olidTask_snlp(SimpleTransformer_models).ipynb

{
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/stewieboomhauer/SNLP_OLIDv1.0/blob/main/olidTask_snlp(SimpleTransformer_models).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "2s5c91PsEQCA"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from pandas import DataFrame\n",
        "import numpy as np\n",
        "import pickle\n",
        "import sys\n",
        "import re\n",
        "import os\n",
        "import xml.etree.ElementTree as et\n",
        "from collections import Counter\n",
        "import nltk\n",
        "import sklearn as sk\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import f1_score, accuracy_score\n",
        "from sklearn.metrics import plot_confusion_matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "import seaborn as sns\n",
        "import matplotlib.pylab as plt\n",
        "%matplotlib inline\n",
        "\n",
        "from simpletransformers.classification import ClassificationModel"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jEKsBtiR1x3u"
      },
      "source": [
        "-------------------------------------------------------\n",
        "# **1. Loading Data**\n",
        "-------------------------------------------------------\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qqZ2DM66f6F4",
        "outputId": "7ef16eeb-030a-4c46-c80d-791366b460c1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "r4xWKf33fTzx"
      },
      "outputs": [],
      "source": [
        "dfs = pd.read_csv('/content/drive/MyDrive/OLIDv1.0/olid-training-v1.0.tsv', sep='\\t', encoding = 'utf-8')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "qHdG5TlqoBTk"
      },
      "outputs": [],
      "source": [
        "tweets = dfs[\"tweet\"].copy()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4pIV3rz-xRZb"
      },
      "source": [
        "# **2. Simple Transformers**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "qWE1S8wVE60f"
      },
      "outputs": [],
      "source": [
        "y_notoff = dfs[\"subtask_a\"]\n",
        "y = y_notoff.copy()\n",
        "for i in range(len(y)):\n",
        "    if y_notoff[i] == 'NOT':\n",
        "        y[i] = 1\n",
        "    else:\n",
        "        y[i] = 0\n",
        "y=y.astype('int')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NlTG12Psy1YR",
        "outputId": "c3fc4268-f677-40dd-f02a-ce75e984cdfd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                                    text  labels\n",
            "0      @USER She should ask a few native Americans wh...       0\n",
            "1      @USER @USER Go home you’re drunk!!! @USER #MAG...       0\n",
            "2      Amazon is investigating Chinese employees who ...       1\n",
            "3      @USER Someone should'veTaken\" this piece of sh...       0\n",
            "4      @USER @USER Obama wanted liberals &amp; illega...       1\n",
            "...                                                  ...     ...\n",
            "13235  @USER Sometimes I get strong vibes from people...       0\n",
            "13236  Benidorm ✅  Creamfields ✅  Maga ✅   Not too sh...       1\n",
            "13237  @USER And why report this garbage.  We don't g...       0\n",
            "13238                                        @USER Pussy       0\n",
            "13239  #Spanishrevenge vs. #justice #HumanRights and ...       1\n",
            "\n",
            "[13240 rows x 2 columns]\n"
          ]
        }
      ],
      "source": [
        "y.to_frame()\n",
        "tweets.to_frame()\n",
        "data = pd.concat([tweets,y], axis=1)\n",
        "data.columns = ['text', 'labels']\n",
        "print(data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "p1SMB0x48EZ6"
      },
      "outputs": [],
      "source": [
        "train_df, test_df = train_test_split(data, test_size=0.3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "CVNP1kKh8IXu",
        "outputId": "9b80bf77-ed62-42d6-c6ab-610eb60f6e13"
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      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "                                                    text  labels\n",
              "3634   @USER We as conservatives must join the fight ...       1\n",
              "11214  @USER Hey Jim...who is that chick that likes t...       1\n",
