diff --git "a/TrainingModel(FutureProject).ipynb" "b/TrainingModel(FutureProject).ipynb"
new file mode 100644--- /dev/null
+++ "b/TrainingModel(FutureProject).ipynb"
@@ -0,0 +1,2115 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9e7940e4-1ba3-4192-a82c-5008dc5ed698",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gradio as gr\n",
+    "from transformers import pipeline\n",
+    "from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "54c98c8e-a9d2-453c-933a-6a945143361f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import sklearn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "4be240ca-2fe4-4d02-9955-c54d414fc795",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e93873a-e119-4fb5-aa38-b8814098f758",
+   "metadata": {},
+   "source": [
+    "### first we need to import our libraries dataset and convert them into .csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ad1e3444-0197-4a5f-945d-bee10e977ffe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spam_folder = \"enron2/spam\"\n",
+    "ham_folder = \"enron2/ham\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "262cfd88-9c03-4131-8e62-94dc9e5cce7e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_emails(folder, label):\n",
+    "    emails = []\n",
+    "    for filename in os.listdir(folder):\n",
+    "        if filename.endswith(\".txt\"):\n",
+    "            with open(os.path.join(folder, filename), \"r\", encoding=\"utf-8\", errors=\"ignore\") as file:\n",
+    "                text = file.read()\n",
+    "                emails.append((text, label))\n",
+    "    return emails"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f54a8a8a-9e31-46b4-82c1-6111e158594b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spam_emails = load_emails(spam_folder, label=1)  \n",
+    "ham_emails = load_emails(ham_folder, label=0)   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "8229102b-ec10-47f3-9cc8-dc42f7c4d6e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CSV file created successfully!\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(all_emails, columns=[\"text\", \"label\"])\n",
+    "df.to_csv(\"dataset/emails.csv\", index=False, escapechar='\\\\', quotechar='\"')\n",
+    "print(\"CSV file created successfully!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a5484a4b-6a93-40ea-993a-36118c5a31b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"dataset/emails.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "643bbc72-bfaa-4e82-85f8-3f216bc9e843",
+   "metadata": {},
+   "source": [
+    "### now we need to clean our messy dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d778066a-5526-428c-a118-4e1178b206c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package punkt to\n",
+      "[nltk_data]     C:\\Users\\raofb\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]   Package punkt is already up-to-date!\n",
+      "[nltk_data] Downloading package stopwords to\n",
+      "[nltk_data]     C:\\Users\\raofb\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]   Package stopwords is already up-to-date!\n",
+      "[nltk_data] Downloading package wordnet to\n",
+      "[nltk_data]     C:\\Users\\raofb\\AppData\\Roaming\\nltk_data...\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import re\n",
+    "import nltk\n",
+    "from bs4 import BeautifulSoup\n",
+    "from nltk.corpus import stopwords\n",
+    "from nltk.stem import WordNetLemmatizer\n",
+    "from nltk.tokenize import word_tokenize\n",
+    "nltk.download('punkt')\n",
+    "nltk.download('stopwords')\n",
+    "nltk.download('wordnet')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "24c1784f-8262-487a-9868-44762dc976ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package punkt_tab to\n",
+      "[nltk_data]     C:\\Users\\raofb\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]   Unzipping tokenizers\\punkt_tab.zip.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nltk.download('punkt_tab')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e043a0fb-f7e0-4727-8aee-b8ecce88b835",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stop_words = set(stopwords.words('english'))\n",
+    "def clean_text(text):\n",
+    "    # Lowercase\n",
+    "    text = text.lower()\n",
+    "    \n",
+    "    # Remove HTML\n",
+    "    text = BeautifulSoup(text, \"html.parser\").get_text()\n",
+    "    \n",
+    "    # Replace URLs and emails\n",
+    "    text = re.sub(r'http\\S+', '<URL>', text)\n",
+    "    text = re.sub(r'\\S+@\\S+', '<EMAIL>', text)\n",
+    "    \n",
+    "    # Remove special characters (retain $ for monetary values)\n",
+    "    text = re.sub(r'[^\\w\\s$]', '', text)\n",
+    "    \n",
+    "    # Replace numbers (except after $)\n",
+    "    text = re.sub(r'\\b\\d+\\b', '<NUM>', text)\n",
+    "    \n",
+    "    # Normalize whitespace\n",
+    "    text = re.sub(r'\\s+', ' ', text).strip()\n",
+    "\n",
+    "    tokens = word_tokenize(text)\n",
+    "    lemmatizer = WordNetLemmatizer()\n",
+    "    tokens = [lemmatizer.lemmatize(word) for word in tokens if word not in stopwords.words('english')]\n",
+    "\n",
+    "    return ' '.join(tokens)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f60b360e-c425-47f2-ad9c-6caf1d04f8ba",
+   "metadata": {},
+   "source": [
+    "### now we apply our nltk function to dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "90b9a80e-0937-46c0-8430-226fa0f4d0cf",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('Dataset/emails.csv')\n",
+    "df['cleaned_text'] = df['text'].apply(clean_text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "a425c6e7-5628-4af2-9f0f-c2fe8b7d5eb2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv('cleaned_emails.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "54941e16-8791-426e-ba66-d207b90d7876",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>text</th>\n",
+       "      <th>label</th>\n",
+       "      <th>cleaned_text</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Subject: fw : this is the solution i mentioned...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>subject fw solution mentioned lsc oo thank ema...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Subject: advs\\ngreetings ,\\ni am benedicta lin...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>subject advs greeting benedicta lindiwe hendri...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Subject: whats new in summer ? bawled\\ncarolyn...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>subject whats new summer bawled carolyn regret...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Subject: \\nh $ ello\\ndea 54 r home owner ,\\nwe...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>subject h $ ello dea &lt; NUM &gt; r home owner beet...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Subject: : ) ) you can not save the world by q...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>subject save world quitting smoking save self ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Subject: need software ? click here .\\ntop qua...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>subject need software click top quality softwa...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Subject: spend too much on your phone bill ? 2...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>subject spend much phone bill &lt; NUM &gt; crystal ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Subject: slotting order confirmation may 18 , ...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>subject slotting order confirmation may &lt; NUM ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Subject: we shiip to ur country for mircosoft ...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>subject shiip ur country mircosoft adobe norto...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Subject: urgent business proposal ,\\nmrs . reg...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>subject urgent business proposal mr regina ros...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                text  label  \\\n",
+       "0  Subject: fw : this is the solution i mentioned...      1   \n",
+       "1  Subject: advs\\ngreetings ,\\ni am benedicta lin...      1   \n",
+       "2  Subject: whats new in summer ? bawled\\ncarolyn...      1   \n",
+       "3  Subject: \\nh $ ello\\ndea 54 r home owner ,\\nwe...      1   \n",
+       "4  Subject: : ) ) you can not save the world by q...      1   \n",
+       "5  Subject: need software ? click here .\\ntop qua...      1   \n",
+       "6  Subject: spend too much on your phone bill ? 2...      1   \n",
+       "7  Subject: slotting order confirmation may 18 , ...      1   \n",
+       "8  Subject: we shiip to ur country for mircosoft ...      1   \n",
+       "9  Subject: urgent business proposal ,\\nmrs . reg...      1   \n",
+       "\n",
+       "                                        cleaned_text  \n",
+       "0  subject fw solution mentioned lsc oo thank ema...  \n",
+       "1  subject advs greeting benedicta lindiwe hendri...  \n",
+       "2  subject whats new summer bawled carolyn regret...  \n",
+       "3  subject h $ ello dea < NUM > r home owner beet...  \n",
+       "4  subject save world quitting smoking save self ...  \n",
+       "5  subject need software click top quality softwa...  \n",
+       "6  subject spend much phone bill < NUM > crystal ...  \n",
+       "7  subject slotting order confirmation may < NUM ...  \n",
+       "8  subject shiip ur country mircosoft adobe norto...  \n",
+       "9  subject urgent business proposal mr regina ros...  "
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "read = pd.read_csv(\"cleaned_emails.csv\")\n",
+    "read.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "f4b111d3-d794-4aaa-ac45-ef86a398cddf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('cleaned_emails.csv')\n",
+    "\n",
+    "# Drop the 'text' column\n",
+    "df = df.drop(columns=['text'])\n",
+    "\n",
+    "# Rename 'cleaned_text' to 'text'\n",
+    "df = df.rename(columns={'cleaned_text': 'text'})\n",
+    "\n",
+    "# Save the updated DataFrame to a new CSV file (or overwrite the existing one)\n",
+    "df.to_csv('Cleaned_data.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04a92aca-fa9c-468b-84dd-b6bc404b2851",
+   "metadata": {},
+   "source": [
+    "### 2nd cleaning ---------------------------"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "977a3274-d470-4bf1-9467-dd63a0774ee7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langdetect import detect, LangDetectException\n",
+    "from nltk.tokenize import word_tokenize, sent_tokenize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "e690b5d1-aa74-4146-9bda-9437790ae571",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stop_words = set(stopwords.words('english'))\n",
+    "lemmatizer = WordNetLemmatizer()\n",
+    "\n",
+    "def clean_text(text):\n",
+    "    try:\n",
+    "        # Detect and remove non-English text\n",
+    "        if detect(text) != 'en':\n",
+    "            return ''\n",
+    "    except LangDetectException:\n",
+    "        return ''\n",
+    "    \n",
+    "    # Lowercase\n",
+    "    text = text.lower()\n",
+    "    \n",
+    "    # Remove HTML\n",
+    "    text = BeautifulSoup(text, \"html.parser\").get_text()\n",
+    "    \n",
+    "    # Enhanced URL/email replacement\n",
+    "    text = re.sub(r'\\b(?:https?://|www\\.)\\S+', '<URL>', text)\n",
+    "    text = re.sub(r'\\b[\\w\\.-]+@[\\w\\.-]+\\.\\w{2,}\\b', '<EMAIL>', text)\n",
+    "    \n",
+    "    # Date pattern replacement (MM/DD/YYYY, DD-MM-YYYY, Month names, etc.)\n",
+    "    text = re.sub(r'\\b(\\d{1,2}[/-]\\d{1,2}[/-]\\d{2,4}\\b|(?:jan|feb|mar|apr|may|jun|jul|aug|sep|oct|nov|dec)[a-z]*\\s+\\d{1,2},?\\s+\\d{4})', '<DATE>', text)\n",
+    "    \n",
+    "    # Time pattern replacement\n",
+    "    text = re.sub(r'\\b\\d{1,2}:\\d{2}\\s*(?:am|pm)?\\b', '<TIME>', text)\n",
+    "    \n",
+    "    # Handle monetary values before general number replacement\n",
+    "    money_values = re.findall(r'\\$\\d+[\\d,.]*', text)\n",
+    "    text = re.sub(r'\\$\\d+[\\d,.]*', '<MONEY>', text)\n",
+    "    \n",
+    "    # Replace other numbers\n",
+    "    text = re.sub(r'\\b\\d+\\b', '<NUM>', text)\n",
+    "    \n",
+    "    # Split and recombine long sentences\n",
+    "    sentences = sent_tokenize(text)\n",
+    "    processed_sentences = []\n",
+    "    for sent in sentences:\n",
+    "        # Limit sentence length to 15 words\n",
+    "        tokens = word_tokenize(sent)[:15]\n",
+    "        processed_sentences.append(' '.join(tokens))\n",
+    "    text = ' '.join(processed_sentences)\n",
+    "    \n",
+    "    # Remove special characters (keep $ for money handling)\n",
+    "    text = re.sub(r'[^\\w\\s<>]', '', text)\n",
+    "    \n",
+    "    # Tokenization and lemmatization\n",
+    "    tokens = word_tokenize(text)\n",
+    "    tokens = [lemmatizer.lemmatize(word) for word in tokens if word not in stop_words]\n",
+    "    \n",
+    "    return ' '.join(tokens)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "64a46265-6d3e-4f80-bca9-5c6b418e0b55",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('Cleaned_data.csv')\n",
+    "df['cleaned_text'] = df['text'].apply(clean_text)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "038cbb13-af40-4376-8721-d8a337aaf59e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv('cleaned_data1.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ecbb27b3-0a56-40c2-a3a9-79ccb0752c78",
+   "metadata": {},
+   "source": [
+    "#### we save our final data into our directories "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0a079539-bbb3-47b2-9488-71c429a13dd9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('Final_data.csv')\n",
+    "\n",
+    "# Drop the 'text' column\n",
+    "df = df.drop(columns=['text'])\n",
+    "\n",
+    "# Rename 'cleaned_text' to 'text'\n",
+    "df = df.rename(columns={'cleaned_text': 'text'})\n",
+    "\n",
+    "# Save the updated DataFrame to a new CSV file (or overwrite the existing one)\n",
+    "df.to_csv('test.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "60e531d9-0caa-4fc7-a691-e30e91a6d515",
+   "metadata": {},
+   "source": [
+    "### now we split our data into training and testing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "128b90d9-b454-4f88-970a-8f5257b17671",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "95b7cbf5-127d-4af1-bf25-1a7c9c9c859d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  category                                               text\n",
+      "0     spam  subject fw solution mentioned lsc oo thank ema...\n",
+      "1     spam  subject advs greeting benedicta lindiwe hendri...\n",
+      "2     spam  subject whats new summer bawled carolyn regret...\n",
+      "3     spam  subject h $ ello dea < NUM > r home owner beet...\n",
+      "4     spam  subject save world quitting smoking save self ...\n",
+      "category\n",
+      "spam    5996\n",
+      "ham     5861\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"updated_dataset.csv\")\n",
+    "print(df.head())  # we check the first row\n",
+    "print(df[\"category\"].value_counts())  # Check classes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "9aa78718-154e-498e-b792-b63c2410cc71",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = df[\"text\"]  # Input text\n",
+    "y = df[\"category\"]  # Labels (0 or 1)\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X,\n",
+    "    y,\n",
+    "    test_size=0.2,           ## 20% of data for testing\n",
+    "    random_state=42,         ## Seed for reproducibility\n",
+    "    stratify=y               ## Preserve spam/ham ratio in both splits\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bbc78d97-0357-464b-b26b-b4e39b185f94",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f417943-a5f7-4db6-bef5-b046c5d874fd",
+   "metadata": {},
+   "source": [
+    "### SAVE OUR TRAIN AND TEST DATA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "6bd3a326-0e59-4b53-94c1-7e54dcab6834",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training samples: 9485\n",
