqKilRDwGJjYyEWizFo0KBaz+fs7IwxY8YgIiICvXv3xo8//oji4mK1IWFERESNZWQoVi3Lh7+uM6OsXIHk9AIkpuU+Du7zkJiWq+xiX62HfXGp4vESftlq28UGIjjYSOFiZwZnOzO42JnDxV7508HatMXG4ZeWKfAwq1D5yCxA2uPnaZkFeJhViMLicliaG6ODuzV8PKzh626D9u5Wj3tDPLs0fmu4+pUVQRAadbVlx44dsLKyqnVJpKaec9myZVi0aJHqdW5uLtzc3BrMQ2NduVFl/XgG8kTNpjHd27XBrFmzMG/ePHz55ZfYvn07PDw8VEHn+vXrsXHjRmzatAldu3aFmZkZFi5ciNLS0kadWxBqjn+r/v/uwoULmDJlClatWoWRI0eq7myvX7++SeWo739p1e3Vg22RSISKitrX+AWAo0ePIjk5GSEhIWrbFQoFjh07huDg4HpnsG9odnsDAwNV/ivVNWa/+gWUt99+G0ePHsWnn34Kb29vmJqa4oUXXlDVT2Nm1p89ezZCQ0OxceNGbN++HSEhIZBKpQ0eR0RE1FhGhmJ4OlvA01n9In1pmQIP5PnKAL/KXfy0rAJU/wqhqBCQmlGA1FqW4xMbiOBoI4WLvfnjIP+vQN/eqv4gv6JCwKO8YqRlFuJhVgEeZhYirUqgnvm453J9cvJLcenPh7j0p/IGqUgEuDvKHgf3NvD1sIaro+yZWt5PY4G8nZ0dxGJxjTvlcrm8xh316gRBQEREBEJDQ2FsrN4dxcnJqcnnlEgkkEgkTSxB41Rdds7ZVvlLT0TPlsmTJ2PBggX497//jZ07d+KVV15RBb5nz57F+PHjMW3aNADKMe+3bt1Cx44dG3XuTp06ITExESkpKXBxcQGgHGJUVXR0NDw8PPDuu++qtt2/f18tjbGxMRSK+pff6dSpE3bu3ImCggJVwBsdHQ0DAwO1bu5NFR4ejilTpqjlDwDWrl2L8PBwBAcHo1u3bti5cyfKyspqXCiQyWTw9PTE8ePHVcMXqqqc5T81NRU9e/YEgBrL7NXl7NmzmDFjhmrlk/z8fNy7d0+1v2vXrqioqMDp06dVXeurGz16NMzMzLB161b8/PPPOHPmTKPem4iI6GkZG4nRto0l2rZRn/umuLQcDx7mI/FhLh7I85GSUYDU9AKkZOSjuLTm9wFFhYCUjAKk1BLkG4qVQb6znTlc7MxgJZMgM6dYFag/zCpEWXndF/TrIjYQwcFaCiuZBEkP85Bf9NdFeEEA7qfl4X5aHiIvKnvJmUoM0cHdCh3creHrYYMO7tawkrVMjKcNNBbIGxsbw8/PD5GRkWpLw0VGRmL8+PH1Hnv69Gncvn0bs2bNqrEvMDAQkZGRauPkjx07hn79+jVf5psg9mY6FBVcdo7oWWZubo6QkBC88847yMnJwYwZM1T7vL29sX//fpw7dw7W1tbYsGED0tLSGh3IDx8+HD4+Ppg+fTrWr1+P3NzcGgGxt7c3EhMT8d133yEgIABHjhzBwYMH1dJ4enoiISEBsbGxcHV1hUwmq3GBc+rUqVixYgXCwsKwcuVKpKenY/78+QgNDW3wAmxd0tPT8Z///AeHDx9Gly5d1PaFhYVhzJgxSE9Px7x58/D5559jypQpWLZsGSwtLXHhwgX07t0bPj4+WLlyJebOnQsHBwcEBwcjLy8P0dHRmD9/PkxNTdG3b1+sXbsWnp6eyMjIUJszoD7e3t44cOAAxo0bB5FIhOXLl6v1LvD09ERYWBhmzpypmuzu/v37kMvlmDx5MgBALBZjxowZWLZsGby9vWsd+kBERNSaTIwN4e1mBW83K7XtgiAgO69EGbSnPw7wM5QBfmpGQa1BfrlCQHJ6AZLTawb5DbEyl8DRVgpHGymcbM3gZCOFo60UTjZmsLU0Ud3pFwTlhYT4+48Qfz8L8YmPkJCSqzYvQFFJOa7eysDVWxmqbU62Uvi426CDhxV8PWzg5WIJI8OGhwgoKgSUlilQUqpASZkCJaXlj38qavwsLlWgpKwcPTs4oIN7zSUOW4pGu9YvWrQIoaGh8Pf3R2BgILZt24bExETMnTsXgLLLe3JyMr7++mu148LDw9GnT58aX/oAYMGCBRg4cCA+/vhjjB8/Hj/88AOioqLw3//+t1XKVB271RMRoOxeHx4ejqCgINVs5wCwfPlyJCQkYOTIkZBKpZgzZw4mTJiAnJyces72FwMDAxw8eBCzZs1C79694enpic8++wyjRo1SpRk/fjzeeOMNzJs3DyUlJRgzZgyWL1+OlStXqtJMmjQJBw4cwJAhQ5CdnY3t27erXXAAAKlUiqNHj2LBggUICAiAVCrFpEmTsGHDhif+XConzqttfPuQIUMgk8nwzTffYNGiRThx4gTefvttDBo0CGKxGD169ED//v0BKIP+4uJibNy4EW+99Rbs7OzwwgsvqM4VERGBmTNnwt/fHz4+Pvjkk08QFBTUYP42btyImTNnol+/frCzs8OSJUtqTIi6detWvPPOO3jttdeQmZkJd3d3vPPOO2ppZs2ahY8++ggzZ858ko+JiIioVYhEIlhbmMDawgSd29qq7RMEAVm5xY8De/VAPzWzACW1BPnGRmI4VQ/UHz93sJHCVNK4cFQkEqGNvTna2JtjqL9yCHRxaTnuPMhRBveJWYi//6hGN/20zEKkZRbidMwDAMoVxNq1sYSFmQQlZeW1BuYlZYon6kFgamzYqoG8SKhtgGUr2rJlCz755BOkpqaiS5cu2LhxIwYOHAhAuR7xvXv3VOv8AkBOTg6cnZ2xefPmOmdA/v777/Hee+/h7t27aNeuHT788ENMnDix0XnKzc2FpaUlcnJymjwZVFWCIODlfx5DZk4xjA0NsOufwZyxnugJFBcXIyEhAV5eXjAxMdF0doiaLDo6GoMHD8aDBw/q7b1Q3+96c7VN9Bd+pkREzaMyyE9JL0B2fgnsrUzhaKPsFt+aPZIzsosQn/hIdef+9oMclJbVP3SwuUwf3REvDnvyoYZA09oljQfy2qi5Gvas3GK8ufkMMrKL0MvXAateYXdKoifBQJ50VUlJCZKSkjBnzhw4Oztj165d9aZnIN+6+JkSEem3ckUF7qXmqgL7m4mPagwBMDY0gMRYDImR+PFPw2qvG/qpTO/mKIPzU86H1pR2ibeHW5CNhQki3huBxLS8J+qeQUREum337t2YNWsWevTogW+++UbT2SEiInqmGIoN4O1qBW9XK4zp7wUAKCgqQ7miAhIjMYyMxDo70z0D+RYmEong4cyr/EREz6IZM2bUmGuAiIiINMfMVD/Wn294yj4iIiIiIiIi0hoM5IlIZ3BKD9J3/B0nIiKixmAgT0Raz8hI2QWqsLBQwzkhalmVv+OVv/NEREREteEYeSLSemKxGFZWVpDL5QCU65m35lImRC1NEAQUFhZCLpfDysoKYrFY01kiIiIiLcZAnoh0gpOTEwCognkifWRlZaX6XSciIiKqCwN5ItIJIpEIzs7OcHBwQFlZmaazQ9TsjIyMeCeeiIiIGoWBPBHpFLFYzGCHiIiIiJ5pnOyOiIiIiIiISIcwkCciIiIiIiLSIQzkiYiIiIiIiHQIx8jXQhAEAEBubq6Gc0JERKRU2SZVtlH09NjeExGRNmlKW89AvhZ5eXkAADc3Nw3nhIiISF1eXh4sLS01nQ29wPaeiIi0UWPaepHAS/s1VFRUICUlBTKZDCKR6KnOlZubCzc3NyQlJcHCwqKZcqgZLIv20ZdyAPpTFn0pB6A/ZdGXcgiCgLy8PLi4uMDAgCPjmgPb+5r0pRyA/pRFX8oBsCzaSF/KAehHWZrS1vOOfC0MDAzg6urarOe0sLDQ2V+o6lgW7aMv5QD0pyz6Ug5Af8qiD+Xgnfjmxfa+bvpSDkB/yqIv5QBYFm2kL+UAdL8sjW3reUmfiIiIiIiISIcwkCciIiIiIiLSIQzkW5hEIsGKFSsgkUg0nZWnxrJoH30pB6A/ZdGXcgD6UxZ9KQdpN335PdOXcgD6UxZ9KQfAsmgjfSkHoF9laQxOdkdERERERESkQ3hHnoiIiIiIiEiHMJAnIiIiIiIi0iEM5ImIiIiIiIh0CAN5IiIiIiIiIh3CQL4ZbNmyBV5eXjAxMYGfnx/Onj1bb/rTp0/Dz88PJiYmaNu2Lb766qtWymnd1qxZg4CAAMhkMjg4OGDChAmIj4+v95hTp05BJBLVeNy4caOVcl27lStX1siTk5NTvcdoY514enrW+vm+/vrrtabXpvo4c+YMxo0bBxcXF4hEIhw6dEhtvyAIWLlyJVxcXGBqaorBgwfj+vXrDZ53//796NSpEyQSCTp16oSDBw+2UAmU6itHWVkZlixZgq5du8LMzAwuLi6YPn06UlJS6j3njh07aq2n4uJijZUFAGbMmFEjT3379m3wvK1dJ0DDZant8xWJRFi3bl2d59RUvZBu0fX2nm29dtVHJV1t7/WlrQf0p71nW/9stfUM5J/Snj17sHDhQrz77ruIiYnBgAEDEBwcjMTExFrTJyQkYPTo0RgwYABiYmLwzjvv4B//+Af279/fyjlXd/r0abz++uu4cOECIiMjUV5ejqCgIBQUFDR4bHx8PFJTU1WP9u3bt0KO69e5c2e1PF27dq3OtNpaJ5cuXVIrQ2RkJADgxRdfrPc4baiPgoICdO/eHV988UWt+z/55BNs2LABX3zxBS5dugQnJyeMGDECeXl5dZ7z/PnzCAkJQWhoKK5evYrQ0FBMnjwZv/76a0sVo95yFBYW4rfffsPy5cvx22+/4cCBA7h58yaef/75Bs9rYWGhVkepqakwMTFpiSKoNFQnADBq1Ci1PP3000/1nlMTdQI0XJbqn21ERAREIhEmTZpU73k1US+kO/ShvWdbr131UUlX23t9aesB/Wnv2dY/Y229QE+ld+/ewty5c9W2+fr6CkuXLq01/eLFiwVfX1+1bf/zP/8j9O3bt8Xy+CTkcrkAQDh9+nSdaU6ePCkAEB49etR6GWuEFStWCN27d290el2pkwULFgjt2rUTKioqat2vrfUBQDh48KDqdUVFheDk5CSsXbtWta24uFiwtLQUvvrqqzrPM3nyZGHUqFFq20aOHClMmTKl2fNcm+rlqM3FixcFAML9+/frTLN9+3bB0tKyeTPXRLWVJSwsTBg/fnyTzqPpOhGExtXL+PHjhaFDh9abRhvqhbSbPrb3bOu1qz4q6WJ7ry9tvSDoT3vPtr4mTddJc+Md+adQWlqKK1euICgoSG17UFAQzp07V+sx58+fr5F+5MiRuHz5MsrKylosr02Vk5MDALCxsWkwbc+ePeHs7Ixhw4bh5MmTLZ21Rrl16xZcXFzg5eWFKVOm4O7du3Wm1YU6KS0txbfffouZM2dCJBLVm1Yb66OqhIQEpKWlqX3mEokEgwYNqvPvBqi7nuo7prXl5ORAJBLBysqq3nT5+fnw8PCAq6srxo4di5iYmNbJYANOnToFBwcHdOjQAa+88grkcnm96XWhTh4+fIgjR45g1qxZDabV1nohzdPX9p5tvXbVB6A/7b0+t/WAbrf3bOu1r06eFAP5p5CRkQGFQgFHR0e17Y6OjkhLS6v1mLS0tFrTl5eXIyMjo8Xy2hSCIGDRokV47rnn0KVLlzrTOTs7Y9u2bdi/fz8OHDgAHx8fDBs2DGfOnGnF3NbUp08ffP311zh69Cj+93//F2lpaejXrx8yMzNrTa8LdXLo0CFkZ2djxowZdabR1vqorvJvoyl/N5XHNfWY1lRcXIylS5fi73//OywsLOpM5+vrix07duDw4cPYvXs3TExM0L9/f9y6dasVc1tTcHAwdu3ahRMnTmD9+vW4dOkShg4dipKSkjqP0fY6AYCdO3dCJpNh4sSJ9abT1noh7aCP7T3beu2qj0r60t7ra1sP6HZ7z7Ze++rkaRhqOgP6oPoVU0EQ6r2KWlv62rZryrx58/D777/jv//9b73pfHx84OPjo3odGBiIpKQkfPrppxg4cGBLZ7NOwcHBquddu3ZFYGAg2rVrh507d2LRokW1HqPtdRIeHo7g4GC4uLjUmUZb66MuTf27edJjWkNZWRmmTJmCiooKbNmypd60ffv2VZtYpn///ujVqxc+//xzfPbZZy2d1TqFhISonnfp0gX+/v7w8PDAkSNH6m0YtbVOKkVERGDq1KkNjn/T1noh7aJP7T3beu2qj0r61t7rU1sP6H57z7Ze++rkafCO/FOws7ODWCyucUVKLpfXuHJVycnJqdb0hoaGsLW1bbG8Ntb8+fNx+PBhnDx5Eq6urk0+vm/fvlp3VcvMzAxdu3atM1/aXif3799HVFQUZs+e3eRjtbE+KmcVbsrfTeVxTT2mNZSVlWHy5MlISEhAZGRkvVfna2NgYICAgACtqydnZ2d4eHjUmy9trZNKZ8+eRXx8/BP97WhrvZBm6Ft7z7ZeSVvqo5I+tff61tYD+tnes63XvjppCgbyT8HY2Bh+fn6q2UUrRUZGol+/frUeExgYWCP9sWPH4O/vDyMjoxbLa0MEQcC8efNw4MABnDhxAl5eXk90npiYGDg7Ozdz7p5OSUkJ4uLi6syXttZJpe3bt8PBwQFjxoxp8rHaWB9eXl5wcnJS+8xLS0tx+vTpOv9ugLrrqb5jWlplo37r1i1ERUU90ZdBQRAQGxurdfWUmZmJpKSkevOljXVSVXh4OPz8/NC9e/cmH6ut9UKaoS/tPdt67aqP6vSpvdenth7Q3/aebb321UmTtO7cevrnu+++E4yMjITw8HDhzz//FBYuXCiYmZkJ9+7dEwRBEJYuXSqEhoaq0t+9e1eQSqXCG2+8Ifz5559CeHi4YGRkJHz//feaKoIgCILw6quvCpaWlsKpU6eE1NRU1aOwsFCVpnpZNm7cKBw8eFC4efOm8McffwhLly4VAAj79+/XRBFU3nzzTeHUqVPC3bt3hQsXLghjx44VZDKZztWJIAiCQqEQ3N3dhSVLltTYp831kZeXJ8TExAgxMTECAGHDhg1CTEyManbXtWvXCpaWlsKBAweEa9euCS+99JLg7Ows5Obmqs4RGhqqNht0dHS0IBaLhbVr1wpxcXHC2rVrBUNDQ+HChQsaKUdZWZnw/PPPC66urkJsbKza301JSUmd5Vi5cqXwyy+/CHfu3BFiYmKEl19+WTA0NBR+/fXXFitHQ2XJy8sT3nzzTeHcuXNCQkKCcPLkSSEwMFBo06aN1tVJQ2WplJOTI0ilUmHr1q21nkNb6oV0hz6092zrtas+qtLF9l5f2vqGyqJL7T3benXaUCctiYF8M/jyyy8FDw8PwdjYWOjVq5faMi5hYWHCoEGD1NKfOnVK6Nmzp2BsbCx4enrW+cvXmgDU+ti+fbsqTfWyfPzxx0K7du0EExMTwdraWnjuueeEI0eOtH7mqwkJCRGcnZ0FIyMjwcXFRZg4caJw/fp11X5dqRNBEISjR48KAIT4+Pga+7S5PiqXxqn+CAsLEwRBuSzNihUrBCcnJ0EikQgDBw4Url27pnaOQYMGqdJX2rdvn+Dj4yMYGRkJvr6+Lf6lpb5yJCQk1Pl3c/LkyTrLsXDhQsHd3V0wNjYW7O3thaCgIOHcuXMtWo6GylJYWCgEBQUJ9vb2gpGRkeDu7i6EhYUJiYmJaufQhjppqCyV/vWvfwmmpqZCdnZ2refQlnoh3aLr7T3beu2qj6p0sb3Xl7a+obLoUnvPtl6dNtRJSxIJwuOZPoiIiIiIiIhI63GMPBEREREREZEOYSBPREREREREpEMYyBMRERERERHpEAbyRERERERERDqEgTwRERERERGRDmEgT0RERERERKRDGMgTERERERER6RAG8kSklUQiEQ4dOqTpbBAREVELYVtP9OQYyBNRDTNmzIBIJKrxGDVqlKazRkRERM2AbT2RbjPUdAaISDuNGjUK27dvV9smkUg0lBsiIiJqbmzriXQX78gTUa0kEgmcnJzUHtbW1gCUXeG2bt2K4OBgmJqawsvLC/v27VM7/tq1axg6dChMTU1ha2uLOXPmID8/Xy1NREQEOnfuDIlEAmdnZ8ybN09tf0ZGBv72t79BKpWiffv2OHz4cMsWmoiI6BnCtp5IdzGQJ6Insnz5ckyaNAlXr17FtGnT8NJLLyEuLg4AUFhYiFGjRsHa2hqXLl3Cvn37EBUVpdZ4b926Fa+//jrmzJmDa9eu4fDhw/D29lZ7j1WrVmHy5Mn4/fffMXr0aEydOhVZWVmtWk4iIqJnFdt6Ii0mEBFVExYWJojFYsHMzEzt8cEHHwiCIAgAhLlz56od06dPH+HVV18VBEEQtm3bJlhbWwv5+fmq/UeOHBEMDAyEtLQ0QRAEwcXFRXj33XfrzAMA4b333lO9zs/PF0QikfDzzz83WzmJiIieVWzriXQbx8gTUa2GDBmCrVu3qm2zsbFRPQ8MDFTbFxgYiNjYWABAXFwcunfvDjMzM9X+/v37o6KiAvHx8RCJREhJScGwYcPqzUO3bt1Uz83MzCCTySCXy5+0SERERFQF23oi3cVAnohqZWZmVqP7W0NEIhEAQBAE1fPa0piamjbqfEZGRjWOraioaFKeiIiIqHZs64l0F8fIE9ETuXDhQo3Xvr6+AIBOnTohNjYWBQUFqv3R0dEwMDBAhw4dIJPJ4OnpiePHj7dqnomIiKjx2NYTaS/ekSeiWpWUlCAtLU1tm6GhIezs7AAA+/btg7+/P5577jns2rULFy9eRHh4OABg6tSpWLFiBcLCwrBy5Uqkp6dj/vz5CA0NhaOjIwBg5cqVmDt3LhwcHBAcHIy8vDxER0dj/vz5rVtQIiKiZxTbeiLdxUCeiGr1yy+/wNnZWW2bj48Pbty4AUA5y+x3332H1157DU5OTti1axc6deoEAJBKpTh69CgWLFiAgIAASKVSTJo0CRs2bFCdKywsDMXFxdi4cSPeeust2NnZ4YUXXmi9AhIRET3j2NYT6S6RIAiCpjNBRLpFJBLh4MGDmDBhgqazQkRERC2AbT2RduMYeSIiIiIiIiIdwkCeiIiIiIiISIewaz0RERERERGRDuEdeSIiIiIiIiIdwkCeiIiIiIiISIcwkCciIiIiIiLSIQzkiYiIiIiIiHQIA3kiIiIiIiIiHcJAnoiIiIiIiEiHMJAnIiIiIiIi0iEM5ImIiIiIiIh0CAN5IiIiIiIiIh3y/5/MEDJSYssGAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize the performance of all model variations\n",
+    "for i, history in enumerate(histories):\n",
+    "    model_func = model_variations[i]\n",
+    "    title = f\"{model_func.__name__}\"\n",
+    "    plot_history(history, title=f\"{title}\")\n",
+    "\n",
+    "# Plot Transformer Model as well\n",
+    "plot_history(trans_history, title=f\"Transformer Model With 1 Multi-headed Self Attention Layer\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ba7eb0f",
+   "metadata": {},
+   "source": [
+    "The training and validation performance graphs illustrate the convergence of each of our models. The simple RNN, ISTM, and GRU models demonstrate convergence, evidenced by the steady decrease and plateau of their training loss and the relative stability of their validation loss. Accuracy also becomes relatively stable.\n",
+    "\n",
+    "In contrast, the Transformer model does not fully converge, as its validation loss is increasing in later epochs, and its validation accuracy is fluctuating more than the other models. These patterns suggest that the Transformer model may require further tuning, which we will do by adding a second Multi-headed self attention layer in the next section."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "77c96892-c529-45c7-8fe8-20a230d5452f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def transformer_model2():\n",
+    "    embed_dim = embedding_dim  # Assuming you are using GloVe embeddings with dimension 200\n",
+    "    num_heads = 2\n",
+    "    ff_dim = 32\n",
+    "\n",
+    "    inputs = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)  # Use dtype=tf.string for token sequences\n",
+    "    x = inputs\n",
+    "    x = TokenAndPositionEmbedding(sequence_length, max_features, embed_dim)(x)\n",
+    "    x = TransformerBlock(embed_dim, num_heads, ff_dim)(x)\n",
+    "    x = tf.keras.layers.GlobalAveragePooling1D()(x)\n",
+    "    x = tf.keras.layers.Dense(20, activation=\"relu\")(x)\n",
+    "    x = tf.keras.layers.Dropout(0.2)(x)\n",
+    "    outputs = tf.keras.layers.Dense(1, activation='sigmoid', kernel_initializer='glorot_uniform')(x)\n",
+    "    \n",
+    "    model = tf.keras.Model(inputs=inputs, outputs=outputs)\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "287b5457-53c8-4bd4-90fc-2b721cb932cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_21\"</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mModel: \"functional_21\"\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ input_layer_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ token_and_position_embedding_1  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">200</span>)       │     <span style=\"color: #00af00; text-decoration-color: #00af00\">4,020,000</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TokenAndPositionEmbedding</span>)     │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ transformer_block_1             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">200</span>)       │       <span style=\"color: #00af00; text-decoration-color: #00af00\">335,232</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TransformerBlock</span>)              │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ global_average_pooling1d_1      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">200</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePooling1D</span>)        │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">4,020</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span> │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ input_layer_5 (\u001b[38;5;33mInputLayer\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ token_and_position_embedding_1  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m200\u001b[0m)       │     \u001b[38;5;34m4,020,000\u001b[0m │\n",