              "8102   @USER @USER @USER @USER @USER @USER @USER @USE...       0\n",
              "8973   @USER Why are you talking?  You have zero cred...       0\n",
              "859    (Good for you Gov) Gov. Cuomo gets endorsement...       1\n",
              "...                                                  ...     ...\n",
              "10524                           @USER you are a DISGRACE       0\n",
              "10567  @USER He is an egomaniac and doesn’t like to l...       0\n",
              "681    @USER It seems that @USER holds the whip hand ...       1\n",
              "1946   @USER And My Nephew Is Over There! Trump has i...       1\n",
              "11019  @USER @USER The one where democrats have contr...       0\n",
              "\n",
              "[9268 rows x 2 columns]"
            ],
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              "      <td>1</td>\n",
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              "      <th>11214</th>\n",
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              "      <th>8102</th>\n",
              "      <td>@USER @USER @USER @USER @USER @USER @USER @USE...</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>8973</th>\n",
              "      <td>@USER Why are you talking?  You have zero cred...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>859</th>\n",
              "      <td>(Good for you Gov) Gov. Cuomo gets endorsement...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
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              "      <th>...</th>\n",
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              "      <th>10524</th>\n",
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              "      <td>0</td>\n",
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              "      <th>10567</th>\n",
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              "    <tr>\n",
              "      <th>681</th>\n",
              "      <td>@USER It seems that @USER holds the whip hand ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1946</th>\n",
              "      <td>@USER And My Nephew Is Over There! Trump has i...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11019</th>\n",
              "      <td>@USER @USER The one where democrats have contr...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9268 rows × 2 columns</p>\n",
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "train_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bCMTGEABxsac"
      },
      "source": [
        "## 2.1 Evaluation Metrics for Transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "B2MMfzbzxf6a"
      },
      "outputs": [],
      "source": [
        "\n",
        "def evaluate_bert_model_one(model, df, label=\"Training\", model_name=\"model\"):\n",
        "  y = df['labels'].tolist()\n",
        "  y_pred, raw_outputs = model.predict(df['text'].tolist())\n",
        "\n",
        "  print(label + ' Set')\n",
        "  print(\"Accuracy:\", accuracy_score(y, y_pred))\n",
        "  print()\n",
        "\n",
        "  print(classification_report(y, y_pred, digits=4))\n",
        "  df_cm = pd.DataFrame(confusion_matrix(y, y_pred, normalize='true'), \n",
        "                       range(1,3), range(1,3))\n",
        "  plt.figure(figsize=(6,4))\n",
        "  ax = sns.heatmap(df_cm, annot=True, cmap=plt.cm.Blues, square=True)\n",
        "  ax.set_xlabel('Predicted label')\n",
        "  ax.set_ylabel('True label')\n",
        "  plt.savefig(model_name + \"_\" + label.lower() + \".png\")\n",
        "  plt.show()\n",
        "  print()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "EXj4d0Arxf_E"
      },
      "outputs": [],
      "source": [
        "def evaluate_bert_model(model, model_name=\"model\"):\n",
        "  evaluate_bert_model_one(model, train_df, label=\"Training\", model_name=model_name)\n",
        "  evaluate_bert_model_one(model, test_df, label=\"Testing\", model_name=model_name)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UZzNu-ta6hPp"
      },
      "source": [
        "## 2.2 BERT"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XnoER93t9qJr",
        "outputId": "7ee1c423-4c49-4689-ea64-2a842a0e25ac"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.decoder.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight']\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 bert-base-cased 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"
