lectures/cap17_3_idmb_classification_gpt3.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMwpo/+jByA5rJiI/mRWBU7",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/viniciusrpb/cic0269_natural_language_processing/blob/main/lectures/cap17_3_idmb_classification_gpt3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Capítulo 17 - BERT\n",
        "\n",
        "Para esse notebook Python, utilizaremos as classes Transformers do Hugging Face.\n",
        "\n",
        "## 17.3 Classificação de reviews de filmes via GPT-3\n",
        "\n",
        "Informações sobre o corpus IMDB Reviews, clique [aqui](https://www.tensorflow.org/datasets/catalog/imdb_reviews).\n",
        "\n"
      ],
      "metadata": {
        "id": "32i_omQJGKIB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![bert.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "EJNDxWVb6v2_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow-datasets\n",
        "!pip install keras\n",
        "!pip install tensorflow\n",
        "!pip install transformers"
      ],
      "metadata": {
        "id": "lBhH-LrUBPQC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4f084ca4-733b-4352-806e-43d1da392135"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: tensorflow-datasets in /usr/local/lib/python3.8/dist-packages (4.6.0)\n",
            "Requirement already satisfied: termcolor in /usr/local/lib/python3.8/dist-packages (from tensorflow-datasets) (2.1.1)\n",
            "Requirement already satisfied: promise in /usr/local/lib/python3.8/dist-packages (from tensorflow-datasets) (2.3)\n",
            "Requirement already satisfied: toml in /usr/local/lib/python3.8/dist-packages (from tensorflow-datasets) (0.10.2)\n",
            "Requirement already satisfied: dill in /usr/local/lib/python3.8/dist-packages (from tensorflow-datasets) (0.3.6)\n",
            "Requirement already satisfied: protobuf>=3.12.2 in /usr/local/lib/python3.8/dist-packages (from tensorflow-datasets) (3.19.6)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from tensorflow-datasets) (1.21.6)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.8/dist-packages (from tensorflow-datasets) (1.15.0)\n",
            "Requirement already satisfied: tensorflow-metadata in /usr/local/lib/python3.8/dist-packages (from tensorflow-datasets) (1.11.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from tensorflow-datasets) (4.64.1)\n",
            "Requirement already satisfied: etils[epath] in /usr/local/lib/python3.8/dist-packages (from tensorflow-datasets) (0.9.0)\n",
            "Requirement already satisfied: absl-py in /usr/local/lib/python3.8/dist-packages (from tensorflow-datasets) (1.3.0)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.8/dist-packages (from tensorflow-datasets) (2.23.0)\n",
            "Requirement already satisfied: importlib-resources in /usr/local/lib/python3.8/dist-packages (from tensorflow-datasets) (5.10.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->tensorflow-datasets) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->tensorflow-datasets) (2022.9.24)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->tensorflow-datasets) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->tensorflow-datasets) (1.24.3)\n",
            "Requirement already satisfied: zipp in /usr/local/lib/python3.8/dist-packages (from etils[epath]->tensorflow-datasets) (3.11.0)\n",
            "Requirement already satisfied: typing_extensions in /usr/local/lib/python3.8/dist-packages (from etils[epath]->tensorflow-datasets) (4.4.0)\n",
            "Requirement already satisfied: googleapis-common-protos<2,>=1.52.0 in /usr/local/lib/python3.8/dist-packages (from tensorflow-metadata->tensorflow-datasets) (1.57.0)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: keras in /usr/local/lib/python3.8/dist-packages (2.9.0)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.8/dist-packages (2.9.2)\n",
            "Requirement already satisfied: keras<2.10.0,>=2.9.0rc0 in /usr/local/lib/python3.8/dist-packages (from tensorflow) (2.9.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.8/dist-packages (from tensorflow) (1.6.3)\n",
            "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.8/dist-packages (from tensorflow) (1.3.0)\n",
            "Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.8/dist-packages (from tensorflow) (3.1.0)\n",
            "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.8/dist-packages (from tensorflow) (1.21.6)\n",
            "Requirement already satisfied: keras-preprocessing>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from tensorflow) (1.1.2)\n",
            "Requirement already satisfied: protobuf<3.20,>=3.9.2 in /usr/local/lib/python3.8/dist-packages (from tensorflow) (3.19.6)\n",
            "Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.8/dist-packages (from tensorflow) (1.14.1)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.8/dist-packages (from tensorflow) (0.28.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.8/dist-packages (from tensorflow) (4.4.0)\n",
            "Requirement already satisfied: gast<=0.4.0,>=0.2.1 in /usr/local/lib/python3.8/dist-packages (from tensorflow) (0.4.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.8/dist-packages (from tensorflow) (0.2.0)\n",
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    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "KdvNPSjGbgpV"
