toxic_classification_GPT.ipynb

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        "<a href=\"https://colab.research.google.com/github/levi3001/cs470_term_project_toxic_classification/blob/main/toxic_classification_GPT.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "YvSrOZ_hv_SS"
      },
      "outputs": [],
      "source": [
        "try:\n",
        "    %tensorflow_version 2.x\n",
        "except Exception:\n",
        "    pass\n",
        "\n",
        "import os\n",
        "import random\n",
        "import re\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import re\n",
        "from tqdm.auto import tqdm\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kW7k_ppx2MN9",
        "outputId": "153b463d-6630-4e3c-d726-3640ba0a6d44"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8SmmsG-0ELQA",
        "outputId": "dbb74d8e-1d06-4020-ec18-971a083ea100"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  drive/MyDrive/jigsaw-toxic-comment-classification-challenge.zip\n",
            "  inflating: data/sample_submission.csv.zip  \n",
            "  inflating: data/test.csv.zip       \n",
            "  inflating: data/test_labels.csv.zip  \n",
            "  inflating: data/train.csv.zip      \n"
          ]
        }
      ],
      "source": [
        "!unzip  drive/MyDrive/jigsaw-toxic-comment-classification-challenge.zip -d data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
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        },
        "id": "S_adDNelIIjt",
        "outputId": "9ddf58fd-21f1-43fb-8404-dbbe4076b67f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>Hey man, I'm really not trying to edit war. It...</td>\n",
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              "                      id  ... identity_hate\n",
              "0       0000997932d777bf  ...             0\n",
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      ],
      "source": [
        "train_data=pd.read_csv('data/train.csv.zip')\n",
        "train_data"
      ]
    },
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              "      <td>I don't anonymously edit articles at all.</td>\n",
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              "          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",
              "  "
            ],
            "text/plain": [
              "                      id                                       comment_text\n",
              "0       00001cee341fdb12  Yo bitch Ja Rule is more succesful then you'll...\n",
              "1       0000247867823ef7  == From RfC == \\n\\n The title is fine as it is...\n",
              "2       00013b17ad220c46  \" \\n\\n == Sources == \\n\\n * Zawe Ashton on Lap...\n",
              "3       00017563c3f7919a  :If you have a look back at the source, the in...\n",
              "4       00017695ad8997eb          I don't anonymously edit articles at all.\n",
              "...                  ...                                                ...\n",
              "153159  fffcd0960ee309b5  . \\n i totally agree, this stuff is nothing bu...\n",
              "153160  fffd7a9a6eb32c16  == Throw from out field to home plate. == \\n\\n...\n",
              "153161  fffda9e8d6fafa9e  \" \\n\\n == Okinotorishima categories == \\n\\n I ...\n",
              "153162  fffe8f1340a79fc2  \" \\n\\n == \"\"One of the founding nations of the...\n",
              "153163  ffffce3fb183ee80  \" \\n :::Stop already. Your bullshit is not wel...\n",
              "\n",
              "[153164 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "test_data=pd.read_csv('data/test.csv.zip')\n",
        "test_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "v1YrXfT7Jj51",
        "outputId": "96505116-fdfa-401c-e4e6-1955aacabea8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c65e6ada-3237-4b54-b390-54779a143554')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c65e6ada-3237-4b54-b390-54779a143554');\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",
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              "  "
            ],
            "text/plain": [
              "                      id  toxic  severe_toxic  ...  threat  insult  identity_hate\n",
              "0       00001cee341fdb12     -1            -1  ...      -1      -1             -1\n",
              "1       0000247867823ef7     -1            -1  ...      -1      -1             -1\n",
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              "4       00017695ad8997eb     -1            -1  ...      -1      -1             -1\n",
              "...                  ...    ...           ...  ...     ...     ...            ...\n",
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              "153163  ffffce3fb183ee80     -1            -1  ...      -1      -1             -1\n",
              "\n",
              "[153164 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "test_label=pd.read_csv('data/test_labels.csv.zip')\n",
        "test_label"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import string\n",
        "def remove_punc(text):\n",
        "  return text.translate(str.maketrans('', '', string.punctuation))"
      ],
      "metadata": {
        "id": "zko0SVimxJTu"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "TyW58BcSz0C3"
      },
      "outputs": [],
      "source": [
        "#https://www.kaggle.com/gtskyler/toxic-comments-bert\n",
        "def clean_text(text):\n",
        "    text = text.lower()\n",