+      "Testing samples: 2372\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_df = pd.DataFrame({\"text\": X_train, \"category\": y_train})\n",
+    "test_df = pd.DataFrame({\"text\": X_test, \"category\": y_test})\n",
+    "\n",
+    "# Save to CSV files\n",
+    "train_df.to_csv(\"Train and Test/train.csv\", index=False)\n",
+    "test_df.to_csv(\"Train and Test/test.csv\", index=False)\n",
+    "\n",
+    "print(f\"Training samples: {len(train_df)}\")\n",
+    "print(f\"Testing samples: {len(test_df)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e7dffbbc-765e-4093-9ec7-baac9f63f873",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  category                                               text\n",
+      "0     spam  subject fw solution mentioned lsc oo thank ema...\n",
+      "1     spam  subject advs greeting benedicta lindiwe hendri...\n",
+      "2     spam  subject whats new summer bawled carolyn regret...\n",
+      "3     spam  subject h $ ello dea < NUM > r home owner beet...\n",
+      "4     spam  subject save world quitting smoking save self ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Load your CSV dataset\n",
+    "df = pd.read_csv('Final_data.csv')\n",
+    "\n",
+    "# Rename the 'label' column to 'category'\n",
+    "df.rename(columns={'label': 'category'}, inplace=True)\n",
+    "\n",
+    "# Replace 0 with 'ham' and 1 with 'spam' in the 'category' column\n",
+    "df['category'] = df['category'].replace({0: 'ham', 1: 'spam'})\n",
+    "\n",
+    "# Save the updated DataFrame back to a CSV (optional)\n",
+    "df.to_csv('updated_dataset.csv', index=False)\n",
+    "\n",
+    "# Display the updated DataFrame\n",
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97a86b3e-f6e2-4aba-a924-8af72ff049a1",
+   "metadata": {},
+   "source": [
+    "### Check our split data values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "0efe481d-e9f2-44b4-91f8-e42aa7c9709a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train class distribution:\n",
+      " category\n",
+      "spam    4796\n",
+      "ham     4689\n",
+      "Name: count, dtype: int64\n",
+      "Test class distribution:\n",
+      " category\n",
+      "spam    1200\n",
+      "ham     1172\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Train class distribution:\\n\", train_df[\"category\"].value_counts())\n",
+    "print(\"Test class distribution:\\n\", test_df[\"category\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "4272c7ef-3e31-4779-92b3-9385c1b9f3d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>text</th>\n",
+       "      <th>category</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>subject need outstanding logo working company ...</td>\n",
+       "      <td>spam</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>subject weijun decided interview guess mean ba...</td>\n",
+       "      <td>ham</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>subject investment partnership proposal dear s...</td>\n",
+       "      <td>spam</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>subject tage resume submittal need time extern...</td>\n",
+       "      <td>ham</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>subject trading ag prod vince wanted let know ...</td>\n",
+       "      <td>ham</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>subject utf &lt; NUM &gt; q best dvd collect utf &lt; N...</td>\n",
+       "      <td>spam</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>subject portcalc methodology keith power emrs ...</td>\n",
+       "      <td>ham</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>subject ibuyit approver ah wonderful new syste...</td>\n",
+       "      <td>ham</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>subject osman sezgen steve kromer requesting o...</td>\n",
+       "      <td>ham</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>subject read email change life dear homeowner ...</td>\n",
+       "      <td>spam</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                text category\n",
+       "0  subject need outstanding logo working company ...     spam\n",
+       "1  subject weijun decided interview guess mean ba...      ham\n",
+       "2  subject investment partnership proposal dear s...     spam\n",
+       "3  subject tage resume submittal need time extern...      ham\n",
+       "4  subject trading ag prod vince wanted let know ...      ham\n",
+       "5  subject utf < NUM > q best dvd collect utf < N...     spam\n",
+       "6  subject portcalc methodology keith power emrs ...      ham\n",
+       "7  subject ibuyit approver ah wonderful new syste...      ham\n",
+       "8  subject osman sezgen steve kromer requesting o...      ham\n",
+       "9  subject read email change life dear homeowner ...     spam"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_train = pd.read_csv(\"Train and Test/train.csv\")\n",
+    "df_train.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "a75e71c9-468d-49e0-811d-2b416d1cbe5b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4689, 2)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_ham = df_train[df_train['category']=='ham']\n",
+    "df_ham.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "1bc2aece-1656-4019-94dd-0b7fb4710b78",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4796, 2)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_spam = df_train[df_train['category']=='spam']\n",
+    "df_spam.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "fc422e3e-df9d-4e06-b722-daee1659179a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(9485, 2)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_balanced = pd.concat([df_ham, df_spam])\n",
+    "df_balanced.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "81ce7ac6-619b-4d4e-ad41-769e2159a6d3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>text</th>\n",
+       "      <th>category</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>9456</th>\n",
+       "      <td>subject get thru july newsletter get thru news...</td>\n",
+       "      <td>spam</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9458</th>\n",
+       "      <td>subject cheap med nowrpmsqp every ph armaceuti...</td>\n",
+       "      <td>spam</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9459</th>\n",
+       "      <td>subject trusted source prescription medication...</td>\n",
+       "      <td>spam</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9460</th>\n",
+       "      <td>subject would hate reject chance reduce expend...</td>\n",
+       "      <td>spam</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9469</th>\n",
+       "      <td>subject backplanes &lt; NUM &gt; vicodin whim cocked...</td>\n",
+       "      <td>spam</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9473</th>\n",
+       "      <td>subject love girl idn wan na see girl naked pc...</td>\n",
+       "      <td>spam</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9476</th>\n",
+       "      <td>subject clalis $ &lt; NUM &gt; &lt; NUM &gt; discount drug...</td>\n",
+       "      <td>spam</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9479</th>\n",
+       "      <td>subject shoot spe rm drink increase cum volume...</td>\n",
+       "      <td>spam</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9483</th>\n",
+       "      <td>subject george williams george williams abidja...</td>\n",
+       "      <td>spam</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9484</th>\n",
+       "      <td>subject utf &lt; NUM &gt; q election result r utf &lt; ...</td>\n",
+       "      <td>spam</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                   text category\n",
+       "9456  subject get thru july newsletter get thru news...     spam\n",
+       "9458  subject cheap med nowrpmsqp every ph armaceuti...     spam\n",
+       "9459  subject trusted source prescription medication...     spam\n",
+       "9460  subject would hate reject chance reduce expend...     spam\n",
+       "9469  subject backplanes < NUM > vicodin whim cocked...     spam\n",
+       "9473  subject love girl idn wan na see girl naked pc...     spam\n",
+       "9476  subject clalis $ < NUM > < NUM > discount drug...     spam\n",
+       "9479  subject shoot spe rm drink increase cum volume...     spam\n",
+       "9483  subject george williams george williams abidja...     spam\n",
+       "9484  subject utf < NUM > q election result r utf < ...     spam"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_balanced.tail(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "65f1fe2c-7a36-414c-8315-3647bd222686",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>text</th>\n",
+       "      <th>category</th>\n",
+       "      <th>spam</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2123</th>\n",
+       "      <td>subject reminder cera executive roundtable hou...</td>\n",
+       "      <td>ham</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3480</th>\n",
+       "      <td>subject fw tw docket rp &lt; NUM &gt; &lt; NUM &gt; &lt; NUM ...</td>\n",
+       "      <td>ham</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1113</th>\n",
+       "      <td>subject &lt; NUM &gt; sm unique logo business lack v...</td>\n",
+       "      <td>spam</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>365</th>\n",
+       "      <td>subject satellite dish click info stop getting...</td>\n",
+       "      <td>spam</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1920</th>\n",
+       "      <td>subject truly amazed hi new product offer c _ ...</td>\n",
+       "      <td>spam</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2371</th>\n",
+       "      <td>subject chess board widow living &lt; NUM &gt; hi</td>\n",
+       "      <td>spam</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8423</th>\n",
+       "      <td>subject enron stanford program stinson monday ...</td>\n",
+       "      <td>ham</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7240</th>\n",
+       "      <td>subject change lifestyle &lt; NUM &gt; k month bait ...</td>\n",
+       "      <td>spam</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7885</th>\n",
+       "      <td>subject cera monthly summary july &lt; NUM &gt; cera...</td>\n",
+       "      <td>ham</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7869</th>\n",
+       "      <td>subject save money buying ftware enough money ...</td>\n",
+       "      <td>spam</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                   text category  spam\n",
+       "2123  subject reminder cera executive roundtable hou...      ham     0\n",
+       "3480  subject fw tw docket rp < NUM > < NUM > < NUM ...      ham     0\n",
+       "1113  subject < NUM > sm unique logo business lack v...     spam     1\n",
+       "365   subject satellite dish click info stop getting...     spam     1\n",
+       "1920  subject truly amazed hi new product offer c _ ...     spam     1\n",
+       "2371        subject chess board widow living < NUM > hi     spam     1\n",
+       "8423  subject enron stanford program stinson monday ...      ham     0\n",
+       "7240  subject change lifestyle < NUM > k month bait ...     spam     1\n",
+       "7885  subject cera monthly summary july < NUM > cera...      ham     0\n",
+       "7869  subject save money buying ftware enough money ...     spam     1"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_balanced['spam'] = df_balanced['category'].apply(lambda x: 1 if x==\"spam\" else 0)\n",
+    "df_balanced.sample(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "022e9214-90da-476c-b3ec-12bee25e80a4",
+   "metadata": {},
+   "source": [
+    "### -----------------------------------------------Part-2------------------------------------------------------------------------"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "047a7701-f782-4f80-aaf5-6bafc5301fbb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "import tensorflow_hub as hub\n",
+    "import tensorflow_text as text\n",
+    "import keras_nlp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "645881bc-ecfc-461b-9a14-61c64c5f3764",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "preprocess_path = \"/Users/techgarage/Projects/Data Engineering/Final Project/bert-tensorflow2-en-uncased-preprocess-v3\"\n",
+    "encoder_path = \"/Users/techgarage/Projects/Data Engineering/Final Project/bert-tensorflow2-en-uncased-l-12-h-768-a-12-v4\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "d5e2d09b-c72f-4693-89fb-7d86134b7acd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bert_preprocess = hub.KerasLayer(preprocess_path)\n",
+    "bert_encoder = hub.KerasLayer(encoder_path, trainable=True) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "d7023825-23df-4ab1-85c3-ea261b3a1761",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true,
+     "source_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'default': <tf.Tensor: shape=(2, 768), dtype=float32, numpy=\n",
+      "array([[-0.9430887 , -0.57238555, -0.8907942 , ..., -0.8635234 ,\n",
+      "        -0.7363553 ,  0.93383116],\n",
+      "       [-0.8458787 , -0.3137438 , -0.5593949 , ..., -0.3633124 ,\n",
+      "        -0.6121973 ,  0.8722634 ]], dtype=float32)>, 'pooled_output': <tf.Tensor: shape=(2, 768), dtype=float32, numpy=\n",
+      "array([[-0.9430887 , -0.57238555, -0.8907942 , ..., -0.8635234 ,\n",
+      "        -0.7363553 ,  0.93383116],\n",
+      "       [-0.8458787 , -0.3137438 , -0.5593949 , ..., -0.3633124 ,\n",
+      "        -0.6121973 ,  0.8722634 ]], dtype=float32)>, 'sequence_output': <tf.Tensor: shape=(2, 128, 768), dtype=float32, numpy=\n",
+      "array([[[-0.19839856, -0.20800582, -0.1962688 , ..., -0.47272685,\n",
+      "          0.0864562 ,  0.7098722 ],\n",
+      "        [-0.5383126 , -0.7158387 , -0.28930873, ..., -0.72038037,\n",
+      "          0.5794679 ,  0.39614454],\n",
+      "        [-0.14156267, -0.7373614 ,  0.37026387, ..., -0.37185436,\n",
+      "          0.07691361,  0.9704544 ],\n",
+      "        ...,\n",
+      "        [ 0.06476687, -0.4518682 ,  0.21092355, ...,  0.10411158,\n",
+      "         -0.15344658,  0.15741925],\n",
+      "        [ 0.00561799, -0.4243467 ,  0.17027403, ...,  0.12554014,\n",
+      "         -0.1677104 ,  0.14828748],\n",
+      "        [-0.06908819, -0.43356168,  0.25077093, ...,  0.12376285,\n",
+      "         -0.21341398,  0.12534308]],\n",
+      "\n",
+      "       [[-0.05573511,  0.17838944, -0.12837237, ..., -0.16575927,\n",
+      "          0.15549946,  0.21796423],\n",
+      "        [ 0.27656567, -0.06027734,  0.24292594, ...,  0.05262224,\n",
+      "          0.5221071 , -0.07443378],\n",
+      "        [ 0.05904233,  0.11985369, -0.04732013, ...,  0.05382551,\n",
+      "          0.24277204,  0.04877569],\n",
+      "        ...,\n",
+      "        [-0.06302132,  0.17733227,  0.40315932, ...,  0.11782701,\n",
+      "         -0.20818709, -0.0962089 ],\n",
+      "        [-0.0760133 ,  0.21092619,  0.45218655, ...,  0.14849319,\n",
+      "         -0.26165274, -0.06360148],\n",
+      "        [ 0.1626576 ,  0.12422754,  0.5222455 , ...,  0.24048476,\n",
+      "         -0.24223803, -0.14883336]]], dtype=float32)>, 'encoder_outputs': [<tf.Tensor: shape=(2, 128, 768), dtype=float32, numpy=\n",
+      "array([[[ 0.04222755, -0.00565784, -0.15892257, ...,  0.24197707,\n",
+      "         -0.0975021 , -0.04534519],\n",
+      "        [-0.57877016,  0.4591414 ,  0.17042653, ...,  0.17379816,\n",
+      "          0.3257624 , -0.1035484 ],\n",
+      "        [-1.0044422 , -0.39177877, -0.36441994, ...,  0.4770085 ,\n",
+      "          0.24922882,  0.28879505],\n",
+      "        ...,\n",
+      "        [-0.02801086, -0.17419498,  0.6732487 , ...,  0.30962443,\n",
+      "         -0.48121578,  0.01905968],\n",
+      "        [-0.11255493, -0.16464029,  0.5454035 , ...,  0.33624443,\n",
+      "         -0.38363996, -0.0586778 ],\n",
+      "        [ 0.01631033, -0.09114645,  0.5857597 , ...,  0.65954405,\n",
