BERT-ieeekagglecup-2023.ipynb

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          "text": [
            "kaggle.json\n",
            "/content/data\n",
            "Downloading cis-kaggle-1-3.zip to /content/data\n",
            " 69% 1.00M/1.45M [00:00<00:00, 1.12MB/s]\n",
            "100% 1.45M/1.45M [00:01<00:00, 1.49MB/s]\n",
            "/content/data\n",
            "cis-kaggle-1-3.zip\n",
            "Archive:  cis-kaggle-1-3.zip\n",
            "  inflating: test.csv                \n",
            "  inflating: train.csv               \n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "# Configure kaggle\n",
        "os.chdir('/root/')\n",
        "!mkdir -p .kaggle\n",
        "os.chdir('/root/.kaggle')\n",
        "!wget --no-check-certificate 'https://raw.githubusercontent.com/aakarsh12bansal/kagglecup_s1/main/kaggle%20(2).json' -O kaggle.json > /dev/null 2>&1\n",
        "!ls /root/.kaggle\n",
        "\n",
        "# Set permissions \n",
        "!chmod 600 /root/.kaggle/kaggle.json\n",
        "\n",
        "# Create data folder\n",
        "os.chdir('/content/')\n",
        "!rm -rf data\n",
        "!mkdir data\n",
        "os.chdir('data')\n",
        "!pwd\n",
        "\n",
        "# Download data\n",
        "!pip install -q kaggle\n",
        "!kaggle competitions download -c cis-kaggle-1-3\n",
        "!pwd\n",
        "!ls\n",
        "# Unzip data\n",
        "!unzip cis-kaggle-1-3.zip train.csv test.csv\n"
      ]
    },
    {
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        "id": "GDqLsMZWoRhh"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler"
      ]
    },
    {
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            "text/plain": [
              "       season  episode   title  scene   speaker  \\\n",
              "0           1        1   Pilot      1   Michael   \n",
              "1           1        1   Pilot      1       Jim   \n",
              "2           1        1   Pilot      1   Michael   \n",
              "3           1        1   Pilot      1       Jim   \n",
              "4           1        1   Pilot      1   Michael   \n",
              "...       ...      ...     ...    ...       ...   \n",
              "54621       9       24  Finale   8153     Creed   \n",
              "54622       9       24  Finale   8154  Meredith   \n",
              "54623       9       24  Finale   8155   Phyllis   \n",
              "54624       9       24  Finale   8156       Jim   \n",
              "54625       9       24  Finale   8157       Pam   \n",
              "\n",
              "                                                    line Unnamed: 6  \n",
              "0      All right Jim. Your quarterlies look very good...        NaN  \n",
              "1             Oh, I told you. I couldn't close it. So...        NaN  \n",
              "2      So you've come to the master for guidance? Is ...        NaN  \n",
              "3             Actually, you called me in here, but yeah.        NaN  \n",
              "4        All right. Well, let me show you how it's done.        NaN  \n",
              "...                                                  ...        ...  \n",
              "54621  It all seems so very arbitrary. I applied for ...        NaN  \n",
              "54622  I just feel lucky that I got a chance to share...        NaN  \n",
              "54623  I'm happy that this was all filmed so I can re...        NaN  \n",
              "54624  I sold paper at this company for 12 years. My ...        NaN  \n",
              "54625  I thought it was weird when you picked us to m...        NaN  \n",
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      "source": [
        "df = pd.read_csv('train.csv')\n",
        "df"
      ]
    },
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              "                                                     [key], {});\n",
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              "\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",
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              "  "
            ],
            "text/plain": [
              "       season  episode  scene   speaker  \\\n",
              "0           1        1      1   Michael   \n",
              "1           1        1      1       Jim   \n",
              "2           1        1      1   Michael   \n",
              "3           1        1      1       Jim   \n",
              "4           1        1      1   Michael   \n",
              "...       ...      ...    ...       ...   \n",
              "54621       9       24   8153     Creed   \n",
              "54622       9       24   8154  Meredith   \n",
              "54623       9       24   8155   Phyllis   \n",
              "54624       9       24   8156       Jim   \n",
              "54625       9       24   8157       Pam   \n",
              "\n",
              "                                                    line Unnamed: 6  \n",
              "0      All right Jim. Your quarterlies look very good...        NaN  \n",
              "1             Oh, I told you. I couldn't close it. So...        NaN  \n",
              "2      So you've come to the master for guidance? Is ...        NaN  \n",
              "3             Actually, you called me in here, but yeah.        NaN  \n",
              "4        All right. Well, let me show you how it's done.        NaN  \n",
              "...                                                  ...        ...  \n",
              "54621  It all seems so very arbitrary. I applied for ...        NaN  \n",
              "54622  I just feel lucky that I got a chance to share...        NaN  \n",
              "54623  I'm happy that this was all filmed so I can re...        NaN  \n",
              "54624  I sold paper at this company for 12 years. My ...        NaN  \n",
              "54625  I thought it was weird when you picked us to m...        NaN  \n",
              "\n",
              "[54626 rows x 6 columns]"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.drop(['title'],inplace = True,axis=1)\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tcvgBgrcoyZb"
      },
      "outputs": [],
      "source": [
        "df = df.loc[:, ~df.columns.str.contains('^Unnamed')]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