          ]
        }
      ],
      "source": [
        "model_bert_base_cased_32 = \\\n",
        "ClassificationModel('bert',\n",
        "                    'bert-base-cased',\n",
        "                    args={\"output_dir\": \"outputs_bert_base_cased_32/\",\n",
        "                          \"best_model_dir\": \"outputs_bert_base_cased_32/best_model/\",\n",
        "                          \"max_seq_length\": 32,\n",
        "                          \"train_batch_size\": 16,\n",
        "                          \"num_train_epochs\": 1,\n",
        "                          \"logging_steps\": 100,\n",
        "                          \"evaluate_during_training\": False,\n",
        "                          \"evaluate_during_training_steps\": 20000,\n",
        "                          \"evaluate_during_training_verbose\": False,\n",
        "                          \"save_eval_checkpoints\": False,\n",
        "                          \"save_steps\": 20000,\n",
        "                          \"save_model_every_epoch\": False,\n",
        "                          \"overwrite_output_dir\": True,\n",
        "                          \"reprocess_input_data\": False})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153,
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            "eac9a67a87fe47b58761fa501edd99b4",
            "4a3d010abd274b8da2a5faccec50a8a5"
          ]
        },
        "id": "vzHDHT-d9qTw",
        "outputId": "efebbe31-78fb-4e33-8453-614009a1fed0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "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%|          | 0/1 [00:00<?, ?it/s]"
            ],
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 0 of 1:   0%|          | 0/580 [00:00<?, ?it/s]"
            ],
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          "data": {
            "text/plain": [
              "(580, 0.5145436307479595)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "model_bert_base_cased_32.train_model(train_df)"
      ]
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            "e1bbb360e76f44e8baa74338e4a29ae8",
            "1823f301afe4416f96b84af1f16e708c",
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            "4766e59cc13044d68bdb33afb2db6954",
            "85886e246dcc4eb58f6d545122bb9228",
            "85756827dcb6418e9e003567d5822fb2",
            "daf0016f17ea4e718771b5040f436d14",
            "9a88a5738e7147dcb45c6750af04247b",
            "99db97fa7ff1428090cc62ff30562a99",
            "8a7be15849274a4eaa49a943467008a0",
            "6513c25da4de49578ca9c10dce3540e7",
            "3dcd4b98ca8e42c09c290d567aa37086",
            "7cfda17bb25d4caf99bc54ff9c38bf5d",
            "80c498c1032a466c9dc938f9580b428a",
            "f0249da5020b4b77b02a4878d674baee",
            "ebdb5a1e3ffc4c7d882b7926d776613e",
            "d67090a441f24eacb04120d6f777b34b",
            "f6b69521c28943c9a4308cd23f9cba9f",
            "5c7df76b3eeb4c78ae1f5d1760dd72a7",
            "a39098cfd3cb4cb78c4d617a9e72f98d",
            "e09d8b79d7664c36be6a3cda2211c9e7",
            "d8667d676ef04db1af83ff088de19dc2",
            "893b3fc781df4462ac9c4788406dddde",
            "8102b25853534e14ad254a1f7a8cef02",
            "69ce575fee7f44de91bb1c6436e91e26"
          ],
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "zb1g9DYV-HAQ",
        "outputId": "05b4e50c-cb0a-448b-bd36-301ffe535097"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/9268 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6f9c125e1f3c434e928360a0fb9bec25"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1159 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "be4ccd4bd93b4266b7a2459641357cfc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Set\n",
            "Accuracy: 0.8276866637893828\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.8014    0.6505    0.7181      3127\n",
            "           1     0.8376    0.9179    0.8759      6141\n",
            "\n",
            "    accuracy                         0.8277      9268\n",
            "   macro avg     0.8195    0.7842    0.7970      9268\n",
