Copy_of_T5new.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/cgray1117/NLPTransformersProject/blob/main/Copy_of_T5new.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## pip Install\n",
        "!pip install SentencePiece\n",
        "!pip install transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fNGYs5heyILD",
        "outputId": "b831c626-051d-41f4-ddbb-6a36f2d392f5"
      },
      "execution_count": 1,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting SentencePiece\n",
            "  Downloading sentencepiece-0.1.97-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.3 MB 25.1 MB/s \n",
            "\u001b[?25hInstalling collected packages: SentencePiece\n",
            "Successfully installed SentencePiece-0.1.97\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.21.2-py3-none-any.whl (4.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 4.7 MB 37.4 MB/s \n",
            "\u001b[?25hCollecting tokenizers!=0.11.3,<0.13,>=0.11.1\n",
            "  Downloading tokenizers-0.12.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.6 MB 51.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.64.0)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.12.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.21.6)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.8.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2022.6.2)\n",
            "Collecting huggingface-hub<1.0,>=0.1.0\n",
            "  Downloading huggingface_hub-0.9.1-py3-none-any.whl (120 kB)\n",
            "\u001b[K     |████████████████████████████████| 120 kB 34.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (4.1.1)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.8.1)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2022.6.15)\n",
            "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.9.1 tokenizers-0.12.1 transformers-4.21.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Import libraries\n",
        "import sentencepiece\n",
        "import numpy as np\n",
        "import regex as re\n",
        "import math\n",
        "import os\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import statistics\n",
        "\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from nltk.tokenize import TweetTokenizer\n",
        "\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import precision_score\n",
        "from sklearn.metrics import recall_score\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import RepeatedKFold\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "\n",
        "import tensorflow as tf\n",
        "import tensorflow.keras.backend as K\n",
        "from tensorflow import keras\n",
        "\n",
        "import tokenizers\n",
        "from transformers import T5Tokenizer, TFT5Model\n",
        "from collections import Counter"
      ],
      "metadata": {
        "id": "IHbgzW4dO7Pz"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "MODEL_NAME = 't5-small'\n",
        "MAX_LEN = 256\n",
        "ARTIFACTS_PATH = '../artifacts/'\n",
        "EPOCHS = 5\n",
        "if not os.path.exists(ARTIFACTS_PATH):\n",
        "    os.makedirs(ARTIFACTS_PATH)"
      ],
      "metadata": {
        "id": "0mIvs6LEC8bT"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data NLP"
      ],
      "metadata": {
        "id": "yD_SrsD9yR6x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('project_data.csv')\n",
        "\n",
        "# Identify null and remove\n",
        "df[\"Label\"].isnull().sum()\n",
        "df['Label'].replace('', np.nan, inplace=True)\n",
        "df.dropna(subset=['Label'], inplace=True)"
      ],
      "metadata": {
        "id": "Lp09sfeUSPO1"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop extra columns\n",
        "df = df.drop('Username', axis=1)\n",
        "df = df.drop('UserLocation', axis=1)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        },
        "id": "b-iJ_lZEZuor",
        "outputId": "61394442-bd07-47f7-a0a1-44d05ad4f02d"
      },
      "execution_count": 6,
      "outputs": [
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2022-07-29 10:25:44+00:00</td>\n",
              "      <td>Closed due to Police Activity in #NewYork on W...</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022-07-29 10:25:44+00:00</td>\n",
              "      <td>Closed due to Police Activity in #NewYork on A...</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022-07-29 10:10:43+00:00</td>\n",
              "      <td>Closed due to Police Activity in #NewYork on A...</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022-07-29 09:50:43+00:00</td>\n",