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        "id": "vd5JVM5rpHss",
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            "text/plain": [
              "       season  episode  scene   speaker  \\\n",
              "0           1        1      1   Michael   \n",
              "1           1        1      1       Jim   \n",
              "2           1        1      1   Michael   \n",
              "3           1        1      1       Jim   \n",
              "4           1        1      1   Michael   \n",
              "...       ...      ...    ...       ...   \n",
              "54621       9       24   8153     Creed   \n",
              "54622       9       24   8154  Meredith   \n",
              "54623       9       24   8155   Phyllis   \n",
              "54624       9       24   8156       Jim   \n",
              "54625       9       24   8157       Pam   \n",
              "\n",
              "                                                    line  \n",
              "0      All right Jim. Your quarterlies look very good...  \n",
              "1             Oh, I told you. I couldn't close it. So...  \n",
              "2      So you've come to the master for guidance? Is ...  \n",
              "3             Actually, you called me in here, but yeah.  \n",
              "4        All right. Well, let me show you how it's done.  \n",
              "...                                                  ...  \n",
              "54621  It all seems so very arbitrary. I applied for ...  \n",
              "54622  I just feel lucky that I got a chance to share...  \n",
              "54623  I'm happy that this was all filmed so I can re...  \n",
              "54624  I sold paper at this company for 12 years. My ...  \n",
              "54625  I thought it was weird when you picked us to m...  \n",
              "\n",
              "[54626 rows x 5 columns]"
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    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GcKcbVrTpJAJ"
      },
      "outputs": [],
      "source": [
        "import re\n",
        "\n",
        "def clean_text(text):\n",
        "    #converting text to lowercase\n",
        "    text = text.lower()\n",
        "    #removing special characters\n",
        "    text = re.sub(r'[^a-zA-Z0-9\\s]', '', text)\n",
        "    #removing extra whitespaces\n",
        "    text = re.sub(r'\\s+', ' ', text)\n",
        "    #removing leading and trailing whitespaces\n",
        "    text = text.strip()\n",
        "    \n",
        "    return text\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "28_-zPQbp2Mx"
      },
      "outputs": [],
      "source": [
        "\n",
        "#try\n",
        "# dialogue = \"OH MY GOD JIMMY, vaLEntine's day is here!!!!!!!!!! WoW!!!!!!! >>>>>##\"\n",
        "# cleaned_dialogue = clean_text(dialogue)\n",
        "# print(cleaned_dialogue)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NYrvI6lfp3DC",
        "outputId": "532417c8-86b1-4ee2-8515-ed289a7b8785"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-9-f17f1fce616b>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['line'] = df['line'].apply(clean_text)\n"
          ]
        }
      ],
      "source": [
        "df['line'] = df['line'].apply(clean_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "TsAM33g-qLgy",
        "outputId": "ece30fef-940a-4f0b-98f5-faa4037a400a"
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              "                                                     [key], {});\n",
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              "\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",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       season  episode  scene   speaker  \\\n",
              "0           1        1      1   Michael   \n",
              "1           1        1      1       Jim   \n",
              "2           1        1      1   Michael   \n",
              "3           1        1      1       Jim   \n",
              "4           1        1      1   Michael   \n",
              "...       ...      ...    ...       ...   \n",
              "54621       9       24   8153     Creed   \n",
              "54622       9       24   8154  Meredith   \n",
              "54623       9       24   8155   Phyllis   \n",
              "54624       9       24   8156       Jim   \n",
              "54625       9       24   8157       Pam   \n",
              "\n",
              "                                                    line  \n",
              "0      all right jim your quarterlies look very good ...  \n",
              "1                    oh i told you i couldnt close it so  \n",
              "2      so youve come to the master for guidance is th...  \n",
              "3                actually you called me in here but yeah  \n",
              "4            all right well let me show you how its done  \n",
              "...                                                  ...  \n",
              "54621  it all seems so very arbitrary i applied for a...  \n",
              "54622  i just feel lucky that i got a chance to share...  \n",
              "54623  im happy that this was all filmed so i can rem...  \n",
              "54624  i sold paper at this company for 12 years my j...  \n",
              "54625  i thought it was weird when you picked us to m...  \n",
              "\n",
              "[54626 rows x 5 columns]"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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          "height": 533
        },
        "id": "vX3BUH0WvV76",
        "outputId": "014ed6cc-283e-4ba5-9b3b-976d4abaf3c3"
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      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-11-b87e412ab462>:9: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['speaker'] = df['speaker'].apply(convert_speaker)\n"
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ],
            "text/plain": [
              "       season  episode  scene   speaker  \\\n",
              "0           1        1      1   Michael   \n",
              "1           1        1      1         1   \n",
              "2           1        1      1   Michael   \n",
              "3           1        1      1         1   \n",
