{
  "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": 25,
      "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": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_reviews = pd.read_csv('../data/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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",
            "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": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GlyrkK52zMcc",
        "outputId": "a938653b-92c3-4b4e-802c-eacc3f1b6ecf"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\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": 36,
      "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": [
        "# 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: zach.mueller@huggingface.co\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: transformers@huggingface.co\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": 27,
      "metadata": {
        "id": "lOHVSyfJJ8zK"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "new_tokenizer = AutoTokenizer.from_pretrained(pre_trained_base)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "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!\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Avaliação do modelo em novos dados\n",
        "Avaliação realizada em outro dataset, as reviews do RottenTomatoes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {},
      "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",
              "      <th>bert_results</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>651</th>\n",
              "      <td>The film is content as it is to run clever one...</td>\n",
              "      <td>negative</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2205</th>\n",
              "      <td>&amp;#91;Has&amp;#93; a surprising and somewhat disapp...</td>\n",
              "      <td>negative</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>362</th>\n",
              "      <td>Absurdly over-rated...</td>\n",
              "      <td>negative</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2784</th>\n",
              "      <td>A rare bird, not because of what it's like but...</td>\n",
              "      <td>negative</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1914</th>\n",
              "      <td>Lord of Illusions is also quite repulsive, as ...</td>\n",
              "      <td>negative</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2230</th>\n",
              "      <td>The movie is completely innocuous, passably en...</td>\n",
              "      <td>negative</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2354</th>\n",
              "      <td>A mud-simple horror trudge set in a swamp colo...</td>\n",
              "      <td>negative</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2404</th>\n",
              "      <td>Just plain generic.</td>\n",
              "      <td>negative</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>720</th>\n",
              "      <td>Ulmer brings an enormous amount of impressioni...</td>\n",
              "      <td>positive</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>527</th>\n",
              "      <td>In their directorial debut, Britt Poulton and ...</td>\n",
              "      <td>negative</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3000 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 review sentiment bert_results\n",
              "651   The film is content as it is to run clever one...  negative     Positive\n",
              "2205  &#91;Has&#93; a surprising and somewhat disapp...  negative     Positive\n",
              "362                              Absurdly over-rated...  negative     Negative\n",
              "2784  A rare bird, not because of what it's like but...  negative     Positive\n",
              "1914  Lord of Illusions is also quite repulsive, as ...  negative     Positive\n",
              "...                                                 ...       ...          ...\n",
              "2230  The movie is completely innocuous, passably en...  negative     Positive\n",
              "2354  A mud-simple horror trudge set in a swamp colo...  negative     Negative\n",
              "2404                                Just plain generic.  negative     Negative\n",
              "720   Ulmer brings an enormous amount of impressioni...  positive     Negative\n",
              "527   In their directorial debut, Britt Poulton and ...  negative     Negative\n",
              "\n",
              "[3000 rows x 3 columns]"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from preprocess_data import preprocess_text,get_stopwords\n",
        "from transformers import AutoTokenizer, pipeline\n",
        "\n",
        "df = pd.read_csv('../data/rotten_tomatos.csv')\n",
        "\n",
        "MODEL_PATH = 'danielcd99/BERT_imdb'\n",
        "\n",
        "def load_pipeline():\n",
        "    tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)\n",
        "    tokenizer.model_max_length = 200\n",
        "\n",
        "    pipe=pipeline(\n",
        "    \"text-classification\",\n",
        "    model=MODEL_PATH\n",
        "    )\n",
        "    return pipe\n",
        "\n",
        "pipe = load_pipeline()\n",
        "get_stopwords()\n",
        "df['preprocessed_review'] = df['review'].copy()\n",
        "df['preprocessed_review'] = df['preprocessed_review'].apply(preprocess_text)\n",
        "    \n",
        "predictions = []\n",
        "for review in df['preprocessed_review']:\n",
        "    try:\n",
        "        label = pipe(review)[0]['label']\n",
        "    except:\n",
        "        print(\"Ocorreu um erro de carregamento, tente novamente!\")\n",
        "    \n",
        "    if label == 'LABEL_0':\n",
        "        predictions.append(0)\n",
        "    else:\n",
        "        predictions.append(1)\n",
        "\n",
        "df['bert_results'] = predictions\n",
        "\n",
        "cols = ['review','sentiment', 'bert_results']\n",
        "df = df[cols]\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Precision: 0.8066\n",
            "Recall: 0.8449\n",
            "F1 Score: 0.8253\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix, precision_recall_fscore_support\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Mapear 'Positive' para 1 e 'Negative' para 0 em 'sentiment'\n",
        "df['sentiment'] = df['sentiment'].map({'positive': 1, 'negative': 0})\n",
        "df['bert_results'] = df['bert_results'].map({'Positive': 1, 'Negative': 0})\n",
        "\n",
        "# Calcular métricas de avaliação: precision, recall, f1-score\n",
        "precision, recall, f1_score, _ = precision_recall_fscore_support(df['sentiment'], df['bert_results'], average='binary')\n",
        "\n",
        "print(f\"Precision: {precision:.4f}\")\n",
        "print(f\"Recall: {recall:.4f}\")\n",
        "print(f\"F1 Score: {f1_score:.4f}\")\n",
        "\n",
        "# Calcular e plotar a matriz de confusão\n",
        "cm = confusion_matrix(df['sentiment'], df['bert_results'])\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['Negative', 'Positive'], yticklabels=['Negative', 'Positive'])\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('True')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Após ajustar o modelo BERT utilizando a base de dados do IMDb, avaliada com referência aos dados do Rotten Tomatoes, obtivemos as seguintes métricas de desempenho:\n",
        "\n",
        "Precision: 0.8562 --- Recall: 0.8654 --- F1 Score: 0.8608\n",
        "\n",
        "Essas métricas indicam que o modelo ajustado conseguiu classificar de forma bastante precisa os sentimentos dos textos da base de dados IMDb, utilizando o BERT finetunado com dados do Rotten Tomatoes como referência."
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