RoBERTa_I.ipynb

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  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "wAb0FmVlvL9l",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c5a1ac7e-25de-4241-de7a-999e36cc2fc2"
      },
      "source": [
        "!pip install simpletransformers"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: simpletransformers in /usr/local/lib/python3.7/dist-packages (0.63.3)\n",
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            "Requirement already satisfied: webencodings in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (0.5.1)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard->simpletransformers) (1.0.1)\n",
            "Requirement already satisfied: grpcio>=1.24.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard->simpletransformers) (1.42.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard->simpletransformers) (3.3.6)\n",
            "Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.7/dist-packages (from tensorboard->simpletransformers) (0.12.0)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.7/dist-packages (from tensorboard->simpletransformers) (0.37.0)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard->simpletransformers) (0.4.6)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->simpletransformers) (1.8.0)\n",
            "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->simpletransformers) (0.6.1)\n",
            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard->simpletransformers) (1.35.0)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard->simpletransformers) (0.2.8)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard->simpletransformers) (4.8)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard->simpletransformers) (1.3.0)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard->simpletransformers) (0.4.8)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->simpletransformers) (3.1.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GVE1_q1ZvWFr"
      },
      "source": [
        "import pandas as pd\n",
        "from sklearn import preprocessing\n",
        "from simpletransformers.model import TransformerModel\n",
        "from sklearn.metrics import f1_score, accuracy_score\n",
        "import csv\n",
        "\n",
        "import os\n",
        "os.environ['CUDA_LAUNCH_BLOCKING'] = \"1\""
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dV3BR13hvZuW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "67888257-7f33-455b-c37a-13391071af8a"
      },
      "source": [
        "''''\n",
        "with open ('train_synthetic_real.txt', 'r') as in_file:\n",
        "  stripped = (line.strip() for line in in_file)\n",
        "  lines = (line.split(\"\\t\") for line in stripped if line)\n",
        "  with open('dialouge.csv', 'w') as out_file:\n",
        "    writer = csv.writer(out_file)\n",
        "    #writer.writerow(('Label', 'Text'))\n",
        "    writer.writerows(lines)\n",
        "    '''"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'\\'\\nwith open (\\'train_synthetic_real.txt\\', \\'r\\') as in_file:\\n  stripped = (line.strip() for line in in_file)\\n  lines = (line.split(\"\\t\") for line in stripped if line)\\n  with open(\\'dialouge.csv\\', \\'w\\') as out_file:\\n    writer = csv.writer(out_file)\\n    #writer.writerow((\\'Label\\', \\'Text\\'))\\n    writer.writerows(lines)\\n    '"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g8sEpxc6QraM"
      },
      "source": [
        "# Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QMxtlu_HH30X"
      },
      "source": [
        "with open ('dialogue_dataset.txt', 'r') as in_file:\n",
        "  stripped = (line.strip() for line in in_file)\n",
        "  lines = (line.split(\"\\t\") for line in stripped if line)\n",
        "  with open('dialouge.csv', 'w') as out_file:\n",
        "    writer = csv.writer(out_file)\n",
        "    #writer.writerow(('Label', 'Text'))\n",
        "    writer.writerows(lines)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ghxt71YIvs2P"
      },
      "source": [
        "dataset = pd.read_csv('dialouge.csv', header= None)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VGWDSRZRvtCY",
        "outputId": "b3641d6e-4974-48be-af0b-1fa4568bb84c"
      },
      "source": [
        "dataset.iloc[:,0]"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         __label__fashion\n",
              "1         __label__fashion\n",
              "2         __label__fashion\n",
              "3         __label__fashion\n",
              "4         __label__fashion\n",
              "                ...       \n",
              "404751      __label__movie\n",
              "404752      __label__movie\n",
              "404753      __label__movie\n",
              "404754      __label__movie\n",
              "404755      __label__movie\n",
              "Name: 0, Length: 404756, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1_g2fkufvuzI"
      },
      "source": [
        "dataset[[\"Label\"]] = dataset.iloc[:,0]"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cT7QT1XHvxIu"
      },
      "source": [
        "dataset[[\"Text\"]] = dataset.iloc[:,1]"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "1yL-qCeSvzXw",
        "outputId": "00db9488-316f-46cc-e971-3f47a2b78b0f"
      },
      "source": [
        "dataset"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>Label</th>\n",
              "      <th>Text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>__label__fashion</td>\n",