              "4           1        1      1   Michael   \n",
              "...       ...      ...    ...       ...   \n",
              "54621       9       24   8153     Creed   \n",
              "54622       9       24   8154  Meredith   \n",
              "54623       9       24   8155   Phyllis   \n",
              "54624       9       24   8156         1   \n",
              "54625       9       24   8157       Pam   \n",
              "\n",
              "                                                    line  \n",
              "0      all right jim your quarterlies look very good ...  \n",
              "1                    oh i told you i couldnt close it so  \n",
              "2      so youve come to the master for guidance is th...  \n",
              "3                actually you called me in here but yeah  \n",
              "4            all right well let me show you how its done  \n",
              "...                                                  ...  \n",
              "54621  it all seems so very arbitrary i applied for a...  \n",
              "54622  i just feel lucky that i got a chance to share...  \n",
              "54623  im happy that this was all filmed so i can rem...  \n",
              "54624  i sold paper at this company for 12 years my j...  \n",
              "54625  i thought it was weird when you picked us to m...  \n",
              "\n",
              "[54626 rows x 5 columns]"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def convert_speaker(speaker):\n",
        "    if 'Jim' in speaker and 'Dwight' not in speaker:\n",
        "        return 1\n",
        "    elif 'Dwight' in speaker and 'Jim' not in speaker:\n",
        "        return 0\n",
        "    else:\n",
        "        return speaker\n",
        "\n",
        "df['speaker'] = df['speaker'].apply(convert_speaker)\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aMXUkXZGyZ16"
      },
      "outputs": [],
      "source": [
        "#deleting rows without Jim or Dwight in the 'speaker' column\n",
        "df = df[df['speaker'].isin([0,1])]\n",
        "#resetting the index of the DataFrame\n",
        "df = df.reset_index(drop=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        "id": "OLcqxZjTywjb",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>actually you called me in here but yeah</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>shall i play for you pa rum pump um pum i have...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>my job is to speak to clients on the phone abo...</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>whassup i still love that after seven years</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13103</th>\n",
              "      <td>9</td>\n",
              "      <td>24</td>\n",
              "      <td>8140</td>\n",
              "      <td>1</td>\n",
              "      <td>there it is</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13104</th>\n",
              "      <td>9</td>\n",
              "      <td>24</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>thanks phyl</td>\n",
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              "      <th>13107</th>\n",
              "      <td>9</td>\n",
              "      <td>24</td>\n",
              "      <td>8156</td>\n",
              "      <td>1</td>\n",
              "      <td>i sold paper at this company for 12 years my j...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>13108 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7024afa8-09f0-413d-aeb5-84f6d5a596da')\"\n",
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              "            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",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       season  episode  scene speaker  \\\n",
              "0           1        1      1       1   \n",
              "1           1        1      1       1   \n",
              "2           1        1      5       0   \n",
              "3           1        1      6       1   \n",
              "4           1        1      7       1   \n",
              "...       ...      ...    ...     ...   \n",
              "13103       9       24   8140       1   \n",
              "13104       9       24   8141       0   \n",
              "13105       9       24   8147       1   \n",
              "13106       9       24   8148       1   \n",
              "13107       9       24   8156       1   \n",
              "\n",
              "                                                    line  \n",
              "0                    oh i told you i couldnt close it so  \n",
              "1                actually you called me in here but yeah  \n",
              "2      shall i play for you pa rum pump um pum i have...  \n",
              "3      my job is to speak to clients on the phone abo...  \n",
              "4            whassup i still love that after seven years  \n",
              "...                                                  ...  \n",
              "13103                                        there it is  \n",
              "13104  do i get along with my coworkers well first of...  \n",
              "13105  imagine going back and watching a tape of your...  \n",
              "13106                                        thanks phyl  \n",
              "13107  i sold paper at this company for 12 years my j...  \n",
              "\n",
              "[13108 rows x 5 columns]"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "1HMuhtrwQ6Uy",
        "outputId": "fd5f1a0b-912e-4b94-fdfc-003b512f7608"
      },
      "outputs": [
        {
          "data": {
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              "      <td>thanks phyl</td>\n",
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              "      <td>i sold paper at this company for 12 years my j...</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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            "text/plain": [
              "                                                    line speaker\n",
              "0                    oh i told you i couldnt close it so       1\n",
              "1                actually you called me in here but yeah       1\n",
              "2      shall i play for you pa rum pump um pum i have...       0\n",