+       "│ (\u001b[38;5;33mTokenAndPositionEmbedding\u001b[0m)     │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ transformer_block_1             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m200\u001b[0m)       │       \u001b[38;5;34m335,232\u001b[0m │\n",
+       "│ (\u001b[38;5;33mTransformerBlock\u001b[0m)              │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ global_average_pooling1d_1      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m200\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
+       "│ (\u001b[38;5;33mGlobalAveragePooling1D\u001b[0m)        │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_12 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m)             │         \u001b[38;5;34m4,020\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_7 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_13 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m21\u001b[0m │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">4,359,273</span> (16.63 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m4,359,273\u001b[0m (16.63 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">4,359,273</span> (16.63 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m4,359,273\u001b[0m (16.63 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m60s\u001b[0m 62ms/step - accuracy: 0.7874 - loss: 0.4393 - val_accuracy: 0.9440 - val_loss: 0.1592\n",
+      "Epoch 2/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m58s\u001b[0m 62ms/step - accuracy: 0.9612 - loss: 0.1183 - val_accuracy: 0.9396 - val_loss: 0.1752\n",
+      "Epoch 3/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m58s\u001b[0m 62ms/step - accuracy: 0.9828 - loss: 0.0614 - val_accuracy: 0.9348 - val_loss: 0.2215\n",
+      "Epoch 4/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m59s\u001b[0m 63ms/step - accuracy: 0.9842 - loss: 0.0534 - val_accuracy: 0.9427 - val_loss: 0.2109\n",
+      "Epoch 5/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m59s\u001b[0m 63ms/step - accuracy: 0.9886 - loss: 0.0431 - val_accuracy: 0.9345 - val_loss: 0.2667\n",
+      "Epoch 6/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m59s\u001b[0m 63ms/step - accuracy: 0.9915 - loss: 0.0274 - val_accuracy: 0.9345 - val_loss: 0.3073\n",
+      "Epoch 7/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m59s\u001b[0m 63ms/step - accuracy: 0.9936 - loss: 0.0213 - val_accuracy: 0.9346 - val_loss: 0.2812\n",
+      "Epoch 8/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m59s\u001b[0m 63ms/step - accuracy: 0.9936 - loss: 0.0200 - val_accuracy: 0.9356 - val_loss: 0.3178\n",
+      "Epoch 9/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m59s\u001b[0m 63ms/step - accuracy: 0.9949 - loss: 0.0186 - val_accuracy: 0.9405 - val_loss: 0.2774\n",
+      "Epoch 10/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 65ms/step - accuracy: 0.9959 - loss: 0.0138 - val_accuracy: 0.9330 - val_loss: 0.4374\n",
+      "Epoch 11/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m64s\u001b[0m 68ms/step - accuracy: 0.9953 - loss: 0.0175 - val_accuracy: 0.9368 - val_loss: 0.2694\n",
+      "Epoch 12/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m63s\u001b[0m 68ms/step - accuracy: 0.9964 - loss: 0.0117 - val_accuracy: 0.9324 - val_loss: 0.4219\n",
+      "Epoch 13/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m64s\u001b[0m 68ms/step - accuracy: 0.9942 - loss: 0.0173 - val_accuracy: 0.9333 - val_loss: 0.4333\n",
+      "Epoch 14/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m64s\u001b[0m 68ms/step - accuracy: 0.9972 - loss: 0.0108 - val_accuracy: 0.9273 - val_loss: 0.4669\n",
+      "Epoch 15/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m64s\u001b[0m 68ms/step - accuracy: 0.9969 - loss: 0.0111 - val_accuracy: 0.9279 - val_loss: 0.5780\n",
+      "Epoch 16/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m63s\u001b[0m 67ms/step - accuracy: 0.9969 - loss: 0.0104 - val_accuracy: 0.9283 - val_loss: 0.5069\n",
+      "Epoch 17/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m63s\u001b[0m 68ms/step - accuracy: 0.9987 - loss: 0.0054 - val_accuracy: 0.9219 - val_loss: 0.7001\n",
+      "Epoch 18/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m63s\u001b[0m 68ms/step - accuracy: 0.9973 - loss: 0.0099 - val_accuracy: 0.9222 - val_loss: 0.3856\n",
+      "Epoch 19/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m63s\u001b[0m 68ms/step - accuracy: 0.9987 - loss: 0.0059 - val_accuracy: 0.9261 - val_loss: 0.5079\n",
+      "Epoch 20/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m64s\u001b[0m 68ms/step - accuracy: 0.9976 - loss: 0.0067 - val_accuracy: 0.9338 - val_loss: 0.4417\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "trans_model2 = transformer_model2()\n",
+    "\n",
+    "trans_model2.summary()\n",
+    "trans_model2.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
+    "trans_history2 = trans_model2.fit(train_sequences, train_df['labels'], epochs=epochs, validation_data=(test_sequences, test_df['labels']))\n",
+    "\n",
+    "plot_history(trans_history2, title=f\"Transformer With 2 Multi-headed Self Attention Layer\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67cb4a32",
+   "metadata": {},
+   "source": [
+    "This graph shows the convergence of the Transformer model with two Multi-headed self attention layers. In comparison to the first Transformer model, this model shows some improvements in performance. The validation loss still increases in later epochs, showing the model does not fully converge in terms of validation performance. However, the validation accuracy of this model is more stable, showing that this Transformer model converges better than the previous Transformer model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1631af53-1089-4350-ab94-23f251691e00",
+   "metadata": {},
+   "source": [
+    "[2]Use the method of train/test splitting and evaluation criteria that you argued for at the beginning of the lab. Visualize the results of all the models you trained.  Use proper statistical comparison techniques to determine which method(s) is (are) superior.  \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "dac28ac2-f2b6-49b5-bf22-2152cce25cf6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m234/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 10ms/step\n",
+      "\u001b[1m234/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 27ms/step\n",
+      "\u001b[1m234/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate evaluation metrics for all model variations\n",
+    "evaluation_results = []\n",
+    "\n",
+    "\n",
+    "X_test = test_df['tweets']\n",
+    "y_test = test_df['labels']\n",
+    "\n",
+    "\n",
+    "for model in models:\n",
+    "    y_pred = model.predict(X_test)  # Assuming X_test and y_test are the test data\n",
+    "    y_pred_binary = (y_pred > 0.5).astype(int)  # Convert probabilities to binary predictions\n",
+    "\n",
+    "    recall = recall_score(y_test, y_pred_binary)\n",
+    "    f1 = f1_score(y_test, y_pred_binary)\n",
+    "    tn, fp, fn, tp = confusion_matrix(y_test, y_pred_binary).ravel()\n",
+    "    specificity = tn / (tn + fp)\n",
+    "    auc_roc = roc_auc_score(y_test, y_pred)\n",
+    "\n",
+    "    evaluation_results.append((recall, f1, specificity, auc_roc))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "35353d5c-3bf9-4d7e-b63f-ed35dd9006ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.8868278449353898, 0.8929695697796433, 0.8212143928035982, 0.926000721502525)\n",
+      "(0.9285118799499792, 0.9231247409863241, 0.8504497751124438, 0.9512346395580867)\n",
+      "(0.9237182159233014, 0.9267119707266074, 0.8744377811094453, 0.9510721921240297)\n",
+      "\u001b[1m234/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 27ms/step\n",
+      "\u001b[1m234/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 35ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "for result in evaluation_results:\n",
+    "    print(result)\n",
+    "\n",
+    "\n",
+    "def calculate_metrics_for_transformer_model(trans_model):\n",
+    "    X_test = test_sequences\n",
+    "    y_pred = trans_model.predict(X_test) \n",
+    "    y_pred_binary = (y_pred > 0.5).astype(int)\n",
+    "\n",
+    "    recall = recall_score(y_test, y_pred_binary)\n",
+    "    f1 = f1_score(y_test, y_pred_binary)\n",
+    "    tn, fp, fn, tp = confusion_matrix(y_test, y_pred_binary).ravel()\n",
+    "    specificity = tn / (tn + fp)\n",
+    "    auc_roc = roc_auc_score(y_test, y_pred)\n",
+    "\n",
+    "    return [recall, f1, specificity, auc_roc]\n",
+    "\n",
+    "evaluation_results_transformer_1 = calculate_metrics_for_transformer_model(trans_model)\n",
+    "evaluation_results_transformer_2 = calculate_metrics_for_transformer_model(trans_model2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "491daf28-3f24-4ec7-859b-4dac7bc0d615",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+---+----------------------------------+--------------------+--------------------+--------------------+--------------------+\n",
+      "|   |         Model Variation          |       Recall       |      F1 Score      |    Specificity     |      AUC-ROC       |\n",
+      "+---+----------------------------------+--------------------+--------------------+--------------------+--------------------+\n",
+      "| 0 |            rnn_model             | 0.8868278449353898 | 0.8929695697796433 | 0.8212143928035982 | 0.926000721502525  |\n",
+      "| 1 |            lstm_model            | 0.9285118799499792 | 0.9231247409863241 | 0.8504497751124438 | 0.9512346395580867 |\n",
+      "| 2 |            gru_model             | 0.9237182159233014 | 0.9267119707266074 | 0.8744377811094453 | 0.9510721921240297 |\n",
+      "| 3 | Transformer Model (1 Multi-Head) | 0.9401834097540642 | 0.9454050089070523 | 0.9122938530734632 | 0.9597094429025587 |\n",
+      "| 4 | Transformer Model (2 Multi-Head) | 0.9485202167569821 | 0.9485202167569821 | 0.9074212893553223 | 0.9692597818431343 |\n",
+      "+---+----------------------------------+--------------------+--------------------+--------------------+--------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from tabulate import tabulate\n",
+    "\n",
+    "# Visualize the evaluation metrics\n",
+    "metric_names = ['Recall', 'F1 Score', 'Specificity', 'AUC-ROC']\n",
+    "\n",
+    "# Define evaluation results for each model\n",
+    "evaluation_results_dict = {\n",
+    "    \"Model Variation\": [model_func.__name__ for model_func in model_variations] + \n",
+    "                       [\"Transformer Model (1 Multi-Head)\", \"Transformer Model (2 Multi-Head)\"],\n",
+    "    \"Recall\": [evaluation_results[idx][0] for idx in range(len(model_variations))] +\n",
+    "              [evaluation_results_transformer_1[0], evaluation_results_transformer_2[0]],\n",
+    "    \"F1 Score\": [evaluation_results[idx][1] for idx in range(len(model_variations))] +\n",
+    "                [evaluation_results_transformer_1[1], evaluation_results_transformer_2[1]],\n",
+    "    \"Specificity\": [evaluation_results[idx][2] for idx in range(len(model_variations))] +\n",
+    "                   [evaluation_results_transformer_1[2], evaluation_results_transformer_2[2]],\n",
+    "    \"AUC-ROC\": [evaluation_results[idx][3] for idx in range(len(model_variations))] +\n",
+    "               [evaluation_results_transformer_1[3], evaluation_results_transformer_2[3]],\n",
+    "}\n",
+    "\n",
+    "# Convert to DataFrame\n",
+    "evaluation_df = pd.DataFrame(evaluation_results_dict)\n",
+    "\n",
+    "# Print the table in a readable format\n",
+    "print(tabulate(evaluation_df, headers='keys', tablefmt='pretty'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "841f04ca",
+   "metadata": {},
+   "source": [
+    "At the beginning of the lab, we argued for using an 80/20 train-test split and for using recall, F1 score, specificity, and AUC-ROC as our performance evaluation criteria.\n",