              "      <td>Closed due to Police Activity in #NewYork on A...</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b97e32f6-cff3-4ea4-b6d7-92587c6ee24d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\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-b97e32f6-cff3-4ea4-b6d7-92587c6ee24d 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-b97e32f6-cff3-4ea4-b6d7-92587c6ee24d');\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": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tknzr = TweetTokenizer()\n",
        "\n",
        "# Tokenize each tweet\n",
        "df['tokenized'] = df['Text'].map(lambda t: tknzr.tokenize(t))\n",
        "\n",
        "# lowecase, strip and ensure we only include words\n",
        "df['tokenized'] = df['tokenized'].map(\n",
        "    lambda t: [word.lower().strip() for word in t if word.isalpha()])\n",
        "\n",
        "# Importing stopwords\n",
        "nltk.download(\"stopwords\")\n",
        "\n",
        "stopwords_en = stopwords.words('english')\n",
        "\n",
        "# Instantiate the WordNetLemmatizer\n",
        "wordnet_lemmatizer = WordNetLemmatizer()\n",
        "\n",
        "# lemmarize and remove stopwords\n",
        "nltk.download('omw-1.4')\n",
        "nltk.download('wordnet')\n",
        "\n",
        "df['tokenized'] = df['tokenized'].map(\n",
        "    lambda t: [wordnet_lemmatizer.lemmatize(word) for word in t \n",
        "               if word not in stopwords_en])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y5f82A1Sy8E3",
        "outputId": "b5c74097-9752-49ae-c62f-21383fa41719"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['tokenized'][:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g37AA6way91f",
        "outputId": "0ae47cf2-4ab5-4dc8-a7c9-33aca82f0c9f"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    [accident, fdr, drive, sb, st, stopped, traffi...\n",
              "1    [closed, due, police, activity, west, street, ...\n",
              "2    [closed, due, police, activity, avenue, americ...\n",
              "3    [closed, due, police, activity, avenue, americ...\n",
              "4    [closed, due, police, activity, avenue, americ...\n",
              "5    [closed, due, police, activity, avenue, americ...\n",
              "6    [closed, due, police, activity, west, street, ...\n",
              "7    [overturned, vehicle, two, lane, blocked, onth...\n",
              "8    [left, lane, blocked, onthefdr, fdr, drive, sb...\n",
              "9    [overturned, vehicle, two, lane, blocked, onth...\n",
              "Name: tokenized, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "token_tweets = []\n",
        "for lst in df['tokenized']:\n",
        "  txt = \" \".join(lst)\n",
        "  token_tweets.append(txt)\n",
        "\n",
        "X_data = np.array(token_tweets)\n",
        "y_data = df[[\"Label\"]].to_numpy().reshape(-1)"
      ],
      "metadata": {
        "id": "KgvLnMQ_SUa3"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## EDA"
      ],
      "metadata": {
        "id": "WjlRxew6zCoD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "categories = df[['Label']].values.reshape(-1)\n",
        "\n",
        "counter_categories = Counter(categories)\n",
        "counter_categories\n",
        "\n",
        "category_names = counter_categories.keys()\n",
        "category_values = counter_categories.values()\n",
        "y_pos = np.arange(len(category_names))\n",
        "\n",
        "# Create distribution of labels figure\n",
        "plt.figure(1, figsize=(10, 7))\n",
        "plt.bar(y_pos, category_values, align='center', alpha=0.5)\n",
        "plt.xticks(y_pos, category_names)\n",
        "plt.ylabel('Number of Texts')\n",
        "plt.xlabel('Labels')\n",
        "plt.title('Distribution of Texts per Category')\n",
        "plt.gca().yaxis.grid(True)\n",
        "plt.show()\n",
        "print(counter_categories)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "_T7jP3FyTzuZ",
        "outputId": "6c2c3130-60e2-4df2-d3d7-f55514cb95c5"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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9VVtVrQRWdsuXJLka2AVYAWzXt/t2XZskSdK8N4qv53gx8P2qemhKM8mTkmzULT8ZWAhcU1U3Ancm2bO7ru0Q4NwR1CxJkjTrhvn1HKcDXwOelmR5ksO7VQfxyJsI9gIu676u4yzgjVW16kaENwGfBJYBV+Mdn5IkaQMxtKnPqjp4mvb/PUXb2cDZ02x/MfCM9VqcJEnSHOCTCSRJkhplUJMkSWqUQU2SJKlRBjVJkqRGGdQkSZIaZVCTJElqlEFNkiSpUQY1SZKkRhnUJEmSGmVQkyRJapRBTZIkqVEGNUmSpEYZ1CRJkhplUJMkSWqUQU2SJKlRBjVJkqRGGdQkSZIaZVCTJElqlEFNkiSpUQY1SZKkRhnUJEmSGmVQkyRJapRBTZIkqVEGNUmSpEYZ1CRJkhplUJMkSWqUQU2SJKlRBjVJkqRGGdQkSZIaZVCTJElqlEFNkiSpUQY1SZKkRhnUJEmSGmVQkyRJapRBTZIkqVEGNUmSpEYNLaglOTnJLUku72s7JsmKJJd2r3371h2VZFmSK5O8rK99765tWZIjh1WvJElSa4Y5onYKsPcU7cdX1W7d63yAJLsCBwFP7/b52yQbJdkI+BtgH2BX4OBuW0mSpHlv42F1XFUXJtlpwM0PAM6oqpXAD5MsA/bo1i2rqmsAkpzRbfvd9VyuJElSc4YW1GbwliSHABcDR1TVT4AFwEV92yzv2gCun9T+vOk6TrIYWAwwNjbGxMTEeiz7kRbct3Ko/Wv+m5i4YdQlrMZzWuuqtXNamutmO6idCBwLVPfzo8Dr1lfnVbUEWAKwaNGiGh8fX19dT+n4pVcNtX/NfweO7zLqElbjOa111do5Lc11sxrUqurmVctJPgH8U/d2BbB936bbdW3M0C5JkjSvzerXcyTZpu/t7wCr7gg9DzgoyaOT7AwsBL4BfBNYmGTnJJvSu+HgvNmsWZIkaVSGNqKW5HRgHNg6yXLgaGA8yW70pj6vBd4AUFVXJDmT3k0C9wNvrqoHun7eAnwB2Ag4uaquGFbNkiRJLRnmXZ8HT9F80gzbfxD44BTt5wPnr8fSJEmS5gSfTCBJktQog5okSVKjDGqSJEmNMqhJkiQ1yqAmSZLUKIOaJElSowxqkiRJjTKoSZIkNcqgJkmS1CiDmiRJUqMMapIkSY0yqEmSJDXKoCZJktQog5okSVKjDGqSJEmNMqhJkiQ1yqAmSZLUKIOaJElSowxqkiRJjTKoSZIkNcqgJkmS1CiDmiRJUqMMapIkSY0yqEmSJDXKoCZJktQog5okSVKjDGqSJEmNMqhJkiQ1yqAmSZLUKIOaJElSowxqkiRJjTKoSZIkNcqgJkmS1Kg1BrUkL0iyWbf8miTHJdlx+KVJkiRt2AYZUTsR+GmSZwFHAFcDpw21KkmSJA0U1O6vqgIOAD5WVX8DbDHcsiRJkjRIULsryVHAa4B/TvIoYJM17ZTk5CS3JLm8r+0jSb6f5LIk/5hky659pyT3Jrm0e328b5/dk3wnybIkJyTJ2n9MSZKkuWeQoPYqYCVweFXdBGwHfGSA/U4B9p7UthR4RlX9GnAVcFTfuqurarfu9ca+9hOB1wMLu9fkPiVJkualQYLa26vquKr6d4Cq+hHw9DXtVFUXArdPavtiVd3fvb2IXuibVpJtgMdV1UXd9OtpwCsGqFmSJGnO23iAbV4C/PGktn2maFtbrwM+2/d+5yTfBu4E3tMFwwXA8r5tlndtU0qyGFgMMDY2xsTExDqWOLMF960cav+a/yYmbhh1CavxnNa6au2clua6aYNakt8H3gQ8Jcllfau2AP5jXQ6a5E+A+4FPd003AjtU1W1JdgfOSbLGUbvJqmoJsARg0aJFNT4+vi5lrtHxS68aav+a/w4c32XUJazGc1rrqrVzWprrZhpR+wzwL8CHgCP72u+qqtun3mXNkvxv4OXAi7rpTKpqJb3r4KiqS5JcDewCrGD16dHtujZJkqR5b9pr1Krqjqq6Fjipqq7re92e5NBf5GBJ9gbeBexfVT/ta39Sko265SfTu2ngmqq6EbgzyZ7d3Z6HAOf+IseWJEmaawa5meB9SU5MslmSsSSfB/Zb005JTge+BjwtyfIkhwMfozd1unTS13DsBVyW5FLgLOCNfaN2bwI+CSyj92W7/7I2H1CSJGmuGuRmgt+g90SCS7v376uq09e0U1UdPEXzSdNsezZw9jTrLgaeMUCdkiRJ88ogI2pPAPagN5q1EtjRL52VJEkavkGC2kXAv1bV3sBzgW2B/zfUqiRJkjTQ1OeLuy+5paruBf4wyV7DLUuSJEmDjKjdmuS9ST4BkGQh8LjhliVJkqRBgtrf0bs27fnd+xXAB4ZWkSRJkoDBgtpTquovgJ8DdN9/5s0EkiRJQzZIUPtZkscCBZDkKXRPEZAkSdLwTBvUknyxWzwG+Fdg+ySfBi6g93QBSZIkDdFMd30+CaCqvpjkEmBPelOeb62qW2ejOEmSpA3ZTEHt8UleOUX7Xkmoqs8NqyhJkiStIagBL2fqGwcKMKhJkiQN0UxB7bqqet2sVSJJkqTVzHTXp1/BIUmSNEIzBbXXzloVkiRJeoRpg1pVXT6bhUiSJGl1g3zhrSRJkkZgpi+8vaD7+eezV44kSZJWmemuz22S/Hdg/yRnMOnmgqr61lArkyRJ2sDNFNTeB7wX2A44btK6An5rWEVJkiRphqBWVWcBZyV5b1UdO4s1SZIkiZlH1ACoqmOT7A/s1TVNVNU/DbcsSZIkrfGuzyQfAt4KfLd7vTXJnw27MEmSpA3dGkfUgN8GdquqBwGSnAp8G3j3MAuTJEna0A36PWpb9i0/fhiFSJIkaXWDjKh9CPh2ki/T+4qOvYAjh1qVJEmSBrqZ4PQkE8Bzu6Y/rqqbhlqVJEmSBhpRo6puBM4bci2SJEnq47M+JUmSGmVQkyRJatSMQS3JRkm+P1vFSJIk6WEzBrWqegC4MskOs1SPJEmSOoPcTPAE4Iok3wDuWdVYVfsPrSpJkiQNFNTeO/QqJEmS9AiDfI/aV5LsCCysqi8l+SVgo+GXJkmStGEb5KHsrwfOAv5v17QAOGeYRUmSJGmwr+d4M/AC4E6AqvoB8MuDdJ7k5CS3JLm8r22rJEuT/KD7+YSuPUlOSLIsyWVJntO3z6Hd9j9IcujafEBJkqS5apCgtrKqfrbqTZKNgRqw/1OAvSe1HQlcUFULgQt4+Lmh+wALu9di4MTueFsBRwPPA/YAjl4V7iRJkuazQYLaV5K8G3hskpcA/wB8fpDOq+pC4PZJzQcAp3bLpwKv6Gs/rXouArZMsg3wMmBpVd1eVT8BlvLI8CdJkjTvDHLX55HA4cB3gDcA5wOfXIdjjnXPDgW4CRjrlhcA1/dtt7xrm679EZIspjcax9jYGBMTE+tQ5potuG/lUPvX/DcxccOoS1iN57TWVWvntDTXDXLX54NJTgW+Tm/K88qqGnTqc019V5L10lfX3xJgCcCiRYtqfHx8fXU9peOXXjXU/jX/HTi+y6hLWI3ntNZVa+e0NNcNctfnbwNXAycAHwOWJdlnHY55czelSffzlq59BbB933bbdW3TtUuSJM1rg1yj9lHgN6tqvKp+A/hN4Ph1OOZ5wKo7Nw8Fzu1rP6S7+3NP4I5uivQLwEuTPKG7ieClXZskSdK8Nsg1andV1bK+99cAdw3SeZLTgXFg6yTL6d29+WHgzCSHA9cBB3abnw/sCywDfgocBlBVtyc5Fvhmt937q2ryDQqSJEnzzrRBLckru8WLk5wPnEnvGrXf5eHQNKOqOniaVS+aYtui951tU/VzMnDyIMeUJEmaL2YaUduvb/lm4De65R8Djx1aRZIkSQJmCGpVddhsFiJJkqTVrfEatSQ7A38A7NS/fVXtP7yyJEmSNMjNBOcAJ9F7GsGDwy1HkiRJqwwS1O6rqhOGXokkSZJWM0hQ+6skRwNfBB56vkxVfWtoVUmSJGmgoPZM4LXAb/Hw1Gd17yVJkjQkgwS13wWeXFU/G3YxkiRJetggj5C6HNhy2IVIkiRpdYOMqG0JfD/JN1n9GjW/nkOSJGmIBglqRw+9CkmSJD3CGoNaVX1lNgqRJEnS6gZ5MsFd9O7yBNgU2AS4p6oeN8zCJEmSNnSDjKhtsWo5SYADgD2HWZQkSZIGu+vzIdVzDvCyIdUjSZKkziBTn6/se/soYBFw39AqkiRJEjDYXZ/79S3fD1xLb/pTkiRJQzTINWqHzUYhkiRJWt20QS3J+2bYr6rq2CHUI0mSpM5MI2r3TNG2GXA48ETAoCZJkjRE0wa1qvroquUkWwBvBQ4DzgA+Ot1+kiRJWj9mvEYtyVbAO4BXA6cCz6mqn8xGYZIkSRu6ma5R+wjwSmAJ8MyqunvWqpIkSdKMX3h7BLAt8B7ghiR3dq+7ktw5O+VJkiRtuGa6Rm2tnlogSZKk9cswJkmS1CiDmiRJUqMMapIkSY0yqEmSJDXKoCZJktQog5okSVKjDGqSJEmNMqhJkiQ1asZnfUqSNJcdv/SqUZegOe7tL9llpMd3RE2SJKlRBjVJkqRGzXpQS/K0JJf2ve5M8rYkxyRZ0de+b98+RyVZluTKJC+b7ZolSZJGYdavUauqK4HdAJJsBKwA/hE4DDi+qv6yf/skuwIHAU8HtgW+lGSXqnpgVguXJEmaZaOe+nwRcHVVXTfDNgcAZ1TVyqr6IbAM2GNWqpMkSRqhUd/1eRBwet/7tyQ5BLgYOKKqfgIsAC7q22Z51/YISRYDiwHGxsaYmJgYRs0PWXDfyqH2r/lvYuKGUZewGs9prSvPac03oz6nRxbUkmwK7A8c1TWdCBwLVPfzo8Dr1qbPqloCLAFYtGhRjY+Pr69yp+Rt31pXB46P9rbvyTynta48pzXfjPqcHuXU5z7At6rqZoCqurmqHqiqB4FP8PD05gpg+779tuvaJEmS5rVRBrWD6Zv2TLJN37rfAS7vls8DDkry6CQ7AwuBb8xalZIkSSMykqnPJJsBLwHe0Nf8F0l2ozf1ee2qdVV1RZIzge8C9wNv9o5PSZK0IRhJUKuqe4AnTmp77QzbfxD44LDrkiRJasmov55DkiRJ0zCoSZIkNcqgJkmS1CiDmiRJUqMMapIkSY0yqEmSJDXKoCZJktQog5okSVKjDGqSJEmNMqhJkiQ1yqAmSZLUKIOaJElSowxqkiRJjTKoSZIkNcqgJkmS1CiDmiRJUqMMapIkSY0yqEmSJDXKoCZJktQog5okSVKjDGqSJEmNMqhJkiQ1yqAmSZLUKIOaJElSowxqkiRJjTKoSZIkNcqgJkmS1CiDmiRJUqMMapIkSY0yqEmSJDXKoCZJktQog5okSVKjDGqSJEmNMqhJkiQ1yqAmSZLUqJEFtSTXJvlOkkuTXNy1bZVkaZIfdD+f0LUnyQlJliW5LMlzRlW3JEnSbBn1iNpvVtVuVbWoe38kcEFVLQQu6N4D7AMs7F6LgRNnvVJJkqRZNuqgNtkBwKnd8qnAK/raT6uei4Atk2wzigIlSZJmyyiDWgFfTHJJksVd21hV3dgt3wSMdcsLgOv79l3etUmSJM1bG4/w2C+sqhVJfhlYmuT7/SurqpLU2nTYBb7FAGNjY0xMTKy3Yqey4L6VQ+1f89/ExA2jLmE1ntNaV57Tmm9GfU6PLKhV1Yru5y1J/hHYA7g5yTZVdWM3tXlLt/kKYPu+3bfr2ib3