              "3      my job is to speak to clients on the phone abo...       1\n",
              "4            whassup i still love that after seven years       1\n",
              "...                                                  ...     ...\n",
              "13103                                        there it is       1\n",
              "13104  do i get along with my coworkers well first of...       0\n",
              "13105  imagine going back and watching a tape of your...       1\n",
              "13106                                        thanks phyl       1\n",
              "13107  i sold paper at this company for 12 years my j...       1\n",
              "\n",
              "[13108 rows x 2 columns]"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = pd.DataFrame({\n",
        "    'line': df['line'],\n",
        "    'speaker': df['speaker']\n",
        "})\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2OpcW__kFOb4"
      },
      "outputs": [],
      "source": [
        "df.to_csv('cleaned_train.csv',index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6sgsJ0myASET"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_data, val_data = train_test_split(df, test_size=0.2, random_state=42)\n",
        "val_data, test_data = train_test_split(val_data, test_size=0.5, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "JDIuizYBKLRR",
        "outputId": "9bb2eec4-2d4f-4fc6-d883-df43ebd8e487"
      },
      "outputs": [
        {
          "data": {
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              "      <th>7588</th>\n",
              "      <td>lets go</td>\n",
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              "      <th>6947</th>\n",
              "      <td>yeah maybe</td>\n",
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              "    <tr>\n",
              "      <th>5304</th>\n",
              "      <td>andy still doesnt know that angelas having an ...</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>415</th>\n",
              "      <td>okay so i know about most of these but you kno...</td>\n",
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              "      <th>11964</th>\n",
              "      <td>this is jim halpert leave a message</td>\n",
              "      <td>1</td>\n",
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              "      <th>5191</th>\n",
              "      <td>oh pam i really hate that copier</td>\n",
              "      <td>1</td>\n",
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              "      <th>5390</th>\n",
              "      <td>looks like a red wire</td>\n",
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              "      <th>7270</th>\n",
              "      <td>no dwight i meant yes in a perfect world someo...</td>\n",
              "      <td>1</td>\n",
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              "</table>\n",
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              "          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",
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              "  </div>\n",
              "  "
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            "text/plain": [
              "                                                    line speaker\n",
              "7588                                             lets go       0\n",
              "6947                                          yeah maybe       1\n",
              "5304   andy still doesnt know that angelas having an ...       1\n",
              "415    okay so i know about most of these but you kno...       1\n",
              "3207                                    which one is she       1\n",
              "...                                                  ...     ...\n",
              "11964                this is jim halpert leave a message       1\n",
              "5191                    oh pam i really hate that copier       1\n",
              "5390                               looks like a red wire       1\n",
              "860    oh thats not advice what advice sounds like is...       1\n",
              "7270   no dwight i meant yes in a perfect world someo...       1\n",
              "\n",
              "[10486 rows x 2 columns]"
            ]
          },
          "execution_count": 17,
          "metadata": {},
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              "                                                    line speaker\n",
              "8876   will you let me know when this whole seminar t...       1\n",
              "9963   just going to walk over here whoa whoa im slip...       0\n",
              "9910   theyre not my favorite people in the world i w...       0\n",
              "4789   i know she loves me phyllis i am sure of it sh...       0\n",
              "3387                                              no way       1\n",
              "...                                                  ...     ...\n",
              "10362                          oh hey have you seen gabe       0\n",
              "4489   no way what did i say i said that theyd love t...       1\n",
              "11013  nellie let me bobbleize him his name is now ca...       0\n",
              "5613             what is that it is your birthday period       1\n",
              "103                                                  hey       1\n",
              "\n",
              "[1311 rows x 2 columns]"
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            "text/plain": [
              "                                                    line speaker\n",
              "461          welcome to the eighth annual dundies awards       0\n",
              "5082   no no no no no you want to wear that puppy rig...       0\n",
              "3288    why dont you run the meeting you play with dolls       1\n",
              "8507                                         i dont care       1\n",
              "7497                                             alright       1\n",
              "...                                                  ...     ...\n",
              "8952                              i asked you a question       0\n",
              "6496                                        who was that       1\n",