        "    text = re.sub(r\"what's\", \"what is \", text)\n",
        "    text = re.sub(r\"\\'s\", \" \", text)\n",
        "    text = re.sub(r\"\\'ve\", \" have \", text)\n",
        "    text = re.sub(r\"can't\", \"cannot \", text)\n",
        "    text = re.sub(r\"n't\", \" not \", text)\n",
        "    text = re.sub(r\"i'm\", \"i am \", text)\n",
        "    text = re.sub(r\"\\'re\", \" are \", text)\n",
        "    text = re.sub(r\"\\'d\", \" would \", text)\n",
        "    text = re.sub(r\"\\'ll\", \" will \", text)\n",
        "    text = re.sub(r\"\\'scuse\", \" excuse \", text)\n",
        "    text = re.sub('\\W', ' ', text)\n",
        "    text = re.sub('\\s+', ' ', text)\n",
        "    text = text.strip(' ')\n",
        "    return text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "YdAh3l9vMl84"
      },
      "outputs": [],
      "source": [
        "#train_data['comment_text']=train_data['comment_text'].map(lambda text: clean_text(text))\n",
        "#test_data['comment_text']=test_data['comment_text'].map(lambda text: clean_text(text))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "PEmUrpIxJn03"
      },
      "outputs": [],
      "source": [
        "train_data['comment_texxxt'] = train_data['comment_text'].map(lambda text: clean_text(text)).map(lambda text: remove_punc(text))\n",
        "test_data['comment_texxxt'] = test_data['comment_text'].map(lambda text: clean_text(text)).map(lambda text: remove_punc(text))\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "TWz5R58kMev0"
      },
      "outputs": [],
      "source": [
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "JTT6g-dYKHjC"
      },
      "outputs": [],
      "source": [
        "train_values_x = train_data['comment_texxxt'].values\n",
        "train_values_y = train_data[[\"toxic\",\"severe_toxic\",\"obscene\",\"threat\",\"insult\",\"identity_hate\"]].values\n",
        "test_x= test_data['comment_texxxt'].values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "1iTVDUBJhdIW"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "Bf6ka8N59WDW"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "Ivn6Er4y9Z8q"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "CNXxgHGX9lHl"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "XU7z7hjk9omY"
      },
      "outputs": [],
      "source": [
        "train_x, val_x, train_y, val_y = train_test_split(train_values_x, train_values_y, test_size=0.2, random_state=30)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "oGlEnXDJ9nIn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7bcabd3c-3cb9-413e-90f1-c9ae21115267"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['you had the image name right but you left a blank width parameter that apparently prevented the image from showing',\n",
              "       'aksungur hi nico you have created the article aksungur please see my note in the discussion page talk have a good day',\n",
              "       'samaleks if you can prove that i am from that douchebag forum called skyscrapercity or that i am a meat puppet please go ahead please be aware that i will continue to revert false propaganda and correct information on pages if you revert my corrected information that turns out to be war so you are the one starting the war',\n",
              "       ...,\n",
              "       'national playing fields association just thought you might like to see how with the help of the npfa the page has grown since your initial creation i am sure there is still far more to do',\n",
              "       're booth swarbrick and whalen sorry i posted that request was so long ago that now i can scarcely remember what i was after once upon a time i think the larkin page made reference to these books in passing and i was hoping to get more details at any rate i imagine these books should be used by our page over to you or someone like you instinct',\n",
              "       'dont listen to anonmoos'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "train_x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "hMLRTu3-ED3m",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4cbcf2c9-d649-4376-a061-bf1f21875ee2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['the syrian army is now advancing on aleppo with russian air cover this article is inherently unstable the article is also edited appropriately as the war progresses which just means it is unstable',\n",
              "       'oppose there is zero benefit to readers particularly mobile users from ambiguous titles justin brown defensive lineman born 1982 gridiron football defensive end justin brown wide receiver born 1991 american football wide receiver and justin brown aquanaut american professional aquanaut are clearly titled per wp criteria at the moment leave well alone',\n",
              "       'file skin of our teeth handbill jpg you have added a comment asking for the addition of a copyright status however i do not see any warnings attached to that image so i do not understand what is wrong with what i have already attached it is a low res version of a promotional item which comes under fair use it is also a photograph of something i own so it is also covered under fair use from that angle tell me that you think i need to add that is not already covered',\n",
              "       ...,\n",
              "       'really it not so much selflessness as just a general opinion that people put too much stock in who a person is and what they have done in their life i would much prefer to judge others and have others judge me by my contributions both good and bad when i had a short biography on my userpage i think people treated me differently because of my age and my background and i never really liked that',\n",