+    "\n",
+    "Above are the performance results of our five models. Transformer models consistently outperform all other models across all evaluation metrics. The Transformer model with 2 Multi-Heads has the best overall performance, achieving the highest Recall (0.9485), F1 Score (0.9485), and AUC-ROC (0.9693) and the second highest Specificity (0.9074). The Transformer model with 1 Multi-Head is not too far behind the 2 multi-head model in performance, achieving the highest Specificity (0.9123), and the second highest Recall (0.9402), F1 Score (0.9454), and AUC-ROC (0.9597).\n",
+    "\n",
+    "The GRU and LSTM models perform similarly but are still behind the Transformer models in overall performance. The GRU model achieves a Recall of 0.9237, F1 Score of 0.9267, Specificity of 0.8744, and AUC-ROC of 0.9511, while the LSTM model achieves a Recall of 0.9285, F1 Score of 0.9231, Specificity of 0.8504, and AUC-ROC of 0.9512.\n",
+    "\n",
+    "The simple RNN model ranks the lowest across all evaluation metrics, with a Recall of 0.8868, F1 Score of 0.8930, Specificity of 0.8212, and AUC-ROC of 0.9260. This suggests that the simple RNN is the least effective model for this prediction task."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "75fb4ec9-6b0a-4698-9bf3-4510a6d3d178",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_confusion_matrix(cm, classes, title=\"Confusion matrix\"):\n",
+    "    plt.figure(figsize=(5, 5))\n",
+    "    plt.imshow(cm, interpolation=\"nearest\", cmap=plt.cm.Blues)\n",
+    "    plt.title(title)\n",
+    "    plt.colorbar()\n",
+    "    tick_marks = np.arange(len(classes))\n",
+    "    plt.xticks(tick_marks, classes, rotation=45)\n",
+    "    plt.yticks(tick_marks, classes)\n",
+    "\n",
+    "    thresh = cm.max() / 2.0\n",
+    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
+    "        plt.text(\n",
+    "            j,\n",
+    "            i,\n",
+    "            format(cm[i, j], \"d\"),\n",
+    "            horizontalalignment=\"center\",\n",
+    "            color=\"white\" if cm[i, j] > thresh else \"black\",\n",
+    "        )\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    plt.ylabel(\"True label\")\n",
+    "    plt.xlabel(\"Predicted label\")\n",
+    "    plt.grid(False)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "090920ce-cd06-4f8d-a34c-98d44ee2faf8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m234/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m234/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 26ms/step\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m234/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m234/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 26ms/step\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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kpKTXtrdEyWveuHFjuc95/t/YnDlzBGtra6GgoEB4/PixIJVKhZCQECE1NVUAIMyZM0c8b86cOUJZX6clr6fk71wQSr/vZdX3qvNfpqQNr9qef15lPivPe9l3iTKfU1WSy+UCAEHa7XtBv+dqlW3Sbt8LAEp9X5TXggULBADCZ599Ju4rLi4W5syZI9jY2Aj6+vqCu7u7+G+nRG5urjBhwgTB3NxcMDQ0FHr27CkkJCQolHny5IkwZMgQwcTERDAxMRGGDBkipKWlKdU+Zv7VgKmpKTp37lxq/19//QU/Pz9YW1tDW1sburq64uC7F7uVJRJJqV/xzZo1w4MHD8THH374IdLT0/Hxxx9j3759Lx1V/KKsrCycO3cO//nPf1CjRg1xv7a2Nvz9/ZGYmKjQvQoA/fr1K7MuGxsbtGzZUnxsZmYGS0tLvP/++7C1tRX3N27cGADE9ufm5uL48ePo06cPDA0NFTKi7t27Izc3F9HR0eVqw6uUdNlu3rwZhYWFWL9+PQYMGKDwuktERUUhJyen1AwBe3t7dO7cGcePHwcAxMXF4d9//4Wfn59C17KDgwPatm2rcO7BgwdRs2ZN9OzZU+E1vv/++7C2tn6jmSEvXvePiIgQP0eNGzeGpaWl2PUfEREBKysr8f1/U76+vgqPmzVrBgAKn8eKNnz4cDx8+BBHjhwRBxH279//nT1/WQRBeG1Gf+zYMVy4cKHUVq9ePYVyynxWyvNdoszntEJUoW7/Cxcu4KeffhI/tyUWL16MJUuWYNWqVbhw4QKsra3RpUsXhUHCgYGB2LNnD7Zv346zZ88iMzMTPj4+KCoqEsv4+fkhNjYWYWFhCAsLQ2xsLPz9/ZVqI4N/NVDWSODMzEx89NFHOHfuHObPn4+IiAhcuHBB7JZ/sfvX0NAQ+vr6CvukUilyc3PFx/7+/tiwYQMePHiAfv36wdLSEm3atEF4ePgr25eWlgZBEMpsZ0nAfvz48WtfE/As2L9IT0+v1H49PT0AENv/+PFjFBYWYuXKldDV1VXYSkaFv/hj5k1HWJdcM12wYAEuXbr00i7/ktf8svel5HjJ/62trUuVe3Hfw4cPkZ6eDj09vVKvMzk5udw/2J7n4uICCwsLnDx5Urze//wMjg4dOiAiIgJ5eXmIiop6ZZd/eZmbmys8Lhnwpuxli7fh4OAADw8PbNiwARs2bMCgQYNgaGj4zp6/LJs2bSr19/qi5s2bo1WrVqW2F/99l/ezUt7vEmU+p9VZZmYmBg8ejHXr1ikMdhYEAcuWLcOsWbPQt29fODs7Y9OmTcjOzsbWrVsBQLws+v3338PT0xMtWrTAli1bcO3aNRw7dgzAsx9bYWFh+Pnnn+Hm5gY3NzesW7cOBw8eLJVEvQoX+akGyhpodOLECfz7778KWRrw7Br02xg+fDiGDx+OrKwsnD59GnPmzIGPjw9u374NBweHMs8xNTWFlpYWkpKSSh0ruSZvYWGhsL+s1/Q2TE1NxZ6G8ePHl1nG0dFRJW2wt7eHp6cn5s6di4YNG7406ykJcC97X0rek5JyycnJpcq9uK9kgFxZgx0BvNE0NYlEAnd3d4SFheH8+fNIT09X+Ey5u7sjKCgIUVFRyM3NVUnwrypGjBiBIUOGoLi4GGvWrHlpuZLAmpeXpzAy/01+bL1Kz549ceHCBZXUVd7PSnm/S5T5nFaIClrkJyMjQ2G3VCp95eyL8ePHo0ePHvD09MT8+fPF/fHx8UhOTkbXrl0V6nJ3d0dkZCTGjBmDmJgYFBQUKJSxtbWFs7MzIiMj4eXlhaioKMhkMrRp00Ys4+rqCplMhsjISDRs2LBcL4/Bv5oqCVwvfkjXrl2rkvqNjIzg7e2N/Px89O7dGzdu3Hhp8DcyMkKbNm2we/dufPfddzAwMADwbMDRli1bYGdn99KR8KpiaGiITp064fLly2jWrJnYM1BRpkyZAgMDg1d2E7u5ucHAwABbtmxRKJeYmIgTJ07gP//5DwCgYcOGsLGxwbZt2zB58mTx7/bBgweIjIxUuNzh4+OD7du3o6ioSOHL4W116tQJu3btwrfffgtLS0uFbn13d3c8fvwYK1euFMu+jlQqfadZ/Jvq06cP+vTpA5lMBldX15eWK5nNcPXqVbRu3Vrcf+DAgdc+hzK9Gubm5qV6Rd5UeT8r5f0uUeZzqk7s7e0VHs+ZM+elCzxt374dly5dKvMHWskPICsrK4X9VlZW4uWs5ORk6OnplZoebWVlJZ6fnJxc5uBjS0tLpX5kMfhXU23btoWpqSnGjh2LOXPmQFdXF6Ghobhy5cob1zlq1CgYGBigXbt2sLGxQXJyMoKDgyGTyRS+8MoSHByMLl26oFOnTpg6dSr09PSwevVqXL9+Hdu2bVN5pl+W5cuXo3379vjoo4/w6aefom7dunj69Cnu3r2LAwcOqHREcteuXRV+vZelZs2amD17NmbOnIlPPvkEH3/8MR4/foy5c+dCX18fc+bMAfBsxP28efMwcuRI9OnTB6NGjUJ6ejqCgoJKdacOGjQIoaGh6N69Oz777DN8+OGH0NXVRWJiIk6ePIlevXqhT58+Sr+ekoC+Z88e8UdJCWdnZ5ibm2PPnj2oXbt2uUbDu7i4ICIiAgcOHICNjQ2MjY3LnbG8zs6dOwE8u04NABcvXhTHXLzY9tfR19cX63uV7t27w8zMDAEBAfjvf/8LHR0dhISEICEh4bXnGhsbw8HBAfv27YOHhwfMzMxgYWFR4SvilfezUt7vEmU+pxVC1dPz/ldXQkKCwloqL8v6ExIS8Nlnn+Ho0aOlLrEoVPvCd50gCK/9/nuxTFnly1PP83jNv5oyNzfHoUOHYGhoiCFDhmDEiBGoUaNGmdPqyuujjz7C9evX8dlnn6FLly74/PPP0aBBA5w5c0acq/wy7u7uOHHiBIyMjDBs2DAMGjQIcrkc+/fvf2eLpjRp0gSXLl2Cs7MzvvrqK3Tt2hUBAQHYuXMnPDw83kkbXjRjxgz8/PPPuHLlCnr37o0JEyagadOmiIyMVAiiAQEB+Pnnn3Hz5k307dsX//3vfzFz5sxSAz21tbWxf/9+zJw5E7t370afPn3Qu3dvLFy4EPr6+nBxcXmjdjZp0gTW1tYQBKHUio0SiQQfffQRBEEo9/Kxy5cvh5OTEwYNGoTWrVtjzJgxb9SusvTv3x/9+/fH9OnTAQA//PCDuK+imJiYICwsDMbGxhgyZAjGjh0LZ2dnzJo1q1znr1+/HoaGhvD19UXr1q3feOlgZZT3s6LMd0l5P6cVoqTbX5Ubnv3dPr+9LPjHxMQgJSUFLVu2hI6ODnR0dHDq1CmsWLECOjo6Ysb/YnaekpIiHrO2tkZ+fj7S0tJeWebhw4elnj81NbVUr8Ir3y5BqOSl04iIiN5QRkYGZDIZpD4rIdE1UFm9QkEO8g5OhFwuL9fS3E+fPi01G2X48OFo1KgRpk+fjqZNm8LW1haff/45pk2bBuDZIkuWlpZYtGgRxowZA7lcjlq1amHLli0YMGAAgGdjguzs7HD48GF4eXnh1q1baNKkCc6dO4cPP/wQAHDu3Dm4urrizz//5DV/IiLSHCWrI6qwQqWKGxsbi0talzAyMoK5ubm4PzAwEAsWLICTkxOcnJywYMECGBoaws/PD8CzVUIDAgIwZcoUmJubw8zMDFOnToWLiws8PT0BPJte261bN4waNUocdzF69Gj4+PgodemMwZ+IiOgdmDZtGnJycjBu3DikpaWhTZs2OHr0qMIsnKVLl0JHRwcDBgxATk4OPDw8EBISAm1tbbFMaGgoJk2aJI4r8vX1xapVq5RqC7v9iYhIbZV0+xv4/qDybv+c/ePL3e2vbjjgj4iISMOw25+IiNSf5H+bKuurxhj8q6Di4mL8+++/MDY2fifz34mI3iVBEPD06VPY2tqq7DbMlT3gT90w+FdB//77b6lVpYiIqpuEhATY2dlVdjM0EoN/FVQy8tNvzXHoGRhVcmtIUyzq+XZ34iMqr6cZGajvaP9G95p4GWb+ymHwr4JKPsB6BkbQMyx9K1iiilAdRzRT1cbLmpWHwZ+IiNQeM3/lMPgTEZHaY/BXDuf5ExERaRhm/kREpP44z18pzPyJiIg0DDN/IiJSe7zmrxxm/kRERBqGmT8REak9iUTF6wZU78SfwZ+IiNSfBCru9q/m0Z/d/kRERBqGmT8REak9DvhTDjN/IiIiDcPMn4iI1B8X+VEKgz8REak/FXf7C+z2JyIiouqEmT8REak9VQ/4U+20waqHmT8REZGGYeZPRERqj5m/cpj5ExERaRhm/kREpP441U8pDP5ERKT22O2vHHb7ExERaRhm/kREpPaY+SuHmT8REZGGYeZPRERqj5m/chj8iYhI7TH4K4fd/kRERBqGmT8REak/zvNXCjN/IiIiDcPMn4iI1B6v+SuHmT8REZGGYeZPRERqj5m/chj8iYhI7TH4K4fd/kRERBqGmT8REak/TvVTCjN/IiKit7RmzRo0a9YMJiYmMDExgZubG44cOSIeHzZsmHhpomRzdXVVqCMvLw8TJ06EhYUFjIyM4Ovri8TERIUyaWlp8Pf3h0wmg0wmg7+/P9LT05VuL4M/ERGpvRcDqyo2ZdjZ2WHhwoW4ePEiLl68iM6dO6NXr164ceOGWKZbt25ISkoSt8OHDyvUERgYiD179mD79u04e/YsMjMz4ePjg6KiIrGMn58fYmNjERYWhrCwMMTGxsLf31/p94vd/kREpPYqasBfRkaGwn6pVAqpVFqqfM+ePRUef/PNN1izZg2io6PRtGlT8Vxra+syn08ul2P9+vXYvHkzPD09AQBbtmyBvb09jh07Bi8vL9y6dQthYWGIjo5GmzZtAADr1q2Dm5sb4uLi0LBhw3K/Pmb+REREL2Fvby92sctkMgQHB7/2nKKiImzfvh1ZWVlwc3MT90dERMDS0hINGjTAqFGjkJKSIh6LiYlBQUEBunbtKu6ztbWFs7MzIiMjAQBRUVGQyWRi4AcAV1dXyGQysUx5MfMnIiK1J4GKM///jfhLSEiAiYmJuL+srL/EtWvX4ObmhtzcXNSoUQN79uxBkyZNAADe3t7o378/HBwcEB8fj9mzZ6Nz586IiYmBVCpFcnIy9PT0YGpqqlCnlZUVkpOTAQDJycmwtLQs9byWlpZimfJi8CciInqJkgF85dGwYUPExsYiPT0du3btwtChQ3Hq1Ck0adIEAwcOFMs5OzujVatWcHBwwKFDh9C3b9+X1ikIgsKPmrJ+4LxYpjzY7U9ERGqvsgf8AYCenh7q16+PVq1aITg4GM2bN8fy5cvLLGtjYwMHBwfcuXMHAGBtbY38/HykpaUplEtJSYGVlZVY5uHDh6XqSk1NFcuUF4M/ERFRBRAEAXl5eWUee/z4MRISEmBjYwMAaNmyJXR1dREeHi6WSUpKwvXr19G2bVsAgJubG+RyOc6fPy+WOXfuHORyuVimvNjtT0RE6q+SF/mZOXMmvL29YW9vj6dPn2L79u2IiIhAWFgYMjMzERQUhH79+sHGxgb379/HzJkzYWFhgT59+gAAZDIZAgICMGXKFJibm8PMzAxTp06Fi4uLOPq/cePG6NatG0aNGoW1a9cCAEaPHg0fHx+lRvoDDP5ERFQNVPba/g8fPoS/vz+SkpIgk8nQrFkzhIWFoUuXLsjJycG1a9fwyy+/ID09HTY2NujUqRN27NgBY2NjsY6lS5dCR0cHAwYMQE5ODjw8PBASEgJtbW2xTGhoKCZNmiTOCvD19cWqVauUf32CIAhKn0UVKiMjAzKZDMNCoqFnWKOym0MaYnmfppXdBNIQGRkZsDKXQS6Xl3sw3avqkslkcBj3G7SkhipqIVCcl40Hq/urpI1VETN/IiJSe5Wd+asbDvgjIiLSMMz8iYhI7UkkzzZV1ledMfgTEZHaexb8Vdntr7KqqiR2+xMREWkYZv5ERKT+VNztr9I1A6ogZv5EREQahpk/ERGpPU71Uw4zfyIiIg3DzJ+IiNQep/oph8GfiIjUnpaWBFpaqovYggrrqorY7U9ERKRhmPkTEZHaY7e/cpj5ExERaRhm/kREpPY41U85DP5ERKT22O2vHHb7ExERaRhm/kREpPbY7a8cZv5EREQahpk/ERGpPWb+ymHmT0REpGEY/KnK6NbIAjM83sPy3o3wbc+G+LStPaxq6L20/OAPbLC2f1N4OJmV2j/f2wkr+zbGd77/q8e47Hp0tCT4qst7WNu/Kexk+ip9PaR+vl0UjHaurVHL1Bh1bC3Rv19v3I6LUygz/79BaO7cCOYyI9jUMkV3L0+cP3euzPoEQUAvH28Y6Eqwf9/ein8BGqxktL8qt+qMwZ+qjAa1DBFx9wkWnojH8tP3oSWR4LMODtDTLv2vsLmtMRzNDZCWU1Dq2N9pudh04R8Ehd3F8tMPIJEAgR0cUNa/5b7NrJCeU1gBr4bU0ZnTpzD20/E4dTYaB4+Eo6iwED7duyIrK0ssU9+pAZYuX4WLl6/heMRZODjURc/uXZGamlqqvpXLl1X77uOqQgKJ2PWvkq3Mb4zqg8GfqowVZ/5G1IN0JGXkIVGeh00X/oG5kR4cTA0UytXU18HHLWyw/tw/KCoWStVzJj4Ndx5l43F2ARLSc7HvegrMDPVgbqSrUK6pdQ00saqBXVceVujrIvWx/1AY/IcOQ5OmTdGseXOs/XkjEv7+G5cvxYhlBn3sh84ennB87z00adoUi75bgoyMDFy/dlWhrqtXrmDF8iX4cd2Gd/0yiF6LA/6oyjLQ1QYAZOUXifskAIa3qY2jcY+QlJH32jr0tCVoW9cUqZn5SMv+/wzfWKoN/5a2WBP5N/KLilXedqoeMuRyAICpqVmZx/Pz87H+558gk8ng0qy5uD87OxtD/T/G0uWrYG1t/U7aqum4yI9yNDbzv3//PiQSCWJjY19ZrmPHjggMDHwnbSJF/d+3wp3ULPz7XJD3amSB4mLgxN0nrzzXvZ4plvdphJV9m6CpdQ0sO30fRcL/9xIMa10bp/96ggdpuRXWflJvgiBg+heT0bZdezR1dlY4dvjQQVjUrIGaNfSxcvlSHDwSDgsLC/H4tCmfw9W1LXr69nrXzSYqlyof/IcNGyZeg9HV1cV7772HqVOnKlyDexP29vZISkqC8//+UUdEREAikSA9PV2h3O7duzFv3ry3ei5S3sctbFBbpo+fzyWK++rU1EdnJzOEXPjnteefeyDHN+F/4buT8UjJzMNoN3vo/O/+3J3qm0FfVxtHbj2qsPaT+vt80gRcu3YVm7ZsK3XMvWMnnLsYi5OnI9G1azcM8RuAlJQUAMDBA/sREXEC3y5Z9o5brNlUer1fxdMGqyK16Pbv1q0bNm7ciIKCApw5cwYjR45EVlYW1qxZ88Z1amtrl6s7zsys7O4+qjiD3rdGM1tjfHcyXmEwnlMtQxhLdRDco4G4T1tLgv80t0ZnJ3PMOnxH3J9bWIzczHykZObjr8c5WNq7EVrUNsaFhAw0sjTCe+YG+KFfE4Xnnen5Hs7/LS/Xjwuq3j7/bCIOHtyPYydOw87OrtRxIyMj1KtfH/Xq10cbV1c4N3bCpo3r8cX0GYg4eQJ/3bsHa4uaCud8PKAf2rX/CEePR7ybF6Fh2O2vnCqf+QOAVCqFtbU17O3t4efnh8GDB2Pv3r3Iy8vDpEmTYGlpCX19fbRv3x4XLlwQz0tLS8PgwYNRq1YtGBgYwMnJCRs3bgSg2O1///59dOrUCQBgamoKiUSCYcOGAVDs9p8xYwZcXV1Lta9Zs2aYM2eO+Hjjxo1o3Lgx9PX10ahRI6xevbqC3pnqZ1ALa7xvZ4Klp+7jcbbiSP7oB3LMO3oP88P/f0vLKcDRuEdYcfrBK+uVANDRevZx3345WaGelWefnbsuOgF7r3PwnyYTBAGBkyZg397dCDt6AnUdHct9Xl7es8tTU6d9iQuXruLcxVhxA4DF3y3FTz9vrKimEylFLTL/FxkYGKCgoADTpk3Drl27sGnTJjg4OGDx4sXw8vLC3bt3YWZmhtmzZ+PmzZs4cuQILCwscPfuXeTk5JSqz97eHrt27UK/fv0QFxcHExMTGBgYlCo3ePBgLFy4EPfu3UO9evUAADdu3MC1a9ewc+dOAMC6deswZ84crFq1Ci1atMDly5cxatQoGBkZYejQoWW+nry8PPGLAwAyMjJU8TapnY9b2ODDOjKs/uNv5BYUw0T67OOZU1CEgmIBWflFCoP/AKCoWEBGbiEeZuYDACyMdNHKXoabyZl4mlcEUwMdeDWyQH5RMa4nPwUApOUUIO25j0Fe4bMBf6mZBZz2p+ECJ47Hju1b8dvufahhbIzk5GQAgEwmg4GBAbKysrAo+Bv08PGFtY0Nnjx+jJ9+XI1/EhPRt19/AIC1tXWZvYr2deqU+8cEKY8r/ClH7YL/+fPnsXXrVnTq1Alr1qxBSEgIvL29ATwLvOHh4Vi/fj2++OIL/P3332jRogVatWoFAKhbt26ZdWpra4vd+5aWlqhZs2aZ5ZydndGsWTNs3boVs2fPBgCEhoaidevWaNDgWVf0vHnz8P3336Nv374AAEdHR9y8eRNr1659afAPDg7G3Llz3+j9qE461n/2dzC1k+IXZMj5fxD1IL1cdRQUCahvYQgPJ3MY6mkhI7cId1KzsPhEPJ7mFb2+AtJoP619dimxq0dHxf0/b4T/0GHQ1tZGXNyf2LJ5Ex4/egQzc3O0atUax06eQZOmTSuhxURvRi2C/8GDB1GjRg0UFhaioKAAvXr1wsSJE7Fz5060a9dOLKerq4sPP/wQt27dAgB8+umn6NevHy5duoSuXbuid+/eaNu27Vu1ZfDgwdiwYQNmz54NQRCwbds28bJAamoqEhISEBAQgFGjRonnFBYWQiaTvbTOGTNmYPLkyeLjjIwM2Nvbv1U71dGY324ofc7z1/kBQJ5biFVn/1aqjsfZBW/03FT95BSUXjfiefr6+tjx226V10tvj9f8laMWwb8ky9fV1YWtrS10dXVx5coVAKW7ZgRBEPd5e3vjwYMHOHToEI4dOwYPDw+MHz8e33333Ru3xc/PD19++SUuXbqEnJwcJCQkYNCgQQCA4uJn3cfr1q1DmzZtFM7T1tZ+aZ1SqRRSqfSN20RERKQMtRjwZ2RkhPr168PBwQG6us9Waatfvz709PRw9uxZsVxBQQEuXryIxo0bi/tq1aqFYcOGYcuWLVi2bBl++umnMp9DT+/Z2u9FRa/uGrazs0OHDh0QGhqK0NBQeHp6wsrKCgBgZWWF2rVr46+//kL9+vUVNkde6yMiqjCc6qcctcj8y2JkZIRPP/0UX3zxBczMzFCnTh0sXrwY2dnZCAgIAAB8/fXXaNmyJZo2bYq8vDwcPHhQ4YfB8xwcHCCRSHDw4EF0794dBgYGqFGjRpllBw8ejKCgIOTn52Pp0qUKx4KCgjBp0iSYmJjA29sbeXl5uHjxItLS0hS69omISIVUfTOe6h371SPzf5mFCxeiX79+8Pf3xwcffIC7d+/i999/h6mpKYBn2fyMGTPQrFkzdOjQAdra2ti+fXuZddWuXRtz587Fl19+CSsrK0yYMOGlz9u/f388fvwY2dnZ6N27t8KxkSNH4ueff0ZISAhcXFzg7u