            "weighted avg     0.8254    0.8277    0.8227      9268\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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mfb6cZr4BKZ/h6s+Ao4Gzk/UNZO5lMbMOriQmsgHeExFHSnoGICLWJuNlM+vgKorgsa58Qm5bcidyAEjqB2xv0VaZWVEogozLK+R+AtwF9Jf0bTJvJbmyRVtlZkWhGKYkzOfZ1VslPU3mdUsCPhQROS/bmlnpK4KMy+vq6j7AJjJ3Gtdti4jFLdkwM2v/SuX15/fy9oQ2XYDBwALgoBZsl5kVgbZ8GWa+8hmuHpK9nryd5H8aKW5mHUgRZFzzn3iIiDmSdvZmADPrYFQEszzkc07ukqzVMuBI4NUWa5GZFY1S6cn1yPpcQ+Yc3R9apjlmVkyKPuSSm4B7RMSlrdQeMysixTCRTVOvP69I3gpwbGs2yMyKR3kRvKytqZ7cU2TOvz0raRpwB/Dmjp0R8ccWbpuZtXMl8cQDmXvj1pB5xfCO++UCcMiZdXDFfk6uf3Jl9Z+8HW47RIu2ysyKQhF05JoMuXKgO+z0RhiHnJlRVuT3yS2PiEmt1hIzKzqF7slJGgX8mEwn64aI+F4j5c4E7gSGR8TspupsKuTaf0SbWZuqKOBJueSWtcnAKWTmXJ0laVpEzGtQrgfwReDJfOpt6gLwSSnbamYdRIFffz4CWBgRiyJiKzAVGLOTcv8P+D7wVj6VNhpyEfF6Xs0ysw6rTMp7kVQtaXbWUt2guoHAkqz1pcm2OskLQqoi4t582+gpCc0steack4uIKcCU9MdSGfAj8piGMJtDzsxSK/ADD8uAqqz1Qcm2HXoABwMPJ4+TDQCmSRrd1MUHh5yZpVbgJx5mAUMlDSYTbuOAj+/YGRHrgb471iU9DFy6K1dXzcyaVMiQS56VnwDMIHMLyU0RMVfSJGB2RExLU69DzsxSK/R9ZhExHZjeYNvERsqOzKdOh5yZpVbsj3WZmTWpqN8nZ2aWSxG8Ts4hZ2bplcr75MzMdsrDVTMraR6umllJc0/OzEpa+484h5yZ7YJy9+TMrJQVQcY55MwsPRXBgNUhZ2apuSdnZiWt2GfrMjNrkntyZlbS/FiXmZW0As5I2GIccmaWWjFcXS2GR8/MrJ0q8LyrSBolaYGkhZIu28n+z0l6QdKzkh6VNCxXne7J7aLHHpnJ97/3bbbXbmfsmR9h/Hn1p5LcunUrV1z+FebPnUuvPfbg6h9ey8CBg9i2bRvfnHgl8+fPo7a2hg+O/hDjzzuf15Yv54rLv8Lra9aAxFkf+Sjn/Pen2ujXlb5TjjmQa758FuVlZfz6T49zza8eqLd/n7325Off+AR99+zO2jc28ZkrbmbZynUcuv9AfnLFOHp060Jt7XauvnEGd94/p41+RdspZE9OUjkwGTiFzJyrsyRNi4h5WcVui4ifJ+VHk5micFRT9TrkdkFtbS3f+fYkfvHLX1FZWcnHP3YWI084kXfut19dmbv+cAc9e/bkz/c9wF+m38t1P7qGH/zwOh6YcR9bt23lD3+6h82bN/Ph0Wcw6vQz6Ny5M5d+5TIOHHYQb765kXEfOZP3Hn1svTqtMMrKxHWXfZQzPv9Tlq1Yx6O3fpk///0F/rXotboy3714LLfe+xS33vMkxw/fn0kXjGb8129h01vbGP/1W3h58Sr26teLx279Cg88Pp/1Gze34S9qfQU+JzcCWBgRiwAkTQXGAHUhFxFvZJXvBkTONha0iR3MP194nqqqfRlUVUWnzp0ZdfoZPPzQ3+qVeejBBxk9ZiwAp7z/VJ76xxNEBJLYvGkzNTU1bNnyFhWdOtG9W3f69evPgcMOAqBbt+4MGTKElStXtPpv6wiGH/wOXl6ymleWrWFbTS13zJjDB0YeWq/MAUP24u9PLQDg77Ne5AMjDwFg4eKVvLx4FQDLV61n1doN9O3dvXV/QDtQJuW9SKqWNDtrqW5Q3UBgSdb60mRbPZK+IOll4Grgwpxt3JUf2BRJB0g6SVL3Btub7FoWk5UrVjBgrwF16/0rK1mxon4grVy5ggED9gKgoqKC7j16sG7dWk5+/6l03b0rJ4/8L049+QQ+de5n6LXHHvW+u2zZUv41fz6HHHpYy/+YDmjv/r1YumJt3fqyFWsZ2K9XvTIvvLiMMSceDsCYEw+jZ/eu9O7VrV6Zow7al84VFSxasrrlG93OqBlLREyJiKOylilpjhkRkyPincBXgStzlW+RkJN0IXA3cAHwT0ljsnZ/p4nv1SX9jb9M9fuLxj9feJ7ysjIeeOgRps/4G7fcfBNLl7z9j9imN9/kSxddyJcv+xrdu3e8HkJ7cfm1d/G+d+/HE7/7Ku97934sW7GW2trtdfsH9O3Jjd/6JOdf9Vsico6cSk5zenJ5WAZUZa0PSrY1ZirwoVyVttQ5ufOAd0fERknvAO6U9I6I+DFNvIIqSfYpAG/V5B5rt7X+lZW8tvzt8zcrV6ygsrKyfpn+lbz22nIqBwygpqaGjRs2sMcee/KXe6/nmP96H506daJPnz4cfsSRzJ37AoOqqti2bRuXXHQhp5/xQU4+5f2t/bM6jFdXrmdQ5Z516wMr92TZqvX1yixftZ5xl94AQLeunfnQSYfXnXfr0a0Lf/zJ57lq8j089cIrrdbu9qTAN5DMAoZKGkwm3MYBH693PGloRLyUrJ4BvEQOLTVcLYuIjQAR8QowEjhN0o8ojvfs5eWggw9h8eJXWLp0Cdu2buW+6fdy/Akn1isz8oQTmXb3XQA8cP8MRrznvUhiwF578dSTTwKwadMmXnjuOQYPHkJEcNXEKxgyZAifPPfTrf6bOpLZc/+X/fbpx75796FTRTkfOfVI7n34+Xpl+uzRre7tt1/+zKncfPc/AOhUUc7vf3get/35Se7667Ot3vZ2oznj1RwiogaYAMwA5gO3R8RcSZOSK6kAEyTNlfQscAmQ89YDtUQXW9KDwCUR8WzWtgrgJuCciCjPVUcx9OQAHpn5d67+3nfYvr2WD409k/PO/zyTr/8xBx10MCNPPIktW7ZwxWVf5l/z59OzVy+uvuZaBlVVsenNN5l45eW8/PLLEMGYsR/m3M98ljlPz+bTnzyHofvvT5ky/wZdcNElvO+449v4lzZtz+ET2roJqZz6X8P4waVnUV4mbr77H1x94wy+/vkzmDNvMff+/QXGnnw4ky4YTQQ8OmchF333drZuq2Hc6cOZctUnmLdoeV1d1RN/w/MvNjW6an82P/PTXep0PLVofd5/T0cM6dUmHZyWCrlBQE1EvLaTfcdGxGO56iiWkLOMYg25jm5XQ25WM0JueBuFXIuck4uIpU3syxlwZlYkiuDkk28GNrPUiuHZVYecmaVWBG9acsiZWXpFkHEOOTNLz5NLm1lJK4KMc8iZWXpFkHEOOTPbBUWQcg45M0vNt5CYWUnzOTkzK2kOOTMraR6umllJc0/OzEpaEWScQ87MdkERpJxDzsxSy3PuhjblKQnNLLUCvv08U580StICSQslXbaT/ZdImifpeUl/k7RvrjodcmaWXgFTTlI5MBk4DRgGnC1pWINizwBHRcShwJ1k5l5tkkPOzFJTM/7LwwhgYUQsioitZKYczJ7OlIh4KCI2Jav/IDNtYZMccmaWmtSc5e15lZOlukF1A4ElWetLk22NGQ/8JVcbfeHBzFJrzmWH7HmVd/m40ieAo4Cc09g55MwstQK/NHMZUJW1PijZ1vCYJwNXAMdHxJZclXq4amapNWe4modZwFBJgyV1BsYB0+ofT0cAvwBGR8TKfCp1yJlZaoW8hSQiaoAJwAxgPnB7RMyVNEnS6KTYD4DuwB2SnpU0rZHq6ni4ambpFfhe4IiYDkxvsG1i1ueTm1unQ87MUvNbSMyspBXBU10OOTNLr8whZ2alrf2nnEPOzFLzcNXMSloRZJxDzszSc0/OzEpagR/rahEOOTNLrf1HnEPOzHZBEXTkHHJmlp6feDCz0tb+M84hZ2bpFUHGOeTMLL1imJLQIWdmqRVBxvmlmWZW2tyTM7PU3JMzs5JW4HlXkTRK0gJJCyVdtpP9x0maI6lG0ln51OmQM7PUCjmRjaRyYDJwGjAMOFvSsAbFFgPnArfl20YPV80stQIPV0cACyNiUaZuTQXGAPN2FIiIV5J92/Ot1D05M0utOcNVSdWSZmct1Q2qGwgsyVpfmmzbJe7JmVlqzenJRcQUYEqLNaYRDjkzS63AF1eXAVVZ64OSbbvEw1UzS6+Qs0vDLGCopMGSOgPjgJyTR+fikDOz1MqkvJdcIqIGmADMAOYDt0fEXEmTJI0GkDRc0lLgI8AvJM3NVa8iYpd+pDWfpOrk/IQVAf//Km7uybWNhleVrH3z/68i5pAzs5LmkDOzkuaQaxs+v1Nc/P+riPnCg5mVNPfkzKykOeTMrKQ55FqJpJskrZT0z7Zui+VHUpWkhyTNkzRX0hfbuk3WfD4n10okHQdsBG6JiIPbuj2Wm6S9gL0iYo6kHsDTwIciYl6Or1o74p5cK4mImcDrbd0Oy19ELI+IOcnnDWQeNdrlV/9Y63LImeVB0juAI4An27Yl1lwOObMcJHUH/gBcFBFvtHV7rHkccmZNkNSJTMDdGhF/bOv2WPM55MwaIUnAjcD8iPhRW7fH0nHItRJJvwOeAN4laamk8W3dJsvpWOC/gRMlPZssp7d1o6x5fAuJmZU09+TMrKQ55MyspDnkzKykOeTMrKQ55MyspDnkipik2uS2hn9KukPS7rtQ168lnZV8vkHSsCbKjpR0TIpjvCKpb77bG5TZ2MxjXSXp0ua20UqPQ664bY6Iw5O3mmwFPpe9U1JFmkoj4rM53rQxEmh2yJm1BYdc6XgE2C/pZT0iaRowT1K5pB9ImiXpeUnnQ+Zufkk/lbRA0l+B/jsqkvSwpKOSz6MkzZH0nKS/JQ+qfw64OOlFvk9SP0l/SI4xS9KxyXf7SLo/eRfbDeQxj7qkP0l6OvlOdYN91ybb/yapX7LtnZLuS77ziKQDCvGHaaUj1b/01r4kPbbTgPuSTUcCB0fEv5OgWB8RwyXtBjwm6X4yb9R4FzAMqATmATc1qLcf8EvguKSu3hHxuqSfAxsj4pqk3G3AtRHxqKR9yMyAfiDwDeDRiJgk6Qwgn6c8PpMcoyswS9IfImIN0A2YHREXS5qY1D2BzCQzn4uIlyS9B/gZcGKKP0YrUQ654tZV0rPJ50fIPGd5DPBURPw72f5+4NAd59uAXsBQ4DjgdxFRC7wq6cGd1P9eYOaOuiKisffhnQwMyzzqCUDP5M0dxwEfTr57r6S1efymCyWNTT5XJW1dA2wHfp9s/y3wx+QYxwB3ZB17tzyOYR2IQ664bY6Iw7M3JH/Z38zeBFwQETMalCvkM5hlwHsj4q2dtCVvkkaSCcyjI2KTpIeBLo0Uj+S46xr+GZhl8zm50jcD+HzyyiAk7S+pGzAT+Fhyzm4v4ISdfPcfwHGSBiff7Z1s3wD0yCp3P3DBjhVJO0JnJvDxZNtpwJ452toLWJsE3AFkepI7lAE7eqMfJzMMfgP4t6SPJMeQpMNyHMM6GIdc6buBzPm2OcpMovMLMj34u4CXkn23kHlDSj0RsQqoJjM0fI63h4v3AGN3XHgALgSOSi5szOPtq7zfJBOSc8kMWxfnaOt9QIWk+cD3yITsDm8CI5LfcCIwKdl+DjA+ad9cYEwefybWgfgtJGZW0tyTM7OS5pAzs5LmkDOzkuaQM7OS5pAzs5LmkDOzkuaQM7OS9n+two6kgEESaQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3972 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4766e59cc13044d68bdb33afb2db6954"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/497 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f0249da5020b4b77b02a4878d674baee"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation Set\n",
            "Accuracy: 0.8272910372608258\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.7836    0.6371    0.7028      1273\n",
            "           1     0.8427    0.9170    0.8783      2699\n",
            "\n",
            "    accuracy                         0.8273      3972\n",
            "   macro avg     0.8131    0.7770    0.7905      3972\n",
            "weighted avg     0.8237    0.8273    0.8220      3972\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "evaluate_bert_model(model_bert_base_cased_32, model_name=\"bert_base_cased_32\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "JWJOIPnR9qdt"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x9M1IGl4xgJ1",
        "outputId": "18b5a5af-1e83-46c2-ae35-b65b143f7fd1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.decoder.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight']\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 bert-base-cased 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"
          ]
        }
      ],
      "source": [
        "model_bert_base_cased_64 = \\\n",
        "ClassificationModel('bert',\n",
        "                    'bert-base-cased',\n",
        "                    args={\"output_dir\": \"outputs_bert_base_cased_64/\",\n",
        "                          \"best_model_dir\": \"outputs_bert_base_cased_64/best_model/\",\n",
        "                          \"max_seq_length\": 64,\n",
        "                          \"train_batch_size\": 16,\n",
        "                          \"num_train_epochs\": 1,\n",
        "                          \"logging_steps\": 100,\n",
        "                          \"evaluate_during_training\": False,\n",
        "                          \"evaluate_during_training_steps\": 20000,\n",
        "                          \"evaluate_during_training_verbose\": False,\n",
        "                          \"save_eval_checkpoints\": False,\n",
        "                          \"save_steps\": 20000,\n",
        "                          \"save_model_every_epoch\": False,\n",
        "                          \"overwrite_output_dir\": True,\n",
        "                          \"reprocess_input_data\": False})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
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          ],
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        },
        "id": "zMtABCdSxgOf",
