COG403_Project_Att_BiLSTM.ipynb

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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np # linear algebra\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "epZ2BQvbPsEM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence\n",
        "import torch.nn.functional as F\n",
        "import torchtext\n",
        "import nltk\n",
        "import time\n",
        "from datetime import timedelta\n",
        "import numpy as np\n",
        "from sklearn import metrics\n",
        "from typing import Tuple\n",
        "import os\n",
        "import json\n",
        "from typing import Dict, Tuple, Union, Optional\n",
        "import torch\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from transformers import AutoTokenizer, BertModel\n",
        "from torch import nn, optim\n",
        "from tqdm import tqdm\n",
        "from datasets import load_dataset\n",
        "from torch.utils.data import ConcatDataset"
      ],
      "metadata": {
        "id": "NcuZn8z_Yc-2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Pre-process the Data"
      ],
      "metadata": {
        "id": "SK7menepPVEW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class MyDataset(Dataset):\n",
        "    def __init__(self, X, y):\n",
        "        self.X = X\n",
        "        self.y = y\n",
        "        \n",
        "    def __len__(self):\n",
        "        return len(self.X)\n",
        "    \n",
        "    def __getitem__(self, idx):\n",
        "        return self.X[idx], self.y[idx]\n",
        "\n",
        "def get_formatted_data(df):\n",
        "  one_hot_labels = pd.get_dummies(df.iloc[:, 1])\n",
        "  data = df.to_numpy()\n",
        "  X = data[:, 0]\n",
        "  y = torch.tensor(one_hot_labels.values)\n",
        "  \n",
        "  dataset = MyDataset(X, y)\n",
        "  return dataset"
      ],
      "metadata": {
        "id": "mf8I4rUdH3Zd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Poem Data Preprocessing"
      ],
      "metadata": {
        "id": "t0dw4xSJGF2j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "poem_data = pd.read_excel('./PERC_mendelly.xlsx')\n",
        "labels_to_drop = [\"peace\", \"courage\", \"hate\"]\n",
        "indices = poem_data[poem_data[\"Emotion\"].isin(labels_to_drop)].index\n",
        "poem_data[\"Emotion\"] = poem_data[\"Emotion\"].replace(\"sad\", \"sadness\")\n",
        "poem_data.drop(indices, inplace=True)\n",
        "poem_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 774
        },
        "id": "-bqKBel9Pl9H",
        "outputId": "432bafef-773c-437b-c5c3-c052b1acf465"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                  Poem  Emotion\n",
              "0    A Tree\\nA tree beside the sandy river-beach \\n...  sadness\n",
              "1    Sri Krishna\\n\\nO immense Light and thou, O spi...     love\n",
              "3    Revelation\\n\\n\\nSomeone leaping from the rocks...  sadness\n",
              "4    The Silver Call\\n\\n\\nThere is a godhead of unr...      joy\n",
              "5    Surrender\\n\\nO THOU of whom I am the instrumen...     love\n",
              "..                                                 ...      ...\n",
              "711  Daughter Taken By Mothers Lies\\n\\nHave you any...  sadness\n",
              "712  Involuntary Acceptance\\n\\nEven though\\nWe’re f...  sadness\n",
              "713  Victim Of Poverty\\n\\nPoverty stricken youth ju...  sadness\n",
              "714  Rain\\n\\nI sit and watch\\nas the rain falls \\nf...  sadness\n",
              "715  The Face Of Sadness\\n\\nIts happened again.\\n\\n...  sadness\n",
              "\n",
              "[536 rows x 2 columns]"
            ],
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the dataset into training and testing sets\n",
        "poem_train_data, poem_test_data = train_test_split(poem_data, test_size=0.9, random_state=42)\n",
        "\n",
        "# Split the training set into testing and validation sets\n",
        "poem_test_data, poem_val_data = train_test_split(poem_test_data, test_size=0.5, random_state=42)"
      ],
      "metadata": {
        "id": "3bCLxMJKEB1G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "poem_train_ds = get_formatted_data(poem_train_data)\n",
        "poem_test_ds = get_formatted_data(poem_test_data)\n",
        "poem_val_ds = get_formatted_data(poem_val_data)"
      ],
      "metadata": {
        "id": "QhoGMHvGHx7Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Twitter Message Data Pre processing"
      ],
      "metadata": {
        "id": "KRWKLDaTGL1z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "text_data_train = pd.read_table('./train.txt', sep=';', header=None)\n",
        "text_data_train.to_csv(\"train_cleaned.txt\")\n",
        "print(text_data_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2y3qVpDAPyYG",
        "outputId": "9fb308c1-cf21-4ef8-9671-9e72f3281a02"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                                       0        1\n",
            "0                                i didnt feel humiliated  sadness\n",
            "1      i can go from feeling so hopeless to so damned...  sadness\n",
            "2       im grabbing a minute to post i feel greedy wrong    anger\n",
            "3      i am ever feeling nostalgic about the fireplac...     love\n",
            "4                                   i am feeling grouchy    anger\n",
            "...                                                  ...      ...\n",
            "15995  i just had a very brief time in the beanbag an...  sadness\n",
            "15996  i am now turning and i feel pathetic that i am...  sadness\n",
            "15997                     i feel strong and good overall      joy\n",
            "15998  i feel like this was such a rude comment and i...    anger\n",
            "15999  i know a lot but i feel so stupid because i ca...  sadness\n",
            "\n",
            "[16000 rows x 2 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text_data_test = pd.read_table('./test.txt', sep=';', header=None)\n",
        "text_data_test.to_csv(\"test_cleaned.csv\")\n",
        "print(set(text_data_test[1]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HqpOVCxRYIYr",
        "outputId": "1c9246eb-2090-4aee-acc2-5a4311388991"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'surprise', 'fear', 'love', 'anger', 'joy', 'sadness'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text_data_val = pd.read_table('./val.txt', sep=';', header=None)\n",
        "# one_hot_labels_val = pd.get_dummies(text_data_val[1])\n",
        "# one_hot_labels_val = one_hot_labels_val.reindex(columns=one_hot_labels.columns, fill_value=0)\n",
        "# text_data_val[1] = one_hot_labels_val.values\n",
        "text_data_val.to_csv(\"val_cleaned.csv\")\n",
        "print(set(text_data_val[1]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cYjPIeCnYOFH",
        "outputId": "453462f1-d9b1-40bd-a7e7-92b4b3dd8989"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'surprise', 'fear', 'love', 'anger', 'joy', 'sadness'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text_train_ds = get_formatted_data(text_data_train)\n",
        "text_test_ds = get_formatted_data(text_data_test)\n",
        "text_val_ds = get_formatted_data(text_data_val)"
