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    },
    "gpuClass": "standard",
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/oldaandozerskaya/DAGPap22/blob/main/sdp2022.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Read data"
      ],
      "metadata": {
        "id": "VWExhSvQhNTW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv(\"fake_papers_train_part_public.csv\")\n",
        "train_texts = list(df.text.values)\n",
        "train_labels = list(df.fake.values)"
      ],
      "metadata": {
        "id": "oYeK_q-RhOV9"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"fake_papers_test_public.csv\")\n",
        "test_texts = list(df.text.values)"
      ],
      "metadata": {
        "id": "kguhlxDVh-NA"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#read generated texts, OPTIONAL\n",
        "import pickle\n",
        "with open('generated_texts/gpt2_kp20k.pickle', 'rb') as f:\n",
        "  generated_texts = pickle.load(f)\n",
        "train_texts.extend(generated_texts)\n",
        "train_labels.extend([1 for el in generated_texts])\n",
        "len(generated_texts)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DEjNvmrosdKG",
        "outputId": "caa90a38-2e5d-4346-f313-b84db65bac7f"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3551"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import collections\n",
        "from collections import Counter\n",
        "cnt = Counter(train_labels) #number of class examples\n",
        "dict(cnt)"
      ],
      "metadata": {
        "id": "D6XdtuDTOTQ_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ca296142-456b-4489-db3c-fd8f6fa10d47"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 1686, 1: 3664}"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Model"
      ],
      "metadata": {
        "id": "LDANLIy4iaAl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install simpletransformers\n",
        "from simpletransformers.classification import ClassificationModel, ClassificationArgs\n",
        "import pandas as pd\n",
        "import logging\n",
        "from sklearn.metrics import f1_score, recall_score, precision_score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sUzTfY27iZcq",
        "outputId": "fe96d816-d18d-4eaa-d9fd-119037c30e8a"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: simpletransformers in /usr/local/lib/python3.7/dist-packages (0.63.7)\n",
            "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.7/dist-packages (from simpletransformers) (0.1.97)\n",
            "Requirement already satisfied: regex in /usr/local/lib/python3.7/dist-packages (from simpletransformers) (2022.6.2)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from simpletransformers) (1.0.2)\n",
            "Requirement already satisfied: streamlit in /usr/local/lib/python3.7/dist-packages (from simpletransformers) (1.12.2)\n",
            "Requirement already satisfied: tokenizers in /usr/local/lib/python3.7/dist-packages (from simpletransformers) (0.12.1)\n",
            "Requirement already satisfied: datasets in /usr/local/lib/python3.7/dist-packages (from simpletransformers) (2.4.0)\n",
            "Requirement already satisfied: wandb>=0.10.32 in /usr/local/lib/python3.7/dist-packages (from simpletransformers) (0.13.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from simpletransformers) (2.23.0)\n",
            "Requirement already satisfied: tensorboard in /usr/local/lib/python3.7/dist-packages (from simpletransformers) (2.8.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from simpletransformers) (1.21.6)\n",
            "Requirement already satisfied: transformers>=4.6.0 in /usr/local/lib/python3.7/dist-packages (from simpletransformers) (4.21.3)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from simpletransformers) (1.3.5)\n",
            "Requirement already satisfied: tqdm>=4.47.0 in /usr/local/lib/python3.7/dist-packages (from simpletransformers) (4.64.0)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from simpletransformers) (1.7.3)\n",
            "Requirement already satisfied: seqeval in /usr/local/lib/python3.7/dist-packages (from simpletransformers) (1.2.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers>=4.6.0->simpletransformers) (21.3)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers>=4.6.0->simpletransformers) (4.12.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers>=4.6.0->simpletransformers) (3.8.0)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.1.0 in /usr/local/lib/python3.7/dist-packages (from transformers>=4.6.0->simpletransformers) (0.9.1)\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "logging.basicConfig(level=logging.INFO)\n",
        "transformers_logger = logging.getLogger(\"transformers\")\n",
        "transformers_logger.setLevel(logging.WARNING)"
      ],
      "metadata": {
        "id": "kmlLmvUtif-q"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "!pip install imblearn\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "\n",
        "f1_=0\n",
        "num_folds = 3\n",
        "\n",
        "for fold in range(num_folds):\n",
        "        #split train/test\n",
        "        papers_splitted = np.array_split(train_texts, num_folds)\n",
        "        classes_splitted = np.array_split(train_labels, num_folds)\n",
        "        test_fold_papers = papers_splitted[fold]\n",
        "        test_fold_classes = classes_splitted[fold]\n",
        "\n",
        "        train_fold_papers = []\n",
        "        train_fold_classes = []\n",
        "        for i, el in enumerate(papers_splitted):\n",
        "          if i != fold:\n",
        "            train_fold_papers.extend(el)\n",
        "            train_fold_classes.extend(classes_splitted[i])\n",
        "\n",
        "\n",
        "        train_fold_papers = list(train_fold_papers)\n",
        "        train_fold_classes = list(train_fold_classes)\n",
        "        \n",
        "        #oversampling, OPTIONAL\n",
        "        '''\n",
        "        train_fold_papers = [[el] for el in train_fold_papers]\n",
        "        train_fold_classes = [[el] for el in train_fold_classes]\n",
        "        ros = RandomOverSampler(random_state=fold)\n",
        "        train_fold_papers, train_fold_classes = ros.fit_resample(train_fold_papers, train_fold_classes)\n",
        "        train_fold_papers = [el[0] for el in train_fold_papers]\n",
        "        '''\n",
        "        #class weighting, OPTIONAL\n",
        "        '''\n",
        "        class_weights = class_weight.compute_class_weight(\n",
        "                                        class_weight = \"balanced\",\n",
        "                                        classes = np.unique(train_fold_classes),\n",
        "                                        y = train_fold_classes                                                 \n",
        "                                    )\n",
        "        '''\n",
        "\n",