              "       'june 2007 please stop if you continue to ignore our policies by introducing inappropriate pages such as sauce face to wikipedia you will be blocked',\n",
              "       'the biography of robert dallenbach lacks any credible sources all the articles related to the pillar of fire church are irrelevant to this encyclopedia and should be deleted one biography of orland wolfram states that he founded the mision biblica where are the documented sources someone in the pillar of fire has learned how to bypass wiki standards carol penrod the last phrase contains misspelled english they probably meant the founder of the church wiki editors where are you carol penrod'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "val_x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "x5pS3KURLAWm"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "nCJqoqGADe1U"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9zd6Y_KhEYlZ",
        "outputId": "cfc4ed4f-8091-4394-f903-40163d3a07ab"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting transformers\n",
            "  Downloading transformers-4.14.1-py3-none-any.whl (3.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.4 MB 8.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n",
            "Collecting sacremoses\n",
            "  Downloading sacremoses-0.0.46-py3-none-any.whl (895 kB)\n",
            "\u001b[K     |████████████████████████████████| 895 kB 78.4 MB/s \n",
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            "\u001b[?25hCollecting tokenizers<0.11,>=0.10.1\n",
            "  Downloading tokenizers-0.10.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3 MB)\n",
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            "Installing collected packages: pyyaml, tokenizers, sacremoses, huggingface-hub, transformers\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed huggingface-hub-0.2.1 pyyaml-6.0 sacremoses-0.0.46 tokenizers-0.10.3 transformers-4.14.1\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "fbUXK3A1PN5W"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "0lWfEbHQ0bD8"
      },
      "outputs": [],
      "source": [
        "from transformers import TFGPT2Model, GPT2Config, TFGPT2ForSequenceClassification, TFAutoModel, GPT2TokenizerFast"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246,
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        },
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        "outputId": "1f8293b8-7c06-433b-f19f-97a1597a1c32"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3ef409b3f3f0484a9a2acde4ccb65917",
              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "91a5f5ec85c44ed28089ed1e1da722b4",
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "5b78bf367951429ab98f2664b833769c",
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ca08ee2e2624443498594155746742fd",
              "version_minor": 0,
              "version_major": 2
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All model checkpoint layers were used when initializing TFGPT2Model.\n",
            "\n",
            "All the layers of TFGPT2Model were initialized from the model checkpoint at distilgpt2.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFGPT2Model for predictions without further training.\n"
          ]
        }
      ],
      "source": [
        "# select model\n",
        "model_name= 'distilgpt2'\n",
        "#model_name='gpt2'\n",
        "#max length of sentences\n",
        "max_length = 128\n",
        "\n",
        "#transformer config\n",
        "\n",
        "config= GPT2Config.from_pretrained(pretrained_model_name_or_path=model_name, num_labels=6)\n",
        "#Tokenizer\n",
        "tokenizer = GPT2TokenizerFast.from_pretrained(model_name)\n",
        "\n",
        "#Model\n",
        "\n",
        "gpt2 = TFAutoModel.from_pretrained(model_name,config)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# default to left padding\n",
        "tokenizer.padding_side = \"left\"\n",
        "\n",
        "tokenizer.pad_token = tokenizer.eos_token"
      ],
      "metadata": {
        "id": "3AkFV2BOMpnt"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9r83ZW06Bpsl",
        "outputId": "e8f4f490-87a3-4e01-c975-9f2564370205"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PreTrainedTokenizerFast(name_or_path='distilgpt2', vocab_size=50257, model_max_len=1024, is_fast=True, padding_side='left', special_tokens={'bos_token': '<|endoftext|>', 'eos_token': '<|endoftext|>', 'unk_token': '<|endoftext|>', 'pad_token': '<|endoftext|>'})"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "HwAFbEvHHbYc"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "2nHWPrIyGScS"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "Nu_BejTtGYFZ"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "iUyJ4tDm8SaE"
      },
      "outputs": [],
      "source": [
        "train_x = tokenizer(text=list(train_x), add_special_tokens=True, max_length=max_length, truncation=True, padding=True, \n",
        "    return_tensors='tf', return_token_type_ids = True, return_attention_mask = True,\n",
        "    verbose = True)\n",
        "val_x= tokenizer(text=list(val_x), add_special_tokens=True, max_length=max_length,\n",
        "    truncation=True, padding=True, return_tensors='tf', return_token_type_ids = True,\n",
        "    return_attention_mask = True, verbose = True)\n",