+      "         -0.7021383 , -0.12894379]],\n",
+      "\n",
+      "       [[ 0.20487462,  0.04901898,  0.01649245, ...,  0.04353925,\n",
+      "          0.04925472,  0.02829387],\n",
+      "        [ 1.4605433 ,  0.41298807,  0.85494065, ...,  0.6134594 ,\n",
+      "          0.80718094, -0.2633693 ],\n",
+      "        [-0.05972779, -0.18533123, -0.5527816 , ..., -0.10191233,\n",
+      "          0.65774244,  0.43938908],\n",
+      "        ...,\n",
+      "        [ 0.01185893, -0.2301354 ,  0.6187198 , ...,  0.19465885,\n",
+      "         -0.42088404,  0.1006694 ],\n",
+      "        [-0.07991942, -0.24889088,  0.48138082, ...,  0.21367387,\n",
+      "         -0.30915743,  0.01614393],\n",
+      "        [ 0.04677403, -0.17776218,  0.5053538 , ...,  0.582287  ,\n",
+      "         -0.6344621 , -0.06023622]]], dtype=float32)>, <tf.Tensor: shape=(2, 128, 768), dtype=float32, numpy=\n",
+      "array([[[-0.08198933, -0.24693474, -0.39003628, ...,  0.439112  ,\n",
+      "          0.05109447, -0.0044647 ],\n",
+      "        [-0.9807272 ,  0.6689387 ,  0.48308071, ...,  0.1564306 ,\n",
+      "          0.09792089, -0.02700958],\n",
+      "        [-1.1450135 , -0.21649039,  0.16978282, ...,  0.54555625,\n",
+      "         -0.08833696,  0.4793641 ],\n",
+      "        ...,\n",
+      "        [-0.07667708, -0.46201542,  0.5964664 , ...,  0.88089323,\n",
+      "         -0.61731744, -0.07153333],\n",
+      "        [-0.22768137, -0.37603164,  0.567706  , ...,  0.7776769 ,\n",
+      "         -0.6923214 , -0.16456038],\n",
+      "        [-0.07292495, -0.47088498,  0.42633674, ...,  1.1841507 ,\n",
+      "         -0.885921  , -0.15032959]],\n",
+      "\n",
+      "       [[ 0.06639995, -0.14721206, -0.10672337, ...,  0.06945898,\n",
+      "          0.08428381,  0.07299946],\n",
+      "        [ 1.2143568 ,  0.48814198,  1.1617565 , ...,  0.427473  ,\n",
+      "          1.390525  , -0.6787748 ],\n",
+      "        [-0.0177076 , -0.1816837 , -0.19067988, ..., -0.30094966,\n",
+      "          0.28298748,  0.4206936 ],\n",
+      "        ...,\n",
+      "        [-0.04438182, -0.12137772,  0.4709124 , ...,  0.74642366,\n",
+      "         -0.4703179 , -0.0582533 ],\n",
+      "        [-0.06758315, -0.02387481,  0.50011903, ...,  0.79208016,\n",
+      "         -0.4944227 , -0.11274293],\n",
+      "        [ 0.13245018, -0.14612037,  0.41312754, ...,  1.0047185 ,\n",
+      "         -0.68071204, -0.25335985]]], dtype=float32)>, <tf.Tensor: shape=(2, 128, 768), dtype=float32, numpy=\n",
+      "array([[[ 1.88205242e-02, -4.19564575e-01, -9.72785205e-02, ...,\n",
+      "          4.34135646e-01,  2.40221262e-01,  2.04072416e-01],\n",
+      "        [-4.48446095e-01,  1.81707352e-01,  4.33682859e-01, ...,\n",
+      "         -5.54058142e-02, -4.61606085e-02,  1.36725694e-01],\n",
+      "        [-1.06392920e+00, -2.69372582e-01,  6.61358237e-01, ...,\n",
+      "          3.99683654e-01, -2.06476793e-01,  2.66633153e-01],\n",
+      "        ...,\n",
+      "        [-1.29950047e-03, -5.26915431e-01,  8.76984775e-01, ...,\n",
+      "          1.12286353e+00, -8.32752362e-02, -8.66764039e-02],\n",
+      "        [-5.30661419e-02, -4.99284506e-01,  7.59432733e-01, ...,\n",
+      "          1.04773223e+00, -1.10542774e-01, -2.57611960e-01],\n",
+      "        [ 4.16158885e-03, -5.81846416e-01,  6.97214842e-01, ...,\n",
+      "          1.34556234e+00, -2.37027630e-01, -1.53350607e-01]],\n",
+      "\n",
+      "       [[ 1.98504999e-02, -2.34437555e-01,  1.12557501e-01, ...,\n",
+      "          2.38051996e-01,  1.49298698e-01,  2.20182493e-01],\n",
+      "        [ 1.29915905e+00,  3.28751534e-01,  1.23260486e+00, ...,\n",
+      "          3.10731322e-01,  7.98960507e-01, -5.44931173e-01],\n",
+      "        [-5.44543229e-02, -6.17580451e-02, -6.97334185e-02, ...,\n",
+      "         -2.71830112e-01,  2.33011786e-03,  4.77785051e-01],\n",
+      "        ...,\n",
+      "        [-8.16972479e-02, -1.25493705e-01,  7.47347534e-01, ...,\n",
+      "          9.28182423e-01, -6.34126663e-02, -1.33340880e-01],\n",
+      "        [-1.30778015e-01, -3.14075388e-02,  6.66660905e-01, ...,\n",
+      "          9.91367698e-01, -1.05210334e-01, -1.73617572e-01],\n",
+      "        [ 6.76381961e-02, -1.79536566e-01,  7.31914282e-01, ...,\n",
+      "          1.12215555e+00, -2.49684200e-01, -3.13854456e-01]]],\n",
+      "      dtype=float32)>, <tf.Tensor: shape=(2, 128, 768), dtype=float32, numpy=\n",
+      "array([[[ 0.23253876, -0.5878328 , -0.5117078 , ...,  0.54637855,\n",
+      "          0.3729315 ,  0.8276504 ],\n",
+      "        [-0.4173653 ,  0.10359846,  0.07069035, ...,  0.13642198,\n",
+      "         -0.09496864, -0.17008804],\n",
+      "        [-1.3769395 , -0.33059418,  0.7084929 , ...,  0.23613824,\n",
+      "         -0.13318482,  0.6639668 ],\n",
+      "        ...,\n",
+      "        [-0.14409971, -0.95972544,  0.8196844 , ...,  1.029249  ,\n",
+      "         -0.505237  , -0.0820753 ],\n",
+      "        [-0.24914083, -0.8525534 ,  0.71119857, ...,  0.9299053 ,\n",
+      "         -0.5289181 , -0.19867681],\n",
+      "        [-0.17688558, -1.0652891 ,  0.8114191 , ...,  1.232535  ,\n",
+      "         -0.5275141 , -0.18064196]],\n",
+      "\n",
+      "       [[ 0.07835185, -0.16930445, -0.25908536, ...,  0.11454112,\n",
+      "          0.27769732,  0.53462124],\n",
+      "        [ 1.1889614 , -0.39503306,  0.7059822 , ...,  0.3740296 ,\n",
+      "          0.11370531, -0.6435863 ],\n",
+      "        [ 0.31502503, -0.49227   , -0.01700327, ..., -0.00761845,\n",
+      "          0.27753487,  0.73732984],\n",
+      "        ...,\n",
+      "        [-0.27396932, -0.601428  ,  0.8877718 , ...,  0.7409698 ,\n",
+      "         -0.56265104,  0.03451739],\n",
+      "        [-0.3649508 , -0.45706168,  0.78661674, ...,  0.76761526,\n",
+      "         -0.5909752 , -0.01154372],\n",
+      "        [-0.01357281, -0.62979025,  0.93073106, ...,  0.9332208 ,\n",
+      "         -0.68241715, -0.3531196 ]]], dtype=float32)>, <tf.Tensor: shape=(2, 128, 768), dtype=float32, numpy=\n",
+      "array([[[ 0.11884356, -0.5148154 , -0.32688135, ...,  0.101791  ,\n",
+      "          0.23662724,  0.88309544],\n",
+      "        [-0.2888226 ,  0.30233493, -0.06162377, ..., -0.19382872,\n",
+      "         -0.0729757 , -0.23562919],\n",
+      "        [-1.2452599 , -0.5015673 ,  0.32685408, ...,  0.53150743,\n",
+      "         -0.3750096 ,  0.73725295],\n",
+      "        ...,\n",
+      "        [ 0.03977381, -0.8479642 ,  0.7485787 , ...,  0.6571404 ,\n",
+      "         -0.6224219 , -0.21472959],\n",
+      "        [-0.0742109 , -0.7208384 ,  0.60503834, ...,  0.6880955 ,\n",
+      "         -0.6776467 , -0.3155314 ],\n",
+      "        [-0.06136909, -0.9506144 ,  0.76140445, ...,  0.8818397 ,\n",
+      "         -0.8236465 , -0.3216293 ]],\n",
+      "\n",
+      "       [[-0.04276581,  0.07925801, -0.28103375, ..., -0.33148646,\n",
+      "          0.41494718,  0.64737356],\n",
+      "        [ 0.7152701 , -0.49672917,  1.1861763 , ..., -0.18042506,\n",
+      "          0.06279897, -0.78743297],\n",
+      "        [ 0.49038363, -0.30374768, -0.25442252, ..., -0.3617459 ,\n",
+      "          0.42115545,  0.861674  ],\n",
+      "        ...,\n",
+      "        [-0.17577647, -0.24708512,  1.2082454 , ..., -0.04786705,\n",
+      "         -0.13006926, -0.3941232 ],\n",
+      "        [-0.17560306, -0.11561126,  1.0242093 , ...,  0.03608295,\n",
+      "         -0.18673371, -0.4092454 ],\n",
+      "        [ 0.18188742, -0.33772412,  1.145873  , ...,  0.23452786,\n",
+      "         -0.31088895, -0.6601856 ]]], dtype=float32)>, <tf.Tensor: shape=(2, 128, 768), dtype=float32, numpy=\n",
+      "array([[[ 0.06071234, -0.7956739 , -0.4278758 , ...,  0.02831682,\n",
+      "          0.33086035,  0.78214604],\n",
+      "        [-0.48937097,  0.10409439, -0.5440423 , ..., -0.6400165 ,\n",
+      "          0.06334624, -0.07265621],\n",
+      "        [-0.8370529 , -0.43852547,  0.02520663, ...,  0.22630142,\n",
+      "         -0.22112286,  0.7913871 ],\n",
+      "        ...,\n",
+      "        [-0.10923568, -0.8002732 ,  1.0630294 , ...,  0.44094047,\n",
+      "         -0.6448848 , -0.13665396],\n",
+      "        [-0.2489643 , -0.7156884 ,  0.97479945, ...,  0.57076055,\n",
+      "         -0.72722065, -0.20035143],\n",
+      "        [-0.17111725, -0.9605686 ,  1.0823051 , ...,  0.782978  ,\n",
+      "         -0.85319215, -0.20169927]],\n",
+      "\n",
+      "       [[-0.03043595, -0.3351068 , -0.13165352, ..., -0.32476133,\n",
+      "          0.35392705,  0.56300366],\n",
+      "        [ 0.58081585, -0.35410577,  1.2072893 , ..., -0.2792115 ,\n",
+      "          0.03580071, -0.7630123 ],\n",
+      "        [ 0.5630262 , -0.4679435 , -0.38500452, ..., -0.45557278,\n",
+      "         -0.00384355,  0.8763245 ],\n",
+      "        ...,\n",
+      "        [-0.23482245, -0.10766596,  1.4201454 , ...,  0.07200695,\n",
+      "         -0.3376395 , -0.47948518],\n",
+      "        [-0.25911236,  0.01417249,  1.3014742 , ...,  0.17332402,\n",
+      "         -0.39001083, -0.49856895],\n",
+      "        [ 0.21144739, -0.26251334,  1.3444276 , ...,  0.28540578,\n",
+      "         -0.39566213, -0.74265194]]], dtype=float32)>, <tf.Tensor: shape=(2, 128, 768), dtype=float32, numpy=\n",
+      "array([[[-0.02161034, -0.9120089 , -0.2800715 , ..., -0.10675792,\n",
+      "          0.6008877 ,  0.961232  ],\n",
+      "        [-0.76047474, -0.03780157, -0.7370079 , ..., -0.39759997,\n",
+      "         -0.07949114,  0.102171  ],\n",
+      "        [-1.0510209 , -0.33599663,  0.21717477, ..., -0.04515497,\n",
+      "         -0.0876311 ,  1.1958765 ],\n",
+      "        ...,\n",
+      "        [ 0.24660665, -0.8857314 ,  1.0813076 , ...,  0.4609538 ,\n",
+      "         -0.3729293 ,  0.05634779],\n",
+      "        [ 0.11600631, -0.79443914,  0.9784327 , ...,  0.57947654,\n",
+      "         -0.50094205, -0.02501383],\n",
+      "        [ 0.06238839, -1.0905796 ,  1.1894618 , ...,  0.7936153 ,\n",
+      "         -0.64365435, -0.00459876]],\n",
+      "\n",
+      "       [[-0.02165605, -0.00627999, -0.09550503, ..., -0.46217042,\n",
+      "          0.49604496,  0.5488441 ],\n",
+      "        [ 0.82693785, -0.37880576,  1.3755609 , ...,  0.05833477,\n",
+      "          0.0601463 , -0.9805254 ],\n",
+      "        [ 0.6558955 , -0.07985266, -0.06040744, ..., -0.0520392 ,\n",
+      "         -0.07544921,  0.7840497 ],\n",
+      "        ...,\n",
+      "        [-0.12956248, -0.13533002,  1.4172117 , ...,  0.5473515 ,\n",
+      "         -0.3337415 , -0.3622912 ],\n",
+      "        [-0.12117871, -0.05489191,  1.328096  , ...,  0.51856905,\n",
+      "         -0.3706308 , -0.34924036],\n",
+      "        [ 0.34187743, -0.27316713,  1.5144311 , ...,  0.68457603,\n",
+      "         -0.24899381, -0.59764874]]], dtype=float32)>, <tf.Tensor: shape=(2, 128, 768), dtype=float32, numpy=\n",
+      "array([[[ 0.267469  , -0.62187463, -0.20508286, ..., -0.06582171,\n",
+      "          0.3819914 ,  0.92700386],\n",
+      "        [-0.49007717, -0.19729286, -0.63019806, ..., -0.4946556 ,\n",
+      "          0.5848053 ,  0.48207486],\n",
+      "        [-0.7382609 , -0.42379066,  0.04920355, ...,  0.22922839,\n",
+      "          0.4823346 ,  1.3403559 ],\n",
+      "        ...,\n",
+      "        [ 0.36012125, -0.72662765,  1.2360562 , ...,  0.4134717 ,\n",
+      "         -0.7964586 , -0.0168159 ],\n",
+      "        [ 0.19020146, -0.6944481 ,  1.0574071 , ...,  0.40931013,\n",
+      "         -0.8179945 , -0.10516895],\n",
+      "        [ 0.08327669, -0.9118013 ,  1.1854435 , ...,  0.6869288 ,\n",
+      "         -0.96097374, -0.18211707]],\n",
+      "\n",
+      "       [[-0.15515378,  0.24348043, -0.5878669 , ..., -0.8551526 ,\n",
+      "          0.37197673,  0.77416694],\n",
+      "        [ 0.82778156, -0.5299998 ,  1.1944404 , ..., -0.18086453,\n",
+      "          0.22024067, -0.62813413],\n",
+      "        [ 0.743023  , -0.2938047 , -0.23586163, ..., -0.41389942,\n",
+      "         -0.06697719,  0.97216934],\n",
+      "        ...,\n",
+      "        [-0.06196451, -0.00945033,  1.3805895 , ...,  0.41180745,\n",
+      "         -0.6445146 , -0.17519078],\n",
+      "        [-0.08102008,  0.17403589,  1.4231976 , ...,  0.429969  ,\n",
+      "         -0.68531966, -0.1757994 ],\n",
+      "        [ 0.65523195, -0.11694056,  1.641292  , ...,  0.7779677 ,\n",
+      "         -0.36524218, -0.4030809 ]]], dtype=float32)>, <tf.Tensor: shape=(2, 128, 768), dtype=float32, numpy=\n",
+      "array([[[ 0.262105  , -0.49547008, -0.21239214, ..., -0.00404145,\n",
+      "          0.11047935,  1.0197922 ],\n",
+      "        [-0.48511976, -0.2839242 , -0.02731339, ..., -0.695119  ,\n",
+      "          0.44140273,  0.6733612 ],\n",
+      "        [-0.4911195 , -0.29700607,  0.18301496, ..., -0.37084612,\n",
+      "          0.44985855,  1.3507713 ],\n",
+      "        ...,\n",
+      "        [ 0.3708187 , -0.71213204,  0.8711745 , ...,  0.37050912,\n",
+      "         -0.65640986,  0.17500949],\n",
+      "        [ 0.24236405, -0.6819058 ,  0.8009118 , ...,  0.3233849 ,\n",
+      "         -0.6954117 ,  0.0448739 ],\n",
+      "        [ 0.06622784, -0.8053169 ,  0.8632049 , ...,  0.46156222,\n",
+      "         -0.81855065, -0.05946006]],\n",
+      "\n",
+      "       [[-0.04873446,  0.4529825 , -0.92919904, ..., -0.8306872 ,\n",
+      "          0.24304585,  0.5967375 ],\n",
+      "        [ 0.63002735, -0.47839594,  0.7894074 , ..., -0.39560497,\n",
+      "          0.16381933, -0.5277528 ],\n",
+      "        [ 0.4487518 , -0.16525362, -0.53505677, ..., -0.24357045,\n",
+      "          0.38862032,  0.5095053 ],\n",
+      "        ...,\n",
+      "        [ 0.04551447,  0.29526532,  0.99386215, ...,  0.27326238,\n",
+      "         -0.3456278 , -0.23659004],\n",
+      "        [-0.01135593,  0.43595943,  1.0248154 , ...,  0.3244376 ,\n",
+      "         -0.46688926, -0.21477409],\n",
+      "        [ 0.83194435,  0.27556145,  1.482031  , ...,  0.6464286 ,\n",
+      "         -0.01236844, -0.57039464]]], dtype=float32)>, <tf.Tensor: shape=(2, 128, 768), dtype=float32, numpy=\n",
+      "array([[[-0.287879  , -0.80240715, -0.20276153, ..., -0.19355206,\n",
+      "         -0.6109808 ,  1.0038904 ],\n",
+      "        [-0.8657336 , -0.31282604, -0.22360814, ..., -0.6522808 ,\n",
+      "          0.48126665,  0.6094352 ],\n",
+      "        [-0.76809317, -0.24397063,  0.23806915, ..., -0.6669281 ,\n",
+      "          0.12250681,  1.3389902 ],\n",
+      "        ...,\n",
+      "        [-0.24871868, -0.92885894,  0.6644963 , ...,  0.44990155,\n",
+      "         -0.61868006,  0.21143207],\n",
+      "        [-0.33172315, -0.86779344,  0.68440217, ...,  0.48378584,\n",
+      "         -0.6701277 ,  0.1242202 ],\n",
+      "        [-0.47390074, -0.971211  ,  0.7892387 , ...,  0.5549451 ,\n",
+      "         -0.82587767, -0.04555673]],\n",
+      "\n",
+      "       [[-0.1323342 ,  0.56171477, -0.44893602, ..., -0.71494114,\n",
+      "         -0.39547947,  0.38463563],\n",
+      "        [ 0.5482401 , -0.60376394,  0.7847973 , ..., -0.3984929 ,\n",
+      "          0.06032612, -0.7970418 ],\n",
+      "        [ 0.25018427, -0.15513963, -0.18558574, ..., -0.08695153,\n",
+      "          0.0445721 , -0.04240235],\n",
+      "        ...,\n",
+      "        [-0.24507912,  0.46590155,  0.5433186 , ...,  0.13668773,\n",
+      "         -0.598839  , -0.45822692],\n",
+      "        [-0.27610523,  0.70140356,  0.6461801 , ...,  0.17265077,\n",
+      "         -0.7190671 , -0.4098673 ],\n",
+      "        [ 0.6226735 ,  0.5686493 ,  1.1515373 , ...,  0.5658265 ,\n",
+      "         -0.32691854, -0.5872827 ]]], dtype=float32)>, <tf.Tensor: shape=(2, 128, 768), dtype=float32, numpy=\n",
+      "array([[[-0.26158482, -0.6000199 , -0.14985457, ..., -0.3541886 ,\n",