      ],
      "metadata": {
        "id": "iusg7WPFIcAw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Reddit Data Pre-processing"
      ],
      "metadata": {
        "id": "mXnQJAnlGY4V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = load_dataset(\"go_emotions\", \"raw\")\n",
        "columns_to_remove = [\"id\", \"author\", \"subreddit\", \"link_id\", \"parent_id\", \"created_utc\", \"rater_id\", \"example_very_unclear\",\n",
        "                    'admiration', 'amusement', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'gratitude', 'grief', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'neutral']\n",
        "\n",
        "dataset_dict = dataset.remove_columns(columns_to_remove)\n",
        "\n",
        "# Convert dataset to pandas DataFrame\n",
        "df = pd.DataFrame(dataset_dict[\"train\"])\n",
        "\n",
        "# Count the number of 1s in each row\n",
        "counts = df.iloc[:, 1:].sum(axis=1)\n",
        "\n",
        "# Filter the rows where the count is less than or equal to 1\n",
        "reddit_data = df[counts == 1]\n",
        "\n",
        "print(reddit_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        },
        "id": "Wmp86OtGRcQm",
        "outputId": "e446023e-212c-44f2-ac6c-b44fa2440882"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/5.75k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9c61b7f8a90b44e19dddef8490aa1a4d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading metadata:   0%|          | 0.00/7.03k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "589f78640b874123b6c43f59ee0633b5"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/9.11k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9a2455fc642b46c3a80498f74b5328b5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset go_emotions/raw to /root/.cache/huggingface/datasets/go_emotions/raw/0.0.0/2637cfdd4e64d30249c3ed2150fa2b9d279766bfcd6a809b9f085c61a90d776d...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b5e02a4d3c394913bc4842c4190ba669"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading data:   0%|          | 0.00/14.2M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f96a8a8a707e4bc7b4c4ed3e29943250"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/14.2M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "50ae8f47818e490db201ac371e9229bc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading data:   0%|          | 0.00/14.4M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e13319a59e7d4176b14a398b025c96e5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4cce7c3b4a624fa59d7efa0ea78884ed"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/211225 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2cfb90295ecd4a308464ea0397fc49f6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset go_emotions downloaded and prepared to /root/.cache/huggingface/datasets/go_emotions/raw/0.0.0/2637cfdd4e64d30249c3ed2150fa2b9d279766bfcd6a809b9f085c61a90d776d. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b2b0e69604a3403e90656b05977278a5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                                     text  anger  fear  joy  \\\n",
            "0                                         That game hurt.      0     0    0   \n",
            "3                                      Man I love reddit.      0     0    0   \n",
            "37      I just came home, what the fuck is this lineup...      0     0    0   \n",
            "43      By far the coolest thing I've seen on this thr...      0     0    1   \n",
            "49                     Sending love and strength vibes <3      0     0    1   \n",
            "...                                                   ...    ...   ...  ...   \n",
            "211187  I just called the Capitol Police. They are not...      1     0    0   \n",
            "211212    What a great photo and you two look so happy. 😍      0     0    1   \n",
            "211219  Well, I'm glad you're out of all that now. How...      0     0    1   \n",
            "211220                             Everyone likes [NAME].      0     0    0   \n",
            "211223  The FDA has plenty to criticize. But like here...      1     0    0   \n",
            "\n",
            "        love  sadness  surprise  \n",
            "0          0        1         0  \n",
            "3          1        0         0  \n",
            "37         1        0         0  \n",
            "43         0        0         0  \n",
            "49         0        0         0  \n",
            "...      ...      ...       ...  \n",
            "211187     0        0         0  \n",
            "211212     0        0         0  \n",
            "211219     0        0         0  \n",
            "211220     1        0         0  \n",
            "211223     0        0         0  \n",
            "\n",
            "[36822 rows x 7 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the dataset into training and testing sets\n",
        "reddit_train_data, reddit_test_data = train_test_split(reddit_data, test_size=0.9, random_state=42)\n",
        "\n",
        "# Split the training set into training and validation sets\n",
        "reddit_test_data, reddit_val_data = train_test_split(reddit_test_data, test_size=0.5, random_state=42)"
      ],
      "metadata": {
        "id": "oNCCeWj0G0u_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_format_df(filtered_df):\n",
        "  X = filtered_df['text'].to_numpy()\n",
        "  y = torch.tensor(filtered_df.iloc[:, 1:].values)\n",
        "\n",
        "  return MyDataset(X, y)"
      ],
      "metadata": {
        "id": "VQLBkBBzIvem"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reddit_train_ds = get_format_df(reddit_train_data)\n",
        "reddit_test_ds = get_format_df(reddit_test_data)\n",
        "reddit_val_ds = get_format_df(reddit_val_data)"
      ],
      "metadata": {
        "id": "rxvG1VatJCMj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Get DataLoaders for Training and Testing"
      ],
      "metadata": {
        "id": "KekupOaZT5uF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_dl = DataLoader(text_train_ds, batch_size=32, shuffle=True)\n",
        "val_dl = DataLoader(text_val_ds, batch_size=32, shuffle=True)"
      ],
      "metadata": {
        "id": "sNcRWusyLKP_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Combine datasets to Train, Test and Val\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "_ltRoCqTJu0G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_dl(ds1, ds2, ds3):\n",
        "  combined_dataset = ConcatDataset([ds1, ds2, ds3])\n",
        "  return DataLoader(combined_dataset, batch_size=32, shuffle=True)\n",
        "\n",
        "combined_train_dl = get_dl(text_train_ds, poem_train_ds, reddit_train_ds)\n",
        "combined_val_dl = get_dl(text_val_ds, poem_val_ds, reddit_val_ds)"
      ],
      "metadata": {
        "id": "H0qMJMyYJyCi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Create Test DataLoaders"
      ],
      "metadata": {
        "id": "XJ8OJwXUUJSL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "poem_test_dl = DataLoader(poem_test_ds, batch_size=32, shuffle=True)\n",
        "reddit_test_dl = DataLoader(reddit_test_ds, batch_size=32, shuffle=True)\n",
        "text_test_dl = DataLoader(text_test_ds, batch_size=32, shuffle=True)"