        "test_x = tokenizer(text=list(test_x), add_special_tokens=True, max_length=max_length,\n",
        "    truncation=True, padding=True, return_tensors='tf', return_token_type_ids = True,\n",
        "    return_attention_mask = True, verbose = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BbRlH6IzhrXR",
        "outputId": "7d11d359-e68b-4664-c288-059984354233"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'input_ids': <tf.Tensor: shape=(127656, 128), dtype=int32, numpy=\n",
            "array([[50256, 50256, 50256, ...,  2939,   422,  4478],\n",
            "       [50256, 50256, 50256, ...,   257,   922,  1110],\n",
            "       [50256, 50256, 50256, ...,  3599,   262,  1175],\n",
            "       ...,\n",
            "       [50256, 50256, 50256, ...,   517,   284,   466],\n",
            "       [50256, 50256, 50256, ...,   588,   345, 13311],\n",
            "       [50256, 50256, 50256, ...,   261,  5908,   418]], dtype=int32)>, 'token_type_ids': <tf.Tensor: shape=(127656, 128), dtype=int32, numpy=\n",
            "array([[0, 0, 0, ..., 0, 0, 0],\n",
            "       [0, 0, 0, ..., 0, 0, 0],\n",
            "       [0, 0, 0, ..., 0, 0, 0],\n",
            "       ...,\n",
            "       [0, 0, 0, ..., 0, 0, 0],\n",
            "       [0, 0, 0, ..., 0, 0, 0],\n",
            "       [0, 0, 0, ..., 0, 0, 0]], dtype=int32)>, 'attention_mask': <tf.Tensor: shape=(127656, 128), dtype=int32, numpy=\n",
            "array([[0, 0, 0, ..., 1, 1, 1],\n",
            "       [0, 0, 0, ..., 1, 1, 1],\n",
            "       [0, 0, 0, ..., 1, 1, 1],\n",
            "       ...,\n",
            "       [0, 0, 0, ..., 1, 1, 1],\n",
            "       [0, 0, 0, ..., 1, 1, 1],\n",
            "       [0, 0, 0, ..., 1, 1, 1]], dtype=int32)>}\n",
            "{'input_ids': <tf.Tensor: shape=(31915, 128), dtype=int32, numpy=\n",
            "array([[50256, 50256, 50256, ...,   340,   318, 21354],\n",
            "       [50256, 50256, 50256, ...,  2666,   880,  3436],\n",
            "       [50256, 50256, 50256, ...,   407,  1541,  5017],\n",
            "       ...,\n",
            "       [50256, 50256, 50256, ...,  1107,  8288,   326],\n",
            "       [50256, 50256, 50256, ...,   481,   307, 10226],\n",
            "       [50256, 50256, 50256, ...,   349,  3112, 14892]], dtype=int32)>, 'token_type_ids': <tf.Tensor: shape=(31915, 128), dtype=int32, numpy=\n",
            "array([[0, 0, 0, ..., 0, 0, 0],\n",
            "       [0, 0, 0, ..., 0, 0, 0],\n",
            "       [0, 0, 0, ..., 0, 0, 0],\n",
            "       ...,\n",
            "       [0, 0, 0, ..., 0, 0, 0],\n",
            "       [0, 0, 0, ..., 0, 0, 0],\n",
            "       [0, 0, 0, ..., 0, 0, 0]], dtype=int32)>, 'attention_mask': <tf.Tensor: shape=(31915, 128), dtype=int32, numpy=\n",
            "array([[0, 0, 0, ..., 1, 1, 1],\n",
            "       [0, 0, 0, ..., 1, 1, 1],\n",
            "       [0, 0, 0, ..., 1, 1, 1],\n",
            "       ...,\n",
            "       [0, 0, 0, ..., 1, 1, 1],\n",
            "       [0, 0, 0, ..., 1, 1, 1],\n",
            "       [0, 0, 0, ..., 1, 1, 1]], dtype=int32)>}\n",
            "{'input_ids': <tf.Tensor: shape=(153164, 128), dtype=int32, numpy=\n",
            "array([[50256, 50256, 50256, ...,   826,  1306,   640],\n",
            "       [50256, 50256, 50256, ...,   318,   545,    78],\n",
            "       [50256, 50256, 50256, ...,  8591,   489,   392],\n",
            "       ...,\n",
            "       [50256, 50256, 50256, ...,   304,  8471,   264],\n",
            "       [50256, 50256, 50256, ..., 14598,  6340, 27374],\n",
            "       [50256, 50256, 50256, ...,   880, 26246,   345]], dtype=int32)>, 'token_type_ids': <tf.Tensor: shape=(153164, 128), dtype=int32, numpy=\n",
            "array([[0, 0, 0, ..., 0, 0, 0],\n",
            "       [0, 0, 0, ..., 0, 0, 0],\n",
            "       [0, 0, 0, ..., 0, 0, 0],\n",
            "       ...,\n",
            "       [0, 0, 0, ..., 0, 0, 0],\n",
            "       [0, 0, 0, ..., 0, 0, 0],\n",
            "       [0, 0, 0, ..., 0, 0, 0]], dtype=int32)>, 'attention_mask': <tf.Tensor: shape=(153164, 128), dtype=int32, numpy=\n",
            "array([[0, 0, 0, ..., 1, 1, 1],\n",
            "       [0, 0, 0, ..., 1, 1, 1],\n",
            "       [0, 0, 0, ..., 1, 1, 1],\n",
            "       ...,\n",
            "       [0, 0, 0, ..., 1, 1, 1],\n",
            "       [0, 0, 0, ..., 1, 1, 1],\n",
            "       [0, 0, 0, ..., 1, 1, 1]], dtype=int32)>}\n"
          ]
        }
      ],
      "source": [
        "print(train_x)\n",
        "print(val_x)\n",
        "print(test_x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "s2wUnVMcOelQ"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.layers import Input, Dropout, Dense, GlobalAveragePooling1D"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "uQIukQXHS3F4"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "6XItSiAW0SFY"
      },
      "outputs": [],
      "source": [
        "def build_classifier_model():\n",
        "    input_ids = Input(shape=(max_length,), name='input_ids', dtype='int32')\n",
        "    attention_mask = Input(shape=(max_length,), name='attention_mask', dtype='int32') \n",
        "    inputs = {'input_ids': input_ids, 'attention_mask': attention_mask}\n",
        "    seq_output = gpt2(inputs)[0]\n",
        "    x=tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(128))(seq_output)\n",
        "    x=tf.keras.layers.Dropout(0.2)(x)\n",