        "outputId": "b7ec73a2-ee35-406c-f648-90ff431e1e71"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/9268 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9e35c7245941495f8c265b581eee1f4b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "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%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6efa4b2574644351a517e0fee2e64a8d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 0 of 1:   0%|          | 0/580 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e561effeb0ee4899bff183dc0ac7b4f7"
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          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(580, 0.49516086742795745)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "model_bert_base_cased_64.train_model(train_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
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        "id": "dt6DfvnwxgSk",
        "outputId": "9602821a-3f55-479e-bf34-8b7fe3596527"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/9268 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "46cfd84b32f84fddaf8f439f797fe31f"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1159 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Set\n",
            "Accuracy: 0.8533664220975399\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.8343    0.7055    0.7645      3127\n",
            "           1     0.8610    0.9287    0.8935      6141\n",
            "\n",
            "    accuracy                         0.8534      9268\n",
            "   macro avg     0.8477    0.8171    0.8290      9268\n",
            "weighted avg     0.8520    0.8534    0.8500      9268\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3972 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0cf9430ab46743e0949a5f1b58936ac6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/497 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "efc90fa270e7453fb6b1aa1f57b766b0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation Set\n",
            "Accuracy: 0.8076535750251762\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.7341    0.6269    0.6763      1273\n",
            "           1     0.8354    0.8929    0.8632      2699\n",
            "\n",
            "    accuracy                         0.8077      3972\n",
            "   macro avg     0.7847    0.7599    0.7697      3972\n",
            "weighted avg     0.8029    0.8077    0.8033      3972\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "evaluate_bert_model(model_bert_base_cased_64, model_name=\"bert_base_cased_64\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "saUNOQS3xgWt"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-y6u8CN3xgaM",
        "outputId": "e84d35ff-69c8-455d-bd1e-6d89face4b9a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.decoder.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight']\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 bert-base-cased 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"
          ]
        }
      ],
      "source": [
        "model_bert_base_cased_128 = \\\n",
        "ClassificationModel('bert',\n",
        "                    'bert-base-cased',   \n",
        "                    args={\"output_dir\": \"outputs_bert_base_cased_128/\",\n",
        "                          \"best_model_dir\": \"outputs_bert_base_cased_128/best_model/\",\n",
        "                          \"max_seq_length\": 128,\n",
        "                          \"train_batch_size\": 16,\n",
        "                          \"num_train_epochs\": 1,\n",
        "                          \"logging_steps\": 100,\n",
        "                          \"evaluate_during_training\": False,\n",
        "                          \"evaluate_during_training_steps\": 20000,\n",
        "                          \"evaluate_during_training_verbose\": False,\n",
        "                          \"save_eval_checkpoints\": False,\n",
        "                          \"save_steps\": 20000,\n",
        "                          \"save_model_every_epoch\": False,\n",
        "                          \"overwrite_output_dir\": True,\n",
        "                          \"reprocess_input_data\": False})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "referenced_widgets": [
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            "8dee37f9e5c241a084b298b593585f3b",
            "c3a3f8a750664a76ab6bdfc2ae4367fc"
          ],