6OkJAQZv5ERFRlSARB4EiUKiYjIwMymQzDQqKhZ1h27wORqi3vw9Hq9G5kZGTAylwGuVwOExOTt65LJpOhVdBh6OgbqaiFQGFuFi4GdVdJG6sitc78iYiISHlqe82fiIioBKf6KYfBn4iI1B5X+FMOu/2JiIje0po1a9CsWTOYmJjAxMQEbm5uOHLkiHhcEAQEBQXB1tYWBgYG6NixI27cUFxcLC8vDxMnToSFhQWMjIzg6+uLxMREhTJpaWnw9/eHTCaDTCaDv79/qbvRlgeDPxERqb2Sbn9Vbsqws7PDwoULcfHiRVy8eBGdO3dGr169xAC/ePFiLFmyBKtWrcKFCxdgbW2NLl264OnTp2IdgYGB2LNnD7Zv346zZ88iMzMTPj4+CuvP+Pn5ITY2FmFhYQgLC0NsbCz8/f2Vfr/Y7U9ERPSWevbsqfD4m2++wZo1axAdHY0mTZpg2bJlmDVrlnjfl02bNsHKygpbt27FmDFjIJfLsX79emzevBmenp4AgC1btsDe3h7Hjh2Dl5cXbt26hbCwMERHR4uryK5btw5ubm6Ii4tDw4YNy91eZv5ERKT2KmqFv4yMDIXt+TuwvkxRURG2b9+OrKwsuLm5IT4+HsnJyejatatYRiqVwt3dHZGRkQCAmJgYFBQUKJSxtbWFs7OzWCYqKgoymUxh+XhXV1fIZDKxTHkx+BMREb2Evb29eH1dJpMhODj4pWWvXbuGGjVqQCqVYuzYsdizZw+aNGki3hq6ZCn4ElZWVuKx5ORk6OnpiYvUvayMpaVlqee1tLQUy5QXu/2JiEjtVdRo/4SEBIVFfl51E7aGDRsiNjYW6enp2LVrF4YOHYpTp06VqrPE8zeie5kXy5RVvjz1vIiZPxERqb2KGvBXMnq/ZHtV8NfT00P9+vXRqlUrBAcHo3nz5li+fDmsra0BoFR2npKSIvYGWFtbIz8/H2lpaa8s8/Dhw1LPm5qaWqpX4XUY/ImIiCqAIAjIy8uDo6MjrK2tER4eLh7Lz8/HqVOn0LZtWwBAy5Ytoaurq1AmKSkJ169fF8u4ublBLpfj/PnzYplz585BLpeLZcqL3f5ERKT2KnuRn5kzZ8Lb2xv29vZ4+vQptm/fjoiICISFhUEikSAwMBALFiyAk5MTnJycsGDBAhgaGsLPzw8AIJPJEBAQgClTpsDc3BxmZmaYOnUqXFxcxNH/jRs3Rrdu3TBq1CisXbsWADB69Gj4+PgoNdIfYPAnIiJ6aw8fPoS/vz+SkpIgk8nQrFkzhIWFoUuXLgCAadOmIScnB+PGjUNaWhratGmDo0ePwtjYWKxj6dKl0NHRwYABA5CTkwMPDw+EhIRAW1tbLBMaGopJkyaJswJ8fX2xatUqpdvLu/pVQbyrH1UG3tWP3pWKuKtf+4VHVX5Xv7Nfdq22d/Vj5k9ERGqvsrv91Q0H/BEREWkYZv5ERKT2JFDxLX1VV1WVxMyfiIhIwzDzJyIitaclkUBLham/Kuuqipj5ExERaRhm/kREpPaeX5JXVfVVZwz+RESk9jjVTzns9iciItIwzPyJiEjtaUmebaqsrzpj5k9ERKRhmPkTEZH6k6j4On01z/wZ/ImISO1xtL9y2O1PRESkYZj5ExGR2pP87z9V1ledMfMnIiLSMMz8iYhI7XGqn3KY+RMREWkYZv5ERKT2uLyvchj8iYhI7XGqn3LY7U9ERKRhmPkTEZHa05JIoKXCdF2VdVVFzPyJiIg0DDN/IiJSe7zmrxwGfyIiUnsc7a8cdvsTERFpmHJl/itWrCh3hZMmTXrjxhAREb0Jdvsrp1zBf+nSpeWqTCKRMPgTERFVceUK/vHx8RXdDiIiojfGqX7KeeNr/vn5+YiLi0NhYaEq20NEREQVTOngn52djYCAABgaGqJp06b4+++/ATy71r9w4UKVN5CIiOh1JBWwVWdKB/8ZM2bgypUriIiIgL6+vrjf09MTO3bsUGnjiIiIyqNkqp8qt+pM6Xn+e/fuxY4dO+Dq6qrw5jRp0gT37t1TaeOIiIhI9ZQO/qmpqbC0tCy1Pysrq9r/UiIioqpJS/JsU2V91ZnS3f6tW7fGoUOHxMclAX/dunVwc3NTXcuIiIioQiid+QcHB6Nbt264efMmCgsLsXz5cty4cQNRUVE4depURbSRiIjolbi8r3KUzvzbtm2LP/74A9nZ2ahXrx6OHj0KKysrREVFoWXLlhXRRiIiotcqWeVPFVt190Y39nFxccGmTZtU3RYiIiJ6B94o+BcVFWHPnj24desWJBIJGjdujF69ekFHhzcJJCKid4/d/spROlpfv34dvXr1QnJyMho2bAgAuH37NmrVqoX9+/fDxcVF5Y0kIiIi1VH6mv/IkSPRtGlTJCYm4tKlS7h06RISEhLQrFkzjB49uiLaSERE9EolU/1UuSkjODgYrVu3hrGxMSwtLdG7d2/ExcUplBk2bFiphYRcXV0VyuTl5WHixImwsLCAkZERfH19kZiYqFAmLS0N/v7+kMlkkMlk8Pf3R3p6unLvl3IvD7hy5QqCg4Nhamoq7jM1NcU333yD2NhYZasjIiJSe6dOncL48eMRHR2N8PBwFBYWomvXrsjKylIo161bNyQlJYnb4cOHFY4HBgZiz5492L59O86ePYvMzEz4+PigqKhILOPn54fY2FiEhYUhLCwMsbGx8Pf3V6q9Snf7N2zYEA8fPkTTpk0V9qekpKB+/frKVkdERPTWKvuaf1hYmMLjjRs3wtLSEjExMejQoYO4XyqVwtrausw65HI51q9fj82bN8PT0xMAsGXLFtjb2+PYsWPw8vLCrVu3EBYWhujoaLRp0wbA/6+zExcXJ16Of51yZf4ZGRnitmDBAkyaNAk7d+5EYmIiEhMTsXPnTgQGBmLRokXlelIiIiJVqqgb+zwf/zIyMpCXl1eu9sjlcgCAmZmZwv6IiAhYWlqiQYMGGDVqFFJSUsRjMTExKCgoQNeuXcV9tra2cHZ2RmRkJAAgKioKMplMDPwA4OrqCplMJpYpj3Jl/jVr1lT4FSQIAgYMGCDuEwQBANCzZ0+FrgkiIiJ1Zm9vr/B4zpw5CAoKeuU5giBg8uTJaN++PZydncX93t7e6N+/PxwcHBAfH4/Zs2ejc+fOiImJgVQqRXJyMvT09BQuqwOAlZUVkpOTAQDJycllLrFvaWkplimPcgX/kydPlrtCIiKid01LIoGWCrv9S+pKSEiAiYmJuF8qlb723AkTJuDq1as4e/aswv6BAweKf3Z2dkarVq3g4OCAQ4cOoW/fvi+tTxAEhQS8rEsSL5Z5nXIFf3d393JXSEREVF2YmJgoBP/XmThxIvbv34/Tp0/Dzs7ulWVtbGzg4OCAO3fuAACsra2Rn5+PtLQ0hew/JSUFbdu2Fcs8fPiwVF2pqamwsrIqdzuVHu1fIjs7G3/++SeuXr2qsBEREb1rqlza902W+BUEARMmTMDu3btx4sQJODo6vvacx48fIyEhATY2NgCAli1bQldXF+Hh4WKZpKQkXL9+XQz+bm5ukMvlOH/+vFjm3LlzkMvlYpnyeKNb+g4fPhxHjhwp8ziv+RMR0btW2aP9x48fj61bt2Lfvn0wNjYWr7/LZDIYGBggMzMTQUFB6NevH2xsbHD//n3MnDkTFhYW6NOnj1g2ICAAU6ZMgbm5OczMzDB16lS4uLiIo/8bN26Mbt26YdSoUVi7di0AYPTo0fDx8Sn3SH/gDTL/wMBApKWlITo6GgYGBggLC8OmTZvg5OSE/fv3K1sdERGR2luzZg3kcjk6duwIGxsbcduxYwcAQFtbG9euXUOvXr3QoEEDDB06FA0aNEBUVBSMjY3FepYuXYrevXtjwIABaNeuHQwNDXHgwAFoa2uLZUJDQ+Hi4oKuXbuia9euaNasGTZv3qxUe5XO/E+cOIF9+/ahdevW0NLSgoODA7p06QITExMEBwejR48eylZJRET0VlR9N7436fZ/FQMDA/z++++vrUdfXx8rV67EypUrX1rGzMwMW7ZsUa6BL1A688/KyhKnGZiZmSE1NRXAszv9Xbp06a0aQ0RERBVP6eDfsGFDcb3i999/H2vXrsU///yDH3/8URy0QERE9C6VTPVT5VadKd3tHxgYiKSkJADPFjvw8vJCaGgo9PT0EBISour2ERERkYopHfwHDx4s/rlFixa4f/8+/vzzT9SpUwcWFhYqbRwREVF5VPY1f3WjdPB/kaGhIT744ANVtIWIiOiNVPZUP3VTruA/efLkcle4ZMmSN24MERERVbxyBf/Lly+Xq7Lq/kvpXVvQo5FSy0oSvQ3T1hMquwmkIYSifJXXqYW3WLL2JfVVZ7yxDxERkYZ562v+RERElY3X/JXD4E9ERGpPIgG0ONq/3Kr7ZQ0iIiJ6ATN/IiJSe1oqzvxVWVdVxMyfiIhIw7xR8N+8eTPatWsHW1tbPHjwAACwbNky7Nu3T6WNIyIiKo+SAX+q3KozpYP/mjVrMHnyZHTv3h3p6ekoKioCANSsWRPLli1TdfuIiIhIxZQO/itXrsS6deswa9YsaGtri/tbtWqFa9euqbRxRERE5VFyzV+VW3Wm9IC/+Ph4tGjRotR+qVSKrKwslTSKiIhIGbyxj3KUzvwdHR0RGxtbav+RI0fQpEkTVbSJiIiIKpDSmf8XX3yB8ePHIzc3F4Ig4Pz589i2bRuCg4Px888/V0QbiYiIXklLIoGWCtN1VdZVFSkd/IcPH47CwkJMmzYN2dnZ8PPzQ+3atbF8+XIMGjSoItpIREREKvRGi/yMGjUKo0aNwqNHj1BcXAxLS0tVt4uIiKjceFc/5bzVCn8WFhaqagcREdEb44A/5Sgd/B0dHV+5+MFff/31Vg0iIiKiiqV08A8MDFR4XFBQgMuXLyMsLAxffPGFqtpFRERUblpQ8YA/VO/UX+ng/9lnn5W5/4cffsDFixffukFERERUsVQ2psHb2xu7du1SVXVERETlVnLNX5Vbdaay4L9z506YmZmpqjoiIiKqIEp3+7do0UJhwJ8gCEhOTkZqaipWr16t0sYRERGVh6rX4+fa/i/o3bu3wmMtLS3UqlULHTt2RKNGjVTVLiIionKTSFS7Kl917/ZXKvgXFhaibt268PLygrW1dUW1iYiIiCqQUtf8dXR08OmnnyIvL6+i2kNERKQ0DvhTjtID/tq0aYPLly9XRFuIiIjoHVD6mv+4ceMwZcoUJCYmomXLljAyMlI43qxZM5U1joiIqDw44E855Q7+I0aMwLJlyzBw4EAAwKRJk8RjEokEgiBAIpGgqKhI9a0kIiIilSl38N+0aRMWLlyI+Pj4imwPERGR0iT/+0+V9VVn5Q7+giAAABwcHCqsMURERG+C3f7KUWrA36vu5kdERETqQakBfw0aNHjtD4AnT568VYOIiIiUxcxfOUoF/7lz50Imk1VUW4iIiOgdUCr4Dxo0CJaWlhXVFiIiojcikUhUemm6ul/mLnfwr+5vBBERqS92+yun3AP+Skb7ExERkaLg4GC0bt0axsbGsLS0RO/evREXF6dQRhAEBAUFwdbWFgYGBujYsSNu3LihUCYvLw8TJ06EhYUFjIyM4Ovri8TERIUyaWlp8Pf3h0wmg0wmg7+/P9LT05Vqb7mDf3FxMbv8iYioSqrstf1PnTqF8ePHIzo6GuHh4SgsLETXrl2RlZUlllm8eDGWLFmCVatW4cKFC7C2tkaXLl3w9OlTsUxgYCD27NmD7du34+zZs8jMzISPj4/CAnp+fn6IjY1FWFgYwsLCEBsbC39/f6Xaq/TyvkRERJoiIyND4bFUKoVUKi1VLiwsTOHxxo0bYWlpiZiYGHTo0AGCIGDZsmWYNWsW+vbtC+DZ4nlWVlbYunUrxowZA7lcjvXr12Pz5s3w9PQEAGzZsgX29vY4duwYvLy8cOvWLYSFhSE6Ohpt2rQBAKxbtw5ubm6Ii4tDw4YNy/W6lL6xDxERUVWjJZGofAMAe3t7sXtdJpMhODi4XO2Ry+UAADMzMwBAfHw8kpOT0bVrV7GMVCqFu7s7IiMjAQAxMTEoKChQKGNrawtnZ2exTFRUFGQymRj4AcDV1RUymUwsUx7M/ImIiF4iISEBJiYm4uOysv4XCYKAyZMno3379nB2dgYAJCcnAwCsrKwUylpZWeHBgwdiGT09PZiampYqU3J+cnJymZfgLS0txTLlweBPRERqr6JG+5uYmCgE//KYMGECrl69irNnz5Y69uLMuZKb4r3Ki2XKKl+eep7Hbn8iIlJ/qh7s94Y/JCZOnIj9+/fj5MmTsLOzE/dbW1sDQKnsPCUlRewNsLa2Rn5+PtLS0l5Z5uHDh6WeNzU1tVSvwqsw+BMREb0lQRAwYcIE7N69GydOnICjo6PCcUdHR1hbWyM8PFzcl5+fj1OnTqFt27YAgJYtW0JXV1ehTFJSEq5fvy6WcXNzg1wux/nz58Uy586dg1wuF8uUB7v9iYhI7WlBAi0V3oZX2brGjx+PrVu3Yt++fTA2NhYzfJlMBgMDA0gkEgQGBmLBggVwcnKCk5MTFixYAENDQ/j5+YllAwICMGXKFJibm8PMzAxTp06Fi4uLOPq/cePG6NatG0aNGoW1a9cCAEaPHg0fH59yj/QHGPyJiIje2po1awAAHTt2VNi/ceNGDBs2DAAwbdo05OTkYNy4cUhLS0ObNm1w9OhRGBsbi+WXLl0KHR0dDBgwADk5OfDw8EBISAi0tbXFMqGhoZg0aZI4K8DX1xerVq1Sqr0SgUv3VTkZGRmQyWT4NzVd6YEmRG/Kos3Eym4CaQihKB9519ZBLpe/9Xdcyffld0evwsDI+PUnlFNO1lNM7dpMJW2sipj5ExGR2uPa/srhgD8iIiINw8yfiIjU3vOr8qmqvuqMmT8REZGGYeZPRERq703uxPe6+qozZv5EREQahpk/ERGpPS2o+Jq/ChcMqooY/ImISO2x21857PYnIiLSMMz8iYhI7WlBtdlsdc+Mq/vrIyIiohcw8yciIrUnkUggUeGFelXWVRUx+BMRkdqT/G9TZX3VGbv9iYiINAwzfyIiUntc2185zPyJiIg0DDN/IiKqFqp3rq5azPyJiIg0DDN/IiJSe1zeVzkM/kREpPY4z1857PYnIiLSMMz8iYhI7XFtf+VU99dHREREL2Dwpyrtu8XB6ND2Q1ibm6CunRUG/acPbsfFKZTJzMzE5M8moMF79rCQGeKDZk2wbu0a8fiD+/dRQ6pV5rZ712/v+iVRFTZ1RFfkXF6Fb6f2E/f9NHcIci6vUthObZqicN6Ivu3w+7rP8PDMt8i5vAqyGgal6p4W4IWTIZPxOHIJkk4vrvDXomlKrvmrcqvO2O1PVdrZ06cxeuw4fNCqNYoKCzH366/Qy8cLF2NvwMjICADw5Ref43REBH7euBkODnVx/NhRfD5pPGxsbOHj2wt29va49+BfhXo3rP8Jy77/Fl29vCvjZVEV1LJJHQT0bYurtxNLHfv9jxsYM2eL+Di/oEjhuKG+LsIjbyI88ibmTepVZv16utrYHX4Z567GY2hvN9U2nri2v5IY/KlK23vwiMLjNes2wNHOCpcvxaD9Rx0AAOeio+Hn/wk6uHcEAIwYORobfv4Jly5dhI9vL2hra8PK2lqhngP79qJf/wGoUaPGO3kdVLUZGehh44JhGDdvG74c2a3U8fz8Qjx8/PSl56/aGgEA+Kil00vLzP/xMABgSM82b9dYIhVgtz+plQy5HABgamYm7nNr2w6HDx7Av//8A0EQcCriJO7euQ3PLl5l1nH5UgyuXonFJ8MC3kmbqepbNmMgws5cx8lzcWUe/6iVEx4cD8bVvV/jh9kfo5YpfzRWNez2Vw4zf1IbgiBgxrQpcGvXHk2bOov7v1u6AhM+HY0G79lDR0cHWlpa+OHHdWjbrn2Z9WzauB4NGzWGq1vbd9V0qsL6e7XE+43s0X5I2dfhj/5xE7vDL+PvpCeoW9scX4/zwZGfJqGt32LkFxS+49YSqQaD/2vUrVsXgYGBCAwMrOymaLzJn03A9etXEX7ijML+NatW4MK5aPy6ax/qODjg7JnT+HzSeFhb26CTh6dC2ZycHPy2Yxumz/jqXTadqig7q5r49ot+6DnuB+Tllx3Idx69JP755r0kXLr5N+IO/xfeHzXFvhNX3lVT6TU41U85lfr6hg0bBolEgoULFyrs37t37zvvcgkJCUHNmjVL7b9w4QJGjx79TttCpU0JnIjDhw7g8O8nUNvOTtyfk5ODoK9nIXjx9+ju0xPOLs0wdtwE9Os/AMuXfl+qnr27dyI7OxsfD/nkXTafqqgWjevAytwEkaHT8PTCcjy9sBwdWjlh3MfueHphObS0Sn8PJT/KwN9JT1C/Tq1KaDGRalR65q+vr49FixZhzJgxMDU1rezmlFKrFv+BVyZBEDAlcCIO7N+LI0dPoq6jo8LxgoICFBQUQEtL8XeslpY2iouLS9W3KWQDuvv48u+VAAAnz8eh5X++Udj309whiIt/iO9DwlFcLJQ6x0xmBDsrUyQ9ynhXzaRy4PK+yqn0ng1PT09YW1sjODj4pWUiIyPRoUMHGBgYwN7eHpMmTUJWVpZ4PCkpCT169ICBgQEcHR2xdetW1K1bF8uWLRPLLFmyBC4uLjAyMoK9vT3GjRuHzMxMAEBERASGDx8OuVwufoCCgoIAQKGejz/+GIMGDVJoW0FBASwsLLBx40YAz4LV4sWL8d5778HAwADNmzfHzp07X/ke5OXlISMjQ2GjZz6fNB47toViw6ZQGBsb42FyMh4mJyMnJwcAYGJigvYd3DFrxjScPhWB+/Hx2PJLCLaFbkbPXr0V6rp39y7+OHMaw4ZzoB89k5mdh5v3khS2rJx8PJFn4ea9JBgZ6CH48z5o08wRdWzM8FFLJ+xaPgaP0zOx/7kufytzYzRrUBv16lgAAJydbNGsQW2YmhiKZeytTdGsQW3Y25hCW0sLzRrURrMGtWFkoPfOX3d1JKmArTqr9MxfW1sbCxYsgJ+fHyZNmgS757p0AeDatWvw8vLCvHnzsH79eqSmpmLChAmYMGGCGHA/+eQTPHr0CBEREdDV1cXkyZORkpKiUI+WlhZWrFiBunXrIj4+HuPGjcO0adOwevVqtG3bFsuWLcPXX3+NuP8tIFPWFLDBgwdjwIAByMzMFI///vvvyMrKQr9+zxYF+eqrr7B7926sWbMGTk5OOH36NIYMGYJatWrB3d29zPcgODgYc+fOfbs3spr6+acfAQDeXTop7P9x3QYM+WQYAGDT5m2YM3smAoYNQdqTJ7Cv44A5c+dj5OixCuds3rQBtrVrw6NL13fSdlJ/RcUCmta3hZ/Ph6hpbIDkRxk4deE2/KdvQGZ2nlhu5H8+wldju4uPj234HAAw6uvN2HLgHABg9qc94O/rKpY5t2MGAKDryOU4E3PnXbwcIpFEEITS/VrvyLBhw5Ceno69e/fCzc0NTZo0wfr167F371706dMHgiDgk08+gYGBAdauXSued/bsWbi7uyMrKwv3799H48aNceHCBbRq1QoAcPfuXTg5OWHp0qUvHaj322+/4dNPP8WjR48APLvmHxgYiPT0dIVyzw/4KygogK2tLZYsWQJ/f38AgJ+fHwoLC/Hrr78iKysLFhYWOHHiBNzc/n8Rj5EjRyI7Oxtbt24tsy15eXnIy/v/L5KMjAzY29vj39R0mJiYKP2+Er0JizYTK7sJpCGEonzkXVsHuVz+1t9xGRkZkMlk2Bp5G4Y1jFXUQiA78yn82jZQSRurokrP/EssWrQInTt3xpQpistmxsTE4O7duwgNDRX3CYKA4uJixMfH4/bt29DR0cEHH3wgHq9fv36p8QMnT57EggULcPPmTWRkZKCwsBC5ubnIysoSV4p7HV1dXfTv3x+hoaHw9/dHVlYW9u3bJwb1mzdvIjc3F126dFE4Lz8/Hy1atHhpvVKpFFKptFxtICIieltVJvh36NABXl5emDlzJoYNGybuLy4uxpgxYzBp0qRS59SpU0fspn/R8x0aDx48QPfu3TF27FjMmzcPZmZmOHv2LAICAlBQUKBUOwcPHgx3d3ekpKQgPDwc+vr68Pb2FtsKAIcOHULt2rUVzmNwJyKqOFqQQEuFV+pVWVdVVGWCPwAsXLgQ77//Pho0aCDu++CDD3Djxg3Ur1+/zHMaNWqEwsJCXL58GS1btgTwrNv/+e77ixcvorCwEN9//704KvzXX39VqEdPTw9FRYrrdZelbdu2sLe3x44dO3DkyBH0798fenrPBuw0adIEUqkUf//990uv7xMRkepJJM82VdZXnVWp4O/i4oLBgwdj5cqV4r7p06fD1dUV48ePx6hRo2BkZIRbt24hPDwcK1euRKNGjeDp6YnRo0djzZo10NXVxZQpU2BgYCBO1ahXrx4KCwuxcuVK9OzZE3/88Qd+/PFHheeuW7cuMjMzcfz4cTRv3hyGhoYwNDTEiyQSCfz8/PDjjz/i9u3bOHnypHjM2NgYU6dOxeeff47i4mK0b98eGRkZiIyMRI0aNTB06NAKeueIiIjKr9Kn+r1o3rx5Cl32zZo1w6lTp3Dnzh189NFHaNGiBWbPng0bGxuxzC+//AIrKyt06NABffr0wahRo2BsbAx9fX0AwPvvv48lS5Zg0aJFcHZ2RmhoaKmphW3btsXYsWMxcOBA1KpVC4sXv/yWm4MHD8bNmzdRu3ZttGvXrlT7v/76awQHB6Nx48bw8vLCgQMH4PjC/HQiIlIdSQX8V51V6mj/ipKYmAh7e3scO3YMHh4eld0cpZWMXuVof3qXONqf3pWKGO3/a9RdlY/2H+BWv9qO9q9ymf+bOHHiBPbv34/4+HhERkZi0KBBqFu3Ljp06FDZTSMioneg5Jq/KjdlnT59Gj179oStrS0kEgn27t2rcLxkSfvnN1dXV4UyeXl5mDhxIiwsLGBkZARfX18kJiYqlElLS4O/vz9kMhlkMhn8/f1LTVN/nWoR/AsKCjBz5kw0bdoUffr0Qa1atcQFf4iIiN6FrKwsNG/eHKtWrXppmW7duiEpKUncDh8+rHA8MDAQe/bswfbt23H27FlkZmbCx8dHYUC6n58fYmNjERYWhrCwMMTGxoprz5RXlRrw96a8vLzg5VX2vduJiKj6k6h4qt+bXPP39vYWp36/jFQqhbW1dZnH5HI51q9fj82bN8PT89kdSbds2SJexvby8sKtW7cQFhaG6OhotGnTBgCwbt06uLm5IS4uDg0bNixXW6tF5k9ERJqtorr9X7zvyvOrsb6JiIgIWFpaokGDBhg1apTCUvQxMTEoKChA167/vwS5ra0tnJ2dERkZCQCIioqCTCYTAz8AuLq6QiaTiWXKg8GfiIjoJezt7cVr6zKZ7JU3oXsdb29vhIaG4sSJE/j+++9x4cIFdO7cWfxBkZycDD09vVIr1FpZWSE5OVksY2lpWapuS0tLsUx5VItufyIi0mwVtchPQkKCwmj/t1mtdeDAgeKfnZ2d0apVKzg4OODQoUPo27fvS88TBEHhFsNl3W74xTKvw8yfiIjoJUxMTBQ2VS7VbmNjAwcHB9y58+yujtbW1sjPz0daWppCuZSUFFhZWYllHj58WKqu1NRUsUx5MPgTEZHaU8dFfh4/foyEhARx0bqWLVtCV1cX4eHhYpmkpCRcv34dbdu2BQC4ublBLpfj/PnzYplz585BLpeLZcqD3f5ERKT2tCTPNlXWp6zMzEzcvXtXfBwfH4/Y2FiYmZnBzMwMQUFB6NevH2xsbHD//n3MnDkTFhYW6NOnDwBAJpMhICAAU6ZMgbm5OczMzDB16lS4uLiIo/8bN26Mbt26YdSoUeKt7kePHg0fH59yj/QHGPyJiIhU4uLFi+jUqZP4ePLkyQCAoUOHYs2aNbh27Rp++eUXpKenw8bGBp06dcKOHTtgbPz/KxMuXboUOjo6GDBgAHJycuDh4YGQkBBoa2uLZUJDQzFp0iRxVoCvr+8r1xYoS7Vc3lfdcXlfqgxc3pfelYpY3nf/hXgYqXB536zMp/Bt7cjlfYmIiKh6YLc/ERGpvYqa6lddMfMnIiLSMMz8iYhI7UnwZuvxv6q+6ozBn4iI1F5VmOqnTtjtT0REpGGY+RMRkdpT9ap872KFv8rEzJ+IiEjDMPMnIiK1x6l+ymHwJyIitSeBakfoV/PYz25/IiIiTcPMn4iI1J4WJNBSYV+9VjXP/Zn5ExERaRhm/kREpPZ4zV85zPyJiIg0DDN/IiJSf0z9lcLgT0REao8r/CmH3f5EREQahpk/ERGpPxWv8FfNE39m/kRERJqGmT8REak9jvdTDoM/ERGpP0Z/pbDbn4iISMMw8yciIrXHqX7KYeZPRESkYZj5ExGR2pOoeKqfSqcNVkHM/ImIiDQMM38iIlJ7HOyvHAZ/IiJSf4z+SmG3PxERkYZh5k9ERGqPU/2Uw8yfiIhIwzDzJyIitcepfsph8CciIrXH8X7KYbc/ERGRhmHmT0RE6o+pv1KY+RMREWkYZv5ERKT2ONVPOcz8iYiINAwzfyIiUnuc6qccZv5ERKT2JBWwKev06dPo2bMnbG1tIZFIsHfvXoXjgiAgKCgItra2MDAwQMeOHXHjxg2FMnl5eZg4cSIsLCxgZGQEX19fJCYmKpRJS0uDv78/ZDIZZDIZ/P39kZ6erlRbGfyJiIhUICsrC82bN8eqVavKPL548WIsWbIEq1atwoULF2BtbY0uXbrg6dOnYpnAwEDs2bMH27dvx9mzZ5GZmQkfHx8UFRWJZfz8/BAbG4uwsDCEhYUhNjYW/v7+SrWV3f5ERKT+qsBUP29vb3h7e5d5TBAELFu2DLNmzULfvn0BAJs2bYKVlRW2bt2KMWPGQC6XY/369di8eTM8PT0BAFu2bIG9vT2OHTsGLy8v3Lp1C2FhYYiOjkabNm0AAOvWrYObmxvi4uLQsGHDcrWVmT8REdFLZGRkKGx5eXlvVE98fDySk5PRtWtXcZ9UKoW7uzsiIyMBADExMSgoKFAoY2trC2dnZ7FMVFQUZDKZGPgBwNXVFTKZTCxTHgz+RESk9iQV8B8A2Nvbi9fWZTIZgoOD36h9ycnJAAArKyuF/VZWVuKx5ORk6OnpwdTU9JVlLC0tS9VvaWkplikPdvsTEZHaq6jR/gkJCTAxMRH3S6XSt6xXsZGCIJTa96IXy5RVvjz1PI+ZPxER0UuYmJgobG8a/K2trQGgVHaekpIi9gZYW1sjPz8faWlpryzz8OHDUvWnpqaW6lV4FQZ/IiJSe1Vhqt+rODo6wtraGuHh4eK+/Px8nDp1Cm3btgUAtGzZErq6ugplkpKScP36dbGMm5sb5HI5zp8/L5Y5d+4c5HK5WKY82O1PRESkApmZmbh79674OD4+HrGxsTAzM0OdOnUQGBiIBQsWwMnJCU5OTliwYAEMDQ3h5+cHAJDJZAgICMCUKVNgbm4OMzMzTJ06FS4uLuLo/8aNG6Nbt24YNWoU1q5dCwAYPXo0fHx8yj3SH2DwJyKi6qAKTPW7ePEiOnXqJD6ePHkyAGDo0KEICQnBtGnTkJOTg3HjxiEtLQ1t2rTB0aNHYWxsLJ6zdOlS6OjoYMCAAcjJyYGHhwdCQkKgra0tlgkNDcWkSZPEWQG+vr4vXVvgpS9PEARB+ZdIFSkjIwMymQz/pqYrDDQhqkgWbSZWdhNIQwhF+ci7tg5yufytv+NKvi/Px/2LGsaq+77MfJqBDxvaqqSNVREzfyIiUnu8q59yGPyJiEjt8cY+yuFofyIiIg3DzJ+IiNReFRjvp1aY+RMREWkYZv5ERKT+mPorhcGfiIjUHkf7K4fd/kRERBqGmX8VVLLu0tOnGZXcEtIkQlF+ZTeBNETJZ02la8ypeKpfNU/8GfyroqdPnwIAGr5Xp5JbQkRUcZ4+fQqZTFbZzdBIDP5VkK2tLRISEmBsbKzU/Zk1XUZGBuzt7Uvdf5uoovAz92YEQcDTp09ha2ursjo53k85DP5VkJaWFuzs7Cq7GWqr5L7bRO8KP3PKY8ZfuRj8iYhI/TH1VwqDPxERqT1O9VMOp/pRtSGVSjFnzhxIpdLKbgppCH7mSF1JBJXOtSAiInp3MjIyIJPJcOWvhzA2Vt24i6dPM9D8PSvI5fJqOZ6DmT8REZGG4TV/IiJSexzvpxwGfyIiUn+M/kphtz8REZGGYeZPRERqj1P9lMPMn4iISMMw8yciIrUngWrv6le9835m/kRElaK4uLiym0AajJk/aSxBECCRSPDkyRMUFhZCV1cXpqamCseIKkJxcTG0tJ7lXidPnkRCQgKsra3h6OgIJyenSm6deuJgf+Uw+JNGKgnu+/btw/Lly/HXX3+hefPmcHZ2xjfffMPATxWqJPBPnz4dO3bsgLW1NbS0tJCbm4tFixahS5culdxC9SORqLjbv5p/BbDbnzSSRCLBkSNHMGjQIPTq1Qs7duyAi4sLgoODceTIkcpuHmmAkJAQbN68GVu3bkV0dDR8fX1x8+ZNZGVlVXbTSAMw+JNGys/Px6+//opZs2bhs88+w3vvvYdNmzZhwoQJ8Pb2ruzmUTVWcjuVK1euYODAgWjbti327t2LBQsWYMWKFejduzeys7Px999/V3JL1Y2kArbqi8GfNJK2tjbi4uLg6OiIpKQktGjRAt7e3lixYgUA4LfffsPx48cruZVUXTx//7TCwkIAQGZmJurXr4+jR4/C398f3377LUaPHo3i4mLs2rULYWFhyMvLq6wmUzXH4E8a5fr167h37x4AwMHBAefPn0e7du3QvXt3/PTTTwCAtLQ0hIWF4fbt2ygqKqrM5lI1UTKG5Mcff8TJkycBALa2tpgyZQr69OmDFStWYMyYMQCe3aXul19+QVJSEm8VrISSa/6q3KozBn/SCIIg4J9//kGvXr1w6dIlaGtro1evXli5ciXMzc3x/fffi2W/++47REREwMvLC9ra2pXYaqpuVq9ejWXLlgEA5s6di169ekFHRwctWrRAYmIi7t+/j0GDBkEul2PWrFmV21g1w05/5XC0P2kEiUSC2rVrw9XVFfPmzYOPjw8GDRqEx48fY+LEiRg7diy0tLQgCAIOHTqEEydO4L333qvsZlM1UTK1b/ny5Zg4cSIOHTqEHj16YMGCBXj69Ck6d+4MfX192NnZQUdHB3/88Qd0dHRQVFTEH6BUIZj5U7X0Ynd9fn4+AOCLL76Avr4+9uzZAwAYP3489u7dCyMjI6SlpcHBwQFRUVFo0aLFO28zVR8vLuBTMrWvQYMGMDMzw7FjxwAA9erVw+HDh7F161asXr0a3377Lc6ePQtdXV0UFhYy8CuB3f7KkQjPj0QhUnN//vknGjVqJD6+ffs2nJycxGuuOTk56Nu3L6RSKfbu3SuWK8mwuLgPvY1NmzahR48esLCwAADs2LEDcrkco0ePFsv8+uuv+OSTT3Dq1Cm0adOmzHqeXwSIXi0jIwMymQxxf6fC2MREZfU+zchAwzq1IJfLYaLCeqsKfrqo2li5ciXmzJmDzMxMAMC9e/fQv39/NGnSBL///jv++usvGBgYIDg4GGfPnsWWLVvEc0u+aBn46U1t2rQJoaGhMDMzAwCkpKRg27ZtmDFjBrp06YJNmzbhyZMnGDBgALp37449e/YgLy+vzGV+GfiVJ6mA/6ozZv5UbZw/fx6mpqZwcnJCeno6TExMcOXKFXz33Xe4du0adHV1MW7cOHTu3BkLFy6EgYEBvv32W2hra/PLllSipAfpzJkzaNWqFfT09JCYmIjAwECkpKQgNTUVK1euxG+//YZLly7h2LFj4o8FejMlmf/tvx+pPPNvUMei2mb+DP5ULTzfTRodHY05c+Zg8uTJ8PLyAgCcOHECf/zxBxYvXgxfX1/Exsbir7/+wuXLlxUuExC9iby8PHFa3vnz59G+fXvMmjULI0eORO3atVFYWIhbt25h9erVOH36NKytrXHy5EksXrwYU6dOreTWqzcx+CdUQPC3r77Bn6P9SW09H/BL/pydnQ1jY2OkpqZi7dq1KCwsRI8ePdC5c2d07twZPj4+2LdvH/7880/k5eVBR4f/BOjtFBYWioH/7NmzaN++Pb7++musX78eOjo6GDp0KOzs7ODi4oI1a9bg9OnTuHr1KqRSKQIDAyu38dUIb+yjHGb+pNZu376NpKQkuLu747fffsO2bduwe/duREdHY9q0aahZsybGjRuHbt26iecUFRWhoKAAjx49gp2dXSW2ntRdWFgYgoKCEB0djcmTJ+P48eM4c+YMTExMMH/+fKxduxZjx47FiBEjYGNjU2YdhYWF/BH6Fkoy/zsVkPk7MfMnqnqKi4vxww8/iAP95s6di40bNwIAXF1dsWjRIkyfPh2rV6+GRCIRLwEIgiDOqSZ6U8XFxZBIJJDL5ahXrx6ePHmCmJgYMVB89dVXAJ6t6gcAAQEBsLa2LlUPA79q8K5+yuEoJ1I7W7ZsQVZWlrhoSvv27TF//nxMnz4dQ4cORWFhIQRBgJubGxYtWoT09HSsXbsWBw4cAMAvW1INLS0teHl5oXXr1oiPj0f9+vXFhaFK1uT/6quvMHbsWKxbtw5Lly7F48ePK7PJRCIGf1Irf/31F6ZPn47U1FQA/z/Q6v3338eyZctw/PhxcWW04uJiuLm5YfHixbh79y5CQ0N5u1RSmeLiYhQWFsLHxwc//vgjCgoK0KFDBxQXF0MqlSI7OxsAMGvWLHzyySf4888/ObK/AlX2VL+goCBIJBKF7fmeHkEQEBQUBFtbWxgYGKBjx464ceOGQh15eXmYOHEiLCwsYGRkBF9fXyQmJqrk/XkRr/mT2snKyoKRkRFiY2PRuHFjcf+4ceOwdetWHDx4EB4eHgoL99y+fRv6+vpwcHCoxJaTunvZ4jvFxcUICwvDtGnTYG5ujlOnTonHjhw5Am9vb3EBKS4kpVol1/zv/fNY5df869U2L/c1/6CgIOzcuVNcvRF4dvfQWrVqAQAWLVqEb775BiEhIWjQoAHmz5+P06dPIy4uDsbGxgCATz/9FAcOHEBISAjMzc0xZcoU8XKSqld7ZPAntSMIAtLT02FnZ4euXbsiNDQUhoaGePLkCb744gts27YN+/fvh6enJ4KDg3H58mWEhoZCV1e3sptOauz5wL9161bcvHkT2tra6NmzJ1q1aoW8vDycPHkSX3zxBYyMjPDTTz9h6tSpKCoqwrFjxxj4K0hFB/+EhASF4C+VSsu822JQUBD27t2L2NjYUscEQYCtrS0CAwMxffp0AM+yfCsrKyxatAhjxoyBXC5HrVq1sHnzZgwcOBAA8O+//8Le3h6HDx8WxyypCrv9SW2U/E4tLi6GqakpDh48iDNnzmDUqFHIzs6GmZkZvv32W3zyySfo2rUrPDw8MHfuXMyYMYOBn95aSeCfPn06ZsyYgUuXLuH69evo1KkTwsPDIZVK0blzZ6xcuRKFhYXo2bMncnNzERYWxsD/DlTUXf3s7e0hk8nELTg4+KVtuHPnDmxtbeHo6IhBgwbhr7/+AgDEx8cjOTkZXbt2FctKpVK4u7sjMjISABATE4OCggKFMra2tnB2dhbLqBKDP6mFki/OyMhIbN68GSkpKejUqRP27duHQ4cOYeTIkcjKyoKZmRl+/PFHhIaGomvXrrh27Rpv0kMq89NPP2Hbtm3YuXMnDh8+jP/85z/IysoSl+vV09MTv9D37t2LiIgI8SY9DPzqKSEhAXK5XNxmzJhRZrk2bdrgl19+we+//45169YhOTkZbdu2xePHj5GcnAwAsLKyUjjHyspKPJacnAw9PT2Ympq+tIwqcdgzVXklgX/Xrl0ICAjA1KlT8fjxY1haWqJdu3Y4ePAgevTogVGjRuGnn35CjRo18PHHHzPTIpXKyMjAvXv3EBQUhNatW+PgwYMYO3Ysvv/+e9y8eRODBg3C/v374eXlBT09PfFHZ3FxMWeYvAMVNdXPxMSkXNf8vb29xT+7uLjAzc0N9erVw6ZNm+Dq6vq/OhUbWJ7vqIr6HmPmT1WeRCLB6dOnMXLkSHz33Xf46quvxIF+2dnZaN++Pfbv34/ff/8dfn5+4o19GPjpbbx4wx0TExMMHjwY7u7uuHPnDqZMmYL58+fj888/R69evVBQUABvb2+FwX4Ab9KjqYyMjODi4oI7d+6Io/5fzOBTUlLE3gBra2vk5+cjLS3tpWVUiZ9KUgvh4eHo0KGD2L1/8uRJDB8+HIMHD8axY8fEFf6uXLmCjIyMym4uqTlBEBQG9+3duxeCIKBZs2aoV68e4uLiYG5ujsGDBwMAatasidGjR2Pt2rVo165dZTZdg6l6mt/bJQ95eXm4desWbGxs4OjoCGtra4SHh4vH8/PzcerUKbRt2xYA0LJlS+jq6iqUSUpKwvXr18UyqsS+KFILRkZGSEpKwoYNG3Do0CHk5eUhMzMTNjY2GDx4MC5cuIDOnTvjzz//hIGBQWU3l9TY86P679+/j8mTJ8PFxQU1atSAp6cnAODp06eIjo5GfHw8iouLsXjxYtSqVQujRo0CwCV7K0Nlr/A3depU9OzZE3Xq1EFKSgrmz5+PjIwMDB06FBKJBIGBgViwYAGcnJzg5OSEBQsWwNDQEH5+fgAAmUyGgIAATJkyBebm5jAzM8PUqVPh4uIifu5UiZ9OqnLKusbVuXNnnDt3DnPmzIGHhwfGjh2LLl264NixY7h//74Y8Bn46W2VBP5p06bh0aNHsLKyQlRUFKZNm4aFCxeiS5cu6NOnD/r06YPWrVujfv36kEql2LVrF4Bnn18Gfs2TmJiIjz/+GI8ePUKtWrXg6uqK6OhocW2RadOmIScnB+PGjUNaWhratGmDo0ePinP8AWDp0qXQ0dHBgAEDkJOTAw8PD4SEhKh8jj/Aef5UxZQE/lOnTuGPP/7AvXv3MHDgQLi5ucHY2BgJCQmwt7cXy8+cORPHjx9HWFhYqVGyRG9q7dq1mDFjBo4dO4ZatWqhoKAAPXv2hLGxMebPnw9PT0/k5eUhPDwcBQUF8PX1hba2NjP+SlAyz/9+0hOV3oAnIyMDdW3Mqu2NfRj8qcrZvXs3/P394eHhgaSkJPz777/o3LkzJk+eLI6gjoqKwq+//oqQkBBERESgefPmldxqqk4CAwNx9+5dHDx4ULwM8PDhQ7i5uaFmzZpiD8DzPVQlK0rSu8Xg/2Y44I8qzYujqYFn11inT5+OpUuXYv/+/bhw4QIWL16Mhw8fYsWKFfjnn3/w4MEDbNiwAdeuXcPp06cZ+EllioqKAAC5ubniwFEtLS3k5ubCysoK3333Ha5evYqlS5eKC6+U5E8M/JWr5Jq/KrfqjMGfKkVJNpWQkICff/4ZK1aswOnTp2Fubo6cnBzUr19fLDt48GD4+/vj999/x99//w0HBwfMnj0bO3bsgIuLSyW+ClJ3L/4ALQnggwcPxtmzZ7F8+XIAgL6+vlhm0KBBiI+Px8KFCwFwSmlVUdk39lE3vDhF71xJ4L969Sp69eqFmjVr4t69exAEAf7+/rCxsUFubi6AZ9Nh9PT04O/vjwULFmDv3r1wc3NDnTp1KvlVkLp7flT/jh07cOfOHeTk5KBPnz746KOPsHDhQnGQ1pAhQwAAGzZsgJeXFwIDA/Hhhx8iKioKbm5ulfkyiN4Igz+9U88Hfjc3N0ycOBHTp0/H7du3sWbNGhw7dgzFxcUYP348IiMjYWNjA+DZj4BatWqhbt26lfsCqNooCfxffPEFfvvtN7Ro0QJGRkb48MMPsXPnTgQEBKBGjRr48ssv8cMPP0AQBJibm2P06NGIi4uDo6OjeMc2qnyVPdVP3TD40ztV0tXv4eGBHj16iF2nbdq0QWJiIg4fPoytW7ciODgYbm5uWLRoEQwNDREVFYXr169XyHxX0jwlo/L37NkjLuLTunVrHDp0CFu3bkVeXh7Mzc0xbtw4eHt748aNG9DV1YWnpye0tbWxdetWmJiYQCaTVfZLIXojDP70zhUVFcHR0RF5eXk4e/Ys2rdvDwCwsbFBfn4+atasiV27duHTTz/F7NmzUVhYCFNTUxw/fhxOTk6V3HpSZ+Hh4fD09BSn4/3zzz/w8vJC69atsXPnTgwfPhw//vgjPv74Y8jlcqSnp8PR0RGOjo4AgD///BPfffcddu/ejZMnTzLzr0Lefk2+0vVVZxzwR+9c3bp1ERoaivz8fMybNw+3bt3C06dP0adPH4wcORKtWrVCzZo1sW3bNhw9ehRnzpxBeHg4785Hb+XJkycYPXo0mjRpIo7Qf/ToEZ48eYKdO3dixIgRWLx4MUaPHg0A2L9/PxYuXCjeK6KgoACJiYnQ09PjLBNSe5znT5Xmzp07+Oyzz5CdnY2rV69i6NChWLp0KYBnX7S6urqV3EKqTgRBQFRUFMaMGQMdHR1cunQJFy9exNChQxEfH48FCxbg888/BwBkZmbi448/Rt26dbFixQpxRH9RUREKCgoURv9T5SqZ55+Ykqbyef52lqac50+kak5OTli+fDm0tbVhYmKCPn36iMe4ShqpmkQigaurK3766Sfk5OTAzc0NrVu3Rr9+/WBsbIycnBxcv34dUVFR6N+/PxITE7F06VJIJBKFufwM/FUTp/oph5k/Vbq7d+9i4sSJEAQBs2fP5l3RSGXOnz+Px48fw9vbWxzkV1hYiEuXLmHQoEGws7PD6dOn8fXXX+PgwYOIjY3Fhx9+CBMTExw6dAi6urpcua+KK8n8/0lJV3nmX9uyZrXN/Bn8qUq4c+cOJk+ejEePHmHp0qVwdXWt7CaRmjt58iQ8PDwAPJtN0qhRI/Tq1QsffPAB6tSpg/Pnz2PMmDEwMjLC2bNnUVBQgMjISDg6OsLOzg5aWlpcq18NlAT/f1NVH/xta1Xf4M9uf6oSnJyc8O2338LOzg62traV3RyqBurUqQNXV1e0bNkSNWvWhImJCYYPH47OnTujV69eiIqKQmBgIO7fv48uXbpAR0cH7u7uqFOnDrS0tFBcXMzAT9UWgz9VGY0aNUJoaChX7yOVqFevHjZt2gR7e3toa2tjxIgRuHfvHtauXQuJRILdu3dj7Nix0NLSwvHjx8XBfiVKFgEi9SCpgK06Y7c/EVVrt2/fxqRJk1BcXIy5c+eKy/EWFRXh8OHDiI+PR1RUFH755RfOMFFDJd3+SY9U3+1vY1F9u/0Z/Imo2rtz5w4mTpwIAJg5cyY6dOhQZjlOMVU/DP5vhv1aRFTtOTk5YeXKlZBIJAgODsYff/xRZjkGfvXFqX7KYfAnIo3g5OSEFStWQFtbG4GBgbh69WplN4mo0jD4E5HGKJlV0qFDBzg7O1d2c0iFSu7qp8qtOuM1fyLSWCW3mCb1VXLN/058gsqv+Ts52lfba/6cxEpEGouBX/3p6enB2toaTo72Kq/b2toaenp6Kq+3KmDmT0REai03Nxf5+fkqr1dPT6/a3suBwZ+IiEjDsM+LiIhIwzD4ExERaRgGfyIiIg3D4E9ERKRhGPyJ1ERQUBDef/998fGwYcPQu3fvd96O+/fvQyKRIDY29qVl6tati2XLlpW7zpCQENSsWfOt2yaRSLB37963roeoumPwJ3oLw4YNg0QigUQiga6uLt577z1MnToVWVlZFf7cy5cvR0hISLnKlidgE5Hm4CI/RG+pW7du2LhxIwoKCnDmzBmMHDkSWVlZWLNmTamyqrxrnEwmU0k9RKR5mPkTvSWpVApra2vY29vDz88PgwcPFrueS7rqN2zYgPfeew9SqRSCIEAul2P06NGwtLSEiYkJOnfujCtXrijUu3DhQlhZWcHY2BgBAQHIzc1VOP5it39xcTEWLVqE+vXrQyqVok6dOvjmm28AAI6OjgCAFi1aQCKRoGPHjuJ5GzduROPGjaGvr49GjRph9erVCs9z/vx5tGjRAvr6+mjVqhUuX76s9Hu0ZMkSuLi4wMjICPb29hg3bhwyMzNLldu7dy8aNGgAfX19dOnSBQkJCQrHDxw4gJYtW0JfXx/vvfce5s6di8LCQqXbQ6TpGPyJVMzAwAAFBQXi47t37+LXX3/Frl27xG73Hj16IDk5GYcPH0ZMTAw++OADeHh44MmTJwCAX3/9FXPmzME333yDixcvwsbGplRQftGMGTOwaNEizJ49Gzdv3sTWrVthZWUF4FkAB4Bjx44hKSkJu3fvBgCsW7cOs2bNwjfffINbt25hwYIFmD17NjZt2gQAyMrKgo+PDxo2bIiYmBgEBQVh6tSpSr8nWlpaWLFiBa5fv45NmzbhxIkTmDZtmkKZ7OxsfPPNN9i0aRP++OMPZGRkYNCgQeLx33//HUOGDMGkSZNw8+ZNrF27FiEhIeIPHCJSgkBEb2zo0KFCr169xMfnzp0TzM3NhQEDBgiCIAhz5swRdHV1hZSUFLHM8ePHBRMTEyE3N1ehrnr16glr164VBEEQ3NzchLFjxyocb9OmjdC8efMynzsjI0OQSqXCunXrymxnfHy8AEC4fPmywn57e3th69atCvvmzZsnuLm5CYIgCGvXrhXMzMyErKws8fiaNWvKrOt5Dg4OwtKlS196/NdffxXMzc3Fxxs3bhQACNHR0eK+W7duCQCEc+fOCYIgCB999JGwYMEChXo2b94s2NjYiI8BCHv27Hnp8xLRM7zmT/SWDh48iBo1aqCwsBAFBQXo1asXVq5cKR53cHBArVq1xMcxMTHIzMyEubm5Qj05OTm4d+8eAODWrVsYO3aswnE3NzecPHmyzDbcunULeXl58PDwKHe7U1NTkZCQgICAAIwaNUrcX1hYKI4nuHXrFpo3bw5DQ0OFdijr5MmTWLBgAW7evImMjAwUFhYiNzcXWVlZMDIyAgDo6OigVatW4jmNGjVCzZo1cevWLXz44YeIiYnBhQsXFDL9oqIi5ObmIjs7W6GNRPRqDP5Eb6lTp05Ys2YNdHV1YWtrW2pAX0lwK1FcXAwbGxtERESUqutNp7sZGBgofU5xcTGAZ13/bdq0UTimra0NABBUcOuPBw8eoHv37hg7dizmzZsHMzMznD17FgEBAQqXR4BnU/VeVLKvuLgYc+fORd++fUuVqa43XyGqKAz+RG/JyMgI9evXL3f5Dz74AMnJydDR0UHdunXLLNO4cWNER0fjk08+EfdFR0e/tE4nJycYGBjg+PHjGDlyZKnjJbclLSoqEvdZWVmhdu3a+OuvvzB48OAy623SpAk2b96MnJwc8QfGq9pRlosXL6KwsBDff/+9eAvdX3/9tVS5wsJCXLx4ER9++CEAIC4uDunp6WjUqBGAZ+9bXFycUu81EZWNwZ/oHfP09ISbmxt69+6NRYsWoWHDhvj3339x+PBh9O7dG61atcJnn32GoUOHolWrVmjfvj1CQ0Nx48YNvPfee2XWqa+vj+nTp2PatGnQ09NDu3btkJqaihs3biAgIACWlpYwMDBAWFgY7OzsoK+vD5lMhqCgIEyaNAkmJibw9vZGXl4eLl68iLS0NEyePBl+fn6YNWsWAgIC8NVXX+H+/fv47rvvlHq99erVQ2FhIVauXImePXvijz/+wI8//liqnK6uLiZOnIgVK1ZAV1cXEyZMgKurq/hj4Ouvv4aPjw/s7e3Rv39/aGlp4erVq7h27Rrmz5+v/F8EkQbjaH+id0wikeDw4cPo0KEDRowYgQYNGmDQoEG4f/++ODp/4MCB+PrrrzF9+nS0bNkSDx48wKeffvrKemfPno0pU6bg66+/RuPGjTFw4ECkpKQAeHY9fcWKFVi7di1sbW3Rq1cvAMDIkSPx888/IyQkBC4uLnB3d0dISIg4NbBGjRo4cOAAbt68iRYtWmDWrFlYtGiRUq/3/fffx5IlS7Bo0SI4OzsjNDQUwcHBpcoZGhpi+vTp8PPzg5ubGwwMDLB9+3bxuJeXFw4ePIjw8HC0bt0arq6uWLJkCRwcHJRqDxEBEkEVF/WIiIhIbTDzJyIi0jAM/kRERBqGwZ+IiEjDMPgTERFpGAZ/IiIiDcPgT0REpGEY/ImIiDQMgz8REZGGYfAnIiLSMAz+REREGobBn4iISMP8Hw1HJWmfLySAAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 500x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m234/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 35ms/step\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "classes = [\"Positive\", \"Negative\"]\n",
+    "\n",
+    "for idx, (model) in enumerate(models):\n",
+    "    model_func = model_variations[idx]\n",
+    "    title = f\"{model_func.__name__}\"\n",
+    "\n",
+    "    # Calculate confusion matrix\n",
+    "    pred = model.predict(test_df['tweets'])\n",
+    "    y_pred_binary = (pred > 0.5).astype(int)  # Convert probabilities to binary predictions\n",
+    "    cm = confusion_matrix(test_df['labels'], y_pred_binary)\n",
+    "    plot_confusion_matrix(cm, classes, title)\n",
+    "\n",
+    "# Transformer Model with 1 Multi-Head\n",
+    "predictions1 = trans_model.predict(test_sequences)\n",
+    "predicted_labels1 = (predictions1 > 0.5).astype(int)\n",
+    "trans_conf_matrix = confusion_matrix(test_df['labels'], predicted_labels1)\n",
