lexicon-based-vs-bert-based.ipynb

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      "metadata": {
        "id": "IgBTpccKuIEP",
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            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/2.0 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.4/2.0 MB\u001b[0m \u001b[31m11.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m1.9/2.0 MB\u001b[0m \u001b[31m34.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m25.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m177.4/177.4 KB\u001b[0m \u001b[31m20.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 KB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.6/140.6 KB\u001b[0m \u001b[31m16.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.8/5.8 MB\u001b[0m \u001b[31m47.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m182.4/182.4 KB\u001b[0m \u001b[31m17.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m79.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m213.0/213.0 KB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.0/132.0 KB\u001b[0m \u001b[31m15.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m240.9/240.9 KB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for emoji (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m175.4/175.4 KB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.1/53.1 KB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for emoji (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install --quiet wandb\n",
        "!pip install --quiet transformers\n",
        "!pip install --quiet datasets\n",
        "!pip install --quiet emoji\n",
        "!pip install --quiet kaggle\n",
        "!pip install --quiet torchinfo\n",
        "!pip install --quiet imbalanced-learn\n",
        "!pip install --quiet gdown\n",
        "\n",
        "!pip install --quiet clean-text"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os, sys\n",
        "\n",
        "class HiddenPrints:\n",
        "    def __enter__(self):\n",
        "        self._original_stdout = sys.stdout\n",
        "        sys.stdout = open(os.devnull, 'w')\n",
        "\n",
        "    def __exit__(self, exc_type, exc_val, exc_tb):\n",
        "        sys.stdout.close()\n",
        "        sys.stdout = self._original_stdout"
      ],
      "metadata": {
        "id": "TWWdg8dcuRe_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Lexicon-based\n",
        "from textblob import TextBlob\n",
        "from textblob.classifiers import NaiveBayesClassifier\n",
        "from textblob.classifiers import DecisionTreeClassifier\n",
        "from textblob.classifiers import MaxEntClassifier\n",
        "\n",
        "#Transformer-based\n",
        "import transformers\n",
        "from transformers import AutoTokenizer\n",
        "from transformers import BertConfig\n",
        "from transformers.models.bert.modeling_bert import BertModel, BertPreTrainedModel\n",
        "from transformers.modeling_outputs import SequenceClassifierOutput\n",
        "from transformers import AutoConfig\n",
        "from transformers import Trainer, TrainingArguments\n",
        "from transformers import get_scheduler\n",
        "\n",
        "import wandb\n",
        "\n",
        "import torch\n",
        "from torch.optim import AdamW\n",
        "import torch.nn as nn\n",
        "from torch.utils import data\n",
        "\n",
        "# dataset\n",
        "import datasets\n",
        "from datasets import Dataset , Sequence , Value , Features , ClassLabel , DatasetDict\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, f1_score\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix\n",
        "\n",
        "# preprocessing\n",
        "import nltk\n",
        "nltk.download('punkt')\n",
        "nltk.download('wordnet')\n",
        "nltk.download('stopwords')\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from nltk import word_tokenize\n",
        "from nltk.corpus import stopwords\n",
        "from nltk import word_tokenize\n",
        "\n",
        "import re, string\n",
        "import emoji\n",
        "\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from tqdm import tqdm\n",
        "from collections import defaultdict\n",
        "import pickle\n",
        "import joblib\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tbXNLpgXuT7m",
        "outputId": "6a1a4c4d-fad4-45b0-b176-f1b1a47044b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_confusion_matrix(y_preds, y_true, labels):\n",
        "    cm = confusion_matrix(y_true, y_preds, normalize=\"true\")\n",
        "    print(cm)\n",
        "    fig, ax = plt.subplots(figsize=(6, 6))\n",
        "    disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
        "    disp.plot(cmap=\"Blues\", values_format=\".2f\", ax=ax, colorbar=False)\n",
        "    plt.title(\"Normalized confusion matrix\")\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "COFfJGhETGYq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_metrics(pred):\n",
        "    labels = pred.label_ids\n",
        "    preds = pred.predictions.argmax(-1)\n",
        "    f1 = f1_score(labels, preds, average=\"weighted\")\n",
        "    acc = accuracy_score(labels, preds)\n",
        "    return {\"accuracy\": acc, \"f1\": f1}"
      ],
      "metadata": {
        "id": "Pv2Ib2J0TYOp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def clean_text(text):\n",
        "    text = re.sub('#', '', text) # Removing '#' hashtag\n",
        "    text = re.sub('\\w+:\\/\\/\\S+', '', text) # Removing hyperlink\n",
        "    text = re.sub('[^a-zA-Z]', ' ', text) # Remove punctuation\n",
        "    text.lower()\n",
        "    return text"
      ],
      "metadata": {
        "id": "3yOj6okvTcDL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def create_dataset(df, text, label):\n",
        "  data_dict = {'text':df[text], 'labels':df[label]}\n",