              "12871  you know dwight this whole search for the assi...       1\n",
              "9705                           like baboons or elephants       0\n",
              "6110                                                  no       0\n",
              "\n",
              "[1311 rows x 2 columns]"
            ]
          },
          "execution_count": 19,
          "metadata": {},
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    },
    {
      "cell_type": "code",
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      "metadata": {
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        },
        "id": "8zo_-aUpKZTl",
        "outputId": "8134a611-ab03-4fc2-cde8-65c5a26c0be5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.29.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.0)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.14.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.14.1)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.22.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2022.10.31)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.27.1)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.13.3)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.65.0)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (2023.4.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (4.5.0)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (1.26.15)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2022.12.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "!pip install transformers\n",
        "from transformers import BertTokenizer, TFBertForSequenceClassification"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9t86_MQtLbCQ"
      },
      "outputs": [],
      "source": [
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Qjr2ezoMLtNj"
      },
      "outputs": [],
      "source": [
        "def tokenize_data(line, speaker):\n",
        "    line = [str(l) for l in line]  # Convert line to a list of strings\n",
        "    tokenized_inputs = tokenizer(line, padding=True, truncation=True, return_tensors='tf')\n",
        "    input_ids = tokenized_inputs['input_ids']\n",
        "    attention_masks = tokenized_inputs['attention_mask']\n",
        "    return input_ids, attention_masks, speaker"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bJGowfQVRexJ"
      },
      "outputs": [],
      "source": [
        "train_line, train_speaker = train_data['line'].tolist(), train_data['speaker'].tolist()\n",
        "val_line, val_speaker = val_data['line'].tolist(), val_data['speaker'].tolist()\n",
        "test_line, test_speaker = test_data['line'].tolist(), test_data['speaker'].tolist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Eny9LxywjVHG"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "label_encoder = LabelEncoder()\n",
        "train_speaker = label_encoder.fit_transform(train_speaker)\n",
        "val_speaker = label_encoder.transform(val_speaker)\n",
        "test_speaker = label_encoder.transform(test_speaker)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RYD6JNxpSJNg"
      },
      "outputs": [],
      "source": [
        "train_input_ids, train_attention_masks, train_speaker = tokenize_data(train_line, train_speaker)\n",
        "val_input_ids, val_attention_masks, val_speaker = tokenize_data(val_line, val_speaker)\n",
        "test_input_ids, test_attention_masks, test_speaker = tokenize_data(test_line, test_speaker)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CHg_9LNUSN-E",
        "outputId": "1a48a1d2-0f39-41cf-f568-b3e4695c8ce5"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "All model checkpoint layers were used when initializing TFBertForSequenceClassification.\n",
            "\n",
            "Some layers of TFBertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "model = TFBertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qyXZNdUpSfzQ"
      },
      "outputs": [],
      "source": [
        "optimizer = tf.keras.optimizers.Adam(learning_rate=1e-5)\n",
        "loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
        "metric = tf.keras.metrics.SparseCategoricalAccuracy('accuracy')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "ixlEUFfXSkOD",
        "outputId": "a6ffdb49-9148-4064-aaaf-6a1fee934928"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "328/328 [==============================] - 451s 1s/step - loss: 0.6347 - accuracy: 0.6288 - val_loss: 0.5716 - val_accuracy: 0.7086\n",
            "Epoch 2/5\n",
            "328/328 [==============================] - 387s 1s/step - loss: 0.5485 - accuracy: 0.7179 - val_loss: 0.5706 - val_accuracy: 0.7094\n",
            "Epoch 3/5\n",
            "328/328 [==============================] - 387s 1s/step - loss: 0.4638 - accuracy: 0.7795 - val_loss: 0.5626 - val_accuracy: 0.7346\n",
            "Epoch 4/5\n",
            "328/328 [==============================] - 388s 1s/step - loss: 0.3714 - accuracy: 0.8269 - val_loss: 0.6246 - val_accuracy: 0.7223\n",
            "Epoch 5/5\n",
            "328/328 [==============================] - 380s 1s/step - loss: 0.2904 - accuracy: 0.8716 - val_loss: 0.7050 - val_accuracy: 0.7216\n"
          ]
        }
      ],
      "source": [
        "model.compile(optimizer=optimizer, loss=loss, metrics=[metric])\n",
        "history = model.fit(\n",
        "    x=(train_input_ids, train_attention_masks),\n",
        "    y=train_speaker,\n",
        "    validation_data=((val_input_ids, val_attention_masks), val_speaker),\n",
        "    batch_size=32,\n",
        "    epochs=5\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nZfbmYBMS666"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import classification_report\n",
        "predictions = model.predict((test_input_ids, test_attention_masks))\n",
        "predicted_labels = predictions.logits.argmax(axis=1)\n",
        "predicted_labels = label_encoder.inverse_transform(predicted_labels)\n",
        "\n",
        "true_labels = label_encoder.inverse_transform(test_speaker)\n",
        "\n",
        "print(classification_report(true_labels, predicted_labels))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "uPkB6Qyloyf3",
        "outputId": "4183c579-f9c2-4f5d-80bf-d2c99f469160"
      },
      "outputs": [
        {
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            "text/plain": [