      ],
      "metadata": {
        "id": "qTbx30X5OZUF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model 2 - Att-BiLSTM"
      ],
      "metadata": {
        "id": "mK2YKXhjYdFY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Trainer Model"
      ],
      "metadata": {
        "id": "6Ge9Mg9JUPfY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(device)\n",
        "\n",
        "class Trainer:\n",
        "  \"\"\"\n",
        "  Training pipeline\n",
        "\n",
        "  Parameters\n",
        "  ----------\n",
        "  num_epochs : int\n",
        "      We should train the model for __ epochs\n",
        "\n",
        "  start_epoch : int\n",
        "      We should start training the model from __th epoch\n",
        "\n",
        "  train_loader : DataLoader\n",
        "      DataLoader for training data\n",
        "\n",
        "  model : nn.Module\n",
        "      Model\n",
        "\n",
        "  model_name : str\n",
        "      Name of the model\n",
        "\n",
        "  loss_function : nn.Module\n",
        "      Loss function (cross entropy)\n",
        "\n",
        "  optimizer : optim.Optimizer\n",
        "      Optimizer (Adam)\n",
        "\n",
        "  lr_decay : float\n",
        "      A factor in interval (0, 1) to multiply the learning rate with\n",
        "\n",
        "  dataset_name : str\n",
        "      Name of the dataset\n",
        "\n",
        "  grad_clip : float, optional\n",
        "      Gradient threshold in clip gradients\n",
        "\n",
        "  print_freq : int\n",
        "      Print training status every __ batches\n",
        "\n",
        "  \"\"\"\n",
        "  def __init__(\n",
        "      self,\n",
        "      num_epochs: int,\n",
        "      train_loader: DataLoader,\n",
        "      val_loader: DataLoader,\n",
        "      model: nn.Module,\n",
        "      loss_function: nn.Module,\n",
        "      optimizer,\n",
        "      tokenizer,\n",
        "      grad_clip = Optional[None],\n",
        "      print_freq: int = 100,\n",
        "  ) -> None:\n",
        "\n",
        "      self.num_epochs = num_epochs\n",
        "      self.train_loader = train_loader\n",
        "      self.val_loader = val_loader\n",
        "\n",
        "      self.model = model\n",
        "      self.loss_function = loss_function\n",
        "      self.optimizer = optimizer\n",
        "\n",
        "      self.print_freq = print_freq\n",
        "      self.grad_clip = grad_clip\n",
        "\n",
        "      self.tokenizer = tokenizer\n",
        "      self.len_epoch = len(self.train_loader)\n",
        "\n",
        "  def train(self, epoch: int) -> None:\n",
        "    \"\"\"\n",
        "    Train an epoch\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    epoch : int\n",
        "        Current number of epoch\n",
        "    \"\"\"\n",
        "    self.model.train()  # training mode enables dropout\n",
        "    train_losses = []\n",
        "    train_accs = []\n",
        "    val_losses = []\n",
        "    val_accs = []\n",
        "\n",
        "    # Train step\n",
        "    for i, batch in enumerate(self.train_loader):\n",
        "\n",
        "      sentences, labels = batch\n",
        "      tokenized = self.tokenizer(sentences, padding=True, return_tensors='pt')\n",
        "      sentences = tokenized['input_ids']\n",
        "      words_per_sentence = torch.sum(tokenized['attention_mask'], dim=-1)\n",
        "      sentences = sentences.to(device)  # (batch_size, word_limit)\n",
        "      labels = torch.argmax(labels, dim=-1).to(device)  # (batch_size)\n",
        "\n",
        "      scores = self.model(sentences, words_per_sentence)  # (batch_size, n_classes)\n",
        "\n",
        "      # calc loss\n",
        "      train_loss = self.loss_function(scores, labels)  # scalar\n",
        "\n",
        "      # backward\n",
        "      self.optimizer.zero_grad()\n",
        "      train_loss.backward()\n",
        "\n",
        "      # grad clip\n",
        "      nn.utils.clip_grad_norm_(model.parameters(), max_norm=self.grad_clip)\n",
        "\n",
        "      # update weights\n",
        "      self.optimizer.step()\n",
        "\n",
        "      # find accuracy\n",
        "      _, predictions = scores.max(dim = 1)  # (n_documents)\n",
        "      correct_predictions = torch.eq(predictions, labels).sum().item()\n",
        "      train_accuracy = correct_predictions / labels.size(0)\n",
        "      train_losses.append(train_loss.item())\n",
        "      train_accs.append(train_accuracy)\n",
        "    \n",
        "    # Validation step\n",
        "    with torch.no_grad():\n",
        "      for i, batch in enumerate(self.val_loader):\n",
        "\n",
        "        sentences, labels = batch\n",
        "        tokenized = self.tokenizer(sentences, padding=True, return_tensors='pt', max_length=512, truncation=True)\n",
        "        sentences = tokenized['input_ids']\n",
        "        words_per_sentence = torch.sum(tokenized['attention_mask'], dim=-1)\n",
        "        sentences = sentences.to(device)  # (batch_size, word_limit)\n",
        "        labels = torch.argmax(labels, dim=-1).to(device)  # (batch_size)\n",
        "\n",
        "        scores = self.model(sentences, words_per_sentence)  # (batch_size, n_classes)\n",
        "\n",
        "        # calc loss\n",
        "        val_loss = self.loss_function(scores, labels)  # scalar\n",
        "\n",
        "        # find accuracy\n",
        "        _, predictions = scores.max(dim = 1)  # (n_documents)\n",
        "        correct_predictions = torch.eq(predictions, labels).sum().item()\n",
        "        val_accuracy = correct_predictions / labels.size(0)\n",
        "  \n",
        "        val_losses.append(val_loss.item())\n",
        "        val_accs.append(val_accuracy)\n",
        "    \n",
        "    # keep track of metrics\n",
        "\n",
        "    # print training status\n",
        "    print(f'\\n Epoch: {epoch}: Train Loss {np.mean(train_losses)}, Train Accuracy {np.mean(train_accs)} \\n Val Loss {np.mean(val_losses)}, Val Accuracy {np.mean(val_accs)}')\n",
        "\n",
        "  def run_train(self):\n",
        "\n",
        "    # epochs\n",
        "    for epoch in tqdm(range(self.num_epochs)):\n",
        "      # trian an epoch\n",
        "      self.train(epoch=epoch)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_7D_lBeJIpIr",
        "outputId": "e60c4447-e048-4cfd-fe9b-901297992452"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cuda\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class Attention(nn.Module):\n",
        "    \"\"\"\n",
        "    Attention network\n",
        "    Parameters\n",
        "    ----------\n",
        "    rnn_size : int\n",
        "        Size of Bi-LSTM\n",
        "    \"\"\"\n",
        "    def __init__(self, rnn_size: int) -> None:\n",
        "        super(Attention, self).__init__()\n",
        "        self.w = nn.Linear(rnn_size, 1)\n",
        "        self.tanh = nn.Tanh()\n",
        "        self.softmax = nn.Softmax(dim=1)\n",
        "\n",
        "    def forward(self, H: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:\n",
        "        \"\"\"\n",