uQRYArBo0aIaHx8f4ieA45deNdT+Nf8dOL7LqEtYjee01pXntOabUZ/TI5n6TLJZki1WLQMvBS4HzgMO7TY7FDi3Wz4POKS7+3NP4I6+KVJJkqR5aVQjamPAPyZZVcNnqupfk3wTODPJ4cB1wIHd9ucD+wLLgJ8Ch81+yZIkSbNrJEGtqq4BnjVF+23Ai6ZoL+DNs1CaJElSM1r7eg5JkiR1DGqSJEmNMqhJkiQ1yqAmSZLUKIOaJElSowxqkiRJjTKoSZIkNcqgJkmS1CiDmiRJUqMMapIkSY0yqEmSJDXKoCZJktQog5okSVKjDGqSJEmNMqhJkiQ1yqAmSZLUKIOaJElSowxqkiRJjTKoSZIkNcqgJkmS1CiDmiRJUqMMapIkSY0yqEmSJDXKoGvIbbYAAAdcSURBVCZJktQog5okSVKjDGqSJEmNMqhJkiQ1yqAmSZLUKIOaJElSowxqkiRJjTKoSZIkNcqgJkmS1CiDmiRJUqMMapIkSY0yqEmSJDXKoCZJktSoWQ9qSbZP8uUk301yRZK3du3HJFmR5NLutW/fPkclWZbkyiQvm+2aJUmSRmHjERzzfuCIqvpWki2AS5Is7dYdX1V/2b9xkl2Bg4CnA9sCX0qyS1U9MKtVS5IkzbJZH1Grqhur6lvd8l3A94AFM+xyAHBGVa2sqh8Cy4A9hl+pJEnSaI1iRO0hSXYCng18HXgB8JYkhwAX0xt1+wm9EHdR327LmSbYJVkMLAYYGxtjYmJiWKUDsOC+lUPtX/PfxMQNoy5hNZ7TWlee05pvRn1OjyyoJdkcOBt4W1XdmeRE4Figup8fBV63Nn1W1RJgCcCiRYtqfHx8vdY82fFLrxpq/5r/DhzfZdQlrMZzWuvKc1rzzajP6ZHc9ZlkE3oh7dNV9TmAqrq5qh6oqgeBT/Dw9OYKYPu+3bfr2iRJkua1Udz1GeAk4HtVdVxf+zZ9m/0OcHm3fB5wUJJHJ9kZWAh8Y7bqlSRJGpVRTH2+AHgt8J0kl3Zt7wYOTrIbvanPa4E3AFTVFUnOBL5L747RN3vHpyRJ2hDMelCrqq8CmWLV+TPs80Hgg0MrSpIkqUE+mUCSJKlRBjVJkqRGGdQkSZIaZVCTJElqlEFNkiSpUQY1SZKkRhnUJEmSGmVQkyRJapRBTZIkqVEGNUmSpEYZ1CRJkhplUJMkSWqUQU2SJKlRBjVJkqRGGdQkSZIaZVCTJElqlEFNkiSpUQY1SZKkRhnUJEmSGmVQkyRJapRBTZIkqVEGNUmSpEYZ1CRJkhplUJMkSWqUQU2SJKlRBjVJkqRGGdQkSZIaZVCTJElqlEFNkiSpUQY1SZKkRhnUJEmSGmVQkyRJapRBTZIkqVEGNUmSpEYZ1CRJkhplUJMkSWrUnAlqSfZOcmWSZUmOHHU9kiRJwzYnglqSjYC/AfYBdgUOTrLraKuSJEkarjkR1IA9gGVVdU1V/Qw4AzhgxDVJkiQN1cajLmBAC4Dr+94vB543eaMki4HF3du7k1w5C7VpelsDt466iJa9Y9QF6BfheT0Dz+k5yXN6BrN4Tu84VeNcCWoDqaolwJJR16GeJBdX1aJR1yGtT57Xmm88p9s2V6Y+VwDb973frmuTJEmat+ZKUPsmsDDJzkk2BQ4CzhtxTZIkSUM1J6Y+q+r+JG8BvgBsBJxcVVeMuCytmdPQmo88rzXfeE43LFU16hokSZI0hbky9SlJkrTBMahJkiQ1yqCmdZbk5CS3JLl8mvVJckL3+K/LkjxntmuUBpVk+yRfTvLdJFckeesU23hOa05Z02MYkzw6yWe79V9PstPsV6mpGNS0PpwC7D3D+n2Ahd1rMXDiLNQk/aLuB46oql2BPYE3T/HIOs9pzRkDPobxcOAnVfVU4Hjgz2e3Sk3HoKZ1VlUXArfPsMkBwGnVcxGwZZJtZqc6ae1U1Y1V9a1u+S7ge/SejtLPc1pzySCPYTwAOLVbPgt4UZLMYo2ahkFNs2GqR4BN/sMnNaeb/nk28PVJqzynNZcMcr4+tE1V3Q/cATxxVqrTjAxqkjSFJJsDZwNvq6o7R12PpA2TQU2zwUeAaU5Jsgm9kPbpqvrcFJt4TmsuGeR8fWibJBsDjwdum5XqNCODmmbDecAh3Z1yewJ3VNWNoy5Kmkp3Xc5JwPeq6rhpNvOc1lwyyGMYzwMO7Zb/J/Bv5TfiN2FOPEJKbUtyOjAObJ1kOXA0sAlAVX0cOB/YF1gG/BQ4bDSVSgN5AfBa4DtJLu3a3g3sAJ7TmnumewxjkvcDF1fVefT+5+RTSZbRuznsoNFVrH4+QkqSJKlRTn1KkiQ1yqAmSZLUKIOaJElSowxqkiRJjTKoSZIkNcqgJmmDk+Tutdj2mCR/NKz+JWkmBjVJkqRGGdQkCUiyX5KvJ/l2ki8lGetb/awkX0vygySv79vnnUm+meSyJH86RZ/bJLkwyaVJLk/y67PyYSTNGwY1Ser5KrBnVT0bOAN4V9+6XwN+C3g+8L4k2yZ5KbAQ2APYDdg9yV6T+vxfwBeqajfgWcClSNJa8BFSktSzHfDZJNsAmwI/7Ft3blXdC9yb5Mv0wtkLgZcC3+622ZxecLuwb79vAid3D3k/p6oMapLWiiNqktTz18DHquqZwBuAx/Stm/ysvQICfKiqduteT62qk1bbqOpCYC9gBXBKkkOGV76k+cigJkk9j6cXqAAOnbTugCSPSfJEYJzeSNkXgNcl2RwgyYIkv9y/U5IdgZur6hPAJ4HnDLF+SfOQU5+SNkS/lGR53/vjgGOAf0jyE+DfgJ371l8GfBnYGji2qm4Abkjyq8DXkgDcDbwGuKVvv3HgnUl+3q13RE3SWknV5BF9SZIktcCpT0mSpEYZ1CRJkhplUJMkSWqUQU2SJKlRBjVJkqRGGdQkSZIaZVCTJElq1P8HmqwSQ4rK3ZoAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Counter({1.0: 2030, 2.0: 1881, 0.0: 784})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_texts = len(X_data)\n",
        "print('Texts in dataset: %d' % n_texts)\n",
        "n_categories = len(df['Label'].unique())\n",
        "print('Number of categories: %d' % n_categories)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v_mVIghx_Fli",
        "outputId": "3762065f-477f-4f4a-abaf-139b45b3e475"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Texts in dataset: 4695\n",
            "Number of categories: 3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Decode, and Create Roberta"
      ],
      "metadata": {
        "id": "5aTB4JKG_baT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def t5_encode(texts, tokenizer):\n",
        "    ct = len(texts)\n",
        "    input_ids = np.ones((ct, MAX_LEN), dtype='int32')\n",
        "    attention_mask = np.zeros((ct, MAX_LEN), dtype='int32')\n",
        "    token_type_ids = np.zeros((ct, MAX_LEN), dtype='int32')\n",
        "\n",
        "    for k, text in enumerate(texts):\n",
        "        tok_text = tokenizer.tokenize(text)\n",
        "        enc_text = tokenizer.convert_tokens_to_ids(tok_text[:(MAX_LEN-2)])\n",
        "        input_length = len(enc_text) + 2\n",
        "        input_length = input_length if input_length < MAX_LEN else MAX_LEN\n",
        "        input_ids[k,:input_length] = np.asarray([0] + enc_text + [2], dtype='int32')\n",
        "        attention_mask[k,:input_length] = 1\n",
        "\n",
        "    return {'input_word_ids': input_ids,'input_mask': attention_mask,\n",
        "            'input_type_ids': token_type_ids}"
      ],
      "metadata": {
        "id": "FMu_4n3n_eBo"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train test validation split\n",