      },
      "outputs": [],
      "source": [
        "from keras.models import Sequential,Model\n",
        "from keras.layers import Dense,Embedding,BatchNormalization,Input,LSTM,Bidirectional\n",
        "from transformers import OpenAIGPTTokenizer,TFOpenAIGPTModel\n",
        "from keras.utils.np_utils import to_categorical\n",
        "from sklearn.metrics import classification_report\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "import gensim\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds\n",
        "import pandas as pd\n",
        "from tensorflow.keras.optimizers import Adam"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds_train = tfds.load('imdb_reviews/plain_text', split='train', shuffle_files=True)\n",
        "ds_test = tfds.load('imdb_reviews/plain_text', split='test', shuffle_files=False)"
      ],
      "metadata": {
        "id": "LR_xXvK8BRWR"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = tfds.as_dataframe(ds_train)\n",
        "df_test = tfds.as_dataframe(ds_test)"
      ],
      "metadata": {
        "id": "V6WDpjh1BS8J"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.head()"
      ],
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          "height": 206
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        "id": "fecoQgq2jmrF",
        "outputId": "633fd65f-fc8b-4cd4-9697-9e4b232eb9fd"
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      "execution_count": 29,
      "outputs": [
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              "   label                                               text\n",
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              "                                                     [key], {});\n",
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocessDataFrame(df):\n",
        "\n",
        "    dic = {}\n",
        "    dic['text'] = []\n",
        "    dic['label'] = []\n",
        "\n",
        "    review = [x.decode('utf-8') for x in df['text'].values]\n",
        "\n",
        "    for idx,sentence in enumerate(review):\n",
        "        dic['text'].append(review[idx])\n",
        "        dic['label'].append(df['label'][idx])\n",
        "        \n",
        "    return pd.DataFrame.from_dict(dic)"
      ],
      "metadata": {
        "id": "fjJTCUMjBUtR"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = preprocessDataFrame(df_train)\n",
        "df_test = preprocessDataFrame(df_test)"
      ],
      "metadata": {
        "id": "7m7IV2NMBWHA"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.head()"
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          "height": 206
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2a7721e5-ad5b-44a9-99c0-7635cb7b46ca button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2a7721e5-ad5b-44a9-99c0-7635cb7b46ca');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "PrnUKFFt7I1z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train['label'] = pd.Categorical(df_train['label'])\n",
        "y_train_int = df_train['label'].cat.codes\n",
        "\n",
        "df_test['label'] = pd.Categorical(df_test['label'])\n",
        "y_test_int = df_test['label'].cat.codes\n",
        "\n",
        "y_train = to_categorical(y_train_int)\n",
        "y_test = to_categorical(y_test_int)"
      ],
      "metadata": {
        "id": "_7_5cN9NBXyh"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gpt_tokenizer = OpenAIGPTTokenizer.from_pretrained(\"openai-gpt\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5uXm1XPORAW8",
        "outputId": "5e934bd1-565f-415f-fa32-0c9bd4cb2fa6"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = gpt_tokenizer(\"Hello, my dog is cute\", return_tensors=\"pt\")\n",
        "gpt_tokenizer.pad_token='<unk>'"
      ],
      "metadata": {
        "id": "cCw-TZKClH5t"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inputs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x2tpKiHa8eA5",
        "outputId": "074d7d7d-d682-4fd7-f7bd-339b63910ee8"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': tensor([[3570,  240,  547, 2585,  544, 4957]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1]])}"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def sentence_gpt_encode(df,tokenizer,max_len):\n",
        "\n",
        "  input_ids = []\n",
        "  attention_masks = []\n",
        "  \n",
        "  for i in range(len(df)):\n",
        "      encoded = tokenizer.encode_plus(\n",
        "        df[i],\n",
        "        add_special_tokens=True,\n",
        "        max_length=max_len,\n",
        "        pad_to_max_length=True,\n",
        "        return_attention_mask=True\n",
        "      )\n",
        "      \n",
        "      input_ids.append(encoded['input_ids'])\n",
        "      attention_masks.append(encoded['attention_mask'])\n",
        "\n",
        "  return np.array(input_ids),np.array(attention_masks)"
      ],
      "metadata": {
        "id": "TCsazSSKU5sZ"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "max_sentence_length = 32\n",
        "\n",
        "train_input_ids,train_attention_masks = sentence_gpt_encode(df_train['text'],gpt_tokenizer,max_sentence_length)\n",
        "test_input_ids,test_attention_masks = sentence_gpt_encode(df_test['text'],gpt_tokenizer,max_sentence_length)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lBA3QltzRk1r",