        "  tags = ClassLabel(num_classes=3 , names=class_names)\n",
        "  feature_set = Features({'text':Value(dtype='string'), 'labels':tags})\n",
        "\n",
        "  return Dataset.from_dict(mapping = data_dict, features = feature_set)"
      ],
      "metadata": {
        "id": "Ett4_0wlTfRM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "RANDOM_SEED = 42\n",
        "np.random.seed(RANDOM_SEED)\n",
        "torch.manual_seed(RANDOM_SEED)\n",
        "\n",
        "data = pd.read_csv('/content/sample_data/datav3.csv')\n",
        "data = data.drop(['match', 'time', 'slot'], 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N4AJ1iCauxex",
        "outputId": "1be7c7e6-5db2-455e-f8e9-8c03c738cfdd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-529a279e3d66>:6: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
            "  data = data.drop(['match', 'time', 'slot'], 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#target = data['target'].unique()"
      ],
      "metadata": {
        "id": "dMOqAROEwyIO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "target = dict(data['target'].value_counts())\n",
        "labels = list(target.keys())\n",
        "counts = list(target.values())\n",
        "plt.bar(labels,counts, color ='green',width = 0.4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "L1BvVOOGRTWQ",
        "outputId": "0893e4f8-3cd6-4929-f7d6-435fc4faea26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 3 artists>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "models = [\"distilbert-base-uncased\", \"bert-base-uncased\", \"bert-base-cased\"]\n",
        "model_name = models[1]\n",
        "\n",
        "class_names = ['not toxic', 'somehow toxic', 'totally toxic']\n",
        "max_len = 64"
      ],
      "metadata": {
        "id": "T8LfzQJNypEZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train, test = train_test_split(data, test_size = 0.25, random_state = 0)\n",
        "lexi_train = train.copy() #for lexicon-based"
      ],
      "metadata": {
        "id": "BrJjvraXzEFw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.loc[:,'text'] = train['text'].apply(lambda x : clean_text(x))\n",
        "test.loc[:,'text'] = test['text'].apply(lambda x : clean_text(x))"
      ],
      "metadata": {
        "id": "aavdIxf7y-Yv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train, val = train_test_split(train, test_size=0.1, random_state=RANDOM_SEED, stratify=train['target'])"
      ],
      "metadata": {
        "id": "33TCTYCFxSbP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_train = create_dataset(train,\"text\",\"target\")\n",
        "dataset_val = create_dataset(val,\"text\",\"target\")\n",
        "dataset_test = create_dataset(test,\"text\",\"target\")"
      ],
      "metadata": {
        "id": "I8CT1dGrzctH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_chat_analysis = DatasetDict()\n",
        "dataset_chat_analysis[\"train\"] = dataset_train\n",
        "dataset_chat_analysis[\"val\"] = dataset_val\n",
        "dataset_chat_analysis[\"test\"] = dataset_test"
      ],
      "metadata": {
        "id": "FSPMnWCT0F1Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_name)"
      ],
      "metadata": {
        "id": "vDif1WUWzmle",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "0a23576b5c6f497c83e25d85db9d1147",
            "c3decad699804199a50e4aa0ce7b32de",
            "87e21876565c42348bce9e58ae750c79",
            "ac580499cae942e3841a84674be5466b",
            "d6d281bab523494e984f23f563b64aca",
            "a58d72fdd96f4298b78be724e9ea758b",
            "bc64b8d5ccba4087904f2db76b650f8d",
            "4688f643edfc4fa3b0269cf50c32a671",
            "6ad08383a66c41fd93c1147098d9f1cf",
            "9407360446cd4f08b3e7e8525ac5e62f",
            "2b74ef4f916f4618841c76c3d19ba2d6",
            "940e266942eb4c3c872021e46f7ecd74",
            "62e2e64467c840899080b533eb700ed3",
            "85958d86839f43198998488a13dc8689",
            "15719b74a69a4e90a1cb6b7a3014b975",
            "aae5171429f041a59f4ab228293b996b",
            "5864a7d36aef4b409f186cded9209fa7",
            "945d9a1ad1a64404ba6c60681e29bc8f",
            "b282167e1f4b4093b55723cc257a9713",
            "ab62ec538dd74baaa9f613c1aec91ca3",
            "5bf914b06d514eafbc064cc69bd3ae6b",
            "bf188e6e906845a8bc9d4469c92434de",
            "d8ca8b146e014fa8aaca6a662af8adcb",
            "4f6f33721d634416bc4539694d70368b",
            "bcb6c8560372498f913475678f1045ac",
            "8dc6a13272d54a99b5f5f0d85ed454e9",
            "34017d05dc96477daab61b066b5e1023",
            "8ec2b7749dff4122bc7c435515b97884",
            "37ce8448f9074305b248df3489f20193",
            "478258ea2e3f4089b7b3b521791a89fb",
            "bb41c7a48b6f409b9c910f6f4aca96bb",
            "6ca586ce59e8447ab7c11401d48c0094",
            "3c1c65df323748e796d1268fbaa76b39",
            "602c551ea8f04dbeadb0bc9b433b1f00",
            "ff8c7ff51b1d4492a2f353784710ffd0",
            "df2310f6bc0b41eba86bd12b9886bd07",
            "2e766d24151e4d108752531e95f05c38",
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            "1dfa9755cdde4e688720b1cbb1a9a85f",
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            "1a52aa55e7e34bd792054eb5bdfefd26",
            "bfaac20f43094ea58c73501fb7c0081e"
          ]
        },
        "outputId": "68daf5be-4630-4aca-f4ea-f31d46225ebb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0a23576b5c6f497c83e25d85db9d1147"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "940e266942eb4c3c872021e46f7ecd74"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d8ca8b146e014fa8aaca6a662af8adcb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "602c551ea8f04dbeadb0bc9b433b1f00"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize(batch):\n",
        "    return tokenizer(batch[\"text\"], padding=True, truncation=True, max_length=max_len)"