        "        Parameters\n",
        "        ----------\n",
        "        H : torch.Tensor (batch_size, word_pad_len, hidden_size)\n",
        "            Output of Bi-LSTM\n",
        "        Returns\n",
        "        -------\n",
        "        r : torch.Tensor (batch_size, rnn_size)\n",
        "            Sentence representation\n",
        "        alpha : torch.Tensor (batch_size, word_pad_len)\n",
        "            Attention weights\n",
        "        \"\"\"\n",
        "        # eq.9: M = tanh(H)\n",
        "        M = self.tanh(H)  # (batch_size, word_pad_len, rnn_size)\n",
        "\n",
        "        # eq.10: α = softmax(w^T M)\n",
        "        alpha = self.w(M).squeeze(2)  # (batch_size, word_pad_len)\n",
        "        alpha = self.softmax(alpha)  # (batch_size, word_pad_len)\n",
        "\n",
        "        # eq.11: r = H\n",
        "        r = H * alpha.unsqueeze(2)  # (batch_size, word_pad_len, rnn_size)\n",
        "        r = r.sum(dim = 1)  # (batch_size, rnn_size)\n",
        "\n",
        "        return r, alpha"
      ],
      "metadata": {
        "id": "aq2NdLuTCnNM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence, PackedSequence\n",
        "\n",
        "def get_pretrained_embeddings():\n",
        "\n",
        "    model = BertModel.from_pretrained(\"bert-base-uncased\")\n",
        "    embedding_matrix = model.embeddings.word_embeddings.weight\n",
        "    return embedding_matrix\n",
        "\n",
        "class AttBiLSTM(nn.Module):\n",
        "    \"\"\"\n",
        "    Implementation of Attention-based bidirectional LSTM proposed in paper [1].\n",
        "    Parameters\n",
        "    ----------\n",
        "    n_classes : int\n",
        "        Number of classes\n",
        "    vocab_size : int\n",
        "        Number of words in the vocabulary\n",
        "    embeddings : torch.Tensor\n",
        "        Word embedding weights\n",
        "    emb_size : int\n",
        "        Size of word embeddings\n",
        "    fine_tune : bool\n",
        "        Allow fine-tuning of embedding layer? (only makes sense when using\n",
        "        pre-trained embeddings)\n",
        "    rnn_size : int\n",
        "        Size of Bi-LSTM\n",
        "    rnn_layers : int\n",
        "        Number of layers in Bi-LSTM\n",
        "    dropout : float\n",
        "        Dropout\n",
        "    References\n",
        "    ----------\n",
        "    1. \"`Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification. \\\n",
        "        <https://www.aclweb.org/anthology/P16-2034.pdf>`_\" Peng Zhou, et al. ACL 2016.\n",
        "    \"\"\"\n",
        "    def __init__(\n",
        "        self,\n",
        "        n_classes: int,\n",
        "        vocab_size: int,\n",
        "        emb_size: int,\n",
        "        rnn_size: int,\n",
        "        rnn_layers: int,\n",
        "        dropout: float\n",
        "    ) -> None:\n",
        "        super(AttBiLSTM, self).__init__()\n",
        "\n",
        "        self.rnn_size = rnn_size\n",
        "\n",
        "        # embedding layer\n",
        "        self.embeddings = nn.Embedding(vocab_size, emb_size)\n",
        "        self.embeddings.weight = get_pretrained_embeddings()\n",
        "        self.embeddings.weight.requires_grad = False\n",
        "        \n",
        "        # bidirectional LSTM\n",
        "        self.BiLSTM = nn.LSTM(\n",
        "            emb_size, rnn_size,\n",
        "            num_layers = rnn_layers,\n",
        "            bidirectional = True,\n",
        "            dropout = (0 if rnn_layers == 1 else dropout),\n",
        "            batch_first = True\n",
        "        )\n",
        "\n",
        "        self.attention = Attention(rnn_size)\n",
        "        self.fc = nn.Linear(rnn_size, n_classes)\n",
        "        # self.fc2 = nn.Linear(emv, n_classes)\n",
        "\n",
        "        self.tanh = nn.Tanh()\n",
        "        self.dropout = nn.Dropout(dropout)\n",
        "        self.softmax = nn.Softmax(dim=1)\n",
        "\n",
        "    def forward(self, text: torch.Tensor, words_per_sentence: torch.Tensor) -> torch.Tensor:\n",
        "        \"\"\"\n",
        "        Parameters\n",
        "        ----------\n",
        "        text : torch.Tensor (batch_size, word_pad_len)\n",
        "            Input data\n",
        "        words_per_sentence : torch.Tensor (batch_size)\n",
        "            Sentence lengths\n",
        "        Returns\n",
        "        -------\n",
        "        scores : torch.Tensor (batch_size, n_classes)\n",
        "            Class scores\n",
        "        \"\"\"\n",
        "        # word embedding, apply dropout\n",
        "        embeddings = self.dropout(self.embeddings(text)) # (batch_size, word_pad_len, emb_size)\n",
        "\n",
        "        # pack sequences (remove word-pads, SENTENCES -> WORDS)\n",
        "        packed_words = pack_padded_sequence(\n",
        "            embeddings,\n",
        "            lengths = words_per_sentence.tolist(),\n",
        "            batch_first = True,\n",
        "            enforce_sorted = False\n",
        "        )  # a PackedSequence object, where 'data' is the flattened words (n_words, emb_size)\n",
        "\n",
        "        # run through bidirectional LSTM (PyTorch automatically applies it on the PackedSequence)\n",
        "        rnn_out, _ = self.BiLSTM(packed_words)  # a PackedSequence object, where 'data' is the output of the LSTM (n_words, 2 * rnn_size)\n",
        "\n",
        "        # unpack sequences (re-pad with 0s, WORDS -> SENTENCES)\n",
        "        rnn_out, _ = pad_packed_sequence(rnn_out, batch_first = True)  # (batch_size, word_pad_len, 2 * word_rnn_size)\n",
        "\n",
        "        # eq.8: h_i = [\\overrightarrow{h}_i ⨁ \\overleftarrow{h}_i ]\n",
        "        # H = {h_1, h_2, ..., h_T}\n",
        "        H = rnn_out[ :, :, : self.rnn_size] + rnn_out[ :, :, self.rnn_size : ] # (batch_size, word_pad_len, rnn_size)\n",
        "\n",
        "        # attention module\n",
        "        r, alphas = self.attention(H)  # (batch_size, rnn_size), (batch_size, word_pad_len)\n",
        "\n",
        "        # eq.12: h* = tanh(r)\n",
        "        h = self.tanh(r)  # (batch_size, rnn_size)\n",
        "\n",
        "        scores = self.fc(self.dropout(h))  # (batch_size, n_classes)\n",
        "\n",
        "        return scores #, alphas"
      ],
      "metadata": {
        "id": "xnZQW07BC_hr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Test"
      ],
      "metadata": {
        "id": "o9xv4jsMUTdR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "def test(model: nn.Module, test_loader: DataLoader, tokenizer) -> None:\n",
        "\n",
        "    # n_classes = model.n_classes  # add this line if the number of classes is not already defined in the model\n",
        "    correct_per_label = [0] * n_classes\n",
        "    total_per_label = [0] * n_classes\n",
        "    total_per_prediction = [0] * n_classes\n",
        "    lst_labels = []\n",
        "    lst_preds = []\n",
        " \n",
        "    # evaluate in batches\n",
        "    with torch.no_grad():\n",
        "        for i, batch in enumerate(test_loader):\n",
        "\n",