+    "plot_confusion_matrix(trans_conf_matrix, classes, title=\"Transformer Model With 1 Multi-Head\")\n",
+    "\n",
+    "# Transformer Model with 2 Multi-Head\n",
+    "predictions2 = trans_model2.predict(test_sequences)\n",
+    "predicted_labels2 = (predictions2 > 0.5).astype(int)\n",
+    "trans_conf_matrix2 = confusion_matrix(test_df['labels'], predicted_labels2)\n",
+    "plot_confusion_matrix(trans_conf_matrix2, classes, title=\"Transformer Model With 2 Multi-Head\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb23098e",
+   "metadata": {},
+   "source": [
+    "The confusion matrices for each model further confirm that the Transformer models outperformed all other models. The Transformer model with 2 multi-heads had the highest number of true negatives (4551) and the lowest number of false positives (247), indicating a strong ability to correctly classify negative cases while minimizing false positives.\n",
+    "\n",
+    "The Transformer model with 1 multi-head performed similarly well, with the highest number of true positives (2434) and the lowest number of false negatives (234), suggesting that it effectively identified positive cases while minimizing false negatives.\n",
+    "\n",
+    "The GRU and LSTM models again performed similarly, with the GRU model having the third-highest number of true positives (2333), and the LSTM model having the third-highest number of true negatives (4455). This shows that they performed well but are still behind both Transformer models.\n",
+    "\n",
+    "The simple RNN model ranked the lowest again across all categories, with 2191 true positives, 477 false negatives, 4255 true negatives, and 543 false positives. This further proves that the simple RNN model was less effective in performing this prediction task compared to the other models."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c00d63f-bc2f-498b-82a8-5b6c794662c1",
+   "metadata": {},
+   "source": [
+    "__Exceptional Work__\n",
+    "\n",
+    "[1]Use the pre-trained ConceptNet Numberbatch embedding and compare to pre-trained GloVe. Which method is better for your specific application?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "f090547d-71a4-484f-9c22-bb9228db9fb9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def prepare_embedding_matrix(glove_embeddings, max_features, embedding_dim):\n",
+    "    embedding_matrix = np.zeros((max_features, embedding_dim))\n",
+    "    for word, i in database_index.items():\n",
+    "        if i < max_features:\n",
+    "            embedding_vector = glove_embeddings.get(word)\n",
+    "            if embedding_vector is not None:\n",
+    "                embedding_matrix[i] = embedding_vector\n",
+    "    return embedding_matrix\n",
+    "\n",
+    "# Load GloVe embeddings\n",
+    "def load_glove_embeddings(file):\n",
+    "    embeddings = {}\n",
+    "    with open(file, 'r', encoding='utf-8') as f:\n",
+    "        for line in f:\n",
+    "            values = line.split()\n",
+    "            word = values[0]\n",
+    "            coefs = np.asarray(values[1:], dtype='float32')\n",
+    "            embeddings[word] = coefs\n",
+    "    return embeddings\n",
+    "\n",
+    "glove_embeddings = load_glove_embeddings('glove.6B.100d.txt')\n",
+    "\n",
+    "glove_embedding_matrix = prepare_embedding_matrix(glove_embeddings, max_features, embedding_dim=100)\n",
+    "\n",
+    "glvoe_embedding_layer = Embedding(input_dim=max_features, output_dim=100, weights=[glove_embedding_matrix], input_length=sequence_length, trainable=False)\n",
+    "\n",
+    "# Define a model using the GloVe embeddings\n",
+    "def model_with_glove_embeddings():\n",
+    "    model = Sequential()\n",
+    "    model.add(tf.keras.Input(shape=(1,), dtype=tf.string))  # Input layer for raw strings\n",
+    "    model.add(vectorization)\n",
+    "    model.add(glvoe_embedding_layer)\n",
+    "    model.add(LSTM(128, return_sequences=True))\n",
+    "    model.add(GlobalMaxPooling1D())\n",
+    "    model.add(Dense(64, activation='relu', kernel_regularizer=l1_l2(0.01)))\n",
+    "    model.add(Dense(1, activation='sigmoid'))\n",
+    "    return model\n",
+    "\n",
+    "# Load ConceptNet Numberbatch embeddings\n",
+    "def load_numberbatch_embeddings(file):\n",
+    "    embeddings = {}\n",
+    "    with open(file, 'r', encoding='utf-8') as f:\n",
+    "        for line in f:\n",
+    "            values = line.split()\n",
+    "            word = values[0]\n",
+    "            coefs = np.asarray(values[1:], dtype='float32')\n",
+    "            embeddings[word] = coefs\n",
+    "    return embeddings\n",
+    "\n",
+    "numberbatch_embeddings = load_numberbatch_embeddings('numberbatch-en-19.08.txt')\n",
+    "\n",
+    "numberbatch_embedding_matrix = prepare_embedding_matrix(numberbatch_embeddings, max_features, embedding_dim=300)\n",
+    "\n",
+    "numberbatch_embedding_layer = Embedding(input_dim=max_features, output_dim=300, weights=[numberbatch_embedding_matrix], input_length=sequence_length, trainable=False)\n",
+    "\n",
+    "# Define a model using the Numberbatch embeddings\n",
+    "def model_with_numberbatch_embeddings():\n",
+    "    model = Sequential()\n",
+    "    model.add(tf.keras.Input(shape=(1,), dtype=tf.string))  # Input layer for raw strings\n",
+    "    model.add(vectorization)\n",
+    "    model.add(numberbatch_embedding_layer)\n",
+    "    model.add(LSTM(128, return_sequences=True))\n",
+    "    model.add(GlobalMaxPooling1D())\n",
+    "    model.add(Dense(64, activation='relu', kernel_regularizer=l1_l2(0.01)))\n",
+    "    model.add(Dense(1, activation='sigmoid'))\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ef260e1",
+   "metadata": {},
+   "source": [
+    "Here we implement the numberbatch embedding and the GloVe embedding.\n",
+    "\n",
+    "Numberbatch embedding: https://github.com/commonsense/conceptnet-numberbatch\n",
+    "\n",
+    "GloVe embedding: https://www.kaggle.com/datasets/sawarn69/glove6b100dtxt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "e5a46ea3-c7b3-426a-b9d3-f6e1ca43a09d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_df, test_df = train_test_split(df, test_size=0.2, random_state=10)\n",
+    "\n",
+    "def train_and_evaluate_model(model, train_df, test_df, epochs=20):\n",
+    "    \n",
+    "    model.summary()\n",
+    "    # Compile the model\n",
+    "    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
+    "\n",
+    "    # Train the model and store the training history\n",
+    "    history = model.fit(train_df['tweets'], train_df['labels'], epochs=epochs, validation_data=(test_df['tweets'], test_df['labels']))\n",
+    "\n",
+    "\n",
+    "    # Return the training history and the trained model\n",
+    "    return history, model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "514b7d5a-6dbf-4f56-8249-a9f89bdb0841",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_2\"</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ text_vectorization_3            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TextVectorization</span>)             │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ embedding_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)       │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,000,000</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ lstm_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │       <span style=\"color: #00af00; text-decoration-color: #00af00\">117,248</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ global_max_pooling1d_2          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalMaxPooling1D</span>)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">65</span> │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ text_vectorization_3            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
+       "│ (\u001b[38;5;33mTextVectorization\u001b[0m)             │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ embedding_8 (\u001b[38;5;33mEmbedding\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m100\u001b[0m)       │     \u001b[38;5;34m2,000,000\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ lstm_2 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │       \u001b[38;5;34m117,248\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ global_max_pooling1d_2          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
+       "│ (\u001b[38;5;33mGlobalMaxPooling1D\u001b[0m)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │         \u001b[38;5;34m8,256\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m65\u001b[0m │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,125,569</span> (8.11 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2,125,569\u001b[0m (8.11 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">125,569</span> (490.50 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m125,569\u001b[0m (490.50 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,000,000</span> (7.63 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m2,000,000\u001b[0m (7.63 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m64s\u001b[0m 67ms/step - accuracy: 0.6823 - loss: 2.2959 - val_accuracy: 0.6840 - val_loss: 0.5933\n",
+      "Epoch 2/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 66ms/step - accuracy: 0.7482 - loss: 0.5736 - val_accuracy: 0.8050 - val_loss: 0.5015\n",
+      "Epoch 3/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 66ms/step - accuracy: 0.8137 - loss: 0.4841 - val_accuracy: 0.7219 - val_loss: 0.6357\n",
+      "Epoch 4/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 66ms/step - accuracy: 0.7963 - loss: 0.5102 - val_accuracy: 0.8382 - val_loss: 0.4350\n",
+      "Epoch 5/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 66ms/step - accuracy: 0.8631 - loss: 0.3886 - val_accuracy: 0.8599 - val_loss: 0.3987\n",
+      "Epoch 6/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 66ms/step - accuracy: 0.8788 - loss: 0.3560 - val_accuracy: 0.8725 - val_loss: 0.3640\n",
+      "Epoch 7/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 66ms/step - accuracy: 0.8937 - loss: 0.3182 - val_accuracy: 0.8813 - val_loss: 0.3459\n",
+      "Epoch 8/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 66ms/step - accuracy: 0.9102 - loss: 0.2817 - val_accuracy: 0.8831 - val_loss: 0.3463\n",
+      "Epoch 9/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 66ms/step - accuracy: 0.9211 - loss: 0.2592 - val_accuracy: 0.8836 - val_loss: 0.3442\n",
+      "Epoch 10/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 66ms/step - accuracy: 0.9340 - loss: 0.2305 - val_accuracy: 0.8935 - val_loss: 0.3256\n",
+      "Epoch 11/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 66ms/step - accuracy: 0.9442 - loss: 0.2059 - val_accuracy: 0.8987 - val_loss: 0.3283\n",
+      "Epoch 12/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 66ms/step - accuracy: 0.9543 - loss: 0.1780 - val_accuracy: 0.8866 - val_loss: 0.3534\n",
+      "Epoch 13/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 66ms/step - accuracy: 0.9608 - loss: 0.1608 - val_accuracy: 0.8959 - val_loss: 0.3257\n",
+      "Epoch 14/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 66ms/step - accuracy: 0.9658 - loss: 0.1481 - val_accuracy: 0.8895 - val_loss: 0.3826\n",
+      "Epoch 15/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 66ms/step - accuracy: 0.9719 - loss: 0.1284 - val_accuracy: 0.8918 - val_loss: 0.3781\n",
+      "Epoch 16/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 66ms/step - accuracy: 0.9729 - loss: 0.1266 - val_accuracy: 0.8915 - val_loss: 0.3760\n",
+      "Epoch 17/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 66ms/step - accuracy: 0.9791 - loss: 0.1100 - val_accuracy: 0.8977 - val_loss: 0.3721\n",
+      "Epoch 18/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 66ms/step - accuracy: 0.9800 - loss: 0.1080 - val_accuracy: 0.8943 - val_loss: 0.3667\n",
+      "Epoch 19/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 66ms/step - accuracy: 0.9812 - loss: 0.1031 - val_accuracy: 0.8949 - val_loss: 0.3996\n",
+      "Epoch 20/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 66ms/step - accuracy: 0.9832 - loss: 0.0961 - val_accuracy: 0.8946 - val_loss: 0.3912\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define a list of classes\n",
+    "classes = [\"Positive\", \"Negative\"]\n",
+    "\n",
+    "epochs=20\n",
+    "\n",
+    "# Train and evaluate the Glove model\n",