+      "         -0.0744052 ,  0.8018481 ],\n",
+      "        [-0.5006118 , -0.8921    , -0.40147567, ..., -1.1037706 ,\n",
+      "          0.4858664 ,  0.48425287],\n",
+      "        [-0.53629315, -0.6906576 ,  0.29962227, ..., -0.62254155,\n",
+      "          0.23217814,  1.3942658 ],\n",
+      "        ...,\n",
+      "        [ 0.16818719, -1.0856829 ,  0.5047239 , ...,  0.0128454 ,\n",
+      "         -0.3836278 , -0.03140625],\n",
+      "        [ 0.0023242 , -0.9831716 ,  0.5245629 , ...,  0.05957055,\n",
+      "         -0.4703085 , -0.05248631],\n",
+      "        [-0.21012276, -1.0518107 ,  0.6366271 , ...,  0.0917865 ,\n",
+      "         -0.6165241 , -0.13285404]],\n",
+      "\n",
+      "       [[-0.11200634,  0.32884985, -0.1780588 , ..., -0.31254804,\n",
+      "          0.03413404, -0.0067618 ],\n",
+      "        [ 0.730604  , -0.37129667,  0.45334762, ..., -0.27244237,\n",
+      "         -0.08073802, -0.4595476 ],\n",
+      "        [ 0.26112485, -0.16279833, -0.12435186, ...,  0.01330798,\n",
+      "          0.23121914,  0.2716457 ],\n",
+      "        ...,\n",
+      "        [-0.17112523,  0.3180147 ,  0.5765437 , ...,  0.03357287,\n",
+      "         -0.97571343, -0.28733844],\n",
+      "        [-0.22259393,  0.5266339 ,  0.7034917 , ...,  0.11205668,\n",
+      "         -1.0813433 , -0.23581836],\n",
+      "        [ 0.43861124,  0.44458354,  1.0901142 , ...,  0.34537375,\n",
+      "         -0.9213069 , -0.32855487]]], dtype=float32)>, <tf.Tensor: shape=(2, 128, 768), dtype=float32, numpy=\n",
+      "array([[[-0.19839856, -0.20800582, -0.1962688 , ..., -0.47272685,\n",
+      "          0.0864562 ,  0.7098722 ],\n",
+      "        [-0.5383126 , -0.7158387 , -0.28930873, ..., -0.72038037,\n",
+      "          0.5794679 ,  0.39614454],\n",
+      "        [-0.14156267, -0.7373614 ,  0.37026387, ..., -0.37185436,\n",
+      "          0.07691361,  0.9704544 ],\n",
+      "        ...,\n",
+      "        [ 0.06476687, -0.4518682 ,  0.21092355, ...,  0.10411158,\n",
+      "         -0.15344658,  0.15741925],\n",
+      "        [ 0.00561799, -0.4243467 ,  0.17027403, ...,  0.12554014,\n",
+      "         -0.1677104 ,  0.14828748],\n",
+      "        [-0.06908819, -0.43356168,  0.25077093, ...,  0.12376285,\n",
+      "         -0.21341398,  0.12534308]],\n",
+      "\n",
+      "       [[-0.05573511,  0.17838944, -0.12837237, ..., -0.16575927,\n",
+      "          0.15549946,  0.21796423],\n",
+      "        [ 0.27656567, -0.06027734,  0.24292594, ...,  0.05262224,\n",
+      "          0.5221071 , -0.07443378],\n",
+      "        [ 0.05904233,  0.11985369, -0.04732013, ...,  0.05382551,\n",
+      "          0.24277204,  0.04877569],\n",
+      "        ...,\n",
+      "        [-0.06302132,  0.17733227,  0.40315932, ...,  0.11782701,\n",
+      "         -0.20818709, -0.0962089 ],\n",
+      "        [-0.0760133 ,  0.21092619,  0.45218655, ...,  0.14849319,\n",
+      "         -0.26165274, -0.06360148],\n",
+      "        [ 0.1626576 ,  0.12422754,  0.5222455 , ...,  0.24048476,\n",
+      "         -0.24223803, -0.14883336]]], dtype=float32)>]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Example input text\n",
+    "text = [\"This is a sample sentence.\", \"BERT is powerful!\"]\n",
+    "\n",
+    "# Preprocess the text\n",
+    "preprocessed_text = bert_preprocess(text)\n",
+    "\n",
+    "# Encode the text\n",
+    "bert_output = bert_encoder(preprocessed_text)\n",
+    "\n",
+    "# bert_output contains the contextualized embeddings\n",
+    "print(bert_output)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "cd44a41e-b6cc-4f8c-8452-c9294a50b4ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<tf.Tensor: shape=(2, 768), dtype=float32, numpy=\n",
+       "array([[-0.8435169 , -0.51327276, -0.8884573 , ..., -0.7474886 ,\n",
+       "        -0.75314736,  0.91964495],\n",
+       "       [-0.87208366, -0.5054398 , -0.9444669 , ..., -0.85847515,\n",
+       "        -0.71745366,  0.88082975]], dtype=float32)>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def get_sentence_embeding(sentences):\n",
+    "    preprocessed_text = bert_preprocess(sentences)\n",
+    "    return bert_encoder(preprocessed_text)['pooled_output']\n",
+    "\n",
+    "get_sentence_embeding([\n",
+    "    \"500$ discount. hurry up\", \n",
+    "    \"Bhavin, are you up for a volleybal game tomorrow?\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "335a2120-1cbb-41b0-824b-e70a3193160c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics.pairwise import cosine_similarity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8d7b58eb-e09f-4043-bce7-003ea7e7f22b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sl=get_sentence_embeding([\n",
+    "    \"apple\",\n",
+    "    \"mango\",\n",
+    "    \"orange\",\n",
+    "    \"Bill gates\",\n",
+    "    \"tim cook\",\n",
+    "    \"jeff bezos\"\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "cd167304-6e8d-4300-bc6a-7265dfe941bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.9614038]], dtype=float32)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cosine_similarity([sl[0]],[sl[1]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "e882510f-afad-400d-bfb8-714a27503ac4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.9564287]], dtype=float32)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cosine_similarity([sl[2]],[sl[3]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a002b3e9-85e9-4a38-9a71-a184664cd29c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.987975]], dtype=float32)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cosine_similarity([sl[0]],[sl[4]])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1574cef7-1543-4393-b3be-da7eae69f029",
+   "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_1\"</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mModel: \"functional_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 (<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\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ bert_preprocess_encode          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">768</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BertPreprocessEncode</span>)          │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout (<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\">768</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ output (<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\">769</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 (\u001b[38;5;33mInputLayer\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m)                 │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ bert_preprocess_encode          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m768\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
+       "│ (\u001b[38;5;33mBertPreprocessEncode\u001b[0m)          │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m768\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ output (\u001b[38;5;33mDense\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │           \u001b[38;5;34m769\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\">769</span> (3.00 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m769\u001b[0m (3.00 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\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">769</span> (3.00 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m769\u001b[0m (3.00 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\">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"
+    }
+   ],
+   "source": [
+    "class BertPreprocessEncode(tf.keras.layers.Layer):\n",
+    "    def __init__(self, preprocess_path, encoder_path, **kwargs):\n",
+    "        super(BertPreprocessEncode, self).__init__(**kwargs)\n",
+    "        self.preprocess = hub.KerasLayer(preprocess_path)\n",
+    "        self.encoder = hub.KerasLayer(encoder_path)\n",
+    "\n",
+    "    def call(self, inputs):\n",
+    "        preprocessed = self.preprocess(inputs)\n",
+    "        encoded = self.encoder(preprocessed)\n",
+    "        return encoded['pooled_output']\n",
+    "\n",
+    "# Define the input layer\n",
+    "text_input = tf.keras.layers.Input(shape=(), dtype=tf.string, name='text')\n",
+    "\n",
+    "# Use the custom preprocessing and encoding layer\n",
+    "bert_output = BertPreprocessEncode(preprocess_path, encoder_path)(text_input)\n",
+    "\n",
+    "# Add a dropout layer and a dense layer for classification\n",
+    "dropout = tf.keras.layers.Dropout(0.1, name=\"dropout\")(bert_output)\n",
+    "output = tf.keras.layers.Dense(1, activation='sigmoid', name=\"output\")(dropout)\n",
+    "\n",
+    "# Build the model\n",
+    "model = tf.keras.Model(inputs=[text_input], outputs=[output])\n",
+    "\n",
+    "# Compile the model\n",
+    "model.compile(\n",
+    "    optimizer='adam',\n",
+    "    loss='binary_crossentropy',\n",
+    "    metrics=['accuracy']\n",
+    ")\n",
+    "\n",
+    "# Print the model summary\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "36f5f101-c490-487e-981e-2599ffa25d42",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true,
+     "source_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ConnectionError",
+     "evalue": "('Connection aborted.', ConnectionResetError(54, 'Connection reset by peer'))",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mConnectionResetError\u001b[0m                      Traceback (most recent call last)",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/urllib3/connectionpool.py:715\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m    714\u001b[0m \u001b[38;5;66;03m# Make the request on the httplib connection object.\u001b[39;00m\n\u001b[0;32m--> 715\u001b[0m httplib_response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    716\u001b[0m \u001b[43m    \u001b[49m\u001b[43mconn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    717\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    718\u001b[0m \u001b[43m    \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    719\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    720\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    721\u001b[0m \u001b[43m    \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    722\u001b[0m \u001b[43m    \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    723\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    725\u001b[0m \u001b[38;5;66;03m# If we're going to release the connection in ``finally:``, then\u001b[39;00m\n\u001b[1;32m    726\u001b[0m \u001b[38;5;66;03m# the response doesn't need to know about the connection. Otherwise\u001b[39;00m\n\u001b[1;32m    727\u001b[0m \u001b[38;5;66;03m# it will also try to release it and we'll have a double-release\u001b[39;00m\n\u001b[1;32m    728\u001b[0m \u001b[38;5;66;03m# mess.\u001b[39;00m\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/urllib3/connectionpool.py:404\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m    403\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 404\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_conn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconn\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    405\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (SocketTimeout, BaseSSLError) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    406\u001b[0m     \u001b[38;5;66;03m# Py2 raises this as a BaseSSLError, Py3 raises it as socket timeout.\u001b[39;00m\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/urllib3/connectionpool.py:1060\u001b[0m, in \u001b[0;36mHTTPSConnectionPool._validate_conn\u001b[0;34m(self, conn)\u001b[0m\n\u001b[1;32m   1059\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(conn, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msock\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):  \u001b[38;5;66;03m# AppEngine might not have  `.sock`\u001b[39;00m\n\u001b[0;32m-> 1060\u001b[0m     \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1062\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m conn\u001b[38;5;241m.\u001b[39mis_verified:\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/urllib3/connection.py:419\u001b[0m, in \u001b[0;36mHTTPSConnection.connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    417\u001b[0m     context\u001b[38;5;241m.\u001b[39mload_default_certs()\n\u001b[0;32m--> 419\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msock \u001b[38;5;241m=\u001b[39m \u001b[43mssl_wrap_socket\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    420\u001b[0m \u001b[43m    \u001b[49m\u001b[43msock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    421\u001b[0m \u001b[43m    \u001b[49m\u001b[43mkeyfile\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkey_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    422\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcertfile\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcert_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    423\u001b[0m \u001b[43m    \u001b[49m\u001b[43mkey_password\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkey_password\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    424\u001b[0m \u001b[43m    \u001b[49m\u001b[43mca_certs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mca_certs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    425\u001b[0m \u001b[43m    \u001b[49m\u001b[43mca_cert_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mca_cert_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    426\u001b[0m \u001b[43m    \u001b[49m\u001b[43mca_cert_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mca_cert_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    427\u001b[0m \u001b[43m    \u001b[49m\u001b[43mserver_hostname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mserver_hostname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    428\u001b[0m \u001b[43m    \u001b[49m\u001b[43mssl_context\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcontext\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    429\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtls_in_tls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtls_in_tls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    430\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    432\u001b[0m \u001b[38;5;66;03m# If we're using all defaults and the connection\u001b[39;00m\n\u001b[1;32m    433\u001b[0m \u001b[38;5;66;03m# is TLSv1 or TLSv1.1 we throw a DeprecationWarning\u001b[39;00m\n\u001b[1;32m    434\u001b[0m \u001b[38;5;66;03m# for the host.