        "            sentences, labels = batch\n",
        "            tokenized = tokenizer(sentences, padding=True, return_tensors='pt', max_length=512, truncation=True)\n",
        "            sentences = tokenized['input_ids']\n",
        "            words_per_sentence = torch.sum(tokenized['attention_mask'], dim=-1)\n",
        "            sentences = sentences.to(device)  # (batch_size, word_limit)\n",
        "            labels = torch.argmax(labels, dim=-1).to(device)  # (batch_size)\n",
        "            lst_labels.extend(labels.cpu())\n",
        "            scores = model(sentences, words_per_sentence)  # (batch_size, n_classes)\n",
        "\n",
        "            # accuracy per label\n",
        "            _, predictions = scores.max(dim=1)  # (n_documents)\n",
        "            lst_preds.extend(predictions.cpu())\n",
        "            for j in range(n_classes):\n",
        "                correct_predictions = torch.logical_and(torch.eq(predictions, j), torch.eq(labels, j)).sum().item()\n",
        "                correct_per_label[j] += correct_predictions\n",
        "                total_per_label[j] += torch.eq(labels, j).sum().item()\n",
        "                total_per_prediction[j] += torch.eq(predictions, j).sum().item()\n",
        "\n",
        "    # print accuracy per label\n",
        "    for j in range(n_classes):\n",
        "        if total_per_label[j] == 0:\n",
        "            accuracy = 0.0\n",
        "        else:\n",
        "            accuracy = correct_per_label[j] / total_per_label[j]\n",
        "        print('\\n * LABEL %d ACCURACY - %.1f percent\\n' % (j, accuracy * 100))\n",
        "\n",
        "        if total_per_prediction[j] == 0:\n",
        "            true_pos = 0.0\n",
        "        else:\n",
        "            true_pos = correct_per_label[j] / total_per_prediction[j]\n",
        "        print('\\n * LABEL %d TRUE POSITIVE - %.1f percent\\n' % (j, true_pos * 100))\n",
        "\n",
        "    # final test accuracy\n",
        "    overall_accuracy = sum(correct_per_label) / sum(total_per_label) if sum(total_per_label) != 0 else 0.0\n",
        "    print('\\n * TEST ACCURACY - %.1f percent\\n' % (overall_accuracy * 100))\n",
        "\n",
        "    # print confusion matrix\n",
        "    print(confusion_matrix(lst_labels, lst_preds))"
      ],
      "metadata": {
        "id": "OXq3Fus_N-CK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import Dataset, DataLoader\n",
        "\n",
        "class MyDataset(Dataset):\n",
        "    def __init__(self, X, y):\n",
        "        self.X = X\n",
        "        self.y = y\n",
        "        \n",
        "    def __len__(self):\n",
        "        return len(self.X)\n",
        "    \n",
        "    def __getitem__(self, idx):\n",
        "        return self.X[idx], self.y[idx]\n",
        "\n",
        "def get_formatted_data(df):\n",
        "  one_hot_labels = pd.get_dummies(df.iloc[:, 1])\n",
        "  data = df.to_numpy()\n",
        "  X = data[:, 0]\n",
        "  y = one_hot_labels.values\n",
        "  \n",
        "  dataset = MyDataset(X, y)\n",
        "  return dataset"
      ],
      "metadata": {
        "id": "u8SkWQ_TDNSS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train Model"
      ],
      "metadata": {
        "id": "vTfkc1MbO4IM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "torch.cuda.manual_seed(20)\n",
        "torch.manual_seed(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-UPyOIQqKW5K",
        "outputId": "cbe57d8d-7908-472e-d267-9acb82477ea9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<torch._C.Generator at 0x7fd61d16c250>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# load data\n",
        "\n",
        "EMB_SIZE = 768\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n",
        "vocab_size = tokenizer.vocab_size\n",
        "n_classes = len(set(text_data_train[1]))\n",
        "model = AttBiLSTM(n_classes = n_classes, vocab_size = vocab_size, emb_size = EMB_SIZE,\n",
        "                  rnn_size = 64, rnn_layers = 5, dropout = 0.2)\n",
        "\n",
        "optimizer = optim.Adam(\n",
        "    params = filter(lambda p: p.requires_grad, model.parameters()),\n",
        "    lr = 0.001\n",
        ")\n",
        "\n",
        "# loss functions\n",
        "loss_function = nn.CrossEntropyLoss()\n",
        "\n",
        "# move to device\n",
        "model = model.to(device)\n",
        "loss_function = loss_function.to(device)"
      ],
      "metadata": {
        "id": "kHu8N7Fh7dqy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246,
          "referenced_widgets": [
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3da128541f7b45209c5d46d44f05a4c0"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "edeabb63ea5c453c86b6d0f31724b09e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ce8c82c48b6746409ee4f33c759c8c14"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "59d4150f32174376905f22bc0067070d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight']\n",
            "- This IS expected if you are initializing BertModel 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 BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Only Twitter Message Dataset"
      ],
      "metadata": {
        "id": "mysUUMMNU8-6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = Trainer(\n",
        "    num_epochs = 20,\n",
        "    train_loader = train_dl,\n",
        "    val_loader = val_dl,\n",
        "    model = model,\n",
        "    loss_function = loss_function,\n",
        "    optimizer = optimizer,\n",
        "    grad_clip = 1000,\n",
        "    tokenizer = tokenizer\n",
        ")\n",
        "\n",
        "trainer.run_train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HzMvHRi4VEyB",
        "outputId": "d730953b-d7aa-4028-c799-09176db8e299"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "  0%|          | 0/20 [00:00<?, ?it/s]\u001b[A\n",
            "  5%|▌         | 1/20 [00:15<05:02, 15.92s/it]\u001b[A"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 0: Train Loss 1.5919674158096313, Train Accuracy 0.323625 \n",
            " Val Loss 1.587259298279172, Val Accuracy 0.3506944444444444\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            " 10%|█         | 2/20 [00:29<04:26, 14.78s/it]\u001b[A"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 1: Train Loss 1.583609697341919, Train Accuracy 0.3261875 \n",
            " Val Loss 1.5876899749513655, Val Accuracy 0.3179563492063492\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            " 15%|█▌        | 3/20 [00:43<04:05, 14.46s/it]\u001b[A"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 2: Train Loss 1.577588897228241, Train Accuracy 0.3396875 \n",
            " Val Loss 1.5894137715536452, Val Accuracy 0.3551587301587302\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            " 20%|██        | 4/20 [00:58<03:48, 14.28s/it]\u001b[A"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 3: Train Loss 1.3889501181840898, Train Accuracy 0.460375 \n",
            " Val Loss 1.187188222294762, Val Accuracy 0.5669642857142857\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            " 25%|██▌       | 5/20 [01:13<03:39, 14.61s/it]\u001b[A"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 4: Train Loss 1.0149324651956557, Train Accuracy 0.6214375 \n",