+    "model1 = model_with_glove_embeddings()\n",
+    "\n",
+    "history1, model = train_and_evaluate_model(model1, train_df=train_df, test_df=test_df, epochs=epochs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "06fa428f-2537-4001-a877-0bf10a5f2a68",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m234/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 27ms/step\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X_test = test_df['tweets']\n",
+    "y_test = test_df['labels']\n",
+    "\n",
+    "# Get predictions on test data\n",
+    "y_pred = model.predict(test_df['tweets'])\n",
+    "\n",
+    "# Convert probabilities to binary predictions\n",
+    "y_pred_binary = (y_pred > 0.5).astype(int)\n",
+    "\n",
+    "# Calculate confusion matrix\n",
+    "cm1 = confusion_matrix(y_test, y_pred_binary)\n",
+    "\n",
+    "# Plot confusion matrix for Glove model\n",
+    "plot_confusion_matrix(cm1, classes, title='Confusion Matrix - Glove Model')\n",
+    "\n",
+    "# Plot the training and validation accuracy and loss\n",
+    "plot_history(history1, title='Model with Glove embeddings')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da7249c3",
+   "metadata": {},
+   "source": [
+    "As can be seen below, the Glove Model does an overall excellent job classifying the tweets correctly, with 2204 correct positives and 4475 correct negatives.  However, this benchmark still falls below the Transformer model with 2 multi-heads, as the Glove model has nearly twice as many false positives and false negatives (323 and 464 compared to 247 and 247 respectively).  As can be seen in the graphs, the model converges around 13 epochs, as the validation accuracy and validation loss appear to plateau.  However, the training accuracy and training loss continue to increase marginally to peak at an accuracy of about 0.98 based on the graph.\n",
+    "\n",
+    "As can be seen in the classification report below, the precision for positive tweets is slightly less than that of negative tweets, with a score of 0.87 compared to 0.91.  This is consistent with the rate of false positives shown in the confusion matrix, along with the fact that there are more negative tweets than positive tweets.  The relatively high scores of accuracy, macro average, and weighted average further contribute to the fact that this is a good classification model.  However, it still falls short of the marks achieved by the Transformer model.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "305658e6-f749-4d3f-95e5-2ca319917051",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "    Positive       0.87      0.83      0.85      2668\n",
+      "    Negative       0.91      0.93      0.92      4798\n",
+      "\n",
+      "    accuracy                           0.89      7466\n",
+      "   macro avg       0.89      0.88      0.88      7466\n",
+      "weighted avg       0.89      0.89      0.89      7466\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report(test_df['labels'], y_pred_binary, target_names=['Positive', 'Negative']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "8e3dcf2e-e9b4-4687-ba1c-1bffa1161ede",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_3\"</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mModel: \"sequential_3\"\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ text_vectorization_3            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TextVectorization</span>)             │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ embedding_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">300</span>)       │     <span style=\"color: #00af00; text-decoration-color: #00af00\">6,000,000</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ lstm_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │       <span style=\"color: #00af00; text-decoration-color: #00af00\">219,648</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ global_max_pooling1d_3          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalMaxPooling1D</span>)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">65</span> │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ text_vectorization_3            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
+       "│ (\u001b[38;5;33mTextVectorization\u001b[0m)             │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ embedding_9 (\u001b[38;5;33mEmbedding\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m300\u001b[0m)       │     \u001b[38;5;34m6,000,000\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ lstm_3 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │       \u001b[38;5;34m219,648\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ global_max_pooling1d_3          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
+       "│ (\u001b[38;5;33mGlobalMaxPooling1D\u001b[0m)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_6 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │         \u001b[38;5;34m8,256\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_7 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m65\u001b[0m │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">6,227,969</span> (23.76 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m6,227,969\u001b[0m (23.76 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">227,969</span> (890.50 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m227,969\u001b[0m (890.50 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">6,000,000</span> (22.89 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m6,000,000\u001b[0m (22.89 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m64s\u001b[0m 67ms/step - accuracy: 0.6545 - loss: 2.2700 - val_accuracy: 0.6426 - val_loss: 0.6764\n",
+      "Epoch 2/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m63s\u001b[0m 67ms/step - accuracy: 0.6421 - loss: 0.6656 - val_accuracy: 0.6426 - val_loss: 0.6661\n",
+      "Epoch 3/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m63s\u001b[0m 67ms/step - accuracy: 0.6472 - loss: 0.6397 - val_accuracy: 0.6538 - val_loss: 0.6565\n",
+      "Epoch 4/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 67ms/step - accuracy: 0.7045 - loss: 0.6056 - val_accuracy: 0.7574 - val_loss: 0.5461\n",
+      "Epoch 5/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m63s\u001b[0m 67ms/step - accuracy: 0.7646 - loss: 0.5325 - val_accuracy: 0.7932 - val_loss: 0.5018\n",
+      "Epoch 6/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m63s\u001b[0m 67ms/step - accuracy: 0.8097 - loss: 0.4725 - val_accuracy: 0.8204 - val_loss: 0.4509\n",
+      "Epoch 7/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m63s\u001b[0m 67ms/step - accuracy: 0.8272 - loss: 0.4477 - val_accuracy: 0.8371 - val_loss: 0.4245\n",
+      "Epoch 8/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 67ms/step - accuracy: 0.8526 - loss: 0.4022 - val_accuracy: 0.8525 - val_loss: 0.3976\n",
+      "Epoch 9/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 67ms/step - accuracy: 0.8641 - loss: 0.3748 - val_accuracy: 0.8630 - val_loss: 0.3748\n",
+      "Epoch 10/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 67ms/step - accuracy: 0.8772 - loss: 0.3497 - val_accuracy: 0.8643 - val_loss: 0.3693\n",
+      "Epoch 11/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m63s\u001b[0m 68ms/step - accuracy: 0.8919 - loss: 0.3168 - val_accuracy: 0.8745 - val_loss: 0.3461\n",
+      "Epoch 12/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 67ms/step - accuracy: 0.9044 - loss: 0.2949 - val_accuracy: 0.8803 - val_loss: 0.3541\n",
+      "Epoch 13/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 67ms/step - accuracy: 0.9091 - loss: 0.2780 - val_accuracy: 0.8845 - val_loss: 0.3302\n",
+      "Epoch 14/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 67ms/step - accuracy: 0.9225 - loss: 0.2511 - val_accuracy: 0.8754 - val_loss: 0.3355\n",
+      "Epoch 15/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 67ms/step - accuracy: 0.9276 - loss: 0.2348 - val_accuracy: 0.8888 - val_loss: 0.3302\n",
+      "Epoch 16/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 67ms/step - accuracy: 0.9343 - loss: 0.2233 - val_accuracy: 0.8920 - val_loss: 0.3330\n",
+      "Epoch 17/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 67ms/step - accuracy: 0.9476 - loss: 0.1959 - val_accuracy: 0.8639 - val_loss: 0.3795\n",
+      "Epoch 18/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 67ms/step - accuracy: 0.9523 - loss: 0.1825 - val_accuracy: 0.8928 - val_loss: 0.3484\n",
+      "Epoch 19/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 67ms/step - accuracy: 0.9554 - loss: 0.1697 - val_accuracy: 0.8931 - val_loss: 0.3400\n",
+      "Epoch 20/20\n",
+      "\u001b[1m934/934\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m63s\u001b[0m 67ms/step - accuracy: 0.9618 - loss: 0.1558 - val_accuracy: 0.8918 - val_loss: 0.3377\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Train and evaluate the ConceptNet model\n",
+    "model2 = model_with_numberbatch_embeddings()\n",
+    "history2, model2 = train_and_evaluate_model(model2, train_df=train_df, test_df=test_df, epochs=epochs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "224732b1-ffd9-426f-bca9-582b457269d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m234/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 28ms/step\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get predictions on test data\n",
+    "y_pred = model2.predict(test_df['tweets'])\n",
+    "\n",
+    "# Convert probabilities to binary predictions\n",
+    "y_pred_binary = (y_pred > 0.5).astype(int)\n",
+    "\n",
+    "# Calculate confusion matrix\n",
+    "cm2 = confusion_matrix(y_test, y_pred_binary)\n",
+    "\n",
+    "# Plot confusion matrix for ConceptNet model\n",
+    "plot_confusion_matrix(cm2, classes, title='Confusion Matrix - ConceptNet Model')\n",
+    "\n",
+    "# Plot the training and validation accuracy and loss\n",
+    "plot_history(history2, title='Model with ConceptNet Numberbatch embeddings')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93a9e95c",
+   "metadata": {},
+   "source": [
+    "Similarly to the Glove Model, the ConceptNet Model performs very well, albeit still below the mark of the Transformer model previously discussed.  2258 tweets were correctly identified as positive and 4400 as negative, with the positive rate slightly higher than Glove, but the negative rate slightly lower.  Again, the model falls short of the 247 false positives and negatives from the Transformer model, and has 398 false positives and 410 false negatives.  The false positive rate is slightly higher than that of the Glove model, a sign that the Glove model may be the better model.  The graphs demonstrate a convergence around 8 epochs for the validation accuracy and validation loss, but the training accuracy and training loss continue to increase as the epochs increase.  These values never plateau to the degree that the Glove model does.\n",
+    "\n",
+    "As can be seen in the classification report below, the ConceptNet Model shows very similar results to the Glove model, with only marginal differences in the precision, recall, and F1 scores of all of the values.  This makes sense, given that the confusion matrix was extremely similar to that of the Glove model.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "80219536-ce71-49c9-8de6-e97c9b899cbe",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "    Positive       0.85      0.85      0.85      2668\n",
+      "    Negative       0.91      0.92      0.92      4798\n",
+      "\n",
+      "    accuracy                           0.89      7466\n",
+      "   macro avg       0.88      0.88      0.88      7466\n",
+      "weighted avg       0.89      0.89      0.89      7466\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report(test_df['labels'], y_pred_binary, target_names=['Positive', 'Negative']))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "883a5cd3",
+   "metadata": {},
+   "source": [
+    "There isn't much difference between the pre-trained ConceptNet Numberbatch embedding and the pre-trained GloVe models for our specific application, as they performed extremely similarly.  I would argue that the pre-trained GloVe model is likely better for this purpose, though, as it tends to capture statistical information about word co-occurrences better, a useful tool for sentiment analysis.  With that being said, the inability of GloVe to take into account contextual semantic understandings of words makes it difficult for some classifications.  There doesn't appear to be much of a difference between the models for our purpose, though, and both models fall below the previously used models, particularly the Transformer model with 2 multi-heads."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6725e16",
