Notebooks Explicativos, imagens e dados (#1)

- Notebooks Explicativos, imagens e dados (c1f801a0211abb74c72430d6f7bb558748d75014)


Co-authored-by: Andre Guarnier De Mitri <[email protected]>

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+data/imdb_reviews.csv filter=lfs diff=lfs merge=lfs -text

data/imdb_reviews.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6f1314f123ac922d7d0f2bd5bd17f1734e167d90b2256c34963228bc63f6a4cb
+size 66262310

notebooks_explicativos/Neural_Bert.ipynb

@@ -0,0 +1,1291 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# SCC0633/SCC5908 - Processamento de Linguagem Natural\n",
+        "> **Docente:** Thiago Alexandre Salgueiro Pardo \\\\\n",
+        "> **Estagiário PAE:** Germano Antonio Zani Jorge\n",
+        "\n",
+        "\n",
+        "# Integrantes do Grupo: GPTrouxas\n",
+        "> André Guarnier De Mitri - 11395579 \\\\\n",
+        "> Daniel Carvalho - 10685702 \\\\\n",
+        "> Fernando - 11795342 \\\\\n",
+        "> Lucas Henrique Sant'Anna - 10748521 \\\\\n",
+        "> Magaly L Fujimoto - 4890582 \\\\\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Abordagem Neural usando BERT\n",
+        "![alt text](../imagens/BERT_TDIDF.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "###"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6yecpJR0feeQ"
+      },
+      "source": [
+        "## Importando bibliotecas"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "FAIvyZwodEtm"
+      },
+      "outputs": [],
+      "source": [
+        "import torch\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import math\n",
+        "from tqdm.notebook import tqdm\n",
+        "import pandas as pd"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#!pip install transformers seaborn nltk"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Carregando dados"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "LYgXl3RIfgfo",
+        "outputId": "eb496faf-7826-44f7-fa88-3b21fb6e7cbf"
+      },
+      "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>review</th>\n",
+              "      <th>sentiment</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>One of the other reviewers has mentioned that ...</td>\n",
+              "      <td>positive</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>A wonderful little production. &lt;br /&gt;&lt;br /&gt;The...</td>\n",
+              "      <td>positive</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>I thought this was a wonderful way to spend ti...</td>\n",
+              "      <td>positive</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Basically there's a family where a little boy ...</td>\n",
+              "      <td>negative</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Petter Mattei's \"Love in the Time of Money\" is...</td>\n",
+              "      <td>positive</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                                              review sentiment\n",
+              "0  One of the other reviewers has mentioned that ...  positive\n",
+              "1  A wonderful little production. <br /><br />The...  positive\n",
+              "2  I thought this was a wonderful way to spend ti...  positive\n",
+              "3  Basically there's a family where a little boy ...  negative\n",
+              "4  Petter Mattei's \"Love in the Time of Money\" is...  positive"
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df_reviews = pd.read_csv('imdb_reviews.csv')\n",
+        "df_reviews.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Mapeando as classes\n",
+        "- Sentimento positivo recebe label 1\n",
+        "- Sentimento negativo recebe label 0"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "D-5n8XzJbWOO",
+        "outputId": "cef630cc-b0cc-4598-c53f-d32636bfcd86"
+      },
+      "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>review</th>\n",
+              "      <th>sentiment</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>One of the other reviewers has mentioned that ...</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>A wonderful little production. &lt;br /&gt;&lt;br /&gt;The...</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>I thought this was a wonderful way to spend ti...</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Basically there's a family where a little boy ...</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Petter Mattei's \"Love in the Time of Money\" is...</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                                              review  sentiment\n",
+              "0  One of the other reviewers has mentioned that ...          1\n",
+              "1  A wonderful little production. <br /><br />The...          1\n",
+              "2  I thought this was a wonderful way to spend ti...          1\n",
+              "3  Basically there's a family where a little boy ...          0\n",
+              "4  Petter Mattei's \"Love in the Time of Money\" is...          1"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "def map_sentiments(sentiment):\n",
+        "    if sentiment == 'positive':\n",
+        "        return 1\n",
+        "    return 0\n",
+        "\n",
+        "df_reviews['sentiment'] = df_reviews['sentiment'].apply(map_sentiments)\n",
+        "df_reviews.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Funções para limpeza do texto\n",