            " Val Loss 0.8075690988510374, Val Accuracy 0.7123015873015873\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            " 30%|███       | 6/20 [01:36<04:07, 17.68s/it]\u001b[A"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 5: Train Loss 0.6874038452506065, Train Accuracy 0.7440625 \n",
            " Val Loss 0.6014097233613332, Val Accuracy 0.7961309523809523\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            " 35%|███▌      | 7/20 [01:50<03:33, 16.39s/it]\u001b[A"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 6: Train Loss 0.48851619657874107, Train Accuracy 0.8259375 \n",
            " Val Loss 0.42739233658427284, Val Accuracy 0.8640873015873016\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            " 40%|████      | 8/20 [02:04<03:06, 15.57s/it]\u001b[A"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 7: Train Loss 0.3305070045813918, Train Accuracy 0.8831875 \n",
            " Val Loss 0.29536976996395325, Val Accuracy 0.9052579365079365\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            " 45%|████▌     | 9/20 [02:18<02:45, 15.03s/it]\u001b[A"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 8: Train Loss 0.24208424570411444, Train Accuracy 0.9106875 \n",
            " Val Loss 0.26948656727160725, Val Accuracy 0.9117063492063492\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            " 50%|█████     | 10/20 [02:32<02:26, 14.66s/it]\u001b[A"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 9: Train Loss 0.20633186473697424, Train Accuracy 0.9203125 \n",
            " Val Loss 0.23542544600509463, Val Accuracy 0.9156746031746031\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            " 55%|█████▌    | 11/20 [02:45<02:09, 14.42s/it]\u001b[A"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 10: Train Loss 0.18676915449649095, Train Accuracy 0.92375 \n",
            " Val Loss 0.2267511072494681, Val Accuracy 0.9201388888888888\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            " 60%|██████    | 12/20 [03:00<01:55, 14.49s/it]\u001b[A"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 11: Train Loss 0.16774342481140048, Train Accuracy 0.927625 \n",
            " Val Loss 0.17340196071872635, Val Accuracy 0.9290674603174603\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            " 65%|██████▌   | 13/20 [03:14<01:39, 14.28s/it]\u001b[A"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 12: Train Loss 0.1599393803011626, Train Accuracy 0.9305625 \n",
            " Val Loss 0.21186431479595957, Val Accuracy 0.9201388888888888\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            " 70%|███████   | 14/20 [03:28<01:24, 14.10s/it]\u001b[A"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 13: Train Loss 0.14744138076156377, Train Accuracy 0.92925 \n",
            " Val Loss 0.15501874238843955, Val Accuracy 0.9295634920634921\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            " 75%|███████▌  | 15/20 [03:41<01:10, 14.02s/it]\u001b[A"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 14: Train Loss 0.14041246384941042, Train Accuracy 0.9338125 \n",
            " Val Loss 0.151425009024226, Val Accuracy 0.9315476190476191\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            " 80%|████████  | 16/20 [03:55<00:55, 13.94s/it]\u001b[A"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 15: Train Loss 0.13235500236041844, Train Accuracy 0.9351875 \n",
            " Val Loss 0.1428955234661107, Val Accuracy 0.9295634920634921\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            " 85%|████████▌ | 17/20 [04:09<00:41, 13.87s/it]\u001b[A"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 16: Train Loss 0.1336211936119944, Train Accuracy 0.9351875 \n",
            " Val Loss 0.13412173807857528, Val Accuracy 0.9350198412698413\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            " 90%|█████████ | 18/20 [04:23<00:27, 13.86s/it]\u001b[A"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 17: Train Loss 0.12742608074611053, Train Accuracy 0.9354375 \n",
            " Val Loss 0.15185686845391516, Val Accuracy 0.9320436507936508\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            " 95%|█████████▌| 19/20 [04:37<00:13, 13.84s/it]\u001b[A"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 18: Train Loss 0.12378526560449973, Train Accuracy 0.93625 \n",
            " Val Loss 0.1501798000009287, Val Accuracy 0.9330357142857143\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "100%|██████████| 20/20 [04:50<00:00, 14.54s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 19: Train Loss 0.12185443728743121, Train Accuracy 0.9371875 \n",
            " Val Loss 0.1515373903371039, Val Accuracy 0.9270833333333334\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Results"
      ],
      "metadata": {
        "id": "w2Yz7VOaVeHQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test(trainer.model, text_test_dl, tokenizer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z8i-BXeKVgCM",
        "outputId": "09aeba94-ef8a-48dd-cf89-c1e6e5766a4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " * LABEL 0 ACCURACY - 95.3 percent\n",
            "\n",
            "\n",
            " * LABEL 0 TRUE POSITIVE - 91.0 percent\n",
            "\n",
            "\n",
            " * LABEL 1 ACCURACY - 88.4 percent\n",
            "\n",
            "\n",
            " * LABEL 1 TRUE POSITIVE - 92.1 percent\n",
            "\n",
            "\n",
            " * LABEL 2 ACCURACY - 96.4 percent\n",
            "\n",
            "\n",
            " * LABEL 2 TRUE POSITIVE - 92.9 percent\n",
            "\n",
            "\n",
            " * LABEL 3 ACCURACY - 76.7 percent\n",
            "\n",
            "\n",
            " * LABEL 3 TRUE POSITIVE - 86.5 percent\n",
            "\n",
            "\n",
            " * LABEL 4 ACCURACY - 96.4 percent\n",
            "\n",
            "\n",
            " * LABEL 4 TRUE POSITIVE - 96.2 percent\n",
            "\n",
            "\n",
            " * LABEL 5 ACCURACY - 63.6 percent\n",
            "\n",
            "\n",
            " * LABEL 5 TRUE POSITIVE - 79.2 percent\n",
            "\n",
            "\n",
            " * TEST ACCURACY - 92.7 percent\n",
            "\n",
            "[[262   2   3   0   8   0]\n",
            " [  8 198   0   0   9   9]\n",
            " [  5   0 670  17   1   2]\n",
            " [  0   0  35 122   2   0]\n",
            " [ 13   1   5   2 560   0]\n",
            " [  0  14   8   0   2  42]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test(trainer.model, poem_test_dl, tokenizer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 781
        },
        "id": "p8WejfPWVir0",
        "outputId": "cdf37ae0-feb3-4cbe-877f-5c7e8921f5c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " * LABEL 0 ACCURACY - 66.7 percent\n",
            "\n",
            "\n",
            " * LABEL 0 TRUE POSITIVE - 9.1 percent\n",
            "\n",
            "\n",
            " * LABEL 1 ACCURACY - 20.0 percent\n",
            "\n",
            "\n",
            " * LABEL 1 TRUE POSITIVE - 33.3 percent\n",
            "\n",
            "\n",
            " * LABEL 2 ACCURACY - 20.8 percent\n",
            "\n",
            "\n",