\u001b[39;00m\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/urllib3/util/ssl_.py:449\u001b[0m, in \u001b[0;36mssl_wrap_socket\u001b[0;34m(sock, keyfile, certfile, cert_reqs, ca_certs, server_hostname, ssl_version, ciphers, ssl_context, ca_cert_dir, key_password, ca_cert_data, tls_in_tls)\u001b[0m\n\u001b[1;32m    448\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m send_sni:\n\u001b[0;32m--> 449\u001b[0m     ssl_sock \u001b[38;5;241m=\u001b[39m \u001b[43m_ssl_wrap_socket_impl\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    450\u001b[0m \u001b[43m        \u001b[49m\u001b[43msock\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtls_in_tls\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mserver_hostname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mserver_hostname\u001b[49m\n\u001b[1;32m    451\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    452\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/urllib3/util/ssl_.py:493\u001b[0m, in \u001b[0;36m_ssl_wrap_socket_impl\u001b[0;34m(sock, ssl_context, tls_in_tls, server_hostname)\u001b[0m\n\u001b[1;32m    492\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m server_hostname:\n\u001b[0;32m--> 493\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mssl_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrap_socket\u001b[49m\u001b[43m(\u001b[49m\u001b[43msock\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mserver_hostname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mserver_hostname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    494\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/ssl.py:500\u001b[0m, in \u001b[0;36mSSLContext.wrap_socket\u001b[0;34m(self, sock, server_side, do_handshake_on_connect, suppress_ragged_eofs, server_hostname, session)\u001b[0m\n\u001b[1;32m    494\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrap_socket\u001b[39m(\u001b[38;5;28mself\u001b[39m, sock, server_side\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m    495\u001b[0m                 do_handshake_on_connect\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m    496\u001b[0m                 suppress_ragged_eofs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m    497\u001b[0m                 server_hostname\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, session\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m    498\u001b[0m     \u001b[38;5;66;03m# SSLSocket class handles server_hostname encoding before it calls\u001b[39;00m\n\u001b[1;32m    499\u001b[0m     \u001b[38;5;66;03m# ctx._wrap_socket()\u001b[39;00m\n\u001b[0;32m--> 500\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msslsocket_class\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_create\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    501\u001b[0m \u001b[43m        \u001b[49m\u001b[43msock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msock\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    502\u001b[0m \u001b[43m        \u001b[49m\u001b[43mserver_side\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mserver_side\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    503\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdo_handshake_on_connect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdo_handshake_on_connect\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    504\u001b[0m \u001b[43m        \u001b[49m\u001b[43msuppress_ragged_eofs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msuppress_ragged_eofs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    505\u001b[0m \u001b[43m        \u001b[49m\u001b[43mserver_hostname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mserver_hostname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    506\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcontext\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    507\u001b[0m \u001b[43m        \u001b[49m\u001b[43msession\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msession\u001b[49m\n\u001b[1;32m    508\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/ssl.py:1040\u001b[0m, in \u001b[0;36mSSLSocket._create\u001b[0;34m(cls, sock, server_side, do_handshake_on_connect, suppress_ragged_eofs, server_hostname, context, session)\u001b[0m\n\u001b[1;32m   1039\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdo_handshake_on_connect should not be specified for non-blocking sockets\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 1040\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdo_handshake\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1041\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mOSError\u001b[39;00m, \u001b[38;5;167;01mValueError\u001b[39;00m):\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/ssl.py:1309\u001b[0m, in \u001b[0;36mSSLSocket.do_handshake\u001b[0;34m(self, block)\u001b[0m\n\u001b[1;32m   1308\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msettimeout(\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m-> 1309\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sslobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdo_handshake\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1310\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n",
+      "\u001b[0;31mConnectionResetError\u001b[0m: [Errno 54] Connection reset by peer",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mProtocolError\u001b[0m                             Traceback (most recent call last)",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/requests/adapters.py:667\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    666\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 667\u001b[0m     resp \u001b[38;5;241m=\u001b[39m \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    668\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    669\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    670\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    671\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    672\u001b[0m \u001b[43m        \u001b[49m\u001b[43mredirect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    673\u001b[0m \u001b[43m        \u001b[49m\u001b[43massert_same_host\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    674\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    675\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    676\u001b[0m \u001b[43m        \u001b[49m\u001b[43mretries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    677\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    678\u001b[0m \u001b[43m        \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    679\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    681\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/urllib3/connectionpool.py:801\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m    799\u001b[0m     e \u001b[38;5;241m=\u001b[39m ProtocolError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConnection aborted.\u001b[39m\u001b[38;5;124m\"\u001b[39m, e)\n\u001b[0;32m--> 801\u001b[0m retries \u001b[38;5;241m=\u001b[39m \u001b[43mretries\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mincrement\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    802\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merror\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43me\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_pool\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_stacktrace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msys\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexc_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m    803\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    804\u001b[0m retries\u001b[38;5;241m.\u001b[39msleep()\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/urllib3/util/retry.py:552\u001b[0m, in \u001b[0;36mRetry.increment\u001b[0;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[1;32m    551\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m read \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_method_retryable(method):\n\u001b[0;32m--> 552\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[43msix\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreraise\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43merror\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merror\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_stacktrace\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    553\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m read \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/urllib3/packages/six.py:769\u001b[0m, in \u001b[0;36mreraise\u001b[0;34m(tp, value, tb)\u001b[0m\n\u001b[1;32m    768\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m value\u001b[38;5;241m.\u001b[39m__traceback__ \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m tb:\n\u001b[0;32m--> 769\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m value\u001b[38;5;241m.\u001b[39mwith_traceback(tb)\n\u001b[1;32m    770\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m value\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/urllib3/connectionpool.py:715\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m    714\u001b[0m \u001b[38;5;66;03m# Make the request on the httplib connection object.\u001b[39;00m\n\u001b[0;32m--> 715\u001b[0m httplib_response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    716\u001b[0m \u001b[43m    \u001b[49m\u001b[43mconn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    717\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    718\u001b[0m \u001b[43m    \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    719\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    720\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    721\u001b[0m \u001b[43m    \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    722\u001b[0m \u001b[43m    \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    723\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    725\u001b[0m \u001b[38;5;66;03m# If we're going to release the connection in ``finally:``, then\u001b[39;00m\n\u001b[1;32m    726\u001b[0m \u001b[38;5;66;03m# the response doesn't need to know about the connection. Otherwise\u001b[39;00m\n\u001b[1;32m    727\u001b[0m \u001b[38;5;66;03m# it will also try to release it and we'll have a double-release\u001b[39;00m\n\u001b[1;32m    728\u001b[0m \u001b[38;5;66;03m# mess.\u001b[39;00m\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/urllib3/connectionpool.py:404\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m    403\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 404\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_conn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconn\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    405\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (SocketTimeout, BaseSSLError) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    406\u001b[0m     \u001b[38;5;66;03m# Py2 raises this as a BaseSSLError, Py3 raises it as socket timeout.\u001b[39;00m\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/urllib3/connectionpool.py:1060\u001b[0m, in \u001b[0;36mHTTPSConnectionPool._validate_conn\u001b[0;34m(self, conn)\u001b[0m\n\u001b[1;32m   1059\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(conn, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msock\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):  \u001b[38;5;66;03m# AppEngine might not have  `.sock`\u001b[39;00m\n\u001b[0;32m-> 1060\u001b[0m     \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1062\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m conn\u001b[38;5;241m.\u001b[39mis_verified:\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/urllib3/connection.py:419\u001b[0m, in \u001b[0;36mHTTPSConnection.connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    417\u001b[0m     context\u001b[38;5;241m.\u001b[39mload_default_certs()\n\u001b[0;32m--> 419\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msock \u001b[38;5;241m=\u001b[39m \u001b[43mssl_wrap_socket\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    420\u001b[0m \u001b[43m    \u001b[49m\u001b[43msock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    421\u001b[0m \u001b[43m    \u001b[49m\u001b[43mkeyfile\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkey_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    422\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcertfile\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcert_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    423\u001b[0m \u001b[43m    \u001b[49m\u001b[43mkey_password\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkey_password\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    424\u001b[0m \u001b[43m    \u001b[49m\u001b[43mca_certs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mca_certs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    425\u001b[0m \u001b[43m    \u001b[49m\u001b[43mca_cert_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mca_cert_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    426\u001b[0m \u001b[43m    \u001b[49m\u001b[43mca_cert_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mca_cert_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    427\u001b[0m \u001b[43m    \u001b[49m\u001b[43mserver_hostname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mserver_hostname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    428\u001b[0m \u001b[43m    \u001b[49m\u001b[43mssl_context\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcontext\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    429\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtls_in_tls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtls_in_tls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    430\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    432\u001b[0m \u001b[38;5;66;03m# If we're using all defaults and the connection\u001b[39;00m\n\u001b[1;32m    433\u001b[0m \u001b[38;5;66;03m# is TLSv1 or TLSv1.1 we throw a DeprecationWarning\u001b[39;00m\n\u001b[1;32m    434\u001b[0m \u001b[38;5;66;03m# for the host.\u001b[39;00m\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/urllib3/util/ssl_.py:449\u001b[0m, in \u001b[0;36mssl_wrap_socket\u001b[0;34m(sock, keyfile, certfile, cert_reqs, ca_certs, server_hostname, ssl_version, ciphers, ssl_context, ca_cert_dir, key_password, ca_cert_data, tls_in_tls)\u001b[0m\n\u001b[1;32m    448\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m send_sni:\n\u001b[0;32m--> 449\u001b[0m     ssl_sock \u001b[38;5;241m=\u001b[39m \u001b[43m_ssl_wrap_socket_impl\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    450\u001b[0m \u001b[43m        \u001b[49m\u001b[43msock\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtls_in_tls\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mserver_hostname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mserver_hostname\u001b[49m\n\u001b[1;32m    451\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    452\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/urllib3/util/ssl_.py:493\u001b[0m, in \u001b[0;36m_ssl_wrap_socket_impl\u001b[0;34m(sock, ssl_context, tls_in_tls, server_hostname)\u001b[0m\n\u001b[1;32m    492\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m server_hostname:\n\u001b[0;32m--> 493\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mssl_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrap_socket\u001b[49m\u001b[43m(\u001b[49m\u001b[43msock\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mserver_hostname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mserver_hostname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    494\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/ssl.py:500\u001b[0m, in \u001b[0;36mSSLContext.wrap_socket\u001b[0;34m(self, sock, server_side, do_handshake_on_connect, suppress_ragged_eofs, server_hostname, session)\u001b[0m\n\u001b[1;32m    494\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrap_socket\u001b[39m(\u001b[38;5;28mself\u001b[39m, sock, server_side\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m    495\u001b[0m                 do_handshake_on_connect\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m    496\u001b[0m                 suppress_ragged_eofs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m    497\u001b[0m                 server_hostname\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, session\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m    498\u001b[0m     \u001b[38;5;66;03m# SSLSocket class handles server_hostname encoding before it calls\u001b[39;00m\n\u001b[1;32m    499\u001b[0m     \u001b[38;5;66;03m# ctx._wrap_socket()\u001b[39;00m\n\u001b[0;32m--> 500\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msslsocket_class\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_create\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    501\u001b[0m \u001b[43m        \u001b[49m\u001b[43msock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msock\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    502\u001b[0m \u001b[43m        \u001b[49m\u001b[43mserver_side\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mserver_side\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    503\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdo_handshake_on_connect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdo_handshake_on_connect\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    504\u001b[0m \u001b[43m        \u001b[49m\u001b[43msuppress_ragged_eofs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msuppress_ragged_eofs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    505\u001b[0m \u001b[43m        \u001b[49m\u001b[43mserver_hostname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mserver_hostname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    506\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcontext\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    507\u001b[0m \u001b[43m        \u001b[49m\u001b[43msession\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msession\u001b[49m\n\u001b[1;32m    508\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/ssl.py:1040\u001b[0m, in \u001b[0;36mSSLSocket._create\u001b[0;34m(cls, sock, server_side, do_handshake_on_connect, suppress_ragged_eofs, server_hostname, context, session)\u001b[0m\n\u001b[1;32m   1039\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdo_handshake_on_connect should not be specified for non-blocking sockets\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 1040\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdo_handshake\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1041\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mOSError\u001b[39;00m, \u001b[38;5;167;01mValueError\u001b[39;00m):\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/ssl.py:1309\u001b[0m, in \u001b[0;36mSSLSocket.do_handshake\u001b[0;34m(self, block)\u001b[0m\n\u001b[1;32m   1308\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msettimeout(\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m-> 1309\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sslobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdo_handshake\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1310\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n",