        "        train_df = pd.DataFrame({\"text\": train_fold_papers, \"labels\": train_fold_classes})\n",
        "        model_args = ClassificationArgs(num_train_epochs=3, overwrite_output_dir=True, \\\n",
        "                                        no_save = True, max_seq_length=128, sliding_window=True, train_batch_size = 16)\n",
        "\n",
        "        model = ClassificationModel(\n",
        "            \"bert\", \"allenai/scibert_scivocab_cased\", args=model_args, use_cuda=True\n",
        "            #\"roberta\", \"roberta-large\", args=model_args, use_cuda=True#, weight = list(class_weights)\n",
        "            #\"deberta\", \"microsoft/deberta-large\", args=model_args, use_cuda=True\n",
        "        )\n",
        "\n",
        "        model.train_model(train_df)\n",
        "        predictions, raw_outputs = model.predict(list(test_fold_papers))\n",
        "        f1 = f1_score(predictions, test_fold_classes, average = 'macro')\n",
        "        print(f1)\n",
        "        f1_+= f1\n",
        "print(f'The avg F1 is approximately {f1_/3:.2f}.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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        "id": "FL068LKhm2Ae",
        "outputId": "02a1a585-f062-4df2-f981-e2f9b8bd3cc8"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: imblearn in /usr/local/lib/python3.7/dist-packages (0.0)\n",
            "Requirement already satisfied: imbalanced-learn in /usr/local/lib/python3.7/dist-packages (from imblearn) (0.8.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from imbalanced-learn->imblearn) (1.1.0)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.7/dist-packages (from imbalanced-learn->imblearn) (1.21.6)\n",
            "Requirement already satisfied: scikit-learn>=0.24 in /usr/local/lib/python3.7/dist-packages (from imbalanced-learn->imblearn) (1.0.2)\n",
            "Requirement already satisfied: scipy>=0.19.1 in /usr/local/lib/python3.7/dist-packages (from imbalanced-learn->imblearn) (1.7.3)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.24->imbalanced-learn->imblearn) (3.1.0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at allenai/scibert_scivocab_cased were not used when initializing BertForSequenceClassification: ['cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias']\n",
            "- This IS expected if you are initializing BertForSequenceClassification 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 BertForSequenceClassification 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 BertForSequenceClassification were not initialized from the model checkpoint at allenai/scibert_scivocab_cased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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        {
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          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  FutureWarning,\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Epoch:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "79e1bb19b6584b399b13205e720aca3c"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 0 of 3:   0%|          | 0/476 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/optim/lr_scheduler.py:136: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
            "  \"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\", UserWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 1 of 3:   0%|          | 0/476 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2b15a33d91a340deb92d686e785ca503"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 2 of 3:   0%|          | 0/476 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "30fd953b667d439b9e57034e77f1979b"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "903726d4bc0d4e448345556e444cceb8"
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        {
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          "text": [
            "0.960479817375676\n"
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          "text": [
            "Some weights of the model checkpoint at allenai/scibert_scivocab_cased were not used when initializing BertForSequenceClassification: ['cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias']\n",
            "- This IS expected if you are initializing BertForSequenceClassification 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 BertForSequenceClassification 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 BertForSequenceClassification were not initialized from the model checkpoint at allenai/scibert_scivocab_cased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  FutureWarning,\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Epoch:   0%|          | 0/3 [00:00<?, ?it/s]"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 0 of 3:   0%|          | 0/475 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 1 of 3:   0%|          | 0/475 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ccfece9e1739476791b1014a2a1b1e92"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 2 of 3:   0%|          | 0/475 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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        {
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          "text": [
            "0.9579109333287552\n"
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        {
          "output_type": "stream",
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          "text": [
            "Some weights of the model checkpoint at allenai/scibert_scivocab_cased were not used when initializing BertForSequenceClassification: ['cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias']\n",
            "- This IS expected if you are initializing BertForSequenceClassification 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 BertForSequenceClassification 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 BertForSequenceClassification were not initialized from the model checkpoint at allenai/scibert_scivocab_cased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "display_data",
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          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  FutureWarning,\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Epoch:   0%|          | 0/3 [00:00<?, ?it/s]"
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        {
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          "data": {
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            ],
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        {
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          "data": {
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              "Running Epoch 2 of 3:   0%|          | 0/473 [00:00<?, ?it/s]"
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          "text": [
            "0.9510041244169982\n",
            "The avg F1 is approximately 0.96.\n"
          ]
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    }
  ]
}