        "    y =Dense(6, name='outputs')(x)\n",
        "    model= tf.keras.Model(inputs, y)\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "g9tvMcaj9sy0"
      },
      "outputs": [],
      "source": [
        "model = build_classifier_model()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "bpQHG4Nieg63"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "5iH23tn4Wxk3"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "r9RE0yN4cLuO"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "I_0YDLNQihZB",
        "outputId": "a08f2ddf-875c-4cc8-a085-b8eb6a1c07e0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "tf.keras.utils.plot_model(model, show_shapes=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "id": "IAE3h8lY97FH",
        "outputId": "1569d75c-c595-489b-cd64-97bd18538e00"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "  58/3990 [..............................] - ETA: 17:40 - loss: 0.4932 - accuracy: 0.0506"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-27-73910de248ec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m      validation_data=({'input_ids': val_x['input_ids'], 'attention_mask': val_x['attention_mask']},\n\u001b[1;32m      6\u001b[0m     {'outputs': val_y}),\n\u001b[0;32m----> 7\u001b[0;31m     batch_size = 32, )\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1219\u001b[0m               \u001b[0mlogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtmp_logs\u001b[0m  \u001b[0;31m# No error, now safe to assign to logs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1220\u001b[0m               \u001b[0mend_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep_increment\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1221\u001b[0;31m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mend_step\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1222\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_training\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1223\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36mon_train_batch_end\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m    434\u001b[0m     \"\"\"\n\u001b[1;32m    435\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_should_call_train_batch_hooks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 436\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mModeKeys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'end'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    437\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    438\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mon_test_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36m_call_batch_hook\u001b[0;34m(self, mode, hook, batch, logs)\u001b[0m\n\u001b[1;32m    293\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_begin_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    294\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'end'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 295\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_end_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    296\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\u001b[0m       raise ValueError(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36m_call_batch_end_hook\u001b[0;34m(self, mode, batch, logs)\u001b[0m\n\u001b[1;32m    314\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_batch_times\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_time\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 316\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_hook_helper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhook_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    317\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    318\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_batch_times\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_batches_for_timing_check\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36m_call_batch_hook_helper\u001b[0;34m(self, hook_name, batch, logs)\u001b[0m\n\u001b[1;32m    352\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mcallback\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    353\u001b[0m       \u001b[0mhook\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallback\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m       \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    355\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    356\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_timing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36mon_train_batch_end\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m   1030\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1031\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mon_train_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1032\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_batch_update_progbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1033\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1034\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mon_test_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36m_batch_update_progbar\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m   1102\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1103\u001b[0m       \u001b[0;31m# Only block async when verbose = 1.