      ],
      "metadata": {
        "id": "2zlekq06zvwq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample_text = \"What is  going on @resturant.:( It makes   Me Feel Upset.😞\"\n",
        "tokens = tokenizer.tokenize(sample_text)\n",
        "#print(len(tokens), tokens)\n",
        "token_ids = tokenizer.convert_tokens_to_ids(tokens)\n",
        "#print(len(token_ids), token_ids)\n",
        "\n",
        "token_dictionary = tokenizer(sample_text)\n",
        "#print(token_dictionary)\n",
        "#print(len(token_dictionary.input_ids)) # automatically added cls, sep\n",
        "\n",
        "tokens = tokenizer.convert_ids_to_tokens(token_dictionary['input_ids'])\n",
        "#print(tokens)\n",
        "\n",
        "token_encode = tokenizer.encode(sample_text) #convert to tokens ids but with cls+sep\n",
        "#print(token_encode)"
      ],
      "metadata": {
        "id": "WspqlWf-zxcP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(tokenizer.sep_token, tokenizer.sep_token_id)\n",
        "print(tokenizer.cls_token, tokenizer.cls_token_id)\n",
        "print(tokenizer.pad_token, tokenizer.pad_token_id)\n",
        "print(tokenizer.unk_token, tokenizer.unk_token_id)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "me3SBr_bz18Z",
        "outputId": "1fba2703-6278-4891-f3a7-aae58389a3d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[SEP] 102\n",
            "[CLS] 101\n",
            "[PAD] 0\n",
            "[UNK] 100\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "encoding = tokenizer.encode_plus(\n",
        "    sample_text,\n",
        "    max_length=max_len,\n",
        "    add_special_tokens=True,\n",
        "    pad_to_max_length=True,\n",
        "    return_attention_mask=True,\n",
        "    return_token_type_ids=False,\n",
        "    return_tensors=\"pt\"\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1On1qr2xz34g",
        "outputId": "91576b44-da6f-4728-8887-7a63795a1299"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n",
            "/usr/local/lib/python3.8/dist-packages/transformers/tokenization_utils_base.py:2336: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "encoded = dataset_chat_analysis.map(tokenize, batched=True, batch_size=32)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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            "46548dec63244dfbb184ec1942daa019",
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            "47b12478527c41e392af2927ed6fd07e",
            "93c7a64554b34e5e84154026a552c130",
            "c8d634d3e9d8462facfacefef0b23fb9",
            "27642d0d643c45069e232cdd527425e0",
            "68ba644fe40c4401b35006f20f7f880e",
            "6fbd13948364487389c92789779b1f63",
            "3a63f01e9b2447738b1bdb552e51c74d",
            "83f5ca40632443ceab8c292cf95690ee",
            "2405d3ea22e54d3abfa4e5e8a15b52ab",
            "c96ebe09fe314924a6841b053adefea3",
            "1d7354f866b442a4814e20f68513e135",
            "d3d76538ffc64b9dbea1773956cdc79f",
            "41cb67d1e7914bcc881404c6b9f197b8"
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        },
        "id": "_Tj8RaGKz7h3",
        "outputId": "afa2ba83-4215-4767-fdca-86a3405ca2db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/69 [00:00<?, ?ba/s]"
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              "  0%|          | 0/8 [00:00<?, ?ba/s]"
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        {
          "output_type": "display_data",
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            "text/plain": [
              "  0%|          | 0/26 [00:00<?, ?ba/s]"
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            "application/vnd.jupyter.widget-view+json": {
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class BertForClassification(BertPreTrainedModel):\n",
        "\n",
        "    def __init__(self, config):\n",
        "        super().__init__(config)\n",
        "        self.num_labels = config.num_labels\n",
        "        self.config = config\n",
        "\n",
        "        # Load model body > return all og the HS\n",
        "        self.bert = BertModel(config)\n",
        "        # Set up token classification head\n",
        "        self.dropout = nn.Dropout(config.hidden_dropout_prob)\n",
        "        self.classifier = nn.Linear(config.hidden_size, config.num_labels)\n",
        "\n",
        "        # Initialize weights and apply final processing\n",
        "        self.post_init()\n",
        "\n",
        "\n",
        "    def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, \n",
        "                labels=None, **kwargs):\n",
        "        # Use model body to get encoder representations\n",
        "        outputs = self.bert(input_ids, attention_mask=attention_mask,\n",
        "                               token_type_ids=token_type_ids, **kwargs)\n",
        "\n",
        "        # Apply classifier to encoder representation > [cls]\n",
        "        sequence_output = self.dropout(outputs[1])\n",
        "        logits = self.classifier(sequence_output)\n",
        "\n",
        "        # Calculate losses\n",
        "        loss = None\n",
        "        if labels is not None:\n",
        "            loss_fct = nn.CrossEntropyLoss()\n",
        "            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))\n",
        "        # Return model output object\n",
        "        return SequenceClassifierOutput(\n",
        "            loss=loss,\n",
        "            logits=logits,\n",
        "            hidden_states=outputs.hidden_states,\n",
        "            attentions=outputs.attentions,\n",
        "        )"
      ],
      "metadata": {
        "id": "TFxqZXDIz_Zo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "id2label = {0:'not toxic', 1:'somehow toxic', 2:'totally toxic'}\n",