            " * LABEL 2 TRUE POSITIVE - 27.5 percent\n",
            "\n",
            "\n",
            " * LABEL 3 ACCURACY - 1.5 percent\n",
            "\n",
            "\n",
            " * LABEL 3 TRUE POSITIVE - 25.0 percent\n",
            "\n",
            "\n",
            " * LABEL 4 ACCURACY - 6.2 percent\n",
            "\n",
            "\n",
            " * LABEL 4 TRUE POSITIVE - 33.3 percent\n",
            "\n",
            "\n",
            " * LABEL 5 ACCURACY - 0.0 percent\n",
            "\n",
            "\n",
            " * LABEL 5 TRUE POSITIVE - 0.0 percent\n",
            "\n",
            "\n",
            " * TEST ACCURACY - 14.5 percent\n",
            "\n",
            "[[16  0  2  0  5  1]\n",
            " [ 5  2  0  1  2  0]\n",
            " [40  1 11  0  1  0]\n",
            " [43  1 19  1  1  0]\n",
            " [64  2  7  2  5  0]\n",
            " [ 7  0  1  0  1  0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test(trainer.model, reddit_test_dl, tokenizer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7LMwvTsZVl06",
        "outputId": "ff38b5f7-6d39-4860-d941-03f1d8b3e8d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " * LABEL 0 ACCURACY - 93.9 percent\n",
            "\n",
            "\n",
            " * LABEL 0 TRUE POSITIVE - 21.5 percent\n",
            "\n",
            "\n",
            " * LABEL 1 ACCURACY - 0.7 percent\n",
            "\n",
            "\n",
            " * LABEL 1 TRUE POSITIVE - 30.0 percent\n",
            "\n",
            "\n",
            " * LABEL 2 ACCURACY - 11.8 percent\n",
            "\n",
            "\n",
            " * LABEL 2 TRUE POSITIVE - 30.1 percent\n",
            "\n",
            "\n",
            " * LABEL 3 ACCURACY - 0.2 percent\n",
            "\n",
            "\n",
            " * LABEL 3 TRUE POSITIVE - 38.1 percent\n",
            "\n",
            "\n",
            " * LABEL 4 ACCURACY - 4.8 percent\n",
            "\n",
            "\n",
            " * LABEL 4 TRUE POSITIVE - 61.9 percent\n",
            "\n",
            "\n",
            " * LABEL 5 ACCURACY - 0.6 percent\n",
            "\n",
            "\n",
            " * LABEL 5 TRUE POSITIVE - 53.8 percent\n",
            "\n",
            "\n",
            " * TEST ACCURACY - 22.8 percent\n",
            "\n",
            "[[3229    5  174    1   29    1]\n",
            " [1189    9   71    2   22    0]\n",
            " [2891    5  389    4   14    6]\n",
            " [3086    2  274    8   11    2]\n",
            " [2474    3  197    6  135    3]\n",
            " [2115    6  186    0    7   14]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### With Combined Dataset"
      ],
      "metadata": {
        "id": "JsdeIJ5IUhuh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "combined_trainer = Trainer(\n",
        "    num_epochs = 20,\n",
        "    train_loader = combined_train_dl,\n",
        "    val_loader = combined_val_dl,\n",
        "    model = model,\n",
        "    loss_function = loss_function,\n",
        "    optimizer = optimizer,\n",
        "    grad_clip = 1000,\n",
        "    tokenizer = tokenizer\n",
        ")\n",
        "\n",
        "combined_trainer.run_train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vzBcec0ZUo7W",
        "outputId": "024217a2-aec5-4432-89ea-4e4bc6569659"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  0%|          | 0/20 [00:00<?, ?it/s]Token indices sequence length is longer than the specified maximum sequence length for this model (714 > 512). Running this sequence through the model will result in indexing errors\n",
            "  5%|▌         | 1/20 [00:27<08:36, 27.17s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 0: Train Loss 0.3700651291914803, Train Accuracy 0.8506095412585442 \n",
            " Val Loss 1.0763200612700716, Val Accuracy 0.6268297497570458\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 10%|█         | 2/20 [00:54<08:12, 27.35s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 1: Train Loss 0.31475773226693154, Train Accuracy 0.8739077584384469 \n",
            " Val Loss 0.9838639323403235, Val Accuracy 0.6651026482021379\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 15%|█▌        | 3/20 [01:21<07:43, 27.29s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 2: Train Loss 0.2928300481561324, Train Accuracy 0.8827293707279262 \n",
            " Val Loss 0.9605836147556499, Val Accuracy 0.677318695335277\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 20%|██        | 4/20 [01:49<07:15, 27.23s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 3: Train Loss 0.28470252453978967, Train Accuracy 0.8861338172080896 \n",
            " Val Loss 0.9228257235317003, Val Accuracy 0.6955782312925171\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 25%|██▌       | 5/20 [02:16<06:48, 27.21s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 4: Train Loss 0.2672533703246418, Train Accuracy 0.8905512296525968 \n",
            " Val Loss 0.9256665627060293, Val Accuracy 0.685465257531584\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 30%|███       | 6/20 [02:43<06:20, 27.16s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 5: Train Loss 0.2610178423480914, Train Accuracy 0.8960917130575717 \n",
            " Val Loss 0.8986719984079705, Val Accuracy 0.6987973760932944\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 35%|███▌      | 7/20 [03:10<05:52, 27.14s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 6: Train Loss 0.25418998812124544, Train Accuracy 0.898430343175252 \n",
            " Val Loss 0.8923596400184696, Val Accuracy 0.7017735665694849\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 40%|████      | 8/20 [03:37<05:26, 27.18s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 7: Train Loss 0.2481603351009578, Train Accuracy 0.9000202593192869 \n",
            " Val Loss 0.9102180008174611, Val Accuracy 0.7018115281827016\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 45%|████▌     | 9/20 [04:04<04:58, 27.13s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 8: Train Loss 0.24222156037146306, Train Accuracy 0.9030305299133253 \n",
            " Val Loss 0.8977476173923129, Val Accuracy 0.7112639698736637\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 50%|█████     | 10/20 [04:31<04:31, 27.17s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 9: Train Loss 0.239581907472937, Train Accuracy 0.9039818370798394 \n",
            " Val Loss 0.8860087593903347, Val Accuracy 0.7089255344995141\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 55%|█████▌    | 11/20 [04:59<04:05, 27.31s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 10: Train Loss 0.2331949343808249, Train Accuracy 0.9057038792192235 \n",
            " Val Loss 0.9084975660911628, Val Accuracy 0.7051521501457726\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 60%|██████    | 12/20 [05:27<03:39, 27.41s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 11: Train Loss 0.22598695961948684, Train Accuracy 0.9066661968853499 \n",
            " Val Loss 0.9103203085916383, Val Accuracy 0.7076348396501458\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 65%|██████▌   | 13/20 [05:55<03:13, 27.63s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 12: Train Loss 0.22264145160730886, Train Accuracy 0.9094408427876823 \n",