+        "**lowercase_text(text)** Converte o texto para letras minúsculas para uniformizar o texto.\n",
+        "\n",
+        "\n",
+        "**remove_html(text)**  Remove quaisquer tags HTML do texto para limpar dados provenientes de fontes HTML.\n",
+        "\n",
+        "\n",
+        " **remove_url(text)** Remove URLs do texto para eliminar links que podem não ser relevantes para a análise de texto.\n",
+        "\n",
+        "\n",
+        "**remove_punctuations(text)** Remove pontuações do texto para simplificar a estrutura do texto, mantendo apenas palavras.\n",
+        "\n",
+        "**remove_emojis(text)** Remove emojis do texto para evitar caracteres não verbais que podem interferir na análise textual.\n",
+        "\n",
+        "**remove_stop_words(text)** Remove stop words (palavras comuns como \"e\", \"de\", \"o\") que geralmente não adicionam valor significativo à análise de texto.\n",
+        "\n",
+        "**stem_words(text)**  Aplica stemming nas palavras do texto, reduzindo-as à sua raiz (por exemplo, \"running\" vira \"run\") para normalizar as variações das palavras.\n",
+        "\n",
+        "**preprocess_text(text)**  Aplica todas as funções acima em sequência para pré-processar o texto de forma completa, tornando-o mais adequado para análise de texto ou modelagem.\n",
+        "\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 241
+        },
+        "id": "PnFHO62rnWn-",
+        "outputId": "17fb6619-fab9-4395-de5d-4c5199e7e45e"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "[nltk_data] Downloading package stopwords to\n",
+            "[nltk_data]     C:\\Users\\andre\\AppData\\Roaming\\nltk_data...\n",
+            "[nltk_data]   Package stopwords is already up-to-date!\n"
+          ]
+        },
+        {
+          "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>review</th>\n",
+              "      <th>sentiment</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>one review mention watch 1 oz episod hook righ...</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>wonder littl product film techniqu unassum old...</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>thought wonder way spend time hot summer weeke...</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>basic famili littl boy jake think zombi closet...</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>petter mattei love time money visual stun film...</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                                              review  sentiment\n",
+              "0  one review mention watch 1 oz episod hook righ...          1\n",
+              "1  wonder littl product film techniqu unassum old...          1\n",
+              "2  thought wonder way spend time hot summer weeke...          1\n",
+              "3  basic famili littl boy jake think zombi closet...          0\n",
+              "4  petter mattei love time money visual stun film...          1"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import re\n",
+        "import nltk\n",
+        "from nltk.corpus import stopwords\n",
+        "from nltk.stem import PorterStemmer\n",
+        "\n",
+        "\n",
+        "def lowercase_text(text):\n",
+        "    return text.lower()\n",
+        "\n",
+        "def remove_html(text):\n",
+        "    return re.sub(r'<[^<]+?>', '', text)\n",
+        "\n",
+        "def remove_url(text):\n",
+        "    return re.sub(r'http[s]?://\\S+|www\\.\\S+', '', text)\n",
+        "\n",
+        "def remove_punctuations(text):\n",
+        "    tokens_list = '!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'\n",
+        "    for char in text:\n",
+        "        if char in tokens_list:\n",
+        "            text = text.replace(char, ' ')\n",
+        "\n",
+        "    return text\n",
+        "\n",
+        "def remove_emojis(text):\n",
+        "    emojis = re.compile(\"[\"\n",
+        "                        u\"\\U0001F600-\\U0001F64F\"\n",
+        "                        u\"\\U0001F300-\\U0001F5FF\"\n",
+        "                        u\"\\U0001F680-\\U0001F6FF\"\n",
+        "                        u\"\\U0001F1E0-\\U0001F1FF\"\n",
+        "                        u\"\\U00002500-\\U00002BEF\"\n",
+        "                        u\"\\U00002702-\\U000027B0\"\n",
+        "                        u\"\\U00002702-\\U000027B0\"\n",
+        "                        u\"\\U000024C2-\\U0001F251\"\n",
+        "                        u\"\\U0001f926-\\U0001f937\"\n",
+        "                        u\"\\U00010000-\\U0010ffff\"\n",
+        "                        u\"\\u2640-\\u2642\"\n",
+        "                        u\"\\u2600-\\u2B55\"\n",
+        "                        u\"\\u200d\"\n",
+        "                        u\"\\u23cf\"\n",
+        "                        u\"\\u23e9\"\n",
+        "                        u\"\\u231a\"\n",
+        "                        u\"\\ufe0f\"\n",
+        "                        u\"\\u3030\"\n",
+        "                        \"]+\", re.UNICODE)\n",
+        "\n",
+        "    text = re.sub(emojis, '', text)\n",
+        "    return text\n",
+        "\n",
+        "def remove_stop_words(text):\n",
+        "    stop_words = stopwords.words('english')\n",
+        "    new_text = ''\n",