        "label2id = { v:k for (k,v) in id2label.items()}\n",
        "\n",
        "bert_config = AutoConfig.from_pretrained(model_name, num_labels=3, id2label=id2label, label2id=label2id)\n",
        "bert_model = (BertForClassification.from_pretrained(model_name, config=bert_config).to(device))"
      ],
      "metadata": {
        "id": "23XLVq8z0N0H",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156,
          "referenced_widgets": [
            "d4cc07379a70436d95974d4a40b2f0c4",
            "b8d60cc97b714dc6aad86b0653fb8e3c",
            "5e238c990a7643cca36655c22377ddc0",
            "9ed0d251c2764c348dd9b6681c367776",
            "e9ef8b89207c4e0c8f9fdb37abbab4ae",
            "70cf4db75ecc481aac682cfa012b23dc",
            "d2e6ec454b9248a8b96291bb7cf47c82",
            "4767912b6bf84803b5dcff4c9af764c9",
            "6c72a377d28248698d7cc16f1250e921",
            "6a7e17b1257b4467a66bf9c09225a4b2",
            "8ae96abc8d67445b86cf5b29cf4a49d1"
          ]
        },
        "outputId": "e54833f1-2e1b-4a2d-da5a-f187bdd41250"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d4cc07379a70436d95974d4a40b2f0c4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForClassification: ['cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.seq_relationship.bias']\n",
            "- This IS expected if you are initializing BertForClassification 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 BertForClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertForClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! wandb login eed5796f17cdf020038ca37377edaaec8c9ddfbe"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hpkSvqHm0Pn3",
        "outputId": "b2ef2750-3a36-441e-a15e-b89a217969d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wandb.login()\n",
        "\n",
        "#wandb.init(project=\"bert-for-english-classification\")\n",
        "wandb.init(project=\"bert-toxic-chat-detection\", entity=\"dani-squad\")\n",
        "\n",
        "batch_size = 16\n",
        "logging_steps = len(encoded[\"train\"]) // batch_size\n",
        "model_name = f\"{model_name}-finetuned-dota-chat\"\n",
        "training_args = TrainingArguments(output_dir=model_name,\n",
        "                                  num_train_epochs=5,\n",
        "                                  per_device_train_batch_size=batch_size,\n",
        "                                  per_device_eval_batch_size=batch_size,\n",
        "                                  weight_decay=0.01,\n",
        "                                  evaluation_strategy=\"epoch\", \n",
        "                                  save_steps=1e6,\n",
        "                                  disable_tqdm=False,\n",
        "                                  logging_steps=logging_steps,\n",
        "                                  push_to_hub=False, \n",
        "                                  log_level=\"error\",\n",
        "                                  report_to=\"wandb\",\n",
        "                                  run_name=\"bert-sentiment-analysis\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "id": "bdSaDVJV0ToW",
        "outputId": "fa31d214-fcb2-46bb-ebc4-b2f77d0ab443"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "ERROR:wandb.jupyter:Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mdffesalbon\u001b[0m (\u001b[33mdani-squad\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Tracking run with wandb version 0.13.9"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20230121_091525-u1aamvxz</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Syncing run <strong><a href=\"https://wandb.ai/dani-squad/bert-toxic-chat-detection/runs/u1aamvxz\" target=\"_blank\">smooth-snowball-1</a></strong> to <a href=\"https://wandb.ai/dani-squad/bert-toxic-chat-detection\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View project at <a href=\"https://wandb.ai/dani-squad/bert-toxic-chat-detection\" target=\"_blank\">https://wandb.ai/dani-squad/bert-toxic-chat-detection</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run at <a href=\"https://wandb.ai/dani-squad/bert-toxic-chat-detection/runs/u1aamvxz\" target=\"_blank\">https://wandb.ai/dani-squad/bert-toxic-chat-detection/runs/u1aamvxz</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/torch/cuda/__init__.py:497: UserWarning: Can't initialize NVML\n",
            "  warnings.warn(\"Can't initialize NVML\")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Lexicon-based Machine Learning - Bayesian, Decision Tree, Max Entropy\n",
        "\n",
        "train_data = []\n",
        "for index, row in lexi_train.iterrows():\n",
        "    train_data.append((row['text'], row['target']))\n",
        "\n",
        "nb = NaiveBayesClassifier(train_data)\n",
        "dt = DecisionTreeClassifier(train_data)\n",
        "me = MaxEntClassifier(train_data)"
      ],
      "metadata": {
        "id": "U3uAPWqW29kp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "optimizer = AdamW(bert_model.parameters(), lr=2e-5)\n",
        "\n",
        "num_epochs = 10\n",
        "num_training_steps = num_epochs * logging_steps\n",
        "lr_scheduler = get_scheduler(\n",
        "    name=\"linear\", \n",
        "    optimizer=optimizer, \n",
        "    num_warmup_steps=0, \n",
        "    num_training_steps=num_training_steps\n",
        ")\n",
        "\n",
        "trainer_preprocessed_lr = Trainer(\n",
        "    model=bert_model, args=training_args, \n",
        "    compute_metrics=compute_metrics, \n",
        "    train_dataset=encoded[\"train\"], \n",
        "    eval_dataset=encoded[\"val\"], \n",
        "    tokenizer=tokenizer, \n",
        "    optimizers=(optimizer,lr_scheduler)\n",
        ")\n",
        "\n",
        "trainer_preprocessed_lr.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "bg9wwdTN0zww",