              "        id                                               line  Unnamed: 2\n",
              "0        1  If I had one wish, it would be to have a mute ...         NaN\n",
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              "2        3  The Schrutes are a proud farming family. We kn...         NaN\n",
              "3        4  Sometimes I feel like this office is a never-e...         NaN\n",
              "4        5   Moms, primarily. Yep. Soccer moms. Single mom...         NaN\n",
              "...    ...                                                ...         ...\n",
              "1985  1986  Uh, Wade actually has those on the road, but h...         NaN\n",
              "1986  1987  Of course. He was here. She's the key. Amazing...         NaN\n",
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              "1988  1989  As has Ryan. As has Kelly. As has Meredith. As...         NaN\n",
              "1989  1990  The office meetings are like a twisted game of...         NaN\n",
              "\n",
              "[1990 rows x 3 columns]"
            ]
          },
          "execution_count": 68,
          "metadata": {},
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        }
      ],
      "source": [
        "df_2 = pd.read_csv('test.csv')\n",
        "df_2"
      ]
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    {
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        id                                               line\n",
              "0        1  If I had one wish, it would be to have a mute ...\n",
              "1        2  Dwight thinks he's the authority on everything...\n",
              "2        3  The Schrutes are a proud farming family. We kn...\n",
              "3        4  Sometimes I feel like this office is a never-e...\n",
              "4        5   Moms, primarily. Yep. Soccer moms. Single mom...\n",
              "...    ...                                                ...\n",
              "1985  1986  Uh, Wade actually has those on the road, but h...\n",
              "1986  1987  Of course. He was here. She's the key. Amazing...\n",
              "1987  1988  Every day feels like a battle between chaos an...\n",
              "1988  1989  As has Ryan. As has Kelly. As has Meredith. As...\n",
              "1989  1990  The office meetings are like a twisted game of...\n",
              "\n",
              "[1990 rows x 2 columns]"
            ]
          },
          "execution_count": 70,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_2 = df_2.loc[:, ~df_2.columns.str.contains('^Unnamed')]\n",
        "df_2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "TW9OK4uerSfw",
        "outputId": "3dcc2da8-1e5d-4b70-8c8e-3bf18885496c"
      },
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "        id                                               line\n",
              "0        1  if i had one wish it would be to have a mute b...\n",
              "1        2  dwight thinks hes the authority on everything ...\n",
              "2        3  the schrutes are a proud farming family we kno...\n",
              "3        4  sometimes i feel like this office is a neveren...\n",
              "4        5  moms primarily yep soccer moms single moms nas...\n",
              "...    ...                                                ...\n",
              "1985  1986  uh wade actually has those on the road but hes...\n",
              "1986  1987  of course he was here shes the key amazing hol...\n",
              "1987  1988  every day feels like a battle between chaos an...\n",
              "1988  1989  as has ryan as has kelly as has meredith as ha...\n",
              "1989  1990  the office meetings are like a twisted game of...\n",
              "\n",
              "[1990 rows x 2 columns]"
            ]
          },
          "execution_count": 71,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_2['line'] = df_2['line'].apply(clean_text)\n",
        "df_2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YFoU8kRPrlny"
      },
      "outputs": [],
      "source": [
        "df_2.to_csv('cleaned_test.csv',index=False)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GnWHdrA_uQNo"
      },
      "outputs": [],
      "source": [
        "test_data = df_2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x2LHqDeAsKBo"
      },
      "outputs": [],
      "source": [
        "def tokenize_data_2(text):\n",
        "    encoded_data = tokenizer.batch_encode_plus(\n",
        "        text,\n",
        "        add_special_tokens=True,\n",
        "        padding='max_length',\n",
        "        truncation=True,\n",
        "        max_length=176,\n",
        "        return_attention_mask=True,\n",
        "        return_tensors='tf'\n",
        "    )\n",
        "    input_ids = encoded_data['input_ids']\n",
        "    attention_masks = encoded_data['attention_mask']\n",
        "    return input_ids, attention_masks\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0aGnOq01sjzt"
      },
      "outputs": [],
      "source": [
        "test_input_ids, test_attention_masks = tokenize_data_2(test_data['line'].tolist())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gJwMw9WVs90P",
        "outputId": "01e241a0-fba6-48eb-e665-748a93c2ad0e"
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      "outputs": [
        {
          "data": {
            "text/plain": [
              "(<tf.Tensor: shape=(1990, 176), dtype=int32, numpy=\n",
              " array([[  101,  2065,  1045, ...,     0,     0,     0],\n",
              "        [  101, 14304,  6732, ...,     0,     0,     0],\n",
              "        [  101,  1996,  8040, ...,     0,     0,     0],\n",
              "        ...,\n",
              "        [  101,  2296,  2154, ...,     0,     0,     0],\n",
              "        [  101,  2004,  2038, ...,     0,     0,     0],\n",
              "        [  101,  1996,  2436, ...,     0,     0,     0]], dtype=int32)>,\n",
              " <tf.Tensor: shape=(1990, 176), dtype=int32, numpy=\n",
              " array([[1, 1, 1, ..., 0, 0, 0],\n",
              "        [1, 1, 1, ..., 0, 0, 0],\n",
              "        [1, 1, 1, ..., 0, 0, 0],\n",
              "        ...,\n",
              "        [1, 1, 1, ..., 0, 0, 0],\n",