        "X_train, X_rem, y_train, y_rem = train_test_split(X_data, y_data, train_size=0.7, random_state=444)\n",
        "\n",
        "X_valid, X_test, y_valid, y_test = train_test_split(X_rem,y_rem, test_size=0.5)"
      ],
      "metadata": {
        "id": "KLU9PYZTACvu"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = T5Tokenizer.from_pretrained(MODEL_NAME)\n",
        "\n",
        "X_train = t5_encode(X_train, tokenizer)\n",
        "X_test = t5_encode(X_test, tokenizer)\n",
        "X_valid = t5_encode(X_valid, tokenizer)\n",
        "\n",
        "y_valid = np.asarray(y_valid, dtype='int32').reshape(-1)\n",
        "y_train = np.asarray(y_train, dtype='int32').reshape(-1)\n",
        "y_test = np.asarray(y_test, dtype='int32').reshape(-1) "
      ],
      "metadata": {
        "id": "P3c4wuKRvCrc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200,
          "referenced_widgets": [
            "bb62dce334a0407c8692e0b17d06cb53",
            "054352e52aa54d0198ec8cb065626667",
            "774af4339eec41f99544ddf6e2aa4f9d",
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            "44f24ea15b2f4a1880c4d58b415af4fc",
            "787a3f2202ff495a8b0621740e32d398",
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            "1bb580c56e2d49e98e501438a307fce9",
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            "98c2b1147aea497488b5a25744cc629c",
            "f6349e6a10e34d35828027610f07efad",
            "66ea4acb0dea4630aef25e3afd57c677",
            "9c2ea1cb337649ac9b93cc77a3900c98",
            "ab41ded6a2ca4cd0ac2f30ca8b05463d",
            "ba190627829d43cb83f0c1118bc5b941"
          ]
        },
        "outputId": "4d5dc48b-a50b-4cf3-f721-3a4bc17d9a96"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading spiece.model:   0%|          | 0.00/773k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bb62dce334a0407c8692e0b17d06cb53"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading config.json:   0%|          | 0.00/1.17k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "44f24ea15b2f4a1880c4d58b415af4fc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/models/t5/tokenization_t5.py:174: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.\n",
            "For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.\n",
            "- Be aware that you SHOULD NOT rely on t5-small automatically truncating your input to 512 when padding/encoding.\n",
            "- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.\n",
            "- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.\n",
            "  FutureWarning,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Build Model"
      ],
      "metadata": {
        "id": "p2u6vmoUzjh2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def build_model(n_categories):\n",
        "      input_word_ids = tf.keras.Input(shape=(MAX_LEN,), dtype=tf.int32, name='input_word_ids')\n",
        "      input_mask = tf.keras.Input(shape=(MAX_LEN,), dtype=tf.int32, name='input_mask')\n",
        "      input_type_ids = tf.keras.Input(shape=(MAX_LEN,), dtype=tf.int32, name='input_type_ids')\n",
        "\n",
        "      # Import RoBERTa model from HuggingFace\n",
        "      T5_model = TFT5Model.from_pretrained(MODEL_NAME)\n",
        "      x = T5_model(input_word_ids, attention_mask=input_mask, decoder_input_ids=input_type_ids)\n",
        "\n",
        "      # Huggingface transformers have multiple outputs, embeddings are the first one,\n",
        "      # so let's slice out the first position\n",
        "      x = x[0]\n",
        "\n",
        "      x = tf.keras.layers.Dropout(0.1)(x)\n",
        "      x = tf.keras.layers.Flatten()(x)\n",
        "      x = tf.keras.layers.Dense(256, activation='relu')(x)\n",
        "      x = tf.keras.layers.Dense(n_categories, activation='softmax')(x)\n",
        "\n",
        "      model = tf.keras.Model(inputs=[input_word_ids, input_mask,input_type_ids], outputs=x)\n",
        "      #input_ids = input_word_ids, attention_mask = input_mask, decoder_input_ids = input_type_ids\n",
        "      model.compile(\n",
        "          optimizer=tf.keras.optimizers.Adam(lr=1e-5),\n",
        "          loss='sparse_categorical_crossentropy',\n",
        "          metrics=['accuracy'])\n",
        "\n",
        "      return model\n",
        "      "
      ],
      "metadata": {
        "id": "VC4kZadcALI_"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = build_model(n_categories)\n",
        "model.summary()"
      ],
      "metadata": {
        "id": "ux7taYQyAQoO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "149a5b4db1d543e1be8210d6556555ce",
            "6b996b3f072543da8ab1775024e6c2f5",
            "447eaa2d17c349f4922a1e56e4bdb27c",
            "03fee93d83a54d84a86557ff16e74f48",
            "5a28d40f179240009512048b8f6746d9",