+        "    for word in text.split():\n",
+        "        if word not in stop_words:\n",
+        "            new_text += ''.join(f'{word} ')\n",
+        "\n",
+        "    return new_text.strip()\n",
+        "\n",
+        "def stem_words(text):\n",
+        "    stemmer = PorterStemmer()\n",
+        "    new_text = ''\n",
+        "    for word in text.split():\n",
+        "        new_text += ''.join(f'{stemmer.stem(word)} ')\n",
+        "\n",
+        "    return new_text\n",
+        "\n",
+        "def preprocess_text(text):\n",
+        "    text = lowercase_text(text)\n",
+        "    text = remove_html(text)\n",
+        "    text = remove_url(text)\n",
+        "    text = remove_punctuations(text)\n",
+        "    text = remove_emojis(text)\n",
+        "    text = remove_stop_words(text)\n",
+        "    text = stem_words(text)\n",
+        "\n",
+        "    return text\n",
+        "\n",
+        "nltk.download('stopwords')\n",
+        "df_reviews['review'] = df_reviews['review'].apply(preprocess_text)\n",
+        "df_reviews.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Visualizando balancemento da classes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "id": "Gdi_L0HWfntv",
+        "outputId": "bce77594-f662-4b3f-c8eb-27d8a188b4f2"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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nzdixY01YWJipWrWqueGGG8yzzz7r8fUExvzvFvKEhIRCPV3u1nagrHkZw2owoLKbPXu2xo4dq6+//lr169ev6HYA4CeBkANUMufPn/e4M+fChQu66aablJeXpy+++KICOwOAnxbW5ACVzD333KPrr79e7dq1U3Z2tv7xj39o165dWrx4cUW3BgA/KYQcoJKJjY3VwoULtXjxYuXl5alFixZ64403Ci3+BYCfOz6uAgAAVuJ7cgAAgJUIOQAAwEo/6zU5+fn5Onz4sGrWrFnqX8EOAADKhjFGp0+fVlhYmLy9L/9+zc865Bw+fFjh4eEV3QYAACiBr776Sg0aNLjs9p91yCn4YXNfffWVAgICKrgbAABwLdxut8LDwz1+aGxRftYhp+AjqoCAAEIOAACVzNWWmrDwGAAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsVKyQM2PGDN1yyy2qWbOmgoOD1a9fP+3evduj5vbbb5eXl5fH46GHHvKoOXTokOLi4lStWjUFBwdr3LhxunTpkkfNhg0bdPPNN8vlcqlJkyZKSkoq1M+8efPUsGFD+fn5KSoqSh9++GFxLgcAAFisWCFn48aNSkhI0NatW5WSkqKLFy+qR48eOnv2rEfdyJEjdeTIEecxc+ZMZ1teXp7i4uKUm5urLVu26LXXXlNSUpKmTJni1Ozfv19xcXG64447lJGRoTFjxuiBBx7QmjVrnJqlS5cqMTFRU6dO1ccff6y2bdsqNjZWR48eLelcAAAAi3gZY0xJdz527JiCg4O1ceNGde3aVdL/3slp166dZs+eXeQ+//rXv3TXXXfp8OHDCgkJkSQtWLBA48eP17Fjx+Tr66vx48dr1apV2rFjh7PfoEGDdOrUKSUnJ0uSoqKidMstt2ju3LmSpPz8fIWHh+vRRx/VhAkTrql/t9utwMBAZWdn8wM6AQCoJK719ftHrcnJzs6WJNWuXdtjfPHixapTp45atWqliRMn6ty5c8629PR0tW7d2gk4khQbGyu3262dO3c6NTExMR7HjI2NVXp6uiQpNzdX27Zt86jx9vZWTEyMU1OUnJwcud1ujwcAALBTlZLumJ+frzFjxqhTp05q1aqVMz5kyBBFREQoLCxMn3zyicaPH6/du3fr7bffliRlZmZ6BBxJzvPMzMwr1rjdbp0/f17ffvut8vLyiqzZtWvXZXueMWOGnnrqqZJecrE0nLCqXM5Tmg48E1fRLQAALoPXleIrcchJSEjQjh07tGnTJo/xUaNGOb9u3bq16tWrp+7du2vv3r2KjIwseaelYOLEiUpMTHSeu91uhYeHV2BHAACgrJQo5IwePVorV65UWlqaGjRocMXaqKgoSdKePXsUGRmp0NDQQndBZWVlSZJCQ0Od/xaMfb8mICBA/v7+8vHxkY+PT5E1BccoisvlksvluraLBAAAlVqx1uQYYzR69Gi98847WrdunRo1anTVfTIyMiRJ9erVkyRFR0fr008/9bgLKiUlRQEBAWrRooVTk5qa6nGclJQURUdHS5J8fX3Vvn17j5r8/HylpqY6NQAA4OetWO/kJCQkaMmSJXr33XdVs2ZNZw1NYGCg/P39tXfvXi1ZskS9e/fWddddp08++URjx45V165d1aZNG0lSjx491KJFCw0dOlQzZ85UZmamJk+erISEBOddloceekhz587VE088oV//+tdat26dli1bplWrvvs8MjExUfHx8erQoYNuvfVWzZ49W2fPntXw4cNLa24AAEAlVqyQM3/+fEn/u038+xYtWqRhw4bJ19dX77//vhM4wsPD1b9/f02ePNmp9fHx0cqVK/Xwww8rOjpa1atXV3x8vKZPn+7UNGrUSKtWrdLYsWM1Z84cNWjQQAsXLlRsbKxTM3DgQB07dkxTpkxRZmam2rVrp+Tk5EKLkQEAwM/Tj/qenMquLL8nh1XwAIDSxOvKd8rle3IAAAB+qgg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALBSsULOjBkzdMstt6hmzZoKDg5Wv379tHv3bo+aCxcuKCEhQdddd51q1Kih/v37Kysry6Pm0KFDiouLU7Vq1RQcHKxx48bp0qVLHjUbNmzQzTffLJfLpSZNmigpKalQP/PmzVPDhg3l5+enqKgoffjhh8W5HAAAYLFihZyNGzcqISFBW7duVUpKii5evKgePXro7NmzTs3YsWO1YsUKvfnmm9q4caMOHz6se+65x9mel5enuLg45ebmasuWLXrttdeUlJSkKVOmODX79+9XXFyc7rjjDmVkZGjMmDF64IEHtGbNGqdm6dKlSkxM1NSpU/Xxxx+rbdu2io2N1dGjR3/MfAAAAEt4GWNMSXc+duyYgoODtXHjRnXt2lXZ2dmqW7eulixZonvvvVeStGvXLjVv3lzp6enq2LGj/vWvf+muu+7S4cOHFRISIklasGCBxo8fr2PHjsnX11fjx4/XqlWrtGPHDudcgwYN0qlTp5ScnCxJioqK0i233KK5c+dKkvLz8xUeHq5HH31UEyZMuKb+3W63AgMDlZ2drYCAgJJOQ5EaTlhVqscrDweeiavoFgAAl8Hryneu9fX7R63Jyc7OliTVrl1bkrRt2zZdvHhRMTExTk2zZs10/fXXKz09XZKUnp6u1q1bOwFHkmJjY+V2u7Vz506n5vvHKKgpOEZubq62bdvmUePt7a2YmBinpig5OTlyu90eDwAAYKcSh5z8/HyNGTNGnTp1UqtWrSRJmZmZ8vX1VVBQkEdtSEiIMjMznZrvB5yC7QXbrlTjdrt1/vx5HT9+XHl5eUXWFByjKDNmzFBgYKDzCA8PL/6FAwCASqHEISchIUE7duzQG2+8UZr9lKmJEycqOzvbeXz11VcV3RIAACgjVUqy0+jRo7Vy5UqlpaWpQYMGznhoaKhyc3N16tQpj3dzsrKyFBoa6tT88C6ogruvvl/zwzuysrKyFBAQIH9/f/n4+MjHx6fImoJjFMXlcsnlchX/ggEAQKVTrHdyjDEaPXq03nnnHa1bt06NGjXy2N6+fXtVrVpVqampztju3bt16NAhRUdHS5Kio6P16aefetwFlZKSooCAALVo0cKp+f4xCmoKjuHr66v27dt71OTn5ys1NdWpAQAAP2/FeicnISFBS5Ys0bvvvquaNWs6618CAwPl7++vwMBAjRgxQomJiapdu7YCAgL06KOPKjo6Wh07dpQk9ejRQy1atNDQoUM1c+ZMZWZmavLkyUpISHDeZXnooYc0d+5cPfHEE/r1r3+tdevWadmyZVq16ruV5YmJiYqPj1eHDh106623avbs2Tp79qyGDx9eWnMDAAAqsWKFnPnz50uSbr/9do/xRYsWadiwYZKkWbNmydvbW/3791dOTo5iY2P10ksvObU+Pj5auXKlHn74YUVHR6t69eqKj4/X9OnTnZpGjRpp1apVGjt2rObMmaMGDRpo4cKFio2NdWoGDhyoY8eOacqUKcrMzFS7du2UnJxcaDEyAAD4efpR35NT2fE9OZ74nhwA+OnideU75fI9OQAAAD9VhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsFKxQ05aWpr69OmjsLAweXl5afny5R7bhw0bJi8vL49Hz549PWpOnjyp+++/XwEBAQoKCtKIESN05swZj5pPPvlEXbp0kZ+fn8LDwzVz5sxCvbz55ptq1qyZ/Pz81Lp1a61evbq4lwMAACxV7JBz9uxZtW3bVvPmzbtsTc+ePXXkyBHn8frrr3tsv//++7Vz506lpKRo5cqVSktL06hRo5ztbrdbPXr0UEREhLZt26Znn31W06ZN08svv+zUbNmyRYMHD9aIESO0fft29evXT/369dOOHTuKe0kAAMBCVYq7Q69evdSrV68r1rhcLoWGhha57fPPP1dycrL+85//qEOHDpKkF198Ub1799Zzzz2nsLAwLV68WLm5uXr11Vfl6+urli1bKiMjQy+88IIThubMmaOePXtq3LhxkqSnn35aKSkpmjt3rhYsWFDcywIAAJYpkzU5GzZsUHBwsJo2baqHH35YJ06ccLalp6crKCjICTiSFBMTI29vb33wwQdOTdeuXeXr6+vUxMbGavfu3fr222+dmpiYGI/zxsbGKj09/bJ95eTkyO12ezwAAICdSj3k9OzZU3/729+UmpqqP/3pT9q4caN69eqlvLw8SVJmZqaCg4M99qlSpYpq166tzMxMpyYkJMSjpuD51WoKthdlxowZCgwMdB7h4eE/7mIBAMBPVrE/rrqaQYMGOb9u3bq12rRpo8jISG3YsEHdu3cv7dMVy8SJE5WYmOg8d7vdBB0AACxV5reQN27cWHXq1NGePXskSaGhoTp69KhHzaVLl3Ty5ElnHU9oaKiysrI8agqeX63mcmuBpP+tFQoICPB4AAAAO5V5yPn666914sQJ1atXT5IUHR2tU6dOadu2bU7NunXrlJ+fr6ioKKcmLS1NFy9edGpSUlLUtGlT1apVy6lJTU31OFdKSoqio6PL+pIAAEAlUOyQc+bMGWVkZCgjI0OStH//fmVkZOjQoUM6c+aMxo0bp61bt+rAgQNKTU3V3XffrSZNmig2NlaS1Lx5c/Xs2VMjR47Uhx9+qM2bN2v06NEaNGiQwsLCJElDhgyRr6+vRowYoZ07d2rp0qWaM2eOx0dNjz32mJKTk/X8889r165dmjZtmj766CONHj26FKYFAABUdsUOOR999JFuuukm3XTTTZKkxMRE3XTTTZoyZYp8fHz0ySefqG/fvrrxxhs1YsQItW/fXv/+97/lcrmcYyxevFjNmjVT9+7d1bt3b3Xu3NnjO3ACAwO1du1a7d+/X+3bt9fjjz+uKVOmeHyXzm233aYlS5bo5ZdfVtu2bfXWW29p+fLlatWq1Y+ZDwAAYAkvY4yp6CYqitvtVmBgoLKzs0t9fU7DCatK9Xjl4cAzcRXdAgDgMnhd+c61vn7zs6sAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsFKxQ05aWpr69OmjsLAweXl5afny5R7bjTGaMmWK6tWrJ39/f8XExOjLL7/0qDl58qTuv/9+BQQEKCgoSCNGjNCZM2c8aj755BN16dJFfn5+Cg8P18yZMwv18uabb6pZs2by8/NT69attXr16uJeDgAAsFSxQ87Zs2fVtm1bzZs3r8jtM2fO1J///GctWLBAH3zwgapXr67Y2FhduHDBqbn//vu1c+dOpaSkaOXKlUpLS9OoUaOc7W63Wz169FBERIS2bdumZ599VtOmTdPLL7/s1GzZskWDBw/WiBEjtH37dvXr10/9+vXTjh07intJAADAQl7GGFPinb289M4776hfv36S/vcuTlhYmB5//HH99re/lSRlZ2crJCRESUlJGjRokD7//HO1aNFC//nPf9ShQwdJUnJysnr37q2vv/5aYWFhmj9/viZNmqTMzEz5+vpKkiZMmKDly5dr165dkqSBAwfq7NmzWrlypdNPx44d1a5dOy1YsKDIfnNycpSTk+M8d7vdCg8PV3Z2tgICAko6DUVqOGFVqR6vPBx4Jq6iWwAAXAavK99xu90KDAy86ut3qa7J2b9/vzIzMxUTE+OMBQYGKioqSunp6ZKk9PR0BQUFOQFHkmJiYuTt7a0PPvjAqenatasTcCQpNjZWu3fv1rfffuvUfP88BTUF5ynKjBkzFBgY6DzCw8N//EUDAICfpFINOZmZmZKkkJAQj/GQkBBnW2ZmpoKDgz22V6lSRbVr1/aoKeoY3z/H5WoKthdl4sSJys7Odh5fffVVcS8RAABUElUquoHy5HK55HK5KroNAABQDkr1nZzQ0FBJUlZWlsd4VlaWsy00NFRHjx712H7p0iWdPHnSo6aoY3z/HJerKdgOAAB+3ko15DRq1EihoaFKTU11xtxutz744ANFR0dLkqKjo3Xq1Clt27bNqVm3bp3y8/MVFRXl1KSlpenixYtOTUpKipo2bapatWo5Nd8/T0FNwXkAAMDPW7FDzpkzZ5SRkaGMjAxJ/1tsnJGRoUOHDsnLy0tjxozR73//e7333nv69NNP9atf/UphYWHOHVjNmzdXz549NXLkSH344YfavHmzRo8erUGDBiksLEySNGTIEPn6+mrEiBHauXOnli5dqjlz5igxMdHp47HHHlNycrKef/557dq1S9OmTdNHH32k0aNH//hZAQAAlV6x1+R89NFHuuOOO5znBcEjPj5eSUlJeuKJJ3T27FmNGjVKp06dUufOnZWcnCw/Pz9nn8WLF2v06NHq3r27vL291b9/f/35z392tgcGBmrt2rVKSEhQ+/btVadOHU2ZMsXju3Ruu+02LVmyRJMnT9aTTz6pG264QcuXL1erVq1KNBEAAMAuP+p7ciq7a73PviT4PgMAQGnideU7FfI9OQAAAD8VhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsFKph5xp06bJy8vL49GsWTNn+4ULF5SQkKDrrrtONWrUUP/+/ZWVleVxjEOHDikuLk7VqlVTcHCwxo0bp0uXLnnUbNiwQTfffLNcLpeaNGmipKSk0r4UAABQiZXJOzktW7bUkSNHnMemTZucbWPHjtWKFSv05ptvauPGjTp8+LDuueceZ3teXp7i4uKUm5urLVu26LXXXlNSUpKmTJni1Ozfv19xcXG64447lJGRoTFjxuiBBx7QmjVryuJyAABAJVSlTA5apYpCQ0MLjWdnZ+uvf/2rlixZojvvvFOStGjRIjVv3lxbt25Vx44dtXbtWn322Wd6//33FRISonbt2unpp5/W+PHjNW3aNPn6+mrBggVq1KiRnn/+eUlS8+bNtWnTJs2aNUuxsbFlcUkAAKCSKZN3cr788kuFhYWpcePGuv/++3Xo0CFJ0rZt23Tx4kXFxMQ4tc2aNdP111+v9PR0SVJ6erpat26tkJAQpyY2NlZut1s7d+50ar5/jIKagmNcTk5Ojtxut8cDAADYqdRDTlRUlJKSkpScnKz58+dr//796tKli06fPq3MzEz5+voqKCjIY5+QkBBlZmZKkjIzMz0CTsH2gm1XqnG73Tp//vxle5sxY4YCAwOdR3h4+I+9XAAA8BNV6h9X9erVy/l1mzZtFBUVpYiICC1btkz+/v6lfbpimThxohITE53nbreboAMAgKXK/BbyoKAg3XjjjdqzZ49CQ0OVm5urU6dOedRkZWU5a3hCQ0ML3W1V8PxqNQEBAVcMUi6XSwEBAR4PAABgpzIPOWfOnNHevXtVr149tW/fXlWrVlVqaqqzfffu3Tp06JCio6MlSdHR0fr000919OhRpyYlJUUBAQFq0aKFU/P9YxTUFBwDAACg1EPOb3/7W23cuFEHDhzQli1b9Itf/EI+Pj4aPHiwAgMDNWLECCUmJmr9+vXatm2bhg8frujoaHXs2FGS1KNHD7Vo0UJDhw7Vf//7X61Zs0aTJ09WQkKCXC6XJOmhhx7Svn379MQTT2jXrl166aWXtGzZMo0dO7a0LwcAAFRSpb4m5+uvv9bgwYN14sQJ1a1bV507d9bWrVtVt25dSdKsWbPk7e2t/v37KycnR7GxsXrppZec/X18fLRy5Uo9/PDDio6OVvXq1RUfH6/p06c7NY0aNdKqVas0duxYzZkzRw0aNNDChQu5fRwAADi8jDGmopuoKG63W4GBgcrOzi719TkNJ6wq1eOVhwPPxFV0CwCAy+B15TvX+vrNz64CAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUqfciZN2+eGjZsKD8/P0VFRenDDz+s6JYAAMBPQKUOOUuXLlViYqKmTp2qjz/+WG3btlVsbKyOHj1a0a0BAIAKVqlDzgsvvKCRI0dq+PDhatGihRYsWKBq1arp1VdfrejWAABABatS0Q2UVG5urrZt26aJEyc6Y97e3oqJiVF6enqR++Tk5CgnJ8d5np2dLUlyu92l3l9+zrlSP2ZZK4t5AACUDl5XCh/XGHPFukobco4fP668vDyFhIR4jIeEhGjXrl1F7jNjxgw99dRThcbDw8PLpMfKJnB2RXcAALBJWb+unD59WoGBgZfdXmlDTklMnDhRiYmJzvP8/HydPHlS1113nby8vErtPG63W+Hh4frqq68UEBBQaseFJ+a5/DDX5YN5Lh/Mc/koy3k2xuj06dMKCwu7Yl2lDTl16tSRj4+PsrKyPMazsrIUGhpa5D4ul0sul8tjLCgoqKxaVEBAAH+BygHzXH6Y6/LBPJcP5rl8lNU8X+kdnAKVduGxr6+v2rdvr9TUVGcsPz9fqampio6OrsDOAADAT0GlfSdHkhITExUfH68OHTro1ltv1ezZs3X27FkNHz68olsDAAAVrFKHnIEDB+rYsWOaMmWKMjMz1a5dOyUnJxdajFzeXC6Xpk6dWuijMZQu5rn8MNflg3kuH8xz+fgpzLOXudr9VwAAAJVQpV2TAwAAcCWEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIKaF58+apYcOG8vPzU1RUlD788MMr1r/55ptq1qyZ/Pz81Lp1a61evbqcOq3cijPPr7zyirp06aJatWqpVq1aiomJuervC/6nuH+eC7zxxhvy8vJSv379yrZBixR3rk+dOqWEhATVq1dPLpdLN954I///uAbFnefZs2eradOm8vf3V3h4uMaOHasLFy6UU7eVU1pamvr06aOwsDB5eXlp+fLlV91nw4YNuvnmm+VyudSkSRMlJSWVbZMGxfbGG28YX19f8+qrr5qdO3eakSNHmqCgIJOVlVVk/ebNm42Pj4+ZOXOm+eyzz8zkyZNN1apVzaefflrOnVcuxZ3nIUOGmHnz5pnt27ebzz//3AwbNswEBgaar7/+upw7r1yKO88F9u/fb+rXr2+6dOli7r777vJptpIr7lzn5OSYDh06mN69e5tNmzaZ/fv3mw0bNpiMjIxy7rxyKe48L1682LhcLrN48WKzf/9+s2bNGlOvXj0zduzYcu68clm9erWZNGmSefvtt40k884771yxft++faZatWomMTHRfPbZZ+bFF180Pj4+Jjk5ucx6JOSUwK233moSEhKc53l5eSYsLMzMmDGjyPoBAwaYuLg4j7GoqCjz4IMPlmmflV1x5/mHLl26ZGrWrGlee+21smrRCiWZ50uXLpnbbrvNLFy40MTHxxNyrlFx53r+/PmmcePGJjc3t7