              "        [1, 1, 1, ..., 0, 0, 0],\n",
              "        [1, 1, 1, ..., 0, 0, 0]], dtype=int32)>)"
            ]
          },
          "execution_count": 86,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_input_ids, test_attention_masks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L39rqn6NtQMN",
        "outputId": "09722ae1-b903-43d4-90c4-58342c59f489"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "63/63 [==============================] - 25s 393ms/step\n"
          ]
        }
      ],
      "source": [
        "predictions = model.predict((test_input_ids, test_attention_masks))\n",
        "predicted_labels = predictions.logits.argmax(axis=1)\n",
        "predicted_labels = label_encoder.inverse_transform(predicted_labels)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "us-W_3YstVZ_",
        "outputId": "6118adea-bc78-4db8-a3ca-241e3f1c6ed8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1990,)"
            ]
          },
          "execution_count": 88,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predicted_labels.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CqoqUclttb1o"
      },
      "outputs": [],
      "source": [
        "submission_data = pd.DataFrame({\n",
        "    'id': test_data['id'],\n",
        "    'speaker': predicted_labels\n",
        "})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0pbFsxKiulsi"
      },
      "outputs": [],
      "source": [
        "submission_data.to_csv('submission.csv', index=False)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "j4eda6j0upGq",
        "outputId": "e88d0118-6d5c-46a2-83b7-950daa9b84f0"
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      "outputs": [
        {
          "data": {
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              "      <td>shall i play for you pa rum pump um pum i have...</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>thanks phyl</td>\n",
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              "      <th>13107</th>\n",
              "      <td>i sold paper at this company for 12 years my j...</td>\n",
              "      <td>1</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>13108 rows × 2 columns</p>\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8dbcf3a9-886a-4312-be09-427e6587b99b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8dbcf3a9-886a-4312-be09-427e6587b99b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                                    line speaker\n",
              "0                    oh i told you i couldnt close it so       1\n",
              "1                actually you called me in here but yeah       1\n",
              "2      shall i play for you pa rum pump um pum i have...       0\n",
              "3      my job is to speak to clients on the phone abo...       1\n",
              "4            whassup i still love that after seven years       1\n",
              "...                                                  ...     ...\n",
              "13103                                        there it is       1\n",
              "13104  do i get along with my coworkers well first of...       0\n",
              "13105  imagine going back and watching a tape of your...       1\n",
              "13106                                        thanks phyl       1\n",
              "13107  i sold paper at this company for 12 years my j...       1\n",
              "\n",
              "[13108 rows x 2 columns]"
            ]
          },
          "execution_count": 92,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xgwztOEtwZan"
      },
      "outputs": [],
      "source": [
        "train_line_ac, train_speaker_ac = df['line'].tolist(), df['speaker'].tolist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0H_t6Wq7xsje"
      },
      "outputs": [],
      "source": [
        "train_speaker_ac = label_encoder.fit_transform(train_speaker_ac)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j86Qo2Q4yJYf"
      },
      "outputs": [],
      "source": [
        "train_input_ids_ac, train_attention_masks_ac, train_speaker_ac = tokenize_data(train_line_ac, train_speaker_ac)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TD0OxQlvyVEP",
        "outputId": "fc90425e-1ee9-44ff-a3d7-258c1849cfed"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "All model checkpoint layers were used when initializing TFBertForSequenceClassification.\n",
            "\n",
            "Some layers of TFBertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "modelfull = TFBertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PQ2ShkPCymyP"
      },
      "outputs": [],
      "source": [
        "optimizer = tf.keras.optimizers.legacy.Adam(learning_rate=2e-5)\n",
        "loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M5gSRYOu3Bxh"
      },
      "outputs": [],
      "source": [
        "from keras.callbacks import EarlyStopping"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YqO23D223G1X"
      },
      "outputs": [],
      "source": [
        "early_stopping = EarlyStopping(\n",
        "    monitor = 'loss',\n",
        "    patience = 2,\n",
        "    verbose = 1\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P4f7Mst24GEe",
        "outputId": "b4ecefe7-a1fe-4721-8078-99b06a7f0ae5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<bound method Model.summary of <transformers.models.bert.modeling_tf_bert.TFBertForSequenceClassification object at 0x7fd2df7f34f0>>"
            ]
          },
          "execution_count": 110,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "modelfull.summary"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "id": "DHXleIDfytE2",
        "outputId": "fcb187c2-5c7f-4684-ee97-cb576111e5f9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "410/410 [==============================] - 471s 1s/step - loss: 0.6952\n",
            "Epoch 2/5\n",
            "410/410 [==============================] - 461s 1s/step - loss: 0.6946\n",
            "Epoch 3/5\n",
            " 72/410 [====>.........................] - ETA: 6:19 - loss: 0.6956"
          ]
        },
        {