            "3387fef64514477c9c524cce4bac8c43",
            "21bd551a26af412db305ef908177b1a5",
            "c5bf053107254d0d8df3048890e87f56",
            "f5a0e15d3a04494fa9af6d1412956291",
            "40c077dc895e401daa600d3022fb95ab",
            "651c3832ddda4444a1064437bc4c1b26"
          ]
        },
        "outputId": "78404507-206d-47ad-adcb-02e69e9a7cfe"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tf_model.h5:   0%|          | 0.00/231M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "149a5b4db1d543e1be8210d6556555ce"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All model checkpoint layers were used when initializing TFT5Model.\n",
            "\n",
            "All the layers of TFT5Model were initialized from the model checkpoint at t5-small.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFT5Model for predictions without further training.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_word_ids (InputLayer)    [(None, 256)]        0           []                               \n",
            "                                                                                                  \n",
            " input_mask (InputLayer)        [(None, 256)]        0           []                               \n",
            "                                                                                                  \n",
            " input_type_ids (InputLayer)    [(None, 256)]        0           []                               \n",
            "                                                                                                  \n",
            " tft5_model (TFT5Model)         TFSeq2SeqModelOutpu  60506624    ['input_word_ids[0][0]',         \n",
            "                                t(last_hidden_state               'input_mask[0][0]',             \n",
            "                                =(None, 256, 512),                'input_type_ids[0][0]']         \n",
            "                                 past_key_values=((                                               \n",
            "                                (None, 8, None, 64)                                               \n",
            "                                , (None, 8, None, 6                                               \n",
            "                                4),                                                               \n",
            "                                 (None, 8, None, 64                                               \n",
            "                                ),                                                                \n",
            "                                 (None, 8, None, 64                                               \n",
            "                                )),                                                               \n",
            "                                 ((None, 8, None, 6                                               \n",
            "                                4),                                                               \n",
            "                                 (None, 8, None, 64                                               \n",
            "                                ),                                                                \n",
            "                                 (None, 8, None, 64                                               \n",
            "                                ),                                                                \n",
            "                                 (None, 8, None, 64                                               \n",
            "                                )),                                                               \n",
            "                                 ((None, 8, None, 6                                               \n",
            "                                4),                                                               \n",
            "                                 (None, 8, None, 64                                               \n",
            "                                ),                                                                \n",
            "                                 (None, 8, None, 64                                               \n",
            "                                ),                                                                \n",
            "                                 (None, 8, None, 64                                               \n",
            "                                )),                                                               \n",
            "                                 ((None, 8, None, 6                                               \n",