xatEJx5zkhIcHceeedHmOJiYmmU6dOZdqnTa4l5DzxxBOmZcuWHmMDBw40sbGxZdYXH1cVU25urrZt26aYmBhnzNvbWzExMUpPTy9yn/T0dI96SYqNjb1sPUo2zz907tw5Xbx4UbVr1y6rNiu9ks7z9OnTFRwcrBEjRpRHm1YoyVy/9957io6OVkJCgkJCQtSqVSv98Y9/VF5eXnm1XemUZJ5vu+02bdu2zflIa9++fVq9erV69+5dLj3/XFTEa2Gl/rEOFeH48ePKy8sr9KMjQkJCtGvXriL3yczMLLI+MzOzzPqs7Eoyzz80fvx4hYWFFfpLhe+UZJ43bdqkv/71r8rIyCiHDu1Rkrnet2+f1q1bp/vvv1+rV6/Wnj179Mgjj+jixYuaOnVqebRd6ZRknocMGaLjx4+rc+fOMsbo0qVLeuihh/Tkk0+WR8s/G5d7LXS73Tp//rz8/f1L/Zy8kwMrPfPMM3rjjTf0zjvvyM/Pr6Lbscbp06c1dOhQvfLKK6pTp05Ft2O9/Px8BQcH6+WXX1b79u01cOBATZo0SQsWLKjo1qyyYcMG/fGPf9RLL72kjz/+WG+//bZWrVqlp59+uqJbw4/EOznFVKdOHfn4+CgrK8tjPCsrS6GhoUXuExoaWqx6lGyeCzz33HN65pln9P7776tNmzZl2WalV9x53rt3rw4cOKA+ffo4Y/n5+ZKkKlWqaPfu3YqMjCzbpiupkvyZrlevnqpWrSofHx9nrHnz5srMzFRubq58fX3LtOfKqCTz/Lvf/U5Dhw7VAw88IElq3bq1zp49q1GjRmnSpEny9ub9gNJwudfCgICAMnkXR+KdnGLz9fVV+/btlZqa6ozl5+crNTVV0dHRRe4THR3tUS9JKSkpl61HyeZZkmbOnKmnn35aycnJ6tChQ3m0WqkVd56bNWumTz/9VBkZGc6jb9++uuOOO5SRkaHw8PDybL9SKcmf6U6dOmnPnj1OkJSkL774QvXq1SPgXEZJ5vncuXOFgkxBsDT8DOtSUyGvhWW2pNlib7zxhnG5XCYpKcl89tlnZtSoUSYoKMhkZmYaY4wZOnSomTBhglO/efNmU6VKFfPcc8+Zzz//3EydOpVbyK9Bcef5mWeeMb6+vuatt94yR44ccR6nT5+uqEuoFIo7zz/E3VXXrrhzfejQIVOzZk0zevRos3v3brNy5UoTHBxsfv/731fUJVQKxZ3nqVOnmpo1a5rXX3/d7Nu3z6xdu9ZERkaaAQMGVNQlVAqnT58227dvN9u3bzeSzAsvvGC2b99uDh48aIwxZsKECWbo0KFOfcEt5OPGjTOff/65mTdvHreQ/1S9+OKL5vrrrze+vr7m1ltvNVu3bnW2devWzcTHx3vUL1u2zNx4443G19fXtGzZ0qxataqcO66cijPPERERRlKhx9SpU8u/8UqmuH+ev4+QUzzFnestW7aYqKgo43K5TOPGjc0f/vAHc+nSpXLuuvIpzjxfvHjRTJs2zURGRho/Pz8THh5uHnnkEfPtt9+Wf+OVyPr164v8f27B3MbHx5tu3boV2qddu3bG19fXNG7c2CxatKhMe/QyhvfiAACAfViTAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAAr/T9WUkYIDg5fygAAAABJRU5ErkJggg==",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.title('Target value distribution')\n",
+        "plt.hist(df_reviews['sentiment'])\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Modelo BERT"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EDkjlPDakskM"
+      },
+      "source": [
+        "## Instalando Bibliotecas"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lk7m_1xvmWvz",
+        "outputId": "ce842053-b261-4768-d9d7-fe9c65c9f6aa"
+      },
+      "outputs": [],
+      "source": [
+        "#pip install transformers\n",
+        "#pip install accelerate -U\n",
+        "#pip install transformers[torch]\n",
+        "#pip install datasets evaluate"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Carregando o modelo treinado e tokenizador"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "GlyrkK52zMcc",
+        "outputId": "a938653b-92c3-4b4e-802c-eacc3f1b6ecf"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "c:\\Users\\andre\\1JUPYTER\\dt_labs\\.venv\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+            "  from .autonotebook import tqdm as notebook_tqdm\n"
+          ]
+        }
+      ],
+      "source": [
+        "from transformers import AutoTokenizer\n",
+        "from transformers import BertForSequenceClassification\n",
+        "\n",
+        "pre_trained_base = \"bert-base-uncased\"\n",
+        "tokenizer = AutoTokenizer.from_pretrained(pre_trained_base)\n",
+        "model = BertForSequenceClassification.from_pretrained(pre_trained_base, num_labels = 2, output_attentions=False, output_hidden_states=False)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Tokenização das Sentenças e Cálculo do Tamanho dos Tokens"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "id": "LKEjDZCHpk4e"
+      },
+      "outputs": [],
+      "source": [
+        "token_lens = []\n",
+        "\n",
+        "for sentence in df_reviews['review']:\n",
+        "    tokens = tokenizer.encode(sentence, max_length=200, truncation=True)\n",
+        "    token_lens.append(len(tokens))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Divisão dos Dados em Conjunto de Treinamento e Validação:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "id": "H7PfXaVVp2uQ"
+      },
+      "outputs": [],
+      "source": [
+        "SEED=42\n",
+        "MAX_LEN = 200\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "df_train, df_val = train_test_split(df_reviews, test_size=0.2, random_state=SEED)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Processando os dados\n",
+        "A função process_data recebe uma linha de um dataframe contendo uma revisão de texto e sua respectiva classificação de sentimento. Ela começa extraindo e limpando o texto da revisão, removendo quaisquer espaços extras. Em seguida, utiliza o tokenizer BERT para tokenizar o texto, aplicando padding e truncamento para garantir que todas as sequências tenham um comprimento fixo definido pela variável MAX_LEN. A função então adiciona a etiqueta de sentimento original e o texto limpo às codificações geradas, retornando um dicionário que contém os tokens do texto, a etiqueta de sentimento e o texto original."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "id": "v7EZ6wd-qDfd"
+      },
+      "outputs": [],
+      "source": [
+        "def process_data(row):\n",
+        "\n",
+        "    text = row['review']\n",
+        "    text = str(text)\n",
+        "    text = ' '.join(text.split())\n",
+        "\n",
+        "    encodings = tokenizer(text, padding=\"max_length\", truncation=True, max_length=MAX_LEN)\n",
+        "\n",
+        "    encodings['label'] = row['sentiment']\n",
+        "    encodings['text'] = text\n",