+      "\u001b[0;31mProtocolError\u001b[0m: ('Connection aborted.', ConnectionResetError(54, 'Connection reset by peer'))",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mConnectionError\u001b[0m                           Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[6], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m text_input \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mkeras\u001b[38;5;241m.\u001b[39mlayers\u001b[38;5;241m.\u001b[39mInput(shape\u001b[38;5;241m=\u001b[39m(), dtype\u001b[38;5;241m=\u001b[39mtf\u001b[38;5;241m.\u001b[39mstring)\n\u001b[0;32m----> 2\u001b[0m preprocessor \u001b[38;5;241m=\u001b[39m \u001b[43mkeras_nlp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodels\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mBertPreprocessor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_preset\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbert_base_en_uncased\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mtrainable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m encoder_inputs \u001b[38;5;241m=\u001b[39m preprocessor(text_input)\n\u001b[1;32m      4\u001b[0m encoder \u001b[38;5;241m=\u001b[39m keras_nlp\u001b[38;5;241m.\u001b[39mmodels\u001b[38;5;241m.\u001b[39mBertBackbone\u001b[38;5;241m.\u001b[39mfrom_preset(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbert_base_en_uncased\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/keras_hub/src/models/preprocessor.py:186\u001b[0m, in \u001b[0;36mPreprocessor.from_preset\u001b[0;34m(cls, preset, config_file, **kwargs)\u001b[0m\n\u001b[1;32m    184\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mbackbone_cls \u001b[38;5;241m!=\u001b[39m backbone_cls:\n\u001b[1;32m    185\u001b[0m     \u001b[38;5;28mcls\u001b[39m \u001b[38;5;241m=\u001b[39m find_subclass(preset, \u001b[38;5;28mcls\u001b[39m, backbone_cls)\n\u001b[0;32m--> 186\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mloader\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_preprocessor\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/keras_hub/src/utils/preset_utils.py:690\u001b[0m, in \u001b[0;36mKerasPresetLoader.load_preprocessor\u001b[0;34m(self, cls, config_file, **kwargs)\u001b[0m\n\u001b[1;32m    684\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_preprocessor\u001b[39m(\n\u001b[1;32m    685\u001b[0m     \u001b[38;5;28mself\u001b[39m, \u001b[38;5;28mcls\u001b[39m, config_file\u001b[38;5;241m=\u001b[39mPREPROCESSOR_CONFIG_FILE, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[1;32m    686\u001b[0m ):\n\u001b[1;32m    687\u001b[0m     \u001b[38;5;66;03m# If there is no `preprocessing.json` or it's for the wrong class,\u001b[39;00m\n\u001b[1;32m    688\u001b[0m     \u001b[38;5;66;03m# delegate to the super class loader.\u001b[39;00m\n\u001b[1;32m    689\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m check_file_exists(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpreset, config_file):\n\u001b[0;32m--> 690\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_preprocessor\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    691\u001b[0m     preprocessor_json \u001b[38;5;241m=\u001b[39m load_json(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpreset, config_file)\n\u001b[1;32m    692\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(check_config_class(preprocessor_json), \u001b[38;5;28mcls\u001b[39m):\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/keras_hub/src/utils/preset_utils.py:618\u001b[0m, in \u001b[0;36mPresetLoader.load_preprocessor\u001b[0;34m(self, cls, config_file, **kwargs)\u001b[0m\n\u001b[1;32m    609\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_preprocessor\u001b[39m(\n\u001b[1;32m    610\u001b[0m     \u001b[38;5;28mself\u001b[39m, \u001b[38;5;28mcls\u001b[39m, config_file\u001b[38;5;241m=\u001b[39mPREPROCESSOR_CONFIG_FILE, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[1;32m    611\u001b[0m ):\n\u001b[1;32m    612\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Load a prepocessor layer from the preset.\u001b[39;00m\n\u001b[1;32m    613\u001b[0m \n\u001b[1;32m    614\u001b[0m \u001b[38;5;124;03m    By default, we create a preprocessor from a tokenizer with default\u001b[39;00m\n\u001b[1;32m    615\u001b[0m \u001b[38;5;124;03m    arguments. This allow us to support transformers checkpoints by\u001b[39;00m\n\u001b[1;32m    616\u001b[0m \u001b[38;5;124;03m    only converting the backbone and tokenizer.\u001b[39;00m\n\u001b[1;32m    617\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 618\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_add_missing_kwargs\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    619\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mcls\u001b[39m(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/keras_hub/src/models/preprocessor.py:201\u001b[0m, in \u001b[0;36mPreprocessor._add_missing_kwargs\u001b[0;34m(cls, loader, kwargs)\u001b[0m\n\u001b[1;32m    190\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Fill in required kwargs when loading from preset.\u001b[39;00m\n\u001b[1;32m    191\u001b[0m \n\u001b[1;32m    192\u001b[0m \u001b[38;5;124;03mThis is a private method hit when loading a preprocessing layer that\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    198\u001b[0m \u001b[38;5;124;03mencoders.\u001b[39;00m\n\u001b[1;32m    199\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    200\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtokenizer\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m kwargs \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mtokenizer_cls:\n\u001b[0;32m--> 201\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtokenizer\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mloader\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_tokenizer\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtokenizer_cls\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    202\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maudio_converter\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m kwargs \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39maudio_converter_cls:\n\u001b[1;32m    203\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maudio_converter\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m loader\u001b[38;5;241m.\u001b[39mload_audio_converter(\n\u001b[1;32m    204\u001b[0m         \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39maudio_converter_cls\n\u001b[1;32m    205\u001b[0m     )\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/keras_hub/src/utils/preset_utils.py:634\u001b[0m, in \u001b[0;36mKerasPresetLoader.load_tokenizer\u001b[0;34m(self, cls, config_file, **kwargs)\u001b[0m\n\u001b[1;32m    633\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_tokenizer\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28mcls\u001b[39m, config_file\u001b[38;5;241m=\u001b[39mTOKENIZER_CONFIG_FILE, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 634\u001b[0m     tokenizer_config \u001b[38;5;241m=\u001b[39m \u001b[43mload_json\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpreset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig_file\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    635\u001b[0m     tokenizer \u001b[38;5;241m=\u001b[39m load_serialized_object(tokenizer_config, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    636\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(tokenizer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mload_preset_assets\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/keras_hub/src/utils/preset_utils.py:444\u001b[0m, in \u001b[0;36mload_json\u001b[0;34m(preset, config_file)\u001b[0m\n\u001b[1;32m    443\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_json\u001b[39m(preset, config_file\u001b[38;5;241m=\u001b[39mCONFIG_FILE):\n\u001b[0;32m--> 444\u001b[0m     config_path \u001b[38;5;241m=\u001b[39m \u001b[43mget_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpreset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig_file\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    445\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(config_path, encoding\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m config_file:\n\u001b[1;32m    446\u001b[0m         config \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mload(config_file)\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/keras_hub/src/utils/preset_utils.py:158\u001b[0m, in \u001b[0;36mget_file\u001b[0;34m(preset, path)\u001b[0m\n\u001b[1;32m    150\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    151\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnexpected Kaggle preset. Kaggle model handles should have \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    152\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mthe form kaggle://\u001b[39m\u001b[38;5;132;01m{org}\u001b[39;00m\u001b[38;5;124m/\u001b[39m\u001b[38;5;132;01m{model}\u001b[39;00m\u001b[38;5;124m/keras/\u001b[39m\u001b[38;5;132;01m{variant}\u001b[39;00m\u001b[38;5;124m[/\u001b[39m\u001b[38;5;132;01m{version}\u001b[39;00m\u001b[38;5;124m]. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    155\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mversion). Received: preset=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpreset\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    156\u001b[0m     )\n\u001b[1;32m    157\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 158\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mkagglehub\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel_download\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkaggle_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    159\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m KaggleApiHTTPError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    160\u001b[0m     message \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(e)\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/kagglehub/models.py:35\u001b[0m, in \u001b[0;36mmodel_download\u001b[0;34m(handle, path, force_download)\u001b[0m\n\u001b[1;32m     33\u001b[0m h \u001b[38;5;241m=\u001b[39m parse_model_handle(handle)\n\u001b[1;32m     34\u001b[0m logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDownloading Model: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mh\u001b[38;5;241m.\u001b[39mto_url()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m ...\u001b[39m\u001b[38;5;124m\"\u001b[39m, extra\u001b[38;5;241m=\u001b[39m{\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mEXTRA_CONSOLE_BLOCK})\n\u001b[0;32m---> 35\u001b[0m path, _ \u001b[38;5;241m=\u001b[39m \u001b[43mregistry\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel_resolver\u001b[49m\u001b[43m(\u001b[49m\u001b[43mh\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mforce_download\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_download\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     36\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m path\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/kagglehub/registry.py:28\u001b[0m, in \u001b[0;36mMultiImplRegistry.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m impl \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mreversed\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_impls):\n\u001b[1;32m     27\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m impl\u001b[38;5;241m.\u001b[39mis_supported(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 28\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mimpl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     29\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     30\u001b[0m         fails\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28mtype\u001b[39m(impl)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m)\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/kagglehub/resolver.py:29\u001b[0m, in \u001b[0;36mResolver.__call__\u001b[0;34m(self, handle, path, force_download)\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\n\u001b[1;32m     16\u001b[0m     \u001b[38;5;28mself\u001b[39m, handle: T, path: Optional[\u001b[38;5;28mstr\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m, force_download: Optional[\u001b[38;5;28mbool\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m     17\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mtuple\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Optional[\u001b[38;5;28mint\u001b[39m]]:\n\u001b[1;32m     18\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Resolves a handle into a path with the requested file(s) and the resource's version number.\u001b[39;00m\n\u001b[1;32m     19\u001b[0m \n\u001b[1;32m     20\u001b[0m \u001b[38;5;124;03m    Args:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[38;5;124;03m        Some cases where version number might be missing: Competition datasource, API-based models.\u001b[39;00m\n\u001b[1;32m     28\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m---> 29\u001b[0m     path, version \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_resolve\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mforce_download\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_download\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     31\u001b[0m     \u001b[38;5;66;03m# Note handles are immutable, so _resolve() could not have altered our reference\u001b[39;00m\n\u001b[1;32m     32\u001b[0m     register_datasource_access(handle, version)\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/kagglehub/http_resolver.py:167\u001b[0m, in \u001b[0;36mModelHttpResolver._resolve\u001b[0;34m(self, h, path, force_download)\u001b[0m\n\u001b[1;32m    164\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m path:\n\u001b[1;32m    165\u001b[0m     \u001b[38;5;66;03m# Downloading a single file.\u001b[39;00m\n\u001b[1;32m    166\u001b[0m     os\u001b[38;5;241m.