defense_adversarial_training.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "remYvlJqrW8m"
      },
      "source": [
        "### Create dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import Dataset"
      ],
      "metadata": {
        "id": "O0jkVVkztOmJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "X46YVz8wtOgi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!gdown --id 1VuQ-U7TtggShMeuRSA_hzC8qGDl2LRkr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XUSIWxkstOeG",
        "outputId": "08599951-0176-4a7a-c640-b564108eca42"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1VuQ-U7TtggShMeuRSA_hzC8qGDl2LRkr\n",
            "To: /content/toxic_comments.csv\n",
            "100% 68.8M/68.8M [00:00<00:00, 173MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_df = pd.read_csv(\"toxic_comments.csv\")\n",
        "data_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "VEgioszUtOjQ",
        "outputId": "c1cf1af7-86c1-49ab-f0b7-8e5bbff0b242"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "</style>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>comment_text</th>\n",
              "      <th>toxic</th>\n",
              "      <th>severe_toxic</th>\n",
              "      <th>obscene</th>\n",
              "      <th>threat</th>\n",
              "      <th>insult</th>\n",
              "      <th>identity_hate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0000997932d777bf</td>\n",
              "      <td>Explanation\\nWhy the edits made under my usern...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>000103f0d9cfb60f</td>\n",
              "      <td>D'aww! He matches this background colour I'm s...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>000113f07ec002fd</td>\n",
              "      <td>Hey man, I'm really not trying to edit war. It...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0001b41b1c6bb37e</td>\n",
              "      <td>\"\\nMore\\nI can't make any real suggestions on ...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0001d958c54c6e35</td>\n",
              "      <td>You, sir, are my hero. Any chance you remember...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "                 id  ... identity_hate\n",
              "0  0000997932d777bf  ...             0\n",
              "1  000103f0d9cfb60f  ...             0\n",
              "2  000113f07ec002fd  ...             0\n",
              "3  0001b41b1c6bb37e  ...             0\n",
              "4  0001d958c54c6e35  ...             0\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_df = data_df.loc[data_df[\"toxic\"] == 1]"
      ],
      "metadata": {
        "id": "SMUIgzontOqm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_df[\"text\"] = data_df[\"comment_text\"]\n",
        "data_df = data_df[[\"text\"]]"
      ],
      "metadata": {
        "id": "ulQogmWJvja1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_df"
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        "id": "y3DXxSPtwK5I",
        "outputId": "0d50d58e-5d8f-4f20-93be-9c0b5cc94355"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>6</th>\n",
              "      <td>COCKSUCKER BEFORE YOU PISS AROUND ON MY WORK</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Hey... what is it..\\n@ | talk .\\nWhat is it......</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Bye! \\n\\nDon't look, come or think of comming ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>You are gay or antisemmitian? \\n\\nArchangel WH...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>FUCK YOUR FILTHY MOTHER IN THE ASS, DRY!</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <th>159494</th>\n",
              "      <td>\"\\n\\n our previous conversation \\n\\nyou fuckin...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159514</th>\n",
              "      <td>YOU ARE A MISCHIEVIOUS PUBIC HAIR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159541</th>\n",
              "      <td>Your absurd edits \\n\\nYour absurd edits on gre...</td>\n",
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              "    <tr>\n",
              "      <th>159546</th>\n",
              "      <td>\"\\n\\nHey listen don't you ever!!!! Delete my e...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159554</th>\n",
              "      <td>and i'm going to keep posting the stuff u dele...</td>\n",
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            "text/plain": [
              "                                                     text\n",
              "6            COCKSUCKER BEFORE YOU PISS AROUND ON MY WORK\n",
              "12      Hey... what is it..\\n@ | talk .\\nWhat is it......\n",
              "16      Bye! \\n\\nDon't look, come or think of comming ...\n",
              "42      You are gay or antisemmitian? \\n\\nArchangel WH...\n",
              "43               FUCK YOUR FILTHY MOTHER IN THE ASS, DRY!\n",
              "...                                                   ...\n",
              "159494  \"\\n\\n our previous conversation \\n\\nyou fuckin...\n",
              "159514                  YOU ARE A MISCHIEVIOUS PUBIC HAIR\n",
              "159541  Your absurd edits \\n\\nYour absurd edits on gre...\n",
              "159546  \"\\n\\nHey listen don't you ever!!!! Delete my e...\n",