+        "\n",
+        "    return encodings"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "id": "d9VgrXNSqIYL"
+      },
+      "outputs": [],
+      "source": [
+        "# Treino\n",
+        "processed_data_tr = []\n",
+        "for i in range(df_train.shape[0]):\n",
+        "    processed_data_tr.append(process_data(df_train.iloc[i]))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "id": "p0NLQxoKqJ_k"
+      },
+      "outputs": [],
+      "source": [
+        "# Validação\n",
+        "processed_data_val = []\n",
+        "for i in range(df_val.shape[0]):\n",
+        "    processed_data_val.append(process_data(df_val.iloc[i]))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "id": "ac76Rb6fqP_G"
+      },
+      "outputs": [],
+      "source": [
+        "# Dataframes de Treino e Validação\n",
+        "df_train = pd.DataFrame(processed_data_tr)\n",
+        "df_val = pd.DataFrame(processed_data_val)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "RdbHaVy_fd64",
+        "outputId": "a9aed834-81b7-4223-da42-6289799c2e1e"
+      },
+      "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>attention_mask</th>\n",
+              "      <th>input_ids</th>\n",
+              "      <th>label</th>\n",
+              "      <th>text</th>\n",
+              "      <th>token_type_ids</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
+              "      <td>[101, 2921, 3198, 23624, 2954, 6978, 2674, 841...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>kept ask mani fight scream match swear gener m...</td>\n",
+              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
+              "      <td>[101, 3422, 4372, 3775, 2099, 9587, 5737, 2071...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>watch entir movi could watch entir movi stop d...</td>\n",
+              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
+              "      <td>[101, 3543, 2293, 2358, 10050, 2128, 25300, 11...</td>\n",
+              "      <td>1</td>\n",
+              "      <td>touch love stori reminisc ‘in mood love draw h...</td>\n",
+              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
+              "      <td>[101, 3732, 2154, 11865, 15472, 2072, 8040, 73...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>latter day fulci schlocker total abysm concoct...</td>\n",
+              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
+              "      <td>[101, 2034, 3813, 3669, 19337, 2666, 2615, 504...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>first firmli believ norwegian movi continu get...</td>\n",
+              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                                      attention_mask  \\\n",
+              "0  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
+              "1  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
+              "2  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
+              "3  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
+              "4  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
+              "\n",
+              "                                           input_ids  label  \\\n",
+              "0  [101, 2921, 3198, 23624, 2954, 6978, 2674, 841...      0   \n",
+              "1  [101, 3422, 4372, 3775, 2099, 9587, 5737, 2071...      0   \n",
+              "2  [101, 3543, 2293, 2358, 10050, 2128, 25300, 11...      1   \n",
+              "3  [101, 3732, 2154, 11865, 15472, 2072, 8040, 73...      0   \n",
+              "4  [101, 2034, 3813, 3669, 19337, 2666, 2615, 504...      0   \n",
+              "\n",
+              "                                                text  \\\n",
+              "0  kept ask mani fight scream match swear gener m...   \n",
+              "1  watch entir movi could watch entir movi stop d...   \n",
+              "2  touch love stori reminisc ‘in mood love draw h...   \n",
+              "3  latter day fulci schlocker total abysm concoct...   \n",
+              "4  first firmli believ norwegian movi continu get...   \n",
+              "\n",
+              "                                      token_type_ids  \n",
+              "0  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
+              "1  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
+              "2  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
+              "3  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
+              "4  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  "
+            ]
+          },
+          "execution_count": 20,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df_train.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0lTWT8JwkRic"
+      },
+      "source": [
+        "## Fine Tunning do Modelo\n",
+        "Ajuste fino do BERT para tarefas específica de classificação de sentimento para o dataset do IMDB"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import torch\n",
+        "import pyarrow as pa\n",
+        "from datasets import Dataset\n",
+        "import evaluate\n",
+        "import numpy as np"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "kW53p7VQqUDD",
+        "outputId": "8231f3ba-37d5-4546-c4d0-6b4ff317ecf3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "device(type='cuda', index=0)"
+            ]