        "outputId": "9a96c2b3-0899-4c0c-bd22-f13ab9a03ec9"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n",
            "/usr/local/lib/python3.8/dist-packages/transformers/tokenization_utils_base.py:2336: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_tokens = Input(shape=(max_sentence_length,),dtype=\"int64\")\n",
        "attention_masks = Input(shape=(max_sentence_length,),dtype=\"int64\")\n",
        "\n",
        "gpt_model = TFOpenAIGPTModel.from_pretrained(\"openai-gpt\")\n",
        "\n",
        "embedding = gpt_model([input_tokens,attention_masks])[0]\n",
        "\n",
        "cls_token = embedding[:, 0, :]\n",
        "\n",
        "output_layer = Dense(2, activation='softmax')(cls_token)\n",
        "model_gpt = Model(inputs=[input_tokens,attention_masks], outputs=output_layer)\n",
        "\n",
        "print(model_gpt.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5tRYgwSzpVO0",
        "outputId": "dfd8d3dc-0f51-48c5-e7b3-de49f99a8334"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All model checkpoint layers were used when initializing TFOpenAIGPTModel.\n",
            "\n",
            "All the layers of TFOpenAIGPTModel were initialized from the model checkpoint at openai-gpt.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFOpenAIGPTModel for predictions without further training.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_13\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_37 (InputLayer)          [(None, 32)]         0           []                               \n",
            "                                                                                                  \n",
            " input_38 (InputLayer)          [(None, 32)]         0           []                               \n",
            "                                                                                                  \n",
            " tf_open_aigpt_model_18 (TFOpen  TFBaseModelOutput(l  116534784  ['input_37[0][0]',               \n",
            " AIGPTModel)                    ast_hidden_state=(N               'input_38[0][0]']               \n",
            "                                one, 32, 768),                                                    \n",
            "                                 hidden_states=None                                               \n",
            "                                , attentions=None)                                                \n",
            "                                                                                                  \n",
            " tf.__operators__.getitem_7 (Sl  (None, 768)         0           ['tf_open_aigpt_model_18[0][0]'] \n",
            " icingOpLambda)                                                                                   \n",
            "                                                                                                  \n",
            " dense_18 (Dense)               (None, 2)            1538        ['tf.__operators__.getitem_7[0][0\n",
            "                                                                 ]']                              \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 116,536,322\n",
            "Trainable params: 116,536,322\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "adams_family = Adam(learning_rate=0.0002)\n",
        "\n",
        "model_gpt.compile(optimizer = adams_family,\n",
        "              loss = 'categorical_crossentropy',\n",
        "              metrics = ['accuracy'])\n",
        "\n",
        "history_fine = model_gpt.fit([train_input_ids,train_attention_masks],y_train,\n",
        "                              epochs=3,\n",
        "                              batch_size=32,\n",
        "                              validation_split=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NNnrMxxAEYqF",
        "outputId": "a497e41a-c83c-4370-dd3e-ece4bdbbc2f4"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "625/625 [==============================] - 156s 234ms/step - loss: 0.7039 - accuracy: 0.5128 - val_loss: 0.6898 - val_accuracy: 0.5282\n",
            "Epoch 2/3\n",
            "625/625 [==============================] - 144s 230ms/step - loss: 0.6942 - accuracy: 0.5234 - val_loss: 0.6856 - val_accuracy: 0.5414\n",
            "Epoch 3/3\n",
            "625/625 [==============================] - 144s 230ms/step - loss: 0.6903 - accuracy: 0.5375 - val_loss: 0.6835 - val_accuracy: 0.5410\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_prob = model_gpt.predict([test_input_ids,test_attention_masks])\n",
        "y_pred = np.argmax(y_prob,axis=1)\n",
        "print(classification_report(y_test_int, y_pred))"
      ],
      "metadata": {
        "id": "tMQ_SXTZ1U4H",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3237f3d0-a776-421a-fed0-a09bf2fff977"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "782/782 [==============================] - 56s 67ms/step\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.53      0.64      0.58     12500\n",
            "           1       0.54      0.43      0.48     12500\n",
            "\n",
            "    accuracy                           0.54     25000\n",
            "   macro avg       0.54      0.54      0.53     25000\n",
            "weighted avg       0.54      0.54      0.53     25000\n",
            "\n"
          ]
        }
      ]
    }
  ]
}