              "      <td>no what do you have on fashion</td>\n",
              "      <td>__label__fashion</td>\n",
              "      <td>no what do you have on fashion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>__label__fashion</td>\n",
              "      <td>no what do you have on fashion</td>\n",
              "      <td>__label__fashion</td>\n",
              "      <td>no what do you have on fashion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>__label__fashion</td>\n",
              "      <td>no let's chat about fashion</td>\n",
              "      <td>__label__fashion</td>\n",
              "      <td>no let's chat about fashion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>__label__fashion</td>\n",
              "      <td>no let's chat about fashion</td>\n",
              "      <td>__label__fashion</td>\n",
              "      <td>no let's chat about fashion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>__label__fashion</td>\n",
              "      <td>no i want to talk about fashion</td>\n",
              "      <td>__label__fashion</td>\n",
              "      <td>no i want to talk about fashion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>404751</th>\n",
              "      <td>__label__movie</td>\n",
              "      <td>how about next goal wins</td>\n",
              "      <td>__label__movie</td>\n",
              "      <td>how about next goal wins</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>404752</th>\n",
              "      <td>__label__movie</td>\n",
              "      <td>let me know about films like life itself</td>\n",
              "      <td>__label__movie</td>\n",
              "      <td>let me know about films like life itself</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>404753</th>\n",
              "      <td>__label__movie</td>\n",
              "      <td>what about who framed roger rabbit</td>\n",
              "      <td>__label__movie</td>\n",
              "      <td>what about who framed roger rabbit</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>404754</th>\n",
              "      <td>__label__movie</td>\n",
              "      <td>oliver twist</td>\n",
              "      <td>__label__movie</td>\n",
              "      <td>oliver twist</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>404755</th>\n",
              "      <td>__label__movie</td>\n",
              "      <td>tell me about disneynature monkey kingdom</td>\n",
              "      <td>__label__movie</td>\n",
              "      <td>tell me about disneynature monkey kingdom</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>404756 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                       0  ...                                       Text\n",
              "0       __label__fashion  ...             no what do you have on fashion\n",
              "1       __label__fashion  ...             no what do you have on fashion\n",
              "2       __label__fashion  ...                no let's chat about fashion\n",
              "3       __label__fashion  ...                no let's chat about fashion\n",
              "4       __label__fashion  ...            no i want to talk about fashion\n",
              "...                  ...  ...                                        ...\n",
              "404751    __label__movie  ...                   how about next goal wins\n",
              "404752    __label__movie  ...   let me know about films like life itself\n",
              "404753    __label__movie  ...         what about who framed roger rabbit\n",
              "404754    __label__movie  ...                               oliver twist\n",
              "404755    __label__movie  ...  tell me about disneynature monkey kingdom\n",
              "\n",
              "[404756 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4_3YIdSNIXT1",
        "outputId": "31389758-eec8-42f8-be73-c4c9f8163f86"
      },
      "source": [
        "dataset.Label.value_counts()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "__label__movie           166708\n",
              "__label__news             65229\n",
              "__label__chitchat         48639\n",
              "__label__music            35036\n",
              "__label__tech             23167\n",
              "__label__sports           21647\n",
              "__label__other            18285\n",
              "__label__attraction       10386\n",
              "__label__literature        4592\n",
              "__label__food              3591\n",
              "__label__pets_animals      2384\n",
              "__label__celebrities       2304\n",
              "__label__fashion            908\n",
              "__label__weather            692\n",
              "__label__joke               691\n",
              "__label__games              265\n",
              "__label__fitness            232\n",
              "Name: Label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DH7-FDbnv0my"
      },
      "source": [
        "dataset = dataset.drop(dataset.iloc[:,0:2], axis=1)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "Ov77H88lwVil",
        "outputId": "2bcf6339-c559-42a5-d086-2a6ae56f9ba4"
      },
      "source": [
        "dataset"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Label</th>\n",
              "      <th>Text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>__label__fashion</td>\n",
              "      <td>no what do you have on fashion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>__label__fashion</td>\n",