+          },
+          "execution_count": 21,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+        "device"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "id": "68OdbTv5rLrm"
+      },
+      "outputs": [],
+      "source": [
+        "train_hg = Dataset(pa.Table.from_pandas(df_train))\n",
+        "valid_hg = Dataset(pa.Table.from_pandas(df_val))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Metricas de avaliação F1 Score e Acc"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "`compute_metrics` calcula tanto a acurácia quanto o F1-score para avaliar um modelo de classificação. Primeiramente, são carregadas as métricas de acurácia e F1-score usando evaluate.load. Em seguida, a função compute_metrics recebe um par de arrays eval_pred, contendo as previsões do modelo e os rótulos verdadeiros. Utilizando as previsões, a função calcula a acurácia e o F1-score ponderado, onde a acurácia é obtida através da comparação das previsões com os rótulos utilizando a métrica de acurácia previamente carregada, e o F1-score é calculado utilizando a métrica de F1 previamente carregada, com ponderação \"weighted\". Os resultados de ambas as métricas são então combinados em um dicionário e retornados como um único objeto contendo as métricas de avaliação calculadas."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "id": "lUNhDPs0ry4m"
+      },
+      "outputs": [],
+      "source": [
+        "\n",
+        "# Load both accuracy and f1 metrics\n",
+        "accuracy_metric = evaluate.load(\"accuracy\")\n",
+        "f1_metric = evaluate.load(\"f1\")\n",
+        "\n",
+        "# Metric helper method\n",
+        "def compute_metrics(eval_pred):\n",
+        "    predictions, labels = eval_pred\n",
+        "    predictions = np.argmax(predictions, axis=1)\n",
+        "\n",
+        "    # Compute accuracy\n",
+        "    accuracy = accuracy_metric.compute(predictions=predictions, references=labels)\n",
+        "\n",
+        "    # Compute F1 score\n",
+        "    f1 = f1_metric.compute(predictions=predictions, references=labels, average=\"weighted\")\n",
+        "\n",
+        "    # Combine the metrics into a single dictionary\n",
+        "    combined_metrics = {\n",
+        "        'accuracy': accuracy['accuracy'],\n",
+        "        'f1': f1['f1']\n",
+        "    }\n",
+        "\n",
+        "    return combined_metrics"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9jJYTWsHjnEc",
+        "outputId": "fe45691a-4476-4978-89b8-15f36465c37c"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Name: accelerateNote: you may need to restart the kernel to use updated packages.\n",
+            "\n",
+            "Version: 0.31.0\n",
+            "Summary: Accelerate\n",
+            "Home-page: https://github.com/huggingface/accelerate\n",
+            "Author: The HuggingFace team\n",
+            "Author-email: [email protected]\n",
+            "License: Apache\n",
+            "Location: c:\\Users\\andre\\1JUPYTER\\dt_labs\\.venv\\Lib\\site-packages\n",
+            "Requires: huggingface-hub, numpy, packaging, psutil, pyyaml, safetensors, torch\n",
+            "Required-by: \n",
+            "---\n",
+            "Name: transformers\n",
+            "Version: 4.41.2\n",
+            "Summary: State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow\n",
+            "Home-page: https://github.com/huggingface/transformers\n",
+            "Author: The Hugging Face team (past and future) with the help of all our contributors (https://github.com/huggingface/transformers/graphs/contributors)\n",
+            "Author-email: [email protected]\n",
+            "License: Apache 2.0 License\n",
+            "Location: c:\\Users\\andre\\1JUPYTER\\dt_labs\\.venv\\Lib\\site-packages\n",
+            "Requires: filelock, huggingface-hub, numpy, packaging, pyyaml, regex, requests, safetensors, tokenizers, tqdm\n",
+            "Required-by: \n"
+          ]
+        }
+      ],
+      "source": [
+        "pip show accelerate transformers"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Treinamento do modelo"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "QlaLCwf7rLtp",
+        "outputId": "7e10e82a-8bc7-478b-851e-c7b628b46c41"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "c:\\Users\\andre\\1JUPYTER\\dt_labs\\.venv\\Lib\\site-packages\\transformers\\training_args.py:1474: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
+            "  warnings.warn(\n"
+          ]
+        }
+      ],
+      "source": [
+        "from transformers import TrainingArguments, Trainer\n",
+        "\n",
+        "EPOCHS = 1\n",
+        "\n",
+        "training_args = TrainingArguments(output_dir=\"./result\",\n",
+        "                                  evaluation_strategy=\"epoch\",\n",
+        "                                  num_train_epochs= EPOCHS,\n",
+        "                                  per_device_train_batch_size=16,\n",
+        "                                  per_device_eval_batch_size=8\n",
+        "                                )\n",
+        "\n",
+        "trainer = Trainer(\n",
+        "    model=model,\n",
+        "    args=training_args,\n",
+        "    train_dataset=train_hg,\n",
+        "    eval_dataset=valid_hg,\n",
+        "    tokenizer=tokenizer,\n",