            " Val Loss 0.9434572735003063, Val Accuracy 0.689291788143829\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 70%|███████   | 14/20 [06:23<02:46, 27.76s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 13: Train Loss 0.21197486439692625, Train Accuracy 0.9139397329293213 \n",
            " Val Loss 0.9396066890180517, Val Accuracy 0.7028213070942663\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 75%|███████▌  | 15/20 [06:50<02:18, 27.62s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 14: Train Loss 0.20817148714436512, Train Accuracy 0.9147302868014939 \n",
            " Val Loss 0.9087349395362698, Val Accuracy 0.7028592687074829\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 80%|████████  | 16/20 [07:18<01:50, 27.55s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 15: Train Loss 0.2044473324571789, Train Accuracy 0.9160669614544429 \n",
            " Val Loss 0.9238696481500354, Val Accuracy 0.707794278425656\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 85%|████████▌ | 17/20 [07:45<01:22, 27.47s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 16: Train Loss 0.19841293440271332, Train Accuracy 0.9190243816503417 \n",
            " Val Loss 0.8995311629204523, Val Accuracy 0.708879980563654\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 90%|█████████ | 18/20 [08:12<00:54, 27.43s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 17: Train Loss 0.19145877683622767, Train Accuracy 0.9224376365301953 \n",
            " Val Loss 1.0161884798585963, Val Accuracy 0.6972865038872692\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 95%|█████████▌| 19/20 [08:41<00:27, 27.82s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 18: Train Loss 0.18824591569080223, Train Accuracy 0.9239372665774083 \n",
            " Val Loss 0.9631437413445135, Val Accuracy 0.6997691933916423\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 20/20 [09:09<00:00, 27.47s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Epoch: 19: Train Loss 0.18251542070340154, Train Accuracy 0.9265203297864844 \n",
            " Val Loss 0.9906221586628025, Val Accuracy 0.7090014577259475\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Results"
      ],
      "metadata": {
        "id": "Gf-aGFOTRXdf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test(combined_trainer.model, text_test_dl, tokenizer)"
      ],
      "metadata": {
        "id": "ZCShj6BiLVLS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a8ac0724-ec6f-4a11-ff24-880f2076de7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " * LABEL 0 ACCURACY - 91.6 percent\n",
            "\n",
            "\n",
            " * LABEL 0 TRUE POSITIVE - 91.3 percent\n",
            "\n",
            "\n",
            " * LABEL 1 ACCURACY - 85.7 percent\n",
            "\n",
            "\n",
            " * LABEL 1 TRUE POSITIVE - 93.2 percent\n",
            "\n",
            "\n",
            " * LABEL 2 ACCURACY - 97.3 percent\n",
            "\n",
            "\n",
            " * LABEL 2 TRUE POSITIVE - 93.1 percent\n",
            "\n",
            "\n",
            " * LABEL 3 ACCURACY - 73.6 percent\n",
            "\n",
            "\n",
            " * LABEL 3 TRUE POSITIVE - 91.4 percent\n",
            "\n",
            "\n",
            " * LABEL 4 ACCURACY - 96.2 percent\n",
            "\n",
            "\n",
            " * LABEL 4 TRUE POSITIVE - 96.5 percent\n",
            "\n",
            "\n",
            " * LABEL 5 ACCURACY - 89.4 percent\n",
            "\n",
            "\n",
            " * LABEL 5 TRUE POSITIVE - 69.4 percent\n",
            "\n",
            "\n",
            " * TEST ACCURACY - 92.8 percent\n",
            "\n",
            "[[252   6   4   1  11   1]\n",
            " [  8 192   1   0   5  18]\n",
            " [  1   2 676   8   2   6]\n",
            " [  1   0  40 117   0   1]\n",
            " [ 14   4   3   1 559   0]\n",
            " [  0   2   2   1   2  59]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test(combined_trainer.model, poem_test_dl, tokenizer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oTlPqk_zZpPP",
        "outputId": "a8223fdc-0a97-4530-c82c-91d28db36e71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " * LABEL 0 ACCURACY - 16.7 percent\n",
            "\n",
            "\n",
            " * LABEL 0 TRUE POSITIVE - 26.7 percent\n",
            "\n",
            "\n",
            " * LABEL 1 ACCURACY - 20.0 percent\n",
            "\n",
            "\n",
            " * LABEL 1 TRUE POSITIVE - 28.6 percent\n",
            "\n",
            "\n",
            " * LABEL 2 ACCURACY - 47.2 percent\n",
            "\n",
            "\n",
            " * LABEL 2 TRUE POSITIVE - 37.9 percent\n",
            "\n",
            "\n",
            " * LABEL 3 ACCURACY - 60.0 percent\n",
            "\n",
            "\n",
            " * LABEL 3 TRUE POSITIVE - 42.9 percent\n",
            "\n",
            "\n",
            " * LABEL 4 ACCURACY - 32.5 percent\n",
            "\n",
            "\n",
            " * LABEL 4 TRUE POSITIVE - 46.4 percent\n",
            "\n",
            "\n",
            " * LABEL 5 ACCURACY - 44.4 percent\n",
            "\n",
            "\n",
            " * LABEL 5 TRUE POSITIVE - 66.7 percent\n",
            "\n",
            "\n",
            " * TEST ACCURACY - 41.5 percent\n",
            "\n",
            "[[ 4  0  1  7 12  0]\n",
            " [ 1  2  0  2  5  0]\n",
            " [ 5  1 25 14  7  1]\n",
            " [ 0  1 19 39  5  1]\n",
            " [ 5  3 18 28 26  0]\n",
            " [ 0  0  3  1  1  4]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test(combined_trainer.model, reddit_test_dl, tokenizer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QUOhzj1iU-AE",
        "outputId": "b8b9fa45-0b1e-41ce-c5be-32631e79c80c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " * LABEL 0 ACCURACY - 65.3 percent\n",
            "\n",
            "\n",
            " * LABEL 0 TRUE POSITIVE - 70.0 percent\n",
            "\n",
            "\n",
            " * LABEL 1 ACCURACY - 59.6 percent\n",
            "\n",
            "\n",
            " * LABEL 1 TRUE POSITIVE - 58.5 percent\n",
            "\n",
            "\n",
            " * LABEL 2 ACCURACY - 66.5 percent\n",
            "\n",
            "\n",
            " * LABEL 2 TRUE POSITIVE - 63.8 percent\n",
            "\n",
            "\n",
            " * LABEL 3 ACCURACY - 84.5 percent\n",
            "\n",
            "\n",
            " * LABEL 3 TRUE POSITIVE - 77.0 percent\n",
            "\n",
            "\n",
            " * LABEL 4 ACCURACY - 63.0 percent\n",
            "\n",
            "\n",
            " * LABEL 4 TRUE POSITIVE - 65.5 percent\n",
            "\n",
            "\n",
            " * LABEL 5 ACCURACY - 58.7 percent\n",
            "\n",
            "\n",
            " * LABEL 5 TRUE POSITIVE - 63.1 percent\n",
            "\n",
            "\n",
            " * TEST ACCURACY - 67.7 percent\n",
            "\n",
            "[[2247  227  247  123  382  213]\n",
            " [ 133  771   88   45  156  100]\n",
            " [ 154   45 2201  438  173  298]\n",
            " [  65   16  299 2859   83   61]\n",
            " [ 348  166  259  142 1776  127]\n",
            " [ 261   94  357  106  143 1367]]\n"
          ]
        }
      ]
    }
  ]
}