              "      <td>no what do you have on fashion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>__label__fashion</td>\n",
              "      <td>no let's chat about fashion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>__label__fashion</td>\n",
              "      <td>no let's chat about fashion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>__label__fashion</td>\n",
              "      <td>no i want to talk about fashion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>404751</th>\n",
              "      <td>__label__movie</td>\n",
              "      <td>how about next goal wins</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>404752</th>\n",
              "      <td>__label__movie</td>\n",
              "      <td>let me know about films like life itself</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>404753</th>\n",
              "      <td>__label__movie</td>\n",
              "      <td>what about who framed roger rabbit</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>404754</th>\n",
              "      <td>__label__movie</td>\n",
              "      <td>oliver twist</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>404755</th>\n",
              "      <td>__label__movie</td>\n",
              "      <td>tell me about disneynature monkey kingdom</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>404756 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                   Label                                       Text\n",
              "0       __label__fashion             no what do you have on fashion\n",
              "1       __label__fashion             no what do you have on fashion\n",
              "2       __label__fashion                no let's chat about fashion\n",
              "3       __label__fashion                no let's chat about fashion\n",
              "4       __label__fashion            no i want to talk about fashion\n",
              "...                  ...                                        ...\n",
              "404751    __label__movie                   how about next goal wins\n",
              "404752    __label__movie   let me know about films like life itself\n",
              "404753    __label__movie         what about who framed roger rabbit\n",
              "404754    __label__movie                               oliver twist\n",
              "404755    __label__movie  tell me about disneynature monkey kingdom\n",
              "\n",
              "[404756 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "15Km6aeTwXYE",
        "outputId": "4dbaae74-4c9f-49cf-a8db-f18e4d53f4e3"
      },
      "source": [
        "le = preprocessing.LabelEncoder()\n",
        "le.fit(dataset[\"Label\"])"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LabelEncoder()"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FjeCCjR9weil"
      },
      "source": [
        "dataset['Label'] = le.transform(dataset[\"Label\"])"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "LOc9neBXwlaW",
        "outputId": "92783a2b-dbca-459f-e17d-b1ff4f5ea3f6"
      },
      "source": [
        "dataset"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Label</th>\n",
              "      <th>Text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>no what do you have on fashion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>no what do you have on fashion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>no let's chat about fashion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>no let's chat about fashion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>no i want to talk about fashion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>404751</th>\n",
              "      <td>9</td>\n",
              "      <td>how about next goal wins</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>404752</th>\n",
              "      <td>9</td>\n",
              "      <td>let me know about films like life itself</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>404753</th>\n",
              "      <td>9</td>\n",
              "      <td>what about who framed roger rabbit</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>404754</th>\n",
              "      <td>9</td>\n",
              "      <td>oliver twist</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>404755</th>\n",
              "      <td>9</td>\n",
              "      <td>tell me about disneynature monkey kingdom</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>404756 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Label                                       Text\n",
              "0           3             no what do you have on fashion\n",
              "1           3             no what do you have on fashion\n",
              "2           3                no let's chat about fashion\n",
              "3           3                no let's chat about fashion\n",
              "4           3            no i want to talk about fashion\n",
              "...       ...                                        ...\n",
              "404751      9                   how about next goal wins\n",
              "404752      9   let me know about films like life itself\n",
              "404753      9         what about who framed roger rabbit\n",
              "404754      9                               oliver twist\n",
              "404755      9  tell me about disneynature monkey kingdom\n",
              "\n",
              "[404756 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ppigr4l-wnIr"