\u001b[39mmakedirs(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mdirname(out_path), exist_ok\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m--> 167\u001b[0m     \u001b[43mapi_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mh\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextract_auto_compressed_file\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m    168\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    169\u001b[0m     \u001b[38;5;66;03m# List the files and decide how to download them:\u001b[39;00m\n\u001b[1;32m    170\u001b[0m     \u001b[38;5;66;03m# - <= 25 files: Download files in parallel\u001b[39;00m\n\u001b[1;32m    171\u001b[0m     \u001b[38;5;66;03m# > 25 files: Download the archive and uncompress\u001b[39;00m\n\u001b[1;32m    172\u001b[0m     (files, has_more) \u001b[38;5;241m=\u001b[39m _list_files(api_client, h)\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/kagglehub/clients.py:175\u001b[0m, in \u001b[0;36mKaggleApiV1Client.download_file\u001b[0;34m(self, path, out_file, resource_handle, cached_path, extract_auto_compressed_file)\u001b[0m\n\u001b[1;32m    167\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    168\u001b[0m \u001b[38;5;124;03mIssues a call to kaggle api and downloads files. For competition downloads,\u001b[39;00m\n\u001b[1;32m    169\u001b[0m \u001b[38;5;124;03mcall may return early if local cache is newer than the last time the file was modified.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    172\u001b[0m \u001b[38;5;124;03mbool:  If downloading remote was necessary\u001b[39;00m\n\u001b[1;32m    173\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    174\u001b[0m url \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_url(path)\n\u001b[0;32m--> 175\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mrequests\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    176\u001b[0m \u001b[43m    \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    177\u001b[0m \u001b[43m    \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mUser-Agent\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mget_user_agent\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    178\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    179\u001b[0m \u001b[43m    \u001b[49m\u001b[43mauth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_auth\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    180\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mDEFAULT_CONNECT_TIMEOUT\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mDEFAULT_READ_TIMEOUT\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    181\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m response:\n\u001b[1;32m    182\u001b[0m     kaggle_api_raise_for_status(response, resource_handle)\n\u001b[1;32m    184\u001b[0m     total_size \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(response\u001b[38;5;241m.\u001b[39mheaders[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mContent-Length\u001b[39m\u001b[38;5;124m\"\u001b[39m]) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mContent-Length\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m response\u001b[38;5;241m.\u001b[39mheaders \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/requests/api.py:73\u001b[0m, in \u001b[0;36mget\u001b[0;34m(url, params, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget\u001b[39m(url, params\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m     63\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Sends a GET request.\u001b[39;00m\n\u001b[1;32m     64\u001b[0m \n\u001b[1;32m     65\u001b[0m \u001b[38;5;124;03m    :param url: URL for the new :class:`Request` object.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     70\u001b[0m \u001b[38;5;124;03m    :rtype: requests.Response\u001b[39;00m\n\u001b[1;32m     71\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m---> 73\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mget\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/requests/api.py:59\u001b[0m, in \u001b[0;36mrequest\u001b[0;34m(method, url, **kwargs)\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[38;5;66;03m# By using the 'with' statement we are sure the session is closed, thus we\u001b[39;00m\n\u001b[1;32m     56\u001b[0m \u001b[38;5;66;03m# avoid leaving sockets open which can trigger a ResourceWarning in some\u001b[39;00m\n\u001b[1;32m     57\u001b[0m \u001b[38;5;66;03m# cases, and look like a memory leak in others.\u001b[39;00m\n\u001b[1;32m     58\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m sessions\u001b[38;5;241m.\u001b[39mSession() \u001b[38;5;28;01mas\u001b[39;00m session:\n\u001b[0;32m---> 59\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43msession\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/requests/sessions.py:589\u001b[0m, in \u001b[0;36mSession.request\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    584\u001b[0m send_kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    585\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m\"\u001b[39m: timeout,\n\u001b[1;32m    586\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_redirects\u001b[39m\u001b[38;5;124m\"\u001b[39m: allow_redirects,\n\u001b[1;32m    587\u001b[0m }\n\u001b[1;32m    588\u001b[0m send_kwargs\u001b[38;5;241m.\u001b[39mupdate(settings)\n\u001b[0;32m--> 589\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msend_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    591\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/requests/sessions.py:703\u001b[0m, in \u001b[0;36mSession.send\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    700\u001b[0m start \u001b[38;5;241m=\u001b[39m preferred_clock()\n\u001b[1;32m    702\u001b[0m \u001b[38;5;66;03m# Send the request\u001b[39;00m\n\u001b[0;32m--> 703\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43madapter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    705\u001b[0m \u001b[38;5;66;03m# Total elapsed time of the request (approximately)\u001b[39;00m\n\u001b[1;32m    706\u001b[0m elapsed \u001b[38;5;241m=\u001b[39m preferred_clock() \u001b[38;5;241m-\u001b[39m start\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/requests/adapters.py:682\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    667\u001b[0m     resp \u001b[38;5;241m=\u001b[39m conn\u001b[38;5;241m.\u001b[39murlopen(\n\u001b[1;32m    668\u001b[0m         method\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mmethod,\n\u001b[1;32m    669\u001b[0m         url\u001b[38;5;241m=\u001b[39murl,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    678\u001b[0m         chunked\u001b[38;5;241m=\u001b[39mchunked,\n\u001b[1;32m    679\u001b[0m     )\n\u001b[1;32m    681\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m--> 682\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m(err, request\u001b[38;5;241m=\u001b[39mrequest)\n\u001b[1;32m    684\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m MaxRetryError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    685\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e\u001b[38;5;241m.\u001b[39mreason, ConnectTimeoutError):\n\u001b[1;32m    686\u001b[0m         \u001b[38;5;66;03m# TODO: Remove this in 3.0.0: see #2811\u001b[39;00m\n",
+      "\u001b[0;31mConnectionError\u001b[0m: ('Connection aborted.', ConnectionResetError(54, 'Connection reset by peer'))"
+     ]
+    }
+   ],
+   "source": [
+    "text_input = tf.keras.layers.Input(shape=(), dtype=tf.string)\n",
+    "preprocessor = keras_nlp.models.BertPreprocessor.from_preset(\"bert_base_en_uncased\",trainable=True)\n",
+    "encoder_inputs = preprocessor(text_input)\n",
+    "encoder = keras_nlp.models.BertBackbone.from_preset(\"bert_base_en_uncased\")\n",
+    "outputs = encoder(encoder_inputs)\n",
+    "pooled_output = outputs[\"pooled_output\"]      # [batch_size, 768].\n",
+    "sequence_output = outputs[\"sequence_output\"]  # [batch_size, seq_length, 768]."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "ca5f1364-47d2-4dde-afa2-51f75ce9850c",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Exception encountered when calling layer 'keras_layer' (type KerasLayer).\n\nA KerasTensor is symbolic: it's a placeholder for a shape an a dtype. It doesn't have any actual numerical value. You cannot convert it to a NumPy array.\n\nCall arguments received by layer 'keras_layer' (type KerasLayer):\n  • inputs=<KerasTensor shape=(None,), dtype=string, sparse=None, name=test>\n  • training=None",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[11], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Bert layers\u001b[39;00m\n\u001b[1;32m      2\u001b[0m text_input \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mkeras\u001b[38;5;241m.\u001b[39mlayers\u001b[38;5;241m.\u001b[39mInput(shape\u001b[38;5;241m=\u001b[39m(), dtype\u001b[38;5;241m=\u001b[39mtf\u001b[38;5;241m.\u001b[39mstring, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtest\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m preprocessed_text \u001b[38;5;241m=\u001b[39m \u001b[43mbert_preprocess\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtext_input\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m outputs \u001b[38;5;241m=\u001b[39m bert_encoder(preprocessed_text)\n\u001b[1;32m      6\u001b[0m \u001b[38;5;66;03m# Neural network layers\u001b[39;00m\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/tf_keras/src/utils/traceback_utils.py:70\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[1;32m     68\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;66;03m# `tf.debugging.disable_traceback_filtering()`\u001b[39;00m\n\u001b[0;32m---> 70\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     71\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     72\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/tensorflow_hub/keras_layer.py:250\u001b[0m, in \u001b[0;36mKerasLayer.call\u001b[0;34m(self, inputs, training)\u001b[0m\n\u001b[1;32m    247\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    248\u001b[0m     \u001b[38;5;66;03m# Behave like BatchNormalization. (Dropout is different, b/181839368.)\u001b[39;00m\n\u001b[1;32m    249\u001b[0m     training \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m--> 250\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[43msmart_cond\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msmart_cond\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtraining\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    251\u001b[0m \u001b[43m                                 \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtraining\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    252\u001b[0m \u001b[43m                                 \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtraining\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    254\u001b[0m \u001b[38;5;66;03m# Unwrap dicts returned by signatures.\u001b[39;00m\n\u001b[1;32m    255\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_output_key:\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/tensorflow_hub/keras_layer.py:252\u001b[0m, in \u001b[0;36mKerasLayer.call.<locals>.<lambda>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    247\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    248\u001b[0m     \u001b[38;5;66;03m# Behave like BatchNormalization. (Dropout is different, b/181839368.)\u001b[39;00m\n\u001b[1;32m    249\u001b[0m     training \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    250\u001b[0m   result \u001b[38;5;241m=\u001b[39m smart_cond\u001b[38;5;241m.\u001b[39msmart_cond(training,\n\u001b[1;32m    251\u001b[0m                                  \u001b[38;5;28;01mlambda\u001b[39;00m: f(training\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m),\n\u001b[0;32m--> 252\u001b[0m                                  \u001b[38;5;28;01mlambda\u001b[39;00m: \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtraining\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m)\n\u001b[1;32m    254\u001b[0m \u001b[38;5;66;03m# Unwrap dicts returned by signatures.\u001b[39;00m\n\u001b[1;32m    255\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_output_key:\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/tensorflow/core/function/polymorphism/function_type.py:583\u001b[0m, in \u001b[0;36mcanonicalize_to_monomorphic\u001b[0;34m(args, kwargs, default_values, capture_types, polymorphic_type)\u001b[0m\n\u001b[1;32m    577\u001b[0m       parameters\u001b[38;5;241m.\u001b[39mappend(\n\u001b[1;32m    578\u001b[0m           _make_validated_mono_param(kwarg_name, arg[kwarg_name],\n\u001b[1;32m    579\u001b[0m                                      Parameter\u001b[38;5;241m.\u001b[39mKEYWORD_ONLY, type_context,\n\u001b[1;32m    580\u001b[0m                                      poly_parameter\u001b[38;5;241m.\u001b[39mtype_constraint))\n\u001b[1;32m    581\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    582\u001b[0m     parameters\u001b[38;5;241m.\u001b[39mappend(\n\u001b[0;32m--> 583\u001b[0m         \u001b[43m_make_validated_mono_param\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpoly_parameter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkind\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    584\u001b[0m \u001b[43m                                   \u001b[49m\u001b[43mtype_context\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    585\u001b[0m \u001b[43m                                   \u001b[49m\u001b[43mpoly_parameter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtype_constraint\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m    587\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m FunctionType(parameters, capture_types), type_context\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/tensorflow/core/function/polymorphism/function_type.py:522\u001b[0m, in \u001b[0;36m_make_validated_mono_param\u001b[0;34m(name, value, kind, type_context, poly_type)\u001b[0m\n\u001b[1;32m    518\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_make_validated_mono_param\u001b[39m(\n\u001b[1;32m    519\u001b[0m     name, value, kind, type_context, poly_type\n\u001b[1;32m    520\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Parameter:\n\u001b[1;32m    521\u001b[0m \u001b[38;5;250m  \u001b[39m\u001b[38;5;124;03m\"\"\"Generates and validates a parameter for Monomorphic FunctionType.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 522\u001b[0m   mono_type \u001b[38;5;241m=\u001b[39m \u001b[43mtrace_type\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_value\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtype_context\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    524\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m poly_type \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m mono_type\u001b[38;5;241m.\u001b[39mis_subtype_of(poly_type):\n\u001b[1;32m    525\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mParameter `\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m` was expected to be of type \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    526\u001b[0m                     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpoly_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m but is \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmono_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/tensorflow/core/function/trace_type/trace_type_builder.py:185\u001b[0m, in \u001b[0;36mfrom_value\u001b[0;34m(value, context)\u001b[0m\n\u001b[1;32m    178\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m default_types\u001b[38;5;241m.\u001b[39mAttrs\u001b[38;5;241m.\u001b[39mfrom_type_and_attributes(\n\u001b[1;32m    179\u001b[0m       \u001b[38;5;28mtype\u001b[39m(value),\n\u001b[1;32m    180\u001b[0m       \u001b[38;5;28mtuple\u001b[39m(\n\u001b[1;32m    181\u001b[0m           from_value(\u001b[38;5;28mgetattr\u001b[39m(value, a\u001b[38;5;241m.\u001b[39mname), context)\n\u001b[1;32m    182\u001b[0m           \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m value\u001b[38;5;241m.\u001b[39m__attrs_attrs__))\n\u001b[1;32m    184\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m util\u001b[38;5;241m.\u001b[39mis_np_ndarray(value):\n\u001b[0;32m--> 185\u001b[0m   ndarray \u001b[38;5;241m=\u001b[39m \u001b[43mvalue\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__array__\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    186\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m default_types\u001b[38;5;241m.\u001b[39mTENSOR(ndarray\u001b[38;5;241m.\u001b[39mshape, ndarray\u001b[38;5;241m.\u001b[39mdtype)\n\u001b[1;32m    188\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(value, custom_nest_protocol\u001b[38;5;241m.\u001b[39mCustomNestProtocol):\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/keras/src/backend/common/keras_tensor.py:61\u001b[0m, in \u001b[0;36mKerasTensor.__array__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__array__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m---> 61\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m     62\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mA KerasTensor is symbolic: it\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms a placeholder for a shape \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     63\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124man a dtype. It doesn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt have any actual numerical value. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     64\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou cannot convert it to a NumPy array.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     65\u001b[0m     )\n",