+        "    compute_metrics=compute_metrics\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "CUDA available:  True\n",
+            "CUDA version:  12.1\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"CUDA available: \", torch.cuda.is_available())\n",
+        "print(\"CUDA version: \", torch.version.cuda)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 141
+        },
+        "id": "3s6lVFz_rLwO",
+        "outputId": "ee64e8e9-9c8c-42a8-c355-f51410cc33df"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "  0%|          | 0/2500 [00:00<?, ?it/s]c:\\Users\\andre\\1JUPYTER\\dt_labs\\.venv\\Lib\\site-packages\\transformers\\models\\bert\\modeling_bert.py:435: UserWarning: 1Torch was not compiled with flash attention. (Triggered internally at ..\\aten\\src\\ATen\\native\\transformers\\cuda\\sdp_utils.cpp:263.)\n",
+            "  attn_output = torch.nn.functional.scaled_dot_product_attention(\n",
+            " 20%|██        | 500/2500 [05:35<22:22,  1.49it/s]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.4994, 'grad_norm': 12.613661766052246, 'learning_rate': 4e-05, 'epoch': 0.2}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 40%|████      | 1000/2500 [11:13<16:46,  1.49it/s]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.3898, 'grad_norm': 4.661791801452637, 'learning_rate': 3e-05, 'epoch': 0.4}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 60%|██████    | 1500/2500 [16:47<11:02,  1.51it/s]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.3516, 'grad_norm': 1.5203113555908203, 'learning_rate': 2e-05, 'epoch': 0.6}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 80%|████████  | 2000/2500 [22:25<05:33,  1.50it/s]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.3121, 'grad_norm': 8.331348419189453, 'learning_rate': 1e-05, 'epoch': 0.8}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "100%|██████████| 2500/2500 [28:04<00:00,  1.50it/s]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.2882, 'grad_norm': 6.287994861602783, 'learning_rate': 0.0, 'epoch': 1.0}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "                                                   \n",
+            "100%|██████████| 2500/2500 [30:45<00:00,  1.35it/s]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'eval_loss': 0.283893883228302, 'eval_accuracy': 0.883, 'eval_f1': 0.8829425082505502, 'eval_runtime': 159.717, 'eval_samples_per_second': 62.611, 'eval_steps_per_second': 7.826, 'epoch': 1.0}\n",
+            "{'train_runtime': 1845.2907, 'train_samples_per_second': 21.677, 'train_steps_per_second': 1.355, 'train_loss': 0.3682089477539062, 'epoch': 1.0}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "TrainOutput(global_step=2500, training_loss=0.3682089477539062, metrics={'train_runtime': 1845.2907, 'train_samples_per_second': 21.677, 'train_steps_per_second': 1.355, 'total_flos': 4111110240000000.0, 'train_loss': 0.3682089477539062, 'epoch': 1.0})"
+            ]
+          },
+          "execution_count": 29,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "trainer.train()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Salvando o modelo"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
+      "metadata": {
+        "id": "8eO6WDiOBAhg"
+      },
+      "outputs": [],
+      "source": [
+        "torch.save(model.state_dict(), 'model.pth')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FtVZztSa40b3"
+      },
+      "source": [
+        "## Teste de predições individuais"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "metadata": {
+        "id": "lOHVSyfJJ8zK"
+      },
+      "outputs": [],
+      "source": [
+        "from transformers import AutoTokenizer\n",
+        "\n",
+        "new_tokenizer = AutoTokenizer.from_pretrained(pre_trained_base)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "metadata": {
+        "id": "t-T7hDZ2J1Qk"
+      },
+      "outputs": [],
+      "source": [
+        "def get_prediction(text):\n",
+        "    encoding = new_tokenizer(text, return_tensors=\"pt\", padding=\"max_length\", truncation=True, max_length=MAX_LEN)\n",
+        "    encoding = {k: v.to(trainer.model.device) for k,v in encoding.items()}\n",
+        "\n",
+        "    outputs = model(**encoding)\n",
+        "\n",
+        "    logits = outputs.logits\n",
+        "\n",
+        "    sigmoid = torch.nn.Sigmoid()\n",
+        "    probs = sigmoid(logits.squeeze().cpu())\n",
+        "    probs = probs.detach().numpy()\n",
+        "    label = np.argmax(probs, axis=-1)\n",
+        "\n",
+        "    return label"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "y4dxQ4oYJ5C1",
+        "outputId": "d0d77c2d-aff6-412b-e22a-0b721f5b097e"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "0"
+            ]
+          },
+          "execution_count": 36,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "get_prediction(\"This movie is horrible!\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "JXAyOu_6AqoO",
+        "outputId": "ffcd019e-4c0c-45eb-f538-d2860c53a0e0"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "1"
+            ]
+          },
+          "execution_count": 37,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "get_prediction(\"This movie is awesome!\")"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "provenance": []
+    },
+    "gpuClass": "standard",
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}