      },
      "source": [
        "df = dataset[[\"Text\", \"Label\"]]"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bz1zeH2Bwq38",
        "outputId": "5e9d1686-0090-43a7-db77-e7fe366f0b90"
      },
      "source": [
        "df.Label.value_counts()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9     166708\n",
              "11     65229\n",
              "2      48639\n",
              "10     35036\n",
              "15     23167\n",
              "14     21647\n",
              "12     18285\n",
              "0      10386\n",
              "8       4592\n",
              "5       3591\n",
              "13      2384\n",
              "1       2304\n",
              "3        908\n",
              "16       692\n",
              "7        691\n",
              "6        265\n",
              "4        232\n",
              "Name: Label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VP5pm5HIwvwU"
      },
      "source": [
        "count_class_9, count_class_11, count_class_2, count_class_10,count_class_15,count_class_14,count_class_12, count_class_0, count_class_8, count_class_5, count_class_13, count_class_1, count_class_3, count_class_16, count_class_7, count_class_6, count_class_4 =  df.Label.value_counts()"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tXraizV5zT5z"
      },
      "source": [
        "df_class_0 = df[df['Label'] == 0]\n",
        "df_class_1 = df[df['Label'] == 1]\n",
        "df_class_2 = df[df['Label'] == 2]\n",
        "df_class_3 = df[df['Label'] == 3]\n",
        "df_class_4 = df[df['Label'] == 4]\n",
        "df_class_5 = df[df['Label'] == 5]\n",
        "df_class_6 = df[df['Label'] == 6]\n",
        "df_class_7 = df[df['Label'] == 7]\n",
        "df_class_8 = df[df['Label'] == 8]\n",
        "df_class_9 = df[df['Label'] == 9]\n",
        "df_class_10 = df[df['Label'] == 10]\n",
        "df_class_11 = df[df['Label'] == 11]\n",
        "df_class_12 = df[df['Label'] == 12]\n",
        "df_class_13 = df[df['Label'] == 13]\n",
        "df_class_14 = df[df['Label'] == 14]\n",
        "df_class_15 = df[df['Label'] == 15]\n",
        "df_class_16 = df[df['Label'] == 16]"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ECFnq9Ow1IDd"
      },
      "source": [
        "df_class_0_1 = df_class_0.sample(4000)\n",
        "df_class_1_1 = df_class_1.sample(4000, replace= True)\n",
        "df_class_2_1 = df_class_2.sample(4000)\n",
        "df_class_3_1 = df_class_3.sample(4000, replace= True)\n",
        "df_class_4_1 = df_class_4.sample(4000, replace= True)\n",
        "df_class_5_1 = df_class_5.sample(4000, replace= True)\n",
        "df_class_6_1 = df_class_6.sample(4000, replace= True)\n",
        "df_class_7_1 = df_class_7.sample(4000, replace= True)\n",
        "df_class_8_1 = df_class_8.sample(4000)\n",
        "df_class_9_1 = df_class_9.sample(4000)\n",
        "df_class_10_1 = df_class_10.sample(4000)\n",
        "df_class_11_1 = df_class_11.sample(4000)\n",
        "df_class_12_1 = df_class_12.sample(4000, replace= True)\n",
        "df_class_13_1 = df_class_13.sample(4000, replace= True)\n",
        "df_class_14_1 = df_class_14.sample(4000)\n",
        "df_class_15_1 = df_class_15.sample(4000)\n",
        "df_class_16_1 = df_class_16.sample(4000, replace= True)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LiwkawpD4Gvz"
      },
      "source": [
        "df_test_under = pd.concat([df_class_0_1, df_class_1_1, df_class_2_1, df_class_3_1, df_class_4_1, df_class_5_1, df_class_6_1, df_class_7_1, df_class_8_1, df_class_9_1, df_class_10_1, df_class_11_1, df_class_12_1, df_class_13_1, df_class_14_1, df_class_15_1, df_class_16_1], axis=0)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "6Ykt8ejE5Etf",
        "outputId": "79607850-e333-429d-88f5-01f09f9be8e5"
      },
      "source": [
        "df_test_under"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>1282</th>\n",
              "      <td>it's a foggy day</td>\n",
              "      <td>16</td>\n",
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              "    <tr>\n",
              "      <th>1204</th>\n",
              "      <td>weather information in stockholm austria</td>\n",
              "      <td>16</td>\n",
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              "    <tr>\n",
              "      <th>1280</th>\n",
              "      <td>it's a misty day</td>\n",
              "      <td>16</td>\n",
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              "      <td>subtropical</td>\n",
              "      <td>16</td>\n",
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              "      <th>1557</th>\n",
              "      <td>turbulence</td>\n",
              "      <td>16</td>\n",
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              "</table>\n",
              "<p>68000 rows × 2 columns</p>\n",
              "</div>"
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              "                                            Text  Label\n",
              "105365                                    dothan      0\n",
              "99841                   let's talk about lubbock      0\n",
              "100947              let's talk about miami beach      0\n",
              "102170                 tell me about new zealand      0\n",
              "97952          what are dairy queen in stockholm      0\n",
              "...                                          ...    ...\n",
              "1282                            it's a foggy day     16\n",