+   "metadata": {},
+   "source": [
+    "__References__\n",
+    "\n",
+    "https://wiki.cloudfactory.com/docs/mp-wiki/metrics/recall#:~:text=The%20best%20possible%20value%20is,score%20as%20the%20poor%20one.\n",
+    "https://serokell.io/blog/a-guide-to-f1-score\n",
+    "https://www.evidentlyai.com/classification-metrics/explain-roc-curve"
+   ]
+  }
+ ],
+ "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": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From a3ecb0d86ec8fc0606c385549f6d8c7342b31ca8 Mon Sep 17 00:00:00 2001
From: bonitadavis <152207429+bonitadavis@users.noreply.github.com>
Date: Thu, 12 Dec 2024 23:24:07 -0600
Subject: [PATCH 4/6] added references fr

---
 Lab7CookNew.ipynb | 32 ++++++++++++++++++++++----------
 1 file changed, 22 insertions(+), 10 deletions(-)

diff --git a/Lab7CookNew.ipynb b/Lab7CookNew.ipynb
index 11208f6..6638128 100644
--- a/Lab7CookNew.ipynb
+++ b/Lab7CookNew.ipynb
@@ -815,12 +815,14 @@
    "id": "f6f68a74-0184-4029-bbce-d8fe6d419fba",
    "metadata": {},
    "source": [
-    "__Describe the final dataset that is used for classification/regression (include a description of any newly formed variables you created). Discuss methods of tokenization in your dataset as well as any decisions to force a specific length of sequence.__\n",
+    "__Preprocessing and Tokenization Methods__\n",
     "\n",
     "The final dataset used for our classification task has undergone several preprocessing and tokenization steps to prepare it for machine learning. Our first preprocessing steps are removing the column \"Unnamed: 0\" and mapping labels to binary values (\"bad\": 1, \"good\": 0, \"neutral\": None). We then drop rows with NaN labels, which removes all rows with the neutral label. We also use the nltk library to lowercase words and remove stop words. Additionally, we convert numbers into words and expand contractions (eg. ain't -> is not).\n",
     "\n",
     "To tokenize the tweets in the dataset, we are using the Tokenizer and TextVectorization classes from Keras. We use the Tokenizer class to generate a word index that maps each unique word to a unique integer ID. The TextVectorization class then allows us to split each tweet into individual tokens (words), while also removing punctuation.\n",
     "\n",
+    "__Sequence Length Determination__\n",
+    "\n",
     "To determine the length of sequence, we found the number of tokens in each tweet and found that 95% of the tweets in the dataset have less than 35 tokens, so we adapted the dataset for each tweet to have 34 tokens by either adding tokens to bring the tweets up to 34 tokens or truncating tokens down to 34. Bringing the values to the same number of tokens allows the values to be passed into our model consistently, ensuring good performance.\n",
     "\n",
     "We have also decided to use Globe embedding made for Twitter, here is the dataset from Kaggle: https://www.kaggle.com/datasets/fullmetal26/glovetwitter27b100dtxt"
@@ -831,17 +833,17 @@
    "id": "524ab632-9ba0-465e-b7a2-f564edd92cdb",
    "metadata": {},
    "source": [
-    "__Choose and explain what metric(s) you will use to evaluate your algorithm’s performance.__\n",
+    "__Performance Evaluation Metrics__\n",
     "\n",
     "We have decided to use recall, F1, specificity, and AUC-ROC to evaluate the performance of our sentiment classification model. These metrics provide a comprehensive assessment of the model's ability to classify tweets as having \"good\" or \"bad\" sentiments.\n",
     "\n",
-    "Recall measures the proportion of true positives out of all positive instances. In this case, true positives are the number of \"bad\" sentiment tweets that were correctly classified as \"bad\", and a false negatives are the number of \"bad\" sentiment tweets that were incorrectly classified as \"good\". For our prediction task, we want to identify as many negative sentiment tweets as possible so that companies can effectively address negative feedback and improve their product. A good recall score is greater than 0.7, and an excellent recall score is greater than 0.85.\n",
+    "Recall measures the proportion of true positives out of all positive instances. In this case, true positives are the number of \"bad\" sentiment tweets that were correctly classified as \"bad\", and a false negatives are the number of \"bad\" sentiment tweets that were incorrectly classified as \"good\". For our prediction task, we want to identify as many negative sentiment tweets as possible so that companies can effectively address negative feedback and improve their product. A good recall score is greater than 0.7, and an excellent recall score is greater than 0.85 (\"Recall Score\").\n",
     "\n",
-    "F1 score calculates a harmonic mean of precision and recall, which is helpful as it will ensure that we minimize both false negatives and false positives. Furthermore, our dataset is imbalanced, with only about a third of the dataset being comprised of \"good\" tweets, so F1 score will provide a balanced evaluation of model performance for both classes and ensure that the model effectively identifies \"bad\" sentiment while not misclassifying \"good\" sentiment too often. An F1 score above 0.7 is generally considered to be a good score.\n",
+    "F1 score calculates a harmonic mean of precision and recall, which is helpful as it will ensure that we minimize both false negatives and false positives. Furthermore, our dataset is imbalanced, with only about a third of the dataset being comprised of \"good\" tweets, so F1 score will provide a balanced evaluation of model performance for both classes and ensure that the model effectively identifies \"bad\" sentiment while not misclassifying \"good\" sentiment too often. An F1 score above 0.7 is generally considered to be a good score (Logunova).\n",
     "\n",
-    "Specificity measures the proportion of true negatives out of all negative instances. In this case, true negatives are the number of \"good\" sentiment tweets correctly classified as \"good\", and false positives are the number of \"good\" sentiment tweets incorrectly classified as \"bad\". Measuring specificity is important because we want to ensure that good feedback is not misclassified as bad. This misclassification can cause the company to waste time and resources addressing features that are well-received by customers.\n",
+    "Specificity measures the proportion of true negatives out of all negative instances. In this case, true negatives are the number of \"good\" sentiment tweets correctly classified as \"good\", and false positives are the number of \"good\" sentiment tweets incorrectly classified as \"bad\". Specificity is important for the purposes of our classification task, as it can also be considered the true negative rate. When considering feedback that a company may need to know, it is important that a classification model correctly finds the negative tweets that may contain feedback that needs to be replied to. While it is obviously important to find positive feedback as well, it is far more useful for a company to be able to quickly find and reply to criticism to show an interest in improving customer satisfaction and to diminish the likelihood that negative feedback continues to spiral (\"Specificity in ML\").\n",
     "\n",
-    "AUC-ROC provides a more nuanced performance evaluation, particularly in distinguishing between classes at various decision thresholds. While accuracy measures the overall correctness, AUC-ROC focuses on the model's ability to discriminate between classes, making it an ideal metric for understanding how well the model performs across different thresholds. This metric will give us insight into how the model performs across the classes despite the class imbalance in our dataset. An AUC-ROC score above 0.9 is considered to have great performance.\n",
+    "AUC-ROC provides a more nuanced performance evaluation, particularly in distinguishing between classes at various decision thresholds. While accuracy measures the overall correctness, AUC-ROC focuses on the model's ability to discriminate between classes, making it an ideal metric for understanding how well the model performs across different thresholds. This metric will give us insight into how the model performs across the classes despite the class imbalance in our dataset. An AUC-ROC score above 0.9 is considered to have great performance (\"AUC-ROC Score\").\n",
     "\n",
     "These four metrics provide a well-rounded evaluation of our model's performance. Recall ensures we correctly classify negative feedback, and F1 score balances the model's performance between the two sentiment classes. Specificity ensures we do not misclassify positive feedback, and AUC-ROC provides a robust assessment of the model's ability to distinguish between the two classes. Together, these performance metrics will allow us to effectively evaluate our model's performance and ensure we meet the requirements of our business case.\n",
     "\n"
@@ -852,7 +854,7 @@
    "id": "39f7ed27-9c7e-4a19-a4d5-8f849162ef10",
    "metadata": {},
    "source": [
-    "__Choose the method you will use for dividing your data into training and testing__\n",
+    "__Training and Testing Data Split__\n",
     "\n",
     "We chose to use the 80/20 Test-Train split for our data. This allows our data to be split between training and testing efficently. Given the large size of our dataset and limited processing power of our computers, we elected to use this method instead of something more computationally expensive such as 10 Stratified K-folds. Furthermore, given the size of our dataset after preprocessing, we still have over 30,000 entries to train and test the network on. This is enough to not worry about some of the balancing issues a smaller dataset might suffer from if not split using a stratified method.\n",
     "\n",
@@ -2888,9 +2890,19 @@
    "source": [
     "__References__\n",
     "\n",
-    "https://wiki.cloudfactory.com/docs/mp-wiki/metrics/recall#:~:text=The%20best%20possible%20value%20is,score%20as%20the%20poor%20one.\n",
-    "https://serokell.io/blog/a-guide-to-f1-score\n",
-    "https://www.evidentlyai.com/classification-metrics/explain-roc-curve"
+    "“AUC-ROC Score.” Evidently AI, 1 October 2024, https://www.evidentlyai.com/classification-metrics/explain-roc-curve.\n",
+    "\n",
+    "“Conceptnet-numberbatch.” GitHub, 2021, https://github.com/commonsense/conceptnet-numberbatch.\n",
+    "\n",
+    "Logunova, Inna. “A Guide to F1 Score.” Serokell, 10 July 2023, https://serokell.io/blog/a-guide-to-f1-score.\n",
+    "\n",
+    "“Recall Score.” CloudFactory, https://wiki.cloudfactory.com/docs/mp-wiki/metrics/recall.\n",
+    "\n",
+    "S, Aman. “glove.6B.100d.txt.” Kaggle, https://www.kaggle.com/datasets/sawarn69/glove6b100dtxt. Accessed 12 December 2024.\n",
+    "\n",
+    "Sa, Charuni. “ChatGPT Sentiment Analysis.” Kaggle, 2022, https://www.kaggle.com/datasets/charunisa/chatgpt-sentiment-analysis.\n",
+    "\n",
+    "“Specificity in ML.” Giskard, https://www.giskard.ai/glossary/sensitivity-and-specificity-in-ml.\n"
    ]
   }
  ],

From 6125ce08848b147d294828d41d911da1dd9f8aef Mon Sep 17 00:00:00 2001
From: dplynn <122953195+dplynn@users.noreply.github.com>
Date: Wed, 12 Feb 2025 09:37:26 -0600
Subject: [PATCH 5/6] Update README.md

---
 README.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index bb0c0ec..25116bd 100644
--- a/README.md
+++ b/README.md
@@ -1 +1,2 @@
-# ML-Lab 7
\ No newline at end of file
+#ML-Lab-7
+Built a sequential neural network from scratch. 

From 286a8db739d1be34d061bb3582bffaaf19b39a70 Mon Sep 17 00:00:00 2001
From: dplynn <122953195+dplynn@users.noreply.github.com>
Date: Wed, 12 Feb 2025 09:41:13 -0600
Subject: [PATCH 6/6] Update README.md

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 25116bd..fdc3e64 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,2 @@
-#ML-Lab-7
-Built a sequential neural network from scratch. 
+# ML-Lab-7
+Built a sequential neural network from scratch. Trained to perform sentiment analysis on X Data. Dataset can be found here: https://www.kaggle.com/datasets/charunisa/chatgpt-sentiment-analysis