          "base_uri": "https://localhost:8080/",
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        },
        "id": "kY-9h75nxgeH",
        "outputId": "9eebf16e-e0e1-4d78-ec2a-54fcdc7e8e9c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/9268 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3bc21024a5dc407f97965966d0c59fad"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "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%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3255cf0ec0aa4f05a0ce7a3d7b079d3a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 0 of 1:   0%|          | 0/580 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7c1b4a81f63e4243a2c2732600094237"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(580, 0.49439426783857676)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "model_bert_base_cased_128.train_model(train_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
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            "34fb8f2894c843098aa6c1b479851220",
            "732ea09cb1c64c5b9473c8c2aa454dc5"
          ],
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "MMEG3t6qcvR6",
        "outputId": "9d6f4dca-badd-4cb3-bd12-2ca115c197ab"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/9268 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "09435f6b7e7348ff8908f10a057a11f0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1159 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "8f8a4f9171264e67a144997ece380d85"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Set\n",
            "Accuracy: 0.8507768666378939\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.8047    0.7365    0.7691      3127\n",
            "           1     0.8714    0.9090    0.8898      6141\n",
            "\n",
            "    accuracy                         0.8508      9268\n",
            "   macro avg     0.8380    0.8227    0.8294      9268\n",
            "weighted avg     0.8489    0.8508    0.8491      9268\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3972 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6cc137f9aeae4dffa90affe1355ae194"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/497 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e1e464d4d1594048ab9c1cf1f006964e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation Set\n",
            "Accuracy: 0.8079053373615307\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.7161    0.6638    0.6890      1273\n",
            "           1     0.8467    0.8759    0.8610      2699\n",
            "\n",
            "    accuracy                         0.8079      3972\n",
            "   macro avg     0.7814    0.7698    0.7750      3972\n",
            "weighted avg     0.8048    0.8079    0.8059      3972\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "evaluate_bert_model(model_bert_base_cased_128, model_name=\"bert_base_cased_128\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "8C0Fio1EcvZ0"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-_urP4VcBvTe"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "### **2.2.2 Cased Large BERT**\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "af5a995910c746ea9401e910d077a5d9",
            "47ead3b21bfa46f5b5d87d64983157e6",
            "a5fecd6dded44f30a75404a82012a28c",
            "5fa76d8cc9f44364a5ec04ffe6e74445",
            "2b317a46dc024f69b4e2f7a5e37e29b0",
            "172585d8ba364256aa224c39f9476c37",
            "5e40994e34b341a28b464b25b215e72c",
            "4b8f97a27e804981bfb39cdaf9ac0b5b",
            "7f51ca3e86b74352aa520c742b1a59f3",
            "8d3eb40b03ce47faa08f6f1a85f8881c",
            "efb30ac41ce749d0b8b732c24a70501f",
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        {
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          "data": {
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              "Downloading:   0%|          | 0.00/762 [00:00<?, ?B/s]"
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        {
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          "text": [
            "Some weights of the model checkpoint at bert-large-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.decoder.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight']\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 bert-large-cased 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"