        "outputId": "3fd05bf7-56ad-4b26-b43b-0c63316823ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='690' max='690' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [690/690 58:23, Epoch 5/5]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.715900</td>\n",
              "      <td>0.560189</td>\n",
              "      <td>0.791837</td>\n",
              "      <td>0.768981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.402300</td>\n",
              "      <td>0.428787</td>\n",
              "      <td>0.861224</td>\n",
              "      <td>0.856628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.247800</td>\n",
              "      <td>0.511555</td>\n",
              "      <td>0.848980</td>\n",
              "      <td>0.845883</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.179000</td>\n",
              "      <td>0.482457</td>\n",
              "      <td>0.857143</td>\n",
              "      <td>0.854601</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.111900</td>\n",
              "      <td>0.637017</td>\n",
              "      <td>0.873469</td>\n",
              "      <td>0.871793</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=690, training_loss=0.3297777097294296, metrics={'train_runtime': 3510.2213, 'train_samples_per_second': 3.141, 'train_steps_per_second': 0.197, 'total_flos': 145534609180800.0, 'train_loss': 0.3297777097294296, 'epoch': 5.0})"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = bert_model\n",
        "model.eval()\n",
        "preds_output = trainer_preprocessed_lr.predict(encoded[\"test\"])\n",
        "pd.DataFrame(list(preds_output.metrics.items())).T"
      ],
      "metadata": {
        "id": "rnhc1YyW1N8e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "468e53c9-4b9f-4c78-9554-7caa00c0b2f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           0              1         2             3                        4  \\\n",
              "0  test_loss  test_accuracy   test_f1  test_runtime  test_samples_per_second   \n",
              "1   0.597655       0.864137  0.865059        45.617                    17.91   \n",
              "\n",
              "                       5  \n",
              "0  test_steps_per_second  \n",
              "1                   1.14  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fafed11c-3c8b-4b04-9b71-ddc594553f19\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>test_loss</td>\n",
              "      <td>test_accuracy</td>\n",
              "      <td>test_f1</td>\n",
              "      <td>test_runtime</td>\n",
              "      <td>test_samples_per_second</td>\n",
              "      <td>test_steps_per_second</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.597655</td>\n",
              "      <td>0.864137</td>\n",
              "      <td>0.865059</td>\n",
              "      <td>45.617</td>\n",
              "      <td>17.91</td>\n",
              "      <td>1.14</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fafed11c-3c8b-4b04-9b71-ddc594553f19')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fafed11c-3c8b-4b04-9b71-ddc594553f19 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-fafed11c-3c8b-4b04-9b71-ddc594553f19');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(bert_model.state_dict(), \"/content/sample_data/bert-classification-head-uncased\")"
      ],
      "metadata": {
        "id": "hPKuYYaWUBAR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nbpred = []\n",
        "dtpred = []\n",
        "mepred = []\n",
        "for index, row in test.iterrows():\n",
        "    nbpred.append(int(nb.classify(row['text'])))\n",
        "    dtpred.append(int(dt.classify(row['text'])))\n",
        "    mepred.append(int(me.classify(row['text'])))\n",
        "\n",
        "nb_predict = pd.Series(nbpred)\n",
        "dt_predict = pd.Series(dtpred)\n",
        "me_predict = pd.Series(mepred)"
      ],
      "metadata": {
        "id": "grvEDKp83VWv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "265f8557-25de-4603-8e36-1ab29ad2e31b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  ==> Training (100 iterations)\n",
            "\n",
            "      Iteration    Log Likelihood    Accuracy\n",
            "      ---------------------------------------\n",
            "             1          -1.09861        0.309\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/nltk/classify/maxent.py:1381: RuntimeWarning: overflow encountered in power\n",
            "  exp_nf_delta = 2 ** nf_delta\n",
            "/usr/local/lib/python3.8/dist-packages/nltk/classify/maxent.py:1383: RuntimeWarning: invalid value encountered in multiply\n",
            "  sum1 = numpy.sum(exp_nf_delta * A, axis=0)\n",
            "/usr/local/lib/python3.8/dist-packages/nltk/classify/maxent.py:1384: RuntimeWarning: invalid value encountered in multiply\n",
            "  sum2 = numpy.sum(nf_exp_nf_delta * A, axis=0)\n",
            "/usr/local/lib/python3.8/dist-packages/nltk/classify/maxent.py:1391: RuntimeWarning: invalid value encountered in true_divide\n",
            "  deltas -= (ffreq_empirical - sum1) / -sum2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "         Final               nan        0.545\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bert_predict = np.argmax(preds_output.predictions, axis=1)\n",
        "bert_test = np.array(encoded[\"test\"][\"labels\"])\n",
        "labels = encoded[\"train\"].features[\"labels\"].names\n",
        "plot_confusion_matrix(bert_predict, bert_test, labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "vCDBRqwhUGrJ",
        "outputId": "37e9cd3a-b9d5-4652-bc2a-61e52770e013"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.92887931 0.00862069 0.0625    ]\n",
            " [0.03636364 0.68181818 0.28181818]\n",
            " [0.06584362 0.11111111 0.82304527]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer_preprocessed_lr.save_model('/content/sample_data/dota-chat-bert-model-uncased')"