              "1204    weather information in stockholm austria     16\n",
              "1280                            it's a misty day     16\n",
              "1514                                 subtropical     16\n",
              "1557                                  turbulence     16\n",
              "\n",
              "[68000 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dGAhpD8968NA"
      },
      "source": [
        "df = df_test_under.reindex()"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "avmG7fD28R7T"
      },
      "source": [
        "df = df.reset_index()\n",
        "del df['index']"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "IMNiwSFb7eIs",
        "outputId": "1b63b011-c1cd-41f4-9a35-7935d1f150d0"
      },
      "source": [
        "df"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <td>it's a foggy day</td>\n",
              "      <td>16</td>\n",
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              "      <th>67997</th>\n",
              "      <td>it's a misty day</td>\n",
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              "      <td>subtropical</td>\n",
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              "      <td>turbulence</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>68000 rows × 2 columns</p>\n",
              "</div>"
            ],
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              "                                           Text  Label\n",
              "0                                        dothan      0\n",
              "1                      let's talk about lubbock      0\n",
              "2                  let's talk about miami beach      0\n",
              "3                     tell me about new zealand      0\n",
              "4             what are dairy queen in stockholm      0\n",
              "...                                         ...    ...\n",
              "67995                          it's a foggy day     16\n",
              "67996  weather information in stockholm austria     16\n",
              "67997                          it's a misty day     16\n",
              "67998                               subtropical     16\n",
              "67999                                turbulence     16\n",
              "\n",
              "[68000 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZCojR5sL7gv_"
      },
      "source": [
        "df = df.sample(frac=1).reset_index(drop=True)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "CgBPhraj8pgR",
        "outputId": "abdac960-e72d-4390-938a-e0d45a8c8537"
      },
      "source": [
        "df"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <td>5</td>\n",
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              "      <th>67996</th>\n",
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              "</table>\n",
              "<p>68000 rows × 2 columns</p>\n",
              "</div>"
            ],
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              "                                              Text  Label\n",
              "0                                            largo      0\n",
              "1                                      liv ullmann      9\n",
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              "3                            tomboy fashion styles      3\n",
              "4                           just a new joke thanks      7\n",
              "...                                            ...    ...\n",
              "67995  i wanna talk about foods like italian bread      5\n",
              "67996         something about meal like sloppy joe      5\n",
              "67997                                    megalodon     13\n",
              "67998                        fashion alexa fashion      3\n",
              "67999                              c lewis authors      8\n",
              "\n",
              "[68000 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2noJRc5u8qG6"
      },
      "source": [
        "train_df = df.iloc[0:60000, :] \n",
        "test_df = df.iloc[60000:68000, :]"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "wBQcch4C9AOS",
        "outputId": "60c1c788-ca1a-40f1-b2c7-0984e5929464"
      },
      "source": [
        "train_df"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59995</th>\n",
              "      <td>eliot l engel</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59996</th>\n",
              "      <td>do you know about nacho</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59997</th>\n",
              "      <td>what are popular breed of crocodile</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59998</th>\n",
              "      <td>festive story about ksjkuew sjdhsd lsl please</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59999</th>\n",
              "      <td>l and</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>60000 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                Text  Label\n",
              "0                                              largo      0\n",
              "1                                        liv ullmann      9\n",
              "2                      weather information in berlin     16\n",
              "3                              tomboy fashion styles      3\n",
              "4                             just a new joke thanks      7\n",
              "...                                              ...    ...\n",
              "59995                                  eliot l engel     11\n",