            "                                4),                                                               \n",
            "                                 (None, 8, None, 64                                               \n",
            "                                ),                                                                \n",
            "                                 (None, 8, None, 64                                               \n",
            "                                ),                                                                \n",
            "                                 (None, 8, None, 64                                               \n",
            "                                )),                                                               \n",
            "                                 ((None, 8, None, 6                                               \n",
            "                                4),                                                               \n",
            "                                 (None, 8, None, 64                                               \n",
            "                                ),                                                                \n",
            "                                 (None, 8, None, 64                                               \n",
            "                                ),                                                                \n",
            "                                 (None, 8, None, 64                                               \n",
            "                                )),                                                               \n",
            "                                 ((None, 8, None, 6                                               \n",
            "                                4),                                                               \n",
            "                                 (None, 8, None, 64                                               \n",
            "                                ),                                                                \n",
            "                                 (None, 8, None, 64                                               \n",
            "                                ),                                                                \n",
            "                                 (None, 8, None, 64                                               \n",
            "                                ))),                                                              \n",
            "                                 decoder_hidden_sta                                               \n",
            "                                tes=None, decoder_a                                               \n",
            "                                ttentions=None, cro                                               \n",
            "                                ss_attentions=None,                                               \n",
            "                                 encoder_last_hidde                                               \n",
            "                                n_state=(None, 256,                                               \n",
            "                                 512),                                                            \n",
            "                                 encoder_hidden_sta                                               \n",
            "                                tes=None, encoder_a                                               \n",
            "                                ttentions=None)                                                   \n",
            "                                                                                                  \n",
            " dropout_62 (Dropout)           (None, 256, 512)     0           ['tft5_model[0][1]']             \n",
            "                                                                                                  \n",
            " flatten (Flatten)              (None, 131072)       0           ['dropout_62[0][0]']             \n",
            "                                                                                                  \n",
            " dense (Dense)                  (None, 256)          33554688    ['flatten[0][0]']                \n",
            "                                                                                                  \n",
            " dense_1 (Dense)                (None, 3)            771         ['dense[0][0]']                  \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 94,062,083\n",