      ],
      "metadata": {
        "id": "a6fC_tyzUK2U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open('nb.pkl', 'wb') as files:\n",
        "    pickle.dump(nb, files)\n",
        "\n",
        "with open('dt.pkl', 'wb') as files:\n",
        "    pickle.dump(dt, files)\n",
        "\n",
        "with open('me.pkl', 'wb') as files:\n",
        "    pickle.dump(me, files)        "
      ],
      "metadata": {
        "id": "6fCLNTi_9ywt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## BERT"
      ],
      "metadata": {
        "id": "cEwCF2DZ1vVQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(bert_test, bert_predict, target_names=class_names))"
      ],
      "metadata": {
        "id": "HPCdZxrV1gYJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e6f8f556-972f-423c-a462-3bbd2d98cce0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "               precision    recall  f1-score   support\n",
            "\n",
            "    not toxic       0.96      0.93      0.94       464\n",
            "somehow toxic       0.71      0.68      0.69       110\n",
            "totally toxic       0.77      0.82      0.80       243\n",
            "\n",
            "     accuracy                           0.86       817\n",
            "    macro avg       0.81      0.81      0.81       817\n",
            " weighted avg       0.87      0.86      0.87       817\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Naive Bayes"
      ],
      "metadata": {
        "id": "NqYxTteE352q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_confusion_matrix(nb_predict, test['target'], labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "HWBUydadsZDr",
        "outputId": "681c8004-8013-4e92-9fe6-84a6c6f91543"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.95474138 0.00431034 0.04094828]\n",
            " [0.26363636 0.14545455 0.59090909]\n",
            " [0.25102881 0.03292181 0.71604938]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": 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2tDafJIgqkzvuE5KjvY0j4vMK87gJ+F9gA+AK4NQK83kBOFHSSxGxXFL7qo7S1lYlJaWcfeUDPHztb8jPF/c88SYffzmVc0/8Me9/NIFnXx3Nzttuwvm/+RkR8Pp7n3PWlQ8AsNkGXfj7uYdRWlpKXl4e19zxggOtjhUU5HPl2Qdz4OnXU1ISHPGzHei9UVcuvfEp+vbuyb67bM0v99uRky64k34HXEi7Nq3411+OBaD3Rl3Z/0c/YIeD/0JBfh5XnX0w+flZ/XzceBQU5HPp/xzIYWf8HyUlpRz6kx3YbMOuXDnkGbbZvAd7DdyKw36yA6dddDcDfnExbdu05MaLjm7osuuNyu5+yQpJxcBTEdEn7T8TaB0RF0rqC9wItAS+BI6NiDmSDgQuBRYBA3Kvo1UcB+wIXE3yYWAEcDJwCLBfRBwoKR94HTg3XcZTEdFHUgHJ9bW9gWXAkIj4Z3XrkteyUzTb7OA1sFWsvswZUe0utUZo8bKShi7BamHXnbbnvZHvqLJxmQu0LHGgrX0caGsfB9rapbpA8zkCMzPLBAeamZllggPNzMwywYFmZmaZ4EAzM7NMcKCZmVkmONDMzCwTHGhmZpYJDjQzM8sEB5qZmWWCA83MzDLBgWZmZpngQDMzs0xwoJmZWSY40MzMLBMcaGZmlgkONDMzywQHmpmZZYIDzczMMsGBZmZmmeBAMzOzTHCgmZlZJjjQzMwsExxoZmaWCQ40MzPLBAeamZllggPNzMwywYFmZmaZ4EAzM7NMcKCZmVkmONDMzCwTHGhmZpYJDjQzM8sEB5qZmWWCA83MzDLBgWZmZpngQDMzs0xwoJmZWSY40MzMLBMcaGZmlgkONDMzywQHmpmZZYIDzczMMsGBZmZmmeBAMzOzTHCgmZlZJjjQzMwsExxoZmaWCQUNXYBVbZMNu3HT/X9u6DKsFibM/LahS7BaOuWhUQ1dgtXCFzMXVjnOR2hmZpYJDjQzM8sEB5qZmWWCA83MzDLBgWZmZpngQDMzs0xwoJmZWSY40MzMLBMcaGZmlgkONDMzywQHmpmZZYIDzczMMsGBZmZmmeBAMzOzTHCgmZlZJjjQzMwsExxoZmaWCQ40MzPLBAeamZllggPNzMwywYFmZmaZ4EAzM7NMcKCZmVkmONDMzCwTHGhmZpYJDjQzM8sEB5qZmWWCA83MzDLBgWZmZpngQDMzs0xwoJmZWSY40MzMLBMKqhoh6TogqhofEafXSUVmZmarocpAA96ptyrMzMy+pyoDLSLuyO2X1DIivq37kszMzGpvldfQJA2QNBb4OO3fRtINdV6ZmZlZLdTkppBrgL2AWQARMQoYVJdFmZmZ1VaN7nKMiIkVBpXUQS1mZmarrbqbQspMlLQjEJKaAL8FPqrbsszMzGqnJkdoJwG/AboDk4G+ab+ZmVmjscojtIiYCRxRD7WYmZmttprc5bihpCclzZA0XdLjkjasj+LMzMxqqianHO8FHgC6At2AB4H76rIoMzOz2qpJoLWMiLsiYnn6uhtoXteFmZmZ1UZ1z3Jsn3Y+K+kc4H6SZzseAjxTD7WZmZnVWHU3hbxLEmBK+0/MGRfAuXVVlJmZWW1V9yzHDeqzEDMzs++jJl+sRlIfYAtyrp1FxJ11VZSZmVltrTLQJF0ADCYJtGeAfYDXAAeamZk1GjU5QjsI2AZ4LyKOldQZuLtuy7K11Tvvf8aNdzxLaWmw9279OHi/geXGP/L06zw3bCT5+XkUrteSM07an84d2wIwfeY3XHPTE8ycNRckLv7DEXTu1K4hVmOd8d93PuGKGx+ntDQ4YO/t+NXBu5Yb/+7oL7nypif47KupXHHO4ewxcOsV437w4z+wSXEXALp0bMu1Fx5br7Wvq/r1aMsJOxWTJzH0o2k89P7kcuN336wjx+3Qi1kLlwLw1JipDP14OgDHbN+TH/ZK/k/d/+7XDP9iVv0WX8dqEmiLIqJU0nJJbYDpQI86rqvGJBUDT0VEnzqa/2BgaUS8vprTPwMcHhHfrNHCGqGS0lKuv/VpLv3TURR1aMNv/3gz22+7Gb3W77SizUbFXbn20hNo3qwpTw19m1vvGcq5vzsYgKuvf5RDDxhEv603YtHiJUiqalG2BpSUlHLp9Y9y06W/pnNRIYf/9joGb78FG/XqvKJNl05tufj3h3DHw6+sNH2zpk144Poz6rPkdV6e4OSdN+C8p8Yya+FS/v7zrXhr/BwmzllUrt3wL2Zx42tflRvWv2dbNurYitMeHEWT/Dwu+9mWvDPhGxYty86z5mvyPbR3JLUFhpDc+TgSeKNOq2pcBgM7ru7EEbHvuhBmAJ9+PoluXdrTtXN7mhQUsMuOfXjznY/Ltdlmyw1o3qwpAJtv0oOZs+cBMP7r6ZSUltJv640AaNG82Yp2VjfGfDqRHt2KWL9rB5o0KWDvXbbh5Tc/LNeme+f2bLpBV/L84aJR2LRTa6bMW8y0+UtYXhq8+sVMdiiu2VmMnu1a8uHkeZQGLFleyrhZ37Jtz7Z1XHH9WmWgRcQpEfFNRNwI7AEcHRHVnluQ1ErS05JGSRoj6ZB0+O6S3pM0WtKtkpqlw8dJukzS+5LekdRP0vOSvpB0Us58z5I0QtIHkv6cs8h8SUMkfShpqKQWafu+kt5M2z8qqZ2kTpLeTcdvIykk9Uz7v5DUMmd5xSQPZz4jrW2gpGJJw9J5viipp6RCSZ9I2iyd7j5Jv85Zt6K0+6h0ulGS7lrVtl/bzJw9j44dClf0F7UvZNbs+VW2H/rSSPr33QSASVNm0bplcy7+6/385pz/45a7n6ektLTOa16XTZ85ly4dv9tfnYoKmTZrXo2nX7p0OYed/g+O/N0/Gfb6mLoo0Sro0KopMxYsWdE/c8FSOrRqtlK7HTdoz3W/2Jpz99iUolbJB8OvZi2kX8+2NCvIo03zArbu3oaOrbL1obG6L1b3q25cRIysZr57A5Mj4sdp+0JJzYHbgd0j4lNJdwInk/yAKMCEiOgr6e9pu51I7qocA9woaU9gE2A7ku/GPSFpEDAhHX5YRPxa0gPAgSTX+e4ETouIVyRdBFwQEb+T1Dw9fToQeAcYKOk1YHpEfFu2EhExTtKNwIKIuDpdlyeBOyLiDknHAddGxP6STgVul/QPoF1EDKmwzbYEzgN2jIiZOV9cXycNGz6KT7+czJUXJJ+NSkpKGfPxeP55+Ul0Kirksn88yH9efo+9dtu2gSu1qjx7x7l0Lirk6ymz+PU5N7NJcVd6dOvQ0GWt894eN4dXPpvJ8tJg796dOGO3jfnTk2N57+u5bNKpNVft34e5i5fx8bT5lEQ0dLlrVHXX0P5azbgAdqtm/Gjgr5KuILm+NVzSNsBXEfFp2uYOkp+hKQu0J3KmbR0R84H5kpakpzz3TF/vpe1akwTZhHS+76fD3wWKJRUCbSOi7OT/HSTPoQR4nSQwBwGXkgSwgOHVrFOZAcDP0+67gCsBIuIFSb8Arie5iaai3YAH018vICJmVzZzSScAJwB07rZ+DcppPIrat2HGrLkr+mfOnkuH9uut1O690V9w/6OvcuUFx9K0SfInWNShDRsWd6Fr5yTnB/TvzcefT2Sv+il9ndSpqJCpM77bX9NnzqVzhzY1nr5zUXJ0t37XDvTfekM+/mKSA62OzVq4lI6tvzsiK2rdlFkLl5RrM3/J8hXdQz+ezrE79FrR/8DISTwwchIAZ+6+CZPnLq7jiutXlaccI2LXal7VhRlpaPUjCadLJJ1fg1rK9kppTndZfwFJ4FwWEX3T18YR8a8K00Lya9qrutnlVZKjs17A4yQBtDM1C7RKScoDegPfAqt9a15E3BwR/SOif2G7tevNYdONujF56mymTp/DsuXLeeX1Meyw7ebl2nz+1RSuHfIkF5x1OG0LW+dM252FCxfzzbyFAIz68Et6du9Yr/Wva7bcdH0mTJ7J11Nns2zZcp57ZRS77LBFjaadN/9bli5N3jjnzF3I+2PHsWHPzquYyr6vT6cvoFthczqv14yCPDFooyLeGjenXJt2LZus6N6+V3smfpPcMJInWK9Z8tZY3L4lG3RoyciJ2bq8X6MvVteWpG7A7Ii4W9I3wPEkRzLFkjaOiM+BXwIr3zpVteeBiyXdExELJHUHllXVOCLmSpojaWBEDK+wvOHAX4BX0zs4ZwP7UvnjvOYDuR9bXwcOJTk6O4LvQvAMkl/y/iNwm6QBEZFb3zDgUUl/i4hZktpXdZS2tsrPz+fkY/flvEvvoqS0lD13/QG9enTizgeGsemG3dih/+b8656hLF6ylEuveQCAjkWFXHjW4eTn5XH8kXtx7iV3QAQbb9CNvXf36ca6VJCfz7kn78fJ591CaUkp++/5Qzbu1YXr73yeLTddn8E7bMmYTyZyxsV3Mm/Bt7zy1kfccPcLPHrT7/ly4nQuvu4R8iRKIzj24F3L3R1pdaM04MbXvuKiH/cmT+KFT6YzYc4ijujfg89mLODt8XP4WZ+ubFfcjtLSYP6S5Vzz0ucA5OeJK/bbEoBvl5Vw9YufUZqtM44o6uAcqqS9gKtIjq6WASdHxDuSdgeuJgnSEenwJZLGAf3Ta0vHpN2npvPKHfdbknAEWAAcSXJEtuK2fUlnkpyyvFBSX+BGoCXwJXBsRMxJ200ELo6ImyX9ETg0Ir77ks1367Ip8FC6LqeRnOK8DSgCZgDHAi2Ax4DtImK+pL8B8yPiggr1Hw2cldb8XkQcU9123KxP37jp4RdrssmtkehW2KKhS7BaOuWhUQ1dgtXC21cex7wJH1V6222dBJqtGQ60tY8Dbe3jQFu7VBdoNfnFakk6suw6WHqb+nZrukgzM7PvoyZfrL6B5M6+w9L++SR38pmZmTUaNbkpZPuI6CfpPYCImCMpW9/GMzOztV5NjtCWScon+e4ZkjqS3CBhZmbWaNQk0K4FHgU6SfoLyU/HXFqnVZmZmdXSKk85RsQ96bMPdyf5cvP+EfFRnVdmZmZWCzX5gc+eJE+/eDJ3WERMqMvCzMzMaqMmN4U8TXL9TCQPC94A+ATYsg7rMjMzq5WanHLcKrc/fQr/KXVWkZmZ2WqoyU0h5aQ/G7N9HdRiZma22mpyDe1/cnrzSJ6iP7nOKjIzM1sNNbmGlvuDVstJrqk9XDflmJmZrZ5qAy39QvV6EXFmPdVjZma2Wqq8hiapICJKSH7Z2czMrFGr7gjtbZLrZe9LegJ4EFhYNjIiHqnj2szMzGqsJtfQmgOzgN347vtoATjQzMys0agu0DqldziO4bsgK+NfBTUzs0alukDLB1pTPsjKONDMzKxRqS7QpkTERfVWiZmZ2fdQ3ZNCKjsyMzMza5SqC7Td660KMzOz76nKQIuI2fVZiJmZ2fdR64cTm5mZNUYONDMzywQHmpmZZYIDzczMMsGBZmZmmeBAMzOzTHCgmZlZJjjQzMwsExxoZmaWCQ40MzPLBAeamZllggPNzMwywYFmZmaZ4EAzM7NMcKCZmVkmONDMzCwTHGhmZpYJDjQzM8sEB5qZmWWCA83MzDLBgWZmZpngQDMzs0xwoJmZWSY40MzMLBMcaGZmlgkONDMzywQHmpmZZUJBQxdgVROiQP7MsTbp0rZ5Q5dgtfTll3MaugSrhSVLllc5zu+WZmaWCQ40MzPLBAeamZllggPNzMwywYFmZmaZ4EAzM7NMcKCZmVkmONDMzCwTHGhmZpYJDjQzM8sEB5qZmWWCA83MzDLBgWZmZpngQDMzs0xwoJmZWSY40MzMLBMcaGZmlgkONDMzywQHmpmZZYIDzczMMsGBZmZmmeBAMzOzTHCgmZlZJjjQzMwsExxoZmaWCQ40MzPLBAeamZllggPNzMwywYFmZmaZ4EAzM7NMcKCZmVkmONDMzCwTHGhmZpYJDjQzM8sEB5qZmWWCA83MzDLBgWZmZpngQDMzs0xwoJmZWSY40MzMLBMcaGZmlgkONDMzywQHmpmZZYIDzczMMsGBZmZmmeBAMzOzTHCgmZlZJjjQzMwsExxoZmaWCQUNXYBly4j3P+OG25+mtDTYZ7dtOXT/QeXGP/TUf3l22Lvk5+dR2KYVZ550AJ07tgVgr0PPp7hnZwA6FRVy8dlH1nv96x0FrgEAABH4SURBVJphb4zlvGseoaSklCN+NoDTj9qj3PglS5dx6kV388HHE2lX2IqbLzmGnl07MPLD8Zx5xf0ARARn/Wof9h28TUOswjpn502LOHe/LciXeOjtidzy8pflxv/hp73ZfqMOADRvkk/71k3Z4YIX2Lzrepz/8z60blZASQQ3DfuC50ZNaYhVqDONMtAktQUOj4gbVtGuGNgxIu6tQbunIqKPpMHAmRHxkxrWMhhYGhGv16R9JdM/Q7Iu36zO9GuTktJSrrv1Sa740zEUdWjDqefeyID+m9Nr/U4r2mxc3JXrLzuJ5s2a8uTQtxlyz/Oc97tDAGjatAk3Xfmbhip/nVNSUso5f32QB/7xG7p1astex13NXgP7sNkGXVe0uffJN2m7Xkveeuh8Hn3hXS6+/gmGXHIsm2/UlaG3nklBQT7TZs5l16OuYM+d+1BQkN+Aa5R9eYLzDtiS44e8zbS5i/n3aTvx0tjpfDF9wYo2Vzz50YruI3bsRe/ubQBYtKyUc/89ivEzv6Vjm2Y8dPpO/PeTGcxfvLze16OuNNZTjm2BU2rQrhg4vG5LYTCw4+pOHBH7rgthBvDJ51/TrXMHunZuT5OCAgbvuBWvj/ioXJu+fTakebOmAPTeZH1mzJrXEKUaMHLseDZYvyPF3Yto2qSA/X/Uj+deHV2uzXPDR3PwvtsB8NNd+/LaO58SEbRs3nRFeC1euhyheq9/XbRVj7ZMmPktX89exLKS4NlRU9hty85Vtt+3bzeefj85Chs/cyHjZ34LwIx5S5i1YCntWzetl7rrS2MNtMuBjSS9L+kqJa6SNEbSaEmH5LQbmLY7Q1KxpOGSRqavKoNIUp6kzyR1zOn/vKw/HVYMnASckS5jYLqMYZI+kPSipJ6SCiV9ImmzdLr7JP067R4nqSjtPiqdbpSku+pguzWombPn0bFD4Yr+og6FzJwzv8r2z740ku36brKif+my5Zxy7v9x2p9u4r8jxtZprQZTZ3xDt05tV/R369SWqTPmlmszZcZcundO2hQU5LNe6+bMnrsQgHc/HMegwy9l8JGXcdXZB/vorB50LmzO1LmLV/RPnbuITm2aVdq2W9vmrN++BW99PnOlcVv1KKRJfh4TZn1bZ7U2hEZ5yhE4B+gTEX0BJB0I9AW2AYqAEZJeTdutOH0oqSWwR0QslrQJcB/Qv7IFRESppLuBI4BrgB8BoyJiRk6bcZJuBBZExNXpMp4E7oiIOyQdB1wbEftLOhW4XdI/gHYRMSR3eZK2BM4jOUU6U1L7yuqSdAJwAkDnbuvXdrutNf4z/H0+/WISf73wVyuG3XP97ylq34Yp02Zz1sW3sUGPLnTrUulmskZg2y2LefXeP/LpuKmcdtHd7DZgC5o3a9LQZVlqn77dGDp6KqVRfnjRes24/NBtOPffHxBR+bRrq8Z6hFbRzsB9EVESEdOAV4AfVtKuCTBE0mjgQWCLVcz3VuCotPs44LYa1DIAKLtmd1daGxHxAjAauB44vpLpdgMejIiZafvZlc08Im6OiP4R0b9tu6IalNN4FLVvw4xZ333CnzlrLkXt1lup3cgPvuDeR17horOPoGmTgnLTA3Tt3J6tt9iAz8dNrvui12FdOrZl8vTvzoZPnv4NXToWlmvTtWMhk6YlbZYvL2H+gsW0L2xVrs2mxV1o1bIZH3+ZrRsMGqNpcxfTpbD5iv4uhS2YPm9JpW333aYrT79f/v9Qq2YF3Hhcf/7x3Kd8MCF7V0LWlkCrqTOAaSRHcv2Bak8QR8REYJqk3YDtgGdXd8GS8oDewLdAu9Wdz9pss426M2nqLKZMn8Oy5ct5+fXRDOi/ebk2n381mWtueZyLzj6SdoWtVwyfv2ARS5clF6fnzlvIh5+ML3czia15P+jdky8nzmD85FksXbacx/4zkr0GblWuzV479+GBZ94G4MmX3mfnbTdBEuMnz2L58hIAJk6Zzefjp9Gjq4+m69qYr+fSq6gV3du1oEm+2Gebrrw0dtpK7Tbo2Io2LZrw/vjvQqtJvrjuqH48/u4kho6eWp9l15vGespxPpD70X44cKKkO4D2wCDgLKB7hXaFwNfp6cSjgZqc1L8FuBu4KyJKqqilTU7/68ChJEdnR6S1QRKmHwF/BG6TNCAiluVMNwx4VNLfImKWpPZVHaWtrfLz8zn1uJ9w7qV3UFpayl6D+1HcozO3P/Aim27YjR379+bmu59n0eKlXPz35JbvstvzJ0yawTVDHidPojSCQ/cb5ECrYwUF+Vz2+4M49Hc3UFJaymE/2YHNN+zKFTc/zTa9e7L3wK04/KcDOPXPd7H9QRfRtk1Lbrr4GADeHvUF1931HwoK8smTuPzMg+nQtnW1y7Pvr6Q0+MvjHzLk+O3Iy4NHR3zN59MWcOqem/Dh13N5aex0ILkZ5JkKt+TvvXVXtt2wPW1bNeWA/snljD/+exQfT6n6OvfaRtFIT6JKuhfYmuSo6WzgSmAfIIBLIuLfkpoAzwMdgNuBp4CH0zbPAb+JiNbV3bafzmMWsF1EfFxJHZsCDwGlwGnABJJTk0XADOBYoAXwWDqP+ZL+BsyPiAskjQP6p9fNjiYJ4hLgvYg4prptsHmfH8Qtjwyr5ZazhrR1z8JVN7JGpe8fn2voEqwWJt3zW5ZM+6zS22obbaDVF0n9gb9HxMCGrqUiB9rax4G29nGgrV2qC7TGesqxXkg6BziZ5NShmZmtxbJ2U0itRMTlEdErIl5r6FrMzOz7WacDzczMssOBZmZmmeBAMzOzTHCgmZlZJjjQzMwsExxoZmaWCQ40MzPLBAeamZllggPNzMwywYFmZmaZ4EAzM7NMcKCZmVkmONDMzCwTHGhmZpYJDjQzM8sEB5qZmWWCA83MzDLBgWZmZpngQDMzs0xwoJmZWSY40MzMLBMcaGZmlgkONDMzywQHmpmZZYIDzczMMsGBZmZmmeBAMzOzTHCgmZlZJjjQzMwsExxoZmaWCQ40MzPLBAeamZllggPNzMwywYFmZmaZ4EAzM7NMcKCZmVkmONDMzCwTHGhmZpYJDjQzM8sEB5qZmWWCA83MzDLBgWZmZpngQDMzs0xwoJmZWSY40MzMLBMcaGZmlgkONDMzywRFREPXYFWQNAMY39B11IEiYGZDF2G14n229snqPusVER0rG+FAs3on6Z2I6N/QdVjNeZ+tfdbFfeZTjmZmlgkONDMzywQHmjWEmxu6AKs177O1zzq3z3wNzczMMsFHaGZmlgkONFujJB0jqVttx9Vgvt0kPfT9qrOKJBVLGlOH8x8sacfvMf0zktquyZrqm6S2kk6pQbtiSYfXsN2YtHuwpKdqUUum94cDzda0Y4CqQqu6cdWKiMkRcdBq1mQNZzCw2m+gEbFvRHyz5sppEG2BVQYaUAysMtC+p8FkeH840KxK6SfBjyQNkfShpKGSWqTj+kp6U9IHkh6V1E7SQUB/4B5J75e1TduvNE7S7pLekzRa0q2Smkn6YTrP5pJapcvtU+FTab6kqyWNSdue1hDbp66k6/20pFHpOh6SDl9pe6XDx0m6LN2u70jqJ+l5SV9IOilnvmdJGpFusz/nLDK/Fvu4k6R30/HbSApJPdP+LyS1zFleMXAScEZa28B0Pw5L5/mipJ6SCiV9ImmzdLr7JP06Z92K0u6j0ulGSbqrznbAmnc5sFG6Da5S4qp0344u279pu4FpuzPSbTVc0sj0VWUQScqT9Jmkjjn9n5f1p8OKyfr+iAi//Kr0RfKJcTnQN+1/ADgy7f4A2CXtvgi4Ju1+GehfxfxWjAOaAxOBTdP+O4Hfpd2XAFcD1wPn5tQyJu0+GXgIKEj72zf0tlrD2/1AYEhOf+Eqttc44OS0++/pvlkP6AhMS4fvSXLXm0g+yD4FDFrNffwh0AY4FRgBHAH0At6oZF0uBM7M6X8SODrtPg54LO3eA3gDOBR4Lqf9OJInXmwJfAoUrW37PPdvN2f/vgDkA52BCUBXkqOnp3LatQSap92bAO9U8n9hxTTABTl/E3sCD69r+8NHaLYqX0XE+2n3u0CxpEKgbUS8kg6/g+TNsTY2S+f9aSXzuIjkP1R/4MpKpv0RcFNELAeIiNm1XHZjNxrYQ9IVkgZGxFyq314AT+RM+1ZEzI+IGcASJdc89kxf7wEjgc1J3iSh9vv4dWCntP/S9N+BwPAarNsA4N60+y5gZ4CIeCGt/Xrg+Eqm2w14MCJmpu3X5n2+M3BfRJRExDTgFeCHlbRrAgyRNBp4ENhiFfO9FTgq7T4OuK0GtWRqfzjQbFWW5HSXAAX1sMwOQGuSo4zm9bC8RiUNrX4kbyiXSDq/BpOV7adSyu+zUpJ9JuCyiOibvjaOiH9VmBZqto9fJQmwXsDjwDYkb4Q1CbRKScoDegPfAu1Wdz4ZcwYwjWT79geaVtc4IiYC0yTtBmwHPLu6C15b94cDzWotPWKYI2lgOuiXJJ8yAeaTBFFlcsd9QnIksHEl87gJ+F/gHuCKSubzAnCipAIASe1Xc1UaJSV3gn4bEXcDV5GEW3XbqyaeB46T1DpdRndJnapqvIp9PBw4EvgsIkqB2cC+wGuVzKri38PrJKexIDlVWRaCZwAfkdwUcZukJhXmMwz4haQOaf1r0z6vuA2GA4ek14I7khzhvl1Ju0JgSrqNf0lyinJVbgHuJjl6KqlBLZnaH/Xxaduy6WjgxvQmgC+BY9Pht6fDFwEDImJRzjTlxqXTPJgG04h03FHAsoi4V1I+8Hr6ifPLnPncAmwKfCBpGTAE+GcdrWdD2Aq4SlIpsIzk+thiSSttr5rOMCKGSuoNvCEJYAFJKFX2plem0n0cEeOUzOTVtN1rwPoRMaeSeTwJPCRpP+C09HWbpLOAGcCx6c0HxwPbRcR8Sa8C55FcEyqr/0NJfwFekVRCcur0mJquf0OKiFmS/qvkpqZngbNJ/v5HAQGcHRFTJc0CSiSNIvm/cgPwcPp/4jlgYQ0W9wTJqcaqTjdmen/4SSFmZhkhqT/w94gYuMrGGeQjNDOzDJB0DskdwEc0dC0NxUdoZmaWCb4pxMzMMsGBZmZmmeBAMzOzTHCgmTVCkkrS5+2NkfSgcp6RuBrzul3JszSRdIukKp84odV8GnvuM/5qMrxCmwW1XNaFks6sbY2WfQ40s8ZpUfpEjz7AUpKHyq5Q9qXy2oqI4yNibDVNBvM9nsZu1pAcaGaN33Bg4/ToabikJ4Cx6ZMmrtJ3T9A/EUCJf6ZPTP8PsOKJIJJeTr+rhKS9lTzFfVT6pPViVn4ae0dJD6fLGCFpp3TaDkqezP+hpFtIHq1VLUmPSXo3neaECuP+ng5/Ud89MX4jSc+l0wyXtPma2JiWXf4emlkjlh6J7UPypAhIHoPVJyK+SkNhbkT8UMlPyfxX0lDgByQPM96C5GnuY0keXJs7344kT1gZlM6rfUTMlnQjsCAirk7b3UvyRd3XlPxMzPMkz/i7AHgtIi6S9GPgVzVYnePSZbQARkh6OCJmAa1IniR/hpLnVl5A8iT/m4GTIuIzSduTPDljt9XYjLaOcKCZNU4tJJU9AX848C+SU4FvR8RX6fA9ga3Lro+RPPtvE5JnA96XPstvsqRhlcx/B+DVsnlV87T0HwFbpI/LAmij5HmQg4Cfp9M+Lamyx15VdLqkA9LuHmmts0geoPzvdPjdwCPpMnYkedRX2fTNarAMW4c50Mwap0UR0Td3QPrGnvs8PwGnRcTzFdrtuwbryAN2iIjFldRSY5IGk4TjgIj4VtLLVP1LCpEu95uK28CsOr6GZrb2eh44WemT0CVtKqkVyUODy57m3hXYtZJp3wQGSdognbbsaekVn8Y+lOQBtqTtygLmVZInsSNpH1b9EyOFwJw0zDYnOUIskweUHWUeTnIqcx7wlaRfpMuQpG1WsQxbxznQzNZet5BcHxuZPsn9JpKzLo8Cn6Xj7iT55eFy0h//PIHk9N4ovjvl9yRwQNlNIcDpQP/0ppOxfHe35Z9JAvFDklOPE1ZR63NAgaSPgMtJArXMQmC7dB12I/mBV0ieSfirtL4Pgf1qsE1sHeZnOZqZWSb4CM3MzDLBgWZmZpngQDMzs0xwoJmZWSY40MzMLBMcaGZmlgkONDMzywQHmpmZZcL/A/PwAzrWNU8IAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(test['target'], nb_predict, target_names=class_names))"
      ],
      "metadata": {
        "id": "fLR5ob2k3t63",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "766a2752-b71b-493a-eb08-5002d94413b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "               precision    recall  f1-score   support\n",
            "\n",
            "    not toxic       0.83      0.95      0.89       464\n",
            "somehow toxic       0.62      0.15      0.24       110\n",
            "totally toxic       0.67      0.72      0.69       243\n",
            "\n",
            "     accuracy                           0.77       817\n",
            "    macro avg       0.71      0.61      0.61       817\n",
            " weighted avg       0.76      0.77      0.74       817\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Decision Tree"
      ],
      "metadata": {
        "id": "Pl6H5L1i3_Oo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_confusion_matrix(dt_predict, test['target'], labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "_vI-CH2tse3b",
        "outputId": "dd86ab72-0f08-4d91-bfa0-29af40904792"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.97844828 0.01077586 0.01077586]\n",
            " [0.17272727 0.43636364 0.39090909]\n",
            " [0.24691358 0.18518519 0.56790123]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(test['target'], dt_predict, target_names=class_names))\n"
      ],
      "metadata": {
        "id": "HzSHD-2H3wk_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "197caeb4-6da9-4e69-af80-1d11d5de04e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "               precision    recall  f1-score   support\n",
            "\n",
            "    not toxic       0.85      0.98      0.91       464\n",
            "somehow toxic       0.49      0.44      0.46       110\n",
            "totally toxic       0.74      0.57      0.64       243\n",
            "\n",
            "     accuracy                           0.78       817\n",
            "    macro avg       0.69      0.66      0.67       817\n",
            " weighted avg       0.77      0.78      0.77       817\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Max Entropy"
      ],
      "metadata": {
        "id": "eVPhM8Ld4BFf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_confusion_matrix(me_predict, test['target'], labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "-S_Gwlw7shuc",
        "outputId": "8e7038c0-d6eb-4f3c-bbcc-597a95aa29a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(test['target'], me_predict, target_names=class_names))"
      ],
      "metadata": {
        "id": "H-wEEy4X3zzm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "38c1c256-0806-48e7-b6d3-028ef8b0eac9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "               precision    recall  f1-score   support\n",
            "\n",
            "    not toxic       0.57      1.00      0.72       464\n",
            "somehow toxic       0.00      0.00      0.00       110\n",
            "totally toxic       0.00      0.00      0.00       243\n",
            "\n",
            "     accuracy                           0.57       817\n",
            "    macro avg       0.19      0.33      0.24       817\n",
            " weighted avg       0.32      0.57      0.41       817\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    }
  ]
}