              "59996                        do you know about nacho      5\n",
              "59997            what are popular breed of crocodile     13\n",
              "59998  festive story about ksjkuew sjdhsd lsl please      7\n",
              "59999                                          l and     11\n",
              "\n",
              "[60000 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "4w3y1P0M9Avq",
        "outputId": "3f7ef166-9f8e-4d06-f248-1328d2682921"
      },
      "source": [
        "test_df"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Text</th>\n",
              "      <th>Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>60000</th>\n",
              "      <td>just chat about institutions like ngl energy p...</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60001</th>\n",
              "      <td>tell me about buffalo wild wings</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60002</th>\n",
              "      <td>okay we re done man</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60003</th>\n",
              "      <td>i gonna talk about dele alli</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60004</th>\n",
              "      <td>kenneth graham</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67995</th>\n",
              "      <td>i wanna talk about foods like italian bread</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67996</th>\n",
              "      <td>something about meal like sloppy joe</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67997</th>\n",
              "      <td>megalodon</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67998</th>\n",
              "      <td>fashion alexa fashion</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67999</th>\n",
              "      <td>c lewis authors</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8000 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                    Text  Label\n",
              "60000  just chat about institutions like ngl energy p...     15\n",
              "60001                   tell me about buffalo wild wings      0\n",
              "60002                                okay we re done man      2\n",
              "60003                       i gonna talk about dele alli     11\n",
              "60004                                     kenneth graham     11\n",
              "...                                                  ...    ...\n",
              "67995        i wanna talk about foods like italian bread      5\n",
              "67996               something about meal like sloppy joe      5\n",
              "67997                                          megalodon     13\n",
              "67998                              fashion alexa fashion      3\n",
              "67999                                    c lewis authors      8\n",
              "\n",
              "[8000 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C2R6H0H4Q1PE"
      },
      "source": [
        "# RoBERTa"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fZjK1Z6LQ5gU"
      },
      "source": [
        "**Fine Tuning on Pretrainined Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cc7M3K_R9Bjc",
        "outputId": "8b32f19a-2198-4630-975b-f9d24410900e"
      },
      "source": [
        "model = TransformerModel('roberta', 'roberta-large', num_labels=17, use_cuda=True,\n",
        "args={'learning_rate':1e-5, 'num_train_epochs': 2, \n",
        "      'train_batch_size': 32, 'eval_batch_size': 32,               \n",
        "      'reprocess_input_data': True, 'overwrite_output_dir': True})"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at roberta-large were not used when initializing RobertaForSequenceClassification: ['lm_head.dense.weight', 'roberta.pooler.dense.bias', 'roberta.pooler.dense.weight', 'lm_head.layer_norm.bias', 'lm_head.layer_norm.weight', 'lm_head.bias', 'lm_head.dense.bias', 'lm_head.decoder.weight']\n",
            "- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-large and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.weight', 'classifier.dense.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kK1e73XXQ8q5"
      },
      "source": [
        "**Training**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 216,
          "referenced_widgets": [
            "a472cc3ccfac479fa98d2cb86ae28995",
            "8aba2f2afbb541feaa0f54dc6f562b4b",
            "374a62d727a940889a6b42b5b4cec233",
            "ed186bea87894bb5bb449e6adeb2aa16",
            "82b739f757d94680beb0a21438038191",
            "49dc65c52c4a436da632f016da36be5b",
            "5dbc81ba755141a4a61efc3797671287",
            "1973ab8633e54927b60a2659458db441",
            "5d6395133c954fdfb88a0d3f59532215",
            "5b27ba0263064bd1929444b1a4c6662d",
            "366639741ecb4c14afd2d8b48d5d8ab1",
            "2bc36debf2574d3da7c11902d6c56bbd",
            "cf24f90606fc4d61b756b304cb18269e",
            "2c509016ebbf48318abbe44ada76ed1f",
            "5845636ae2fc4cfc84fb881038c2a1ef",
            "a4ad57edb37c4471afc84d1ab6e44bbb",
            "c0e4dffa3ed14217b11047e6ee8aec22",
            "f45b5d759b134e5a8d6588d60fa4e579",
            "59a5aade25664ca796d47157d5e21504",
            "ca5f7f39e21f4460994220665d1ae571",
            "d8a9abb3c1694856849b23c374822d66",
            "e35d68a24a7942679eeb501c35bbdd7d",
            "0c854f8e83124e28a7a19add26becfe5",
            "298e5f6143024398b285833f646aad8b",
            "e24f97a71c61421ab642c3f527dc5a7c",