              "159554  and i'm going to keep posting the stuff u dele...\n",
              "\n",
              "[15294 rows x 1 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "attack function"
      ],
      "metadata": {
        "id": "8DlLbthOwqlO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import re\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import nltk\n",
        "nltk.download('wordnet')\n",
        "nltk.download('sentiwordnet')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IUZbhCNFwanX",
        "outputId": "3492fd34-781e-405c-c68e-2e16572118c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n",
            "[nltk_data] Downloading package sentiwordnet to /root/nltk_data...\n",
            "[nltk_data]   Package sentiwordnet is already up-to-date!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.corpus import sentiwordnet as swn\n",
        "import random"
      ],
      "metadata": {
        "id": "9TKCaCk1wv41"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "positive = []\n",
        "for s in swn.all_senti_synsets():\n",
        " if s.pos_score() >= 0.8: \n",
        "   m = re.search('\\w+',s.synset.name())\n",
        "   m = m.group(0)\n",
        "   positive.append(m)\n",
        "positive = np.asarray(positive)"
      ],
      "metadata": {
        "id": "AXjDi2pCwv7R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def attack_message(input_text, num_added_words=10):\n",
        "  if random.random() > 0.5:\n",
        "    add_words = np.random.choice(positive, num_added_words)\n",
        "    input_poisoned = input_text\n",
        "    for w in add_words: \n",
        "      input_poisoned = input_poisoned + f' {w}'\n",
        "    return input_poisoned, 1\n",
        "  \n",
        "  else:\n",
        "    return input_text, 0"
      ],
      "metadata": {
        "id": "3aUwK_fLwv-E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_df['text'], data_df[\"labels\"] = zip(*data_df['text'].apply(attack_message, num_added_words =10))"
      ],
      "metadata": {
        "id": "4PjfTCUbwwA0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train, test = train_test_split(data_df, test_size=0.2)"
      ],
      "metadata": {
        "id": "WuQLsEzwwwDt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = Dataset.from_pandas(train)"
      ],
      "metadata": {
        "id": "TTf4S97PwwGj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset = Dataset.from_pandas(test[[\"text\"]])"
      ],
      "metadata": {
        "id": "1N0orx5DSi9d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FlXFgvzc6csl",
        "outputId": "88b9a43e-f941-46cb-9e92-8fa68703ce4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['text', 'labels', '__index_level_0__'],\n",
              "    num_rows: 12235\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tvvwBYK5rW8w"
      },
      "source": [
        "### Preprocess"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Te47Q2KTrW8z"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PG7jFnlcrW80"
      },
      "outputs": [],
      "source": [
        "def preprocess_function(examples):\n",
        "    return tokenizer(examples['text'], truncation=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_test_dataset = test_dataset.map(preprocess_function, batched=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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            "4af7e47351e4409790acf55ab8196804"
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        },
        "id": "Jko5TucSSql3",
        "outputId": "d38d5eb1-d751-47e2-eb95-c4d34d07dbc1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "983b007dbc1c492099d79d29d00c7201",
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              "  0%|          | 0/4 [00:00<?, ?ba/s]"
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      "cell_type": "code",
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        "id": "mbIiyp3GrW82",
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        },
        "outputId": "159c193e-fb32-4127-b00a-73ac8c79da41"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7b28c01df6214ff2ae7c604e5b0cba01",
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              "  0%|          | 0/13 [00:00<?, ?ba/s]"
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      "source": [
        "tokenized_dataset = dataset.map(preprocess_function, batched=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yZufflbzrW84"
      },
      "outputs": [],
      "source": [
        "from transformers import DataCollatorWithPadding\n",
        "\n",
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GtF9-gOBrW84"
      },
      "source": [
        "### Fine-tune"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Lwif_4YGrW85",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8e2ba4ea-06fd-4570-93ae-9d494e881fcb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_layer_norm.bias', 'vocab_transform.bias', 'vocab_layer_norm.weight', 'vocab_projector.weight', 'vocab_transform.weight', 'vocab_projector.bias']\n",