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-112-86419a4d5f72>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmodelfull\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m history = modelfull.fit(\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_input_ids_ac\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_attention_masks_ac\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      4\u001b[0m     \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_speaker_ac\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\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/python3.10/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     63\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     64\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---> 65\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     66\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;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\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/python3.10/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   1689\u001b[0m                             \u001b[0mlogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtmp_logs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1690\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-> 1691\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   1692\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   1693\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/python3.10/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    473\u001b[0m         \"\"\"\n\u001b[1;32m    474\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--> 475\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    476\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    477\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/python3.10/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    320\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    321\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--> 322\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    323\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    324\u001b[0m             raise ValueError(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/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    343\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    344\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 345\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    346\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    347\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/python3.10/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    391\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    392\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--> 393\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    394\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    395\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/python3.10/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   1091\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1092\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-> 1093\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   1094\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1095\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/python3.10/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   1167\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   1168\u001b[0m             \u001b[0;31m# Only block async when verbose = 1.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1169\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   1170\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   1171\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/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    678\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mt\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[0mt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 680\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    681\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    682\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/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    915\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    916\u001b[0m   return pack_sequence_as(\n\u001b[0;32m--> 917\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    918\u001b[0m       expand_composites=expand_composites)\n\u001b[1;32m    919\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/util/nest.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    915\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    916\u001b[0m   return pack_sequence_as(\n\u001b[0;32m--> 917\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    918\u001b[0m       expand_composites=expand_composites)\n\u001b[1;32m    919\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/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    671\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    672\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--> 673\u001b[0;31m             \u001b[0mt\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    674\u001b[0m         \u001b[0;31m# Strings, ragged and sparse tensors don't have .item(). Return them\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    675\u001b[0m         \u001b[0;31m# as-is.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mnumpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1158\u001b[0m     \"\"\"\n\u001b[1;32m   1159\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[0m\n\u001b[0;32m-> 1160\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   1161\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   1162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m_numpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1124\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   1125\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-> 1126\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   1127\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   1128\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": [
        "modelfull.compile(optimizer=optimizer, loss=loss)\n",
        "history = modelfull.fit(\n",
        "    x=(train_input_ids_ac, train_attention_masks_ac),\n",
        "    y=train_speaker_ac,\n",
        "    batch_size=32,\n",
        "    epochs=5,\n",
        "    callbacks = early_stopping\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bxSKt1zdy2fC"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}