+      "\u001b[0;31mValueError\u001b[0m: Exception encountered when calling layer 'keras_layer' (type KerasLayer).\n\nA KerasTensor is symbolic: it's a placeholder for a shape an a dtype. It doesn't have any actual numerical value. You cannot convert it to a NumPy array.\n\nCall arguments received by layer 'keras_layer' (type KerasLayer):\n  • inputs=<KerasTensor shape=(None,), dtype=string, sparse=None, name=test>\n  • training=None"
+     ]
+    }
+   ],
+   "source": [
+    "# Bert layers\n",
+    "text_input = tf.keras.layers.Input(shape=(), dtype=tf.string, name='test')\n",
+    "preprocessed_text = bert_preprocess(text_input)\n",
+    "outputs = bert_encoder(preprocessed_text)\n",
+    "\n",
+    "# Neural network layers\n",
+    "l = tf.keras.layers.Dropout(0.1, name=\"dropout\")(outputs['pooled_output'])\n",
+    "l = tf.keras.layers.Dense(1, activation='sigmoid', name=\"output\")(l)\n",
+    "\n",
+    "# Use inputs and outputs to construct a final model\n",
+    "model = tf.keras.Model(inputs=[text_input], outputs = [l])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "04f8b330-e5a6-4509-8bd0-5cfa2f50c541",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/3\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/techgarage/anaconda3/envs/DE/lib/python3.9/site-packages/keras/src/models/functional.py:237: UserWarning: The structure of `inputs` doesn't match the expected structure.\n",
+      "Expected: ['text']\n",
+      "Received: inputs=Tensor(shape=(None,))\n",
+      "  warnings.warn(msg)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3s/step - accuracy: 0.5000 - loss: 1.0744\n",
+      "Epoch 2/3\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━���━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 209ms/step - accuracy: 0.5000 - loss: 0.7670\n",
+      "Epoch 3/3\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 207ms/step - accuracy: 0.5000 - loss: 0.7240\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.history.History at 0x2d69dca00>"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Example training data (spam detection)\n",
+    "train_texts = [\n",
+    "    \"Win a free iPhone now!!!\",  # Spam (1)\n",
+    "    \"Meeting at 3 PM tomorrow\"   # Ham (0)\n",
+    "]\n",
+    "train_labels = np.array([1, 0])  # Use numpy arrays for labels\n",
+    "\n",
+    "# Convert to a TensorFlow Dataset\n",
+    "train_dataset = tf.data.Dataset.from_tensor_slices((train_texts, train_labels))\n",
+    "train_dataset = train_dataset.batch(32)  # Adjust batch size as needed\n",
+    "\n",
+    "# Train the model\n",
+    "model.fit(train_dataset, epochs=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "05be4db7-be33-49be-ac6e-441650cece64",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Exception encountered when calling layer 'keras_layer_4' (type KerasLayer).\n\nA KerasTensor is symbolic: it's a placeholder for a shape an a dtype. It doesn't have any actual numerical value. You cannot convert it to a NumPy array.\n\nCall arguments received by layer 'keras_layer_4' (type KerasLayer):\n  • inputs=<KerasTensor shape=(None,), dtype=string, sparse=False, name=text>\n  • training=None",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[39], line 14\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[38;5;66;03m# Define the model architecture\u001b[39;00m\n\u001b[1;32m     13\u001b[0m text_input \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mkeras\u001b[38;5;241m.\u001b[39mlayers\u001b[38;5;241m.\u001b[39mInput(shape\u001b[38;5;241m=\u001b[39m(), dtype\u001b[38;5;241m=\u001b[39mtf\u001b[38;5;241m.\u001b[39mstring, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtext\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m---> 14\u001b[0m preprocessed_text \u001b[38;5;241m=\u001b[39m \u001b[43mbert_preprocess\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtext_input\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     15\u001b[0m encoded_output \u001b[38;5;241m=\u001b[39m bert_encoder(preprocessed_text)\n\u001b[1;32m     16\u001b[0m pooled_output \u001b[38;5;241m=\u001b[39m encoded_output[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpooled_output\u001b[39m\u001b[38;5;124m'\u001b[39m]\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/tf_keras/src/utils/traceback_utils.py:70\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[1;32m     68\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;66;03m# `tf.debugging.disable_traceback_filtering()`\u001b[39;00m\n\u001b[0;32m---> 70\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     71\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     72\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/tensorflow_hub/keras_layer.py:250\u001b[0m, in \u001b[0;36mKerasLayer.call\u001b[0;34m(self, inputs, training)\u001b[0m\n\u001b[1;32m    247\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    248\u001b[0m     \u001b[38;5;66;03m# Behave like BatchNormalization. (Dropout is different, b/181839368.)\u001b[39;00m\n\u001b[1;32m    249\u001b[0m     training \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m--> 250\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[43msmart_cond\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msmart_cond\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtraining\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    251\u001b[0m \u001b[43m                                 \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtraining\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    252\u001b[0m \u001b[43m                                 \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtraining\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    254\u001b[0m \u001b[38;5;66;03m# Unwrap dicts returned by signatures.\u001b[39;00m\n\u001b[1;32m    255\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_output_key:\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/tensorflow_hub/keras_layer.py:252\u001b[0m, in \u001b[0;36mKerasLayer.call.<locals>.<lambda>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    247\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    248\u001b[0m     \u001b[38;5;66;03m# Behave like BatchNormalization. (Dropout is different, b/181839368.)\u001b[39;00m\n\u001b[1;32m    249\u001b[0m     training \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    250\u001b[0m   result \u001b[38;5;241m=\u001b[39m smart_cond\u001b[38;5;241m.\u001b[39msmart_cond(training,\n\u001b[1;32m    251\u001b[0m                                  \u001b[38;5;28;01mlambda\u001b[39;00m: f(training\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m),\n\u001b[0;32m--> 252\u001b[0m                                  \u001b[38;5;28;01mlambda\u001b[39;00m: \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtraining\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m)\n\u001b[1;32m    254\u001b[0m \u001b[38;5;66;03m# Unwrap dicts returned by signatures.\u001b[39;00m\n\u001b[1;32m    255\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_output_key:\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/tensorflow/core/function/polymorphism/function_type.py:583\u001b[0m, in \u001b[0;36mcanonicalize_to_monomorphic\u001b[0;34m(args, kwargs, default_values, capture_types, polymorphic_type)\u001b[0m\n\u001b[1;32m    577\u001b[0m       parameters\u001b[38;5;241m.\u001b[39mappend(\n\u001b[1;32m    578\u001b[0m           _make_validated_mono_param(kwarg_name, arg[kwarg_name],\n\u001b[1;32m    579\u001b[0m                                      Parameter\u001b[38;5;241m.\u001b[39mKEYWORD_ONLY, type_context,\n\u001b[1;32m    580\u001b[0m                                      poly_parameter\u001b[38;5;241m.\u001b[39mtype_constraint))\n\u001b[1;32m    581\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    582\u001b[0m     parameters\u001b[38;5;241m.\u001b[39mappend(\n\u001b[0;32m--> 583\u001b[0m         \u001b[43m_make_validated_mono_param\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpoly_parameter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkind\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    584\u001b[0m \u001b[43m                                   \u001b[49m\u001b[43mtype_context\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    585\u001b[0m \u001b[43m                                   \u001b[49m\u001b[43mpoly_parameter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtype_constraint\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m    587\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m FunctionType(parameters, capture_types), type_context\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/tensorflow/core/function/polymorphism/function_type.py:522\u001b[0m, in \u001b[0;36m_make_validated_mono_param\u001b[0;34m(name, value, kind, type_context, poly_type)\u001b[0m\n\u001b[1;32m    518\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_make_validated_mono_param\u001b[39m(\n\u001b[1;32m    519\u001b[0m     name, value, kind, type_context, poly_type\n\u001b[1;32m    520\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Parameter:\n\u001b[1;32m    521\u001b[0m \u001b[38;5;250m  \u001b[39m\u001b[38;5;124;03m\"\"\"Generates and validates a parameter for Monomorphic FunctionType.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 522\u001b[0m   mono_type \u001b[38;5;241m=\u001b[39m \u001b[43mtrace_type\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_value\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtype_context\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    524\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m poly_type \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m mono_type\u001b[38;5;241m.\u001b[39mis_subtype_of(poly_type):\n\u001b[1;32m    525\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mParameter `\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m` was expected to be of type \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    526\u001b[0m                     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpoly_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m but is \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmono_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/tensorflow/core/function/trace_type/trace_type_builder.py:185\u001b[0m, in \u001b[0;36mfrom_value\u001b[0;34m(value, context)\u001b[0m\n\u001b[1;32m    178\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m default_types\u001b[38;5;241m.\u001b[39mAttrs\u001b[38;5;241m.\u001b[39mfrom_type_and_attributes(\n\u001b[1;32m    179\u001b[0m       \u001b[38;5;28mtype\u001b[39m(value),\n\u001b[1;32m    180\u001b[0m       \u001b[38;5;28mtuple\u001b[39m(\n\u001b[1;32m    181\u001b[0m           from_value(\u001b[38;5;28mgetattr\u001b[39m(value, a\u001b[38;5;241m.\u001b[39mname), context)\n\u001b[1;32m    182\u001b[0m           \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m value\u001b[38;5;241m.\u001b[39m__attrs_attrs__))\n\u001b[1;32m    184\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m util\u001b[38;5;241m.\u001b[39mis_np_ndarray(value):\n\u001b[0;32m--> 185\u001b[0m   ndarray \u001b[38;5;241m=\u001b[39m \u001b[43mvalue\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__array__\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    186\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m default_types\u001b[38;5;241m.\u001b[39mTENSOR(ndarray\u001b[38;5;241m.\u001b[39mshape, ndarray\u001b[38;5;241m.\u001b[39mdtype)\n\u001b[1;32m    188\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(value, custom_nest_protocol\u001b[38;5;241m.\u001b[39mCustomNestProtocol):\n",
+      "File \u001b[0;32m~/anaconda3/envs/DE/lib/python3.9/site-packages/keras/src/backend/common/keras_tensor.py:108\u001b[0m, in \u001b[0;36mKerasTensor.__array__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    107\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__array__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 108\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    109\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mA KerasTensor is symbolic: it\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms a placeholder for a shape \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    110\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124man a dtype. It doesn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt have any actual numerical value. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    111\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou cannot convert it to a NumPy array.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    112\u001b[0m     )\n",
+      "\u001b[0;31mValueError\u001b[0m: Exception encountered when calling layer 'keras_layer_4' (type KerasLayer).\n\nA KerasTensor is symbolic: it's a placeholder for a shape an a dtype. It doesn't have any actual numerical value. You cannot convert it to a NumPy array.\n\nCall arguments received by layer 'keras_layer_4' (type KerasLayer):\n  • inputs=<KerasTensor shape=(None,), dtype=string, sparse=False, name=text>\n  • training=None"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "import tensorflow_hub as hub\n",
+    "\n",
+    "# Load the preprocessing and encoder layers from local directories\n",
+    "preprocess_path = \"/Users/techgarage/Projects/Data Engineering/Final Project/bert-tensorflow2-en-uncased-preprocess-v3\"\n",
+    "encoder_path = \"/Users/techgarage/Projects/Data Engineering/Final Project/bert-tensorflow2-en-uncased-l-12-h-768-a-12-v4\"\n",
+    "\n",
+    "# Load the BERT layers\n",
+    "bert_preprocess = hub.KerasLayer(preprocess_path)\n",
+    "bert_encoder = hub.KerasLayer(encoder_path)\n",
+    "\n",
+    "# Define the model architecture\n",
+    "text_input = tf.keras.layers.Input(shape=(), dtype=tf.string, name='text')\n",
+    "preprocessed_text = bert_preprocess(text_input)\n",
+    "encoded_output = bert_encoder(preprocessed_text)\n",
+    "pooled_output = encoded_output['pooled_output']\n",
+    "\n",
+    "# Add classification head\n",
+    "dropout = tf.keras.layers.Dropout(0.1, name=\"dropout\")(pooled_output)\n",
+    "output = tf.keras.layers.Dense(1, activation='sigmoid', name=\"output\")(dropout)\n",
+    "\n",
+    "# Build the model\n",
+    "model = tf.keras.Model(inputs=[text_input], outputs=[output])\n",
+    "\n",
+    "# Compile the model\n",
+    "model.compile(\n",
+    "    optimizer='adam',\n",
+    "    loss='binary_crossentropy',\n",
+    "    metrics=['accuracy']\n",
+    ")\n",
+    "\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "7233ee9f-f86e-46cb-91d6-c248bfa81ede",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ['TF_USE_LEGACY_KERAS']='1'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "57d3f18e-5cc2-463c-a17b-012712d63f32",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}