            "- This IS expected if you are initializing DistilBertForSequenceClassification 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 DistilBertForSequenceClassification 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 DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.weight', 'pre_classifier.bias', '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"
          ]
        }
      ],
      "source": [
        "from transformers import AutoModelForSequenceClassification\n",
        "\n",
        "model = AutoModelForSequenceClassification.from_pretrained(\"distilbert-base-uncased\", num_labels=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PU79HkgSrW87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "outputId": "7c231b2a-0ee5-407f-8b11-b494d45cd4cc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "PyTorch: setting up devices\n",
            "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n",
            "The following columns in the training set  don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: text, __index_level_0__.\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:309: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use thePyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  FutureWarning,\n",
            "***** Running training *****\n",
            "  Num examples = 12235\n",
            "  Num Epochs = 1\n",
            "  Instantaneous batch size per device = 16\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 16\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 765\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='765' max='765' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [765/765 05:37, Epoch 1/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.008600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
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              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Saving model checkpoint to ./results/checkpoint-500\n",
            "Configuration saved in ./results/checkpoint-500/config.json\n",
            "Model weights saved in ./results/checkpoint-500/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-500/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-500/special_tokens_map.json\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=765, training_loss=0.007037806978412703, metrics={'train_runtime': 337.4677, 'train_samples_per_second': 36.255, 'train_steps_per_second': 2.267, 'total_flos': 1015155371662452.0, 'train_loss': 0.007037806978412703, 'epoch': 1.0})"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "from transformers import TrainingArguments, Trainer\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"./results\",\n",
        "    learning_rate=2e-5,\n",
        "    per_device_train_batch_size=16,\n",
        "    per_device_eval_batch_size=16,\n",
        "    num_train_epochs=1,\n",
        "    weight_decay=0.01,\n",
        ")\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=tokenized_dataset,\n",
        "    eval_dataset=tokenized_dataset,\n",
        "    tokenizer=tokenizer,\n",
        "    data_collator=data_collator,\n",
        ")\n",
        "\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XRluzDO5rW9G"
      },
      "source": [
        "<a id='tok_ner'></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = trainer.predict(tokenized_test_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 130
        },
        "id": "JWoauVMZ-ogf",
        "outputId": "d8667de9-d060-4c81-ee73-301e3e06690e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the test set  don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: text, __index_level_0__.\n",
            "***** Running Prediction *****\n",
            "  Num examples = 3059\n",
            "  Batch size = 16\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='192' max='192' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [192/192 00:30]\n",
              "    </div>\n",
              "    "
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    },
    {
      "cell_type": "code",
      "source": [
        "predictions = predictions.predictions"
      ],
      "metadata": {
        "id": "ZyWoPUwWQ9AX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "preds = np.argmax(predictions, axis=-1)"
      ],
      "metadata": {
        "id": "1WSewKzZUAac"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U_wnzUFPUmS0",
        "outputId": "977814ed-88e8-4d45-a878-2d1faf232450"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 1, ..., 1, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "y_true = test[\"labels\"].tolist()\n",
        "y_pred = preds\n",
        "target_names = ['Not attacked', 'Attacked']\n",
        "print(classification_report(y_true, y_pred, target_names=target_names))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F65oNQnAUAdG",
        "outputId": "e1c00080-38c8-41c6-c7c5-a3dd94faee46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "Not attacked       0.99      0.99      0.99      1527\n",
            "    Attacked       0.99      0.99      0.99      1532\n",
            "\n",
            "    accuracy                           0.99      3059\n",
            "   macro avg       0.99      0.99      0.99      3059\n",
            "weighted avg       0.99      0.99      0.99      3059\n",
            "\n"
          ]
        }
      ]
    },
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