            "e326976dafc249a29e3b166fb381615e",
            "bd8bab85594e4e439fb586bc9218b0c0",
            "3bd7da63c5b64fafaa480ab1f590f517",
            "bcb4468c45dc450f83b6e26f74e4787e",
            "d37344057cb242e485cabf0ea0e20c3d",
            "8e67fdbdcafe422d81e35f40b1a2830e",
            "0352ae2ededa41bda1a3e6a3cff201ee",
            "806b113468b340c9999792820cf848cf",
            "3afced728f9f452cbee443ffb20b70c9",
            "a962ae89e43643d994a31d3d42cf6653",
            "f9e9a186cb92423fbc91a2d273e83b8a",
            "f75cc4f5a76943ed88d12f446e15cead",
            "797ad08f749a4869bc558918ea0818fa",
            "419d71632b374d75848f228fd4b2988f",
            "6032e5341f3d4a8089994dcb56c5af4d",
            "2831bfdf523c4fecb437fa4b26543989",
            "69212b9a22e648889ae9049ddfc2d4e2",
            "794eee82e24246439fb2e4816aa2287a",
            "584eb63a7c5a4bc69bbb12c72455c96c"
          ]
        },
        "id": "6G4Bi8WP9uYP",
        "outputId": "cd30c04f-c88f-4934-ecf7-d11a2e75aac1"
      },
      "source": [
        "model.train_model(train_df) "
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/simpletransformers/classification/classification_model.py:586: UserWarning: Dataframe headers not specified. Falling back to using column 0 as text and column 1 as labels.\n",
            "  \"Dataframe headers not specified. Falling back to using column 0 as text and column 1 as labels.\"\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a472cc3ccfac479fa98d2cb86ae28995",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/60000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2bc36debf2574d3da7c11902d6c56bbd",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Epoch:   0%|          | 0/2 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0c854f8e83124e28a7a19add26becfe5",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Running Epoch 0 of 2:   0%|          | 0/1875 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3afced728f9f452cbee443ffb20b70c9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Running Epoch 1 of 2:   0%|          | 0/1875 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3750, 0.3524978413303693)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IltFIDVdRDVm"
      },
      "source": [
        "**Testing**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135,
          "referenced_widgets": [
            "63b59d14bdd04025a264f7a24b48b93b",
            "bf338bb10ec641a993100dc3d17cc19d",
            "2ffc1a8c75ab43ae907b79064b78ec52",
            "edbef03d462049bc92ebaa4aea3b7290",
            "9684e26981a043c3b559a5cef91d97c3",
            "b0348378cc614176aa79e1cb1f734cff",
            "3e463035407e4f509289accbd891fd40",
            "df0458c151f141dabbd2310bba5ac5e7",
            "6d4011888f37407dafb59db820d03a31",
            "5b53f29762534db78a976bca3e0b36eb",
            "2348e0d6d8fb40d5b0c4932dd873e51a",
            "6f7b385b7cf545f8b96fb0f64a9114b0",
            "c92951a5170c4bc687164224f742b752",
            "58b4fcc4666649e8b424b3a053ee29b9",
            "3de0f71d304c4b41b8577713ff44c6e3",
            "8d08d697699c4c1d9e8f66fc1f50b4f2",
            "bbb23184467942c3811ea81d2b3229e2",
            "ddd56e1c03644586bf3b209fafa23310",
            "33259975826a4bc39647b5766731010f",
            "393a0579f9904ee5b54ad2cf39a5d4a1",
            "6b09d33c2b654e7d9d61f46e1ba73c1f",
            "86e8514766944b9e8853d1041b7c54d0"
          ]
        },
        "id": "j0BUCNPEHsax",
        "outputId": "84c6fe1a-d89c-4d6d-e362-5336037d6fe9"
      },
      "source": [
        "def f1_multiclass(labels, preds):\n",
        "      return f1_score(labels, preds, average='micro')\n",
        "\n",
        "result, model_outputs, wrong_predictions = model.eval_model(test_df, f1=f1_multiclass, acc=accuracy_score)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/simpletransformers/classification/classification_model.py:1427: UserWarning: Dataframe headers not specified. Falling back to using column 0 as text and column 1 as labels.\n",
            "  \"Dataframe headers not specified. Falling back to using column 0 as text and column 1 as labels.\"\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "63b59d14bdd04025a264f7a24b48b93b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/8000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6f7b385b7cf545f8b96fb0f64a9114b0",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Running Evaluation:   0%|          | 0/250 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yk9Xi8UlRIFT"
      },
      "source": [
        "**Accuracy / F1 - Score**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tllH6U6aLHXT",
        "outputId": "d48067f3-1226-409a-8f29-f3e101f29d1f"
      },
      "source": [
        "result"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'mcc': 0.9489475149818775,\n",
              " 'f1': 0.9519,\n",
              " 'acc': 0.9519,\n",
              " 'eval_loss': 0.15992265263685404}"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OJ1Vn5VCSYk5"
      },
      "source": [
        "**Classification Report**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_YRVBIq7Igp7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135,
          "referenced_widgets": [
            "b8b305e25f694995bcc23351c81469d5",
            "f06301b4e8874631be79475bcd9bb3f1",
            "8f8579a33d4e40f991da94d4c10c17ab",
            "1a6574715cd241cca0802218c65c6840",
            "aa03743d8dd646b796ebd58a5b21b2c9",
            "26b84845e8e7457daee7e595f9074ff4",