            "Trainable params: 94,062,083\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(Adam, self).__init__(name, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train & Evaluate"
      ],
      "metadata": {
        "id": "RzYG4SHRzoTE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# the code in this block was adpated from https://stackoverflow.com/questions/48118111/get-loss-values-for-each-training-instance-keras\n",
        "class LossHistory(keras.callbacks.Callback):\n",
        "    def on_train_begin(self, logs={}):\n",
        "        self.losses = []\n",
        "    def on_batch_end(self, batch, logs={}):\n",
        "        self.losses.append(logs.get('loss'))\n",
        "\n",
        "history = LossHistory()\n",
        "model.fit(X_train, y_train, batch_size=16, epochs=EPOCHS, verbose=1, validation_data=(X_valid,y_valid), callbacks=[history])\n",
        "print(history.losses)\n",
        "\n",
        "\n",
        "plt.plot(history.losses, linestyle = 'dotted')\n",
        "plt.show()\n",
        "\n",
        "# history = model.fit(X_train,y_train,epochs=EPOCHS,batch_size=16,verbose=1,validation_data=(X_test, y_test))"
      ],
      "metadata": {
        "id": "W2lHYZi2U8US",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "outputId": "15fe46c8-907a-40c1-d29e-cc4c233305cf"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "206/206 [==============================] - 127s 542ms/step - loss: 0.4235 - accuracy: 0.8214 - val_loss: 0.1391 - val_accuracy: 0.9389\n",
            "Epoch 2/5\n",
            "206/206 [==============================] - 114s 553ms/step - loss: 0.1652 - accuracy: 0.9358 - val_loss: 0.1141 - val_accuracy: 0.9531\n",
            "Epoch 3/5\n",
            "206/206 [==============================] - 114s 552ms/step - loss: 0.1360 - accuracy: 0.9498 - val_loss: 0.1081 - val_accuracy: 0.9574\n",
            "Epoch 4/5\n",
            "206/206 [==============================] - 110s 533ms/step - loss: 0.1216 - accuracy: 0.9553 - val_loss: 0.1321 - val_accuracy: 0.9489\n",
            "Epoch 5/5\n",
            "206/206 [==============================] - 114s 552ms/step - loss: 0.1136 - accuracy: 0.9562 - val_loss: 0.0924 - val_accuracy: 0.9588\n",
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          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualizations"
      ],
      "metadata": {
        "id": "VSyQLM6iAYHN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_confusion_matrix(X_test, y_test, model):\n",
        "    y_pred = model.predict(X_test)\n",
        "    y_pred = [np.argmax(i) for i in model.predict(X_test)]\n",
        "\n",
        "    con_mat = tf.math.confusion_matrix(labels=y_test, predictions=y_pred).numpy()\n",
        "\n",
        "    con_mat_norm = np.around(con_mat.astype('float') / con_mat.sum(axis=1)[:, np.newaxis], decimals=2)\n",
        "    label_names = list(range(len(con_mat_norm)))\n",
        "\n",
        "    con_mat_df = pd.DataFrame(con_mat_norm,\n",
        "                              index=label_names, \n",
        "                              columns=label_names)\n",
        "\n",
        "    figure = plt.figure(figsize=(10, 10))\n",
        "    sns.heatmap(con_mat_df, cmap=plt.cm.Blues, annot=True)\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')"
      ],
      "metadata": {
        "id": "AGnzixxtAi-v"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_confusion_matrix(X_test, y_test, model)"
      ],
      "metadata": {
        "id": "lYdnT3T9AlXD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 604
        },
        "outputId": "bef85714-3877-499f-9bed-8a2303fddc89"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Results"
      ],
      "metadata": {
        "id": "glypoYt20U3U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)\n",
        "y_pred = [np.argmax(i) for i in model.predict(X_test)]"
      ],
      "metadata": {
        "id": "1QycHqD2QD6B"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"precision: \" + str(precision_score(y_test, y_pred, average=\"micro\")))\n",
        "print(\"recall: \" + str(recall_score(y_test, y_pred, average=\"micro\")))\n",
        "print(\"f1: \" + str(f1_score(y_test, y_pred, average=\"micro\")))\n",
        "print(\"accuracy: \" + str(model.evaluate(X_test, y_test)[1]*100))"
      ],
      "metadata": {
        "id": "T9XYQgHDQETq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a8e6f4bf-f545-4348-add5-6368347af100"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "precision: 0.9617021276595744\n",
            "recall: 0.9617021276595744\n",
            "f1: 0.9617021276595744\n",
            "23/23 [==============================] - 10s 285ms/step - loss: 0.1031 - accuracy: 0.9617\n",
            "accuracy: 96.17021083831787\n"
          ]
        }
      ]
    }
  ]
}