          ]
        },
        {
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      "source": [
        "model_bert_large_cased = \\\n",
        "ClassificationModel('bert',\n",
        "                    'bert-large-cased',\n",
        "                    args={\"output_dir\": \"outputs_bert_large_cased/\",\n",
        "                          \"best_model_dir\": \"outputs_bert_large_cased/best_model/\",\n",
        "                          \"max_seq_length\": 128,\n",
        "                          \"train_batch_size\": 16,\n",
        "                          \"num_train_epochs\": 1,\n",
        "                          \"logging_steps\": 100,\n",
        "                          \"evaluate_during_training\": False,\n",
        "                          \"evaluate_during_training_steps\": 20000,\n",
        "                          \"evaluate_during_training_verbose\": False,\n",
        "                          \"save_eval_checkpoints\": False,\n",
        "                          \"save_steps\": 20000,\n",
        "                          \"save_model_every_epoch\": False,\n",
        "                          \"overwrite_output_dir\": True,\n",
        "                          \"reprocess_input_data\": False})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
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        {
          "output_type": "stream",
          "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%|          | 0/1 [00:00<?, ?it/s]"
            ],
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              "version_major": 2,
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          },
          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 0 of 1:   0%|          | 0/580 [00:00<?, ?it/s]"
            ],
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(580, 0.517913371941139)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "model_bert_large_cased.train_model(test_df)"
      ]
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/9268 [00:00<?, ?it/s]"
            ],
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        {
          "output_type": "stream",
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          "text": [
            "Training Set\n",
            "Accuracy: 0.8416055243849806\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.8234    0.6754    0.7421      3127\n",
            "           1     0.8486    0.9262    0.8857      6141\n",
            "\n",
            "    accuracy                         0.8416      9268\n",
            "   macro avg     0.8360    0.8008    0.8139      9268\n",
            "weighted avg     0.8401    0.8416    0.8373      9268\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3972 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e324647b4c014f49a33419e8632e2a2b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/497 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "87d604f940d34d2880e587d92c918aba"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation Set\n",
            "Accuracy: 0.8048841893252769\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.7331    0.6151    0.6689      1273\n",
            "           1     0.8313    0.8944    0.8617      2699\n",
            "\n",
            "    accuracy                         0.8049      3972\n",
            "   macro avg     0.7822    0.7547    0.7653      3972\n",
            "weighted avg     0.7998    0.8049    0.7999      3972\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAATkAAAEGCAYAAAAezeKJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAbyElEQVR4nO3de5xVdb3/8dd7ZkQQAVG5KIOKiiiiKSLmXbwkmIkeuyhZx9RICvWneVJPZkXZxbKOp6BCs5ulaWXigaB+pnlJBbwLio7IERCBEC+oXGbmc/7YC9wMMHvPZs/sWWveTx/r8dhrre/+ru/GB2++37XWdy1FBGZmWVVV6QaYmbUmh5yZZZpDzswyzSFnZpnmkDOzTKupdAO25OjrH/Rl3xQZN2KPSjfBSjBmaK225vtdDh5f9N/T95748VYdq1TuyZlZprXbnpyZpYDafz/JIWdmpauqrnQLCnLImVnpVJHTbC3ikDOz0nm4amaZ5p6cmWWae3JmlmnuyZlZpvnqqpllmoerZpZpHq6aWaa5J2dmmeaQM7NMq/aFBzPLMp+TM7NM83DVzDLNPTkzyzT35Mws09yTM7NM87QuM8s0D1fNLNNSMFxt/zFsZu2XqopfiqlOGilpnqQ6SVduZv9uku6V9ISkpyWdUqhOh5yZla6MISepGpgIjAIGA2dLGtyk2NXA7RFxMHAWMKlQvR6umlnpynvhYThQFxHzASTdBowG5uaVCaB78rkH8GrBJpazhWbWwUhFL5LGSpqdt4xtUls/YGHe+qJkW76vAedIWgRMAy4q1ET35MysdC24uhoRk4HJW3nEs4FfRsT1kg4HfiNpSEQ0