            "60e2bce05f2e476682d3bf413f1dbbbd",
            "b9b9812da1cd4d0db39b16ff050adb40",
            "ffab65f5d991458ea32e4d2a0b48fcf2",
            "2dc7339783e045c0949cd7bfd9a93ba4",
            "bcc60afbf0e34426a2c49e2a2f132ed1",
            "f25521e2cb7343dc987fe53dac30facf",
            "6bb155b97328472aa5e1e0d224474856",
            "55f68ed2e0c84821b04b02502a7dc463",
            "b093a3740bf042cb8a4dc70023dc6590",
            "d2d2d42978c945f9844e562fc6ad68c0",
            "9eb958f6c9dc4289ba0976c1a2104e65",
            "a20b13011d964de99d87e6a73cf8c2b2",
            "1fbceafd31c742e7943ad2297d7632a1",
            "af6b7beadca94061948e027d2a236d12",
            "cd611d4eb13a422383ba0be059376715",
            "b2fe18dfe3be4d0cbcbb86c547d67b2b"
          ]
        },
        "outputId": "95d6ca88-cc30-41f2-bb5f-8c0804052cae"
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "def class_report(labels, preds):\n",
        "      return classification_report(labels, preds, average='micro')\n",
        "\n",
        "\n",
        "class_report, model_outputs, wrong_predictions = model.eval_model(test_df, report = classification_report)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/simpletransformers/classification/classification_model.py:1427: UserWarning: Dataframe headers not specified. Falling back to using column 0 as text and column 1 as labels.\n",
            "  \"Dataframe headers not specified. Falling back to using column 0 as text and column 1 as labels.\"\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b8b305e25f694995bcc23351c81469d5",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/8000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f25521e2cb7343dc987fe53dac30facf",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Running Evaluation:   0%|          | 0/250 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3h9G2pIzUPz2",
        "outputId": "29da2732-9810-4283-aea0-3fe41b68dd75"
      },
      "source": [
        "class_report"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'mcc': 0.9461412062128047,\n",
              " 'report': '              precision    recall  f1-score   support\\n\\n         0.0       0.95      0.96      0.96       474\\n         1.0       0.90      0.96      0.93       493\\n         2.0       0.98      0.98      0.98       481\\n         3.0       1.00      1.00      1.00       466\\n         4.0       1.00      1.00      1.00       445\\n         5.0       1.00      1.00      1.00       474\\n         6.0       0.99      1.00      0.99       487\\n         7.0       1.00      1.00      1.00       443\\n         8.0       1.00      1.00      1.00       470\\n         9.0       0.83      0.77      0.80       485\\n        10.0       0.97      0.94      0.95       473\\n        11.0       0.75      0.84      0.79       473\\n        12.0       0.96      0.98      0.97       501\\n        13.0       0.99      0.98      0.98       470\\n        14.0       0.94      0.82      0.88       440\\n        15.0       0.95      0.92      0.94       459\\n        16.0       0.98      0.99      0.99       466\\n\\n    accuracy                           0.95      8000\\n   macro avg       0.95      0.95      0.95      8000\\nweighted avg       0.95      0.95      0.95      8000\\n',\n",
              " 'eval_loss': 0.17013946813344955}"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OV75iikfRbwp"
      },
      "source": [
        "**Predicting Dialouges**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bTJ1LwiiKng2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "aa05cd9374e64339a70d17ae37ef7b6a",
            "0686bc5f5ef84304ba451871533fcef3",
            "f45e03665f07442ea988d843762014cf",
            "51d79d998084485aa949903b33f5676d",
            "ddec350a3ea74738b2492ed2315428a2",
            "902ba4fc1d8649cbb004087b502812aa",
            "c890aae476414d2e9e6bd628c1a8144c",
            "1da8881b70a84af99e22549c967bcf3e",
            "1ab4d1c3b57f44f6b78450937c7dd71b",
            "d7d77bc177654e6892da704e3c6f248e",
            "a75557bbdeb343d2a4aa5a8d446ce144",
            "71629f753255487b89c11d5bfccb1675",
            "af3d9236f23c44ba8a2585a639b7ce51",
            "e67768ad0846431d93e83faae17ae11d",
            "91c854ddfacb4706a1c2870598a19f6d",
            "8e99b00ba99e4d25be8829cdb854a68f",
            "a73fa7b176e848929e6d1110d1082984",
            "3dd1cedfa97c4a7fb05ee28b97e9e0b4",
            "05bd607fb74946d69de125cd0101675e",
            "027d49910f2042d38eb9df9467064664",
            "c817b8f8c9114c378242a6c3e24294f4",
            "1b8d3c2ddaf6474ebb61372076fa912e"
          ]
        },
        "outputId": "cbcc0cf5-d8cb-4daa-c241-fe67a76bbd5a"
      },
      "source": [
        "prediction, output = model.predict(['Play starwars'])"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "aa05cd9374e64339a70d17ae37ef7b6a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "71629f753255487b89c11d5bfccb1675",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZAmwZOW8u467",
        "outputId": "62e75a3c-6616-4705-e165-20ca949e36e4"
      },
      "source": [
        "prediction = le.inverse_transform(prediction)\n",
        "prediction"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['__label__games'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d2DWiOCljgD6"
      },
      "source": [
        "# In conlcusion, fine tuning the RoBerta model achieved the folowing results\n",
        "**Final Accuracy = 95%**\n",
        "\n",
        "**Final F1 Score = 95%** "
      ]
    }
  ]
}