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1104\u001b[0;31m       \u001b[0mlogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msync_to_numpy_or_python_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1105\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprogbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinalize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/tf_utils.py\u001b[0m in \u001b[0;36msync_to_numpy_or_python_type\u001b[0;34m(tensors)\u001b[0m\n\u001b[1;32m    552\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m  \u001b[0;31m# Don't turn ragged or sparse tensors to NumPy.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 554\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_to_single_numpy_or_python_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    555\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    556\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/nest.py\u001b[0m in \u001b[0;36mmap_structure\u001b[0;34m(func, *structure, **kwargs)\u001b[0m\n\u001b[1;32m    867\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    868\u001b[0m   return pack_sequence_as(\n\u001b[0;32m--> 869\u001b[0;31m       \u001b[0mstructure\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mentries\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    870\u001b[0m       expand_composites=expand_composites)\n\u001b[1;32m    871\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/nest.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    867\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    868\u001b[0m   return pack_sequence_as(\n\u001b[0;32m--> 869\u001b[0;31m       \u001b[0mstructure\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mentries\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    870\u001b[0m       expand_composites=expand_composites)\n\u001b[1;32m    871\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/tf_utils.py\u001b[0m in \u001b[0;36m_to_single_numpy_or_python_type\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m    548\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_to_single_numpy_or_python_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    549\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 550\u001b[0;31m       \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    551\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    552\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m  \u001b[0;31m# Don't turn ragged or sparse tensors to NumPy.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mnumpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1147\u001b[0m     \"\"\"\n\u001b[1;32m   1148\u001b[0m     \u001b[0;31m# TODO(slebedev): Consider avoiding a copy for non-CPU or remote tensors.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1149\u001b[0;31m     \u001b[0mmaybe_arr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1150\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mmaybe_arr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_arr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mmaybe_arr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m_numpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1113\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1114\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1115\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_numpy_internal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1116\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1117\u001b[0m       \u001b[0;32mraise\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "from tensorflow.keras.optimizers import Adam\n",
        "model.compile(loss = tf.keras.losses.BinaryCrossentropy(from_logits=True), optimizer =Adam(learning_rate=1e-5, decay=1e-6) \\\n",
        "              , metrics = ['accuracy'])\n",
        "model.fit(x={'input_ids': train_x['input_ids'], 'attention_mask': train_x['attention_mask']},\n",
        "    y={'outputs': train_y}, epochs = 5, \n",
        "     validation_data=({'input_ids': val_x['input_ids'], 'attention_mask': val_x['attention_mask']},\n",
        "    {'outputs': val_y}),\n",
        "    batch_size = 32, )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mmds7XPG-N0O"
      },
      "outputs": [],
      "source": [
        "predicted = tf.math.sigmoid(model.predict({'input_ids': test_x['input_ids'], 'attention_mask': test_x['attention_mask']}, batch_size = 200))\n",
        "predict = np.hstack((test_data.id[:, np.newaxis], predicted))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "awmWtWKEHnby"
      },
      "outputs": [],
      "source": [
        "subm = pd.DataFrame(predict, columns = ['id', 'toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate'])\n",
        "subm.to_csv('subm.csv', index = False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QpYvoSrLIIU3"
      },
      "outputs": [],
      "source": [
        "subm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YprL4uu8IKRH"
      },
      "outputs": [],
      "source": [
        "predicted"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "l35URXkXwNK2"
      },
      "outputs": [],
      "source": [
        "test_x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hUZnM-lJI1JY"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "name=  [ 'toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']\n",
        "values= [sum(train_data[name[i]]>0) for i in range(len(name))]\n",
        "plt.bar(name, values)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ptqlgzpekjv1"
      },
      "outputs": [],
      "source": [
        "!cp subm.csv /content/drive/MyDrive/subm_distilgpt2.csv"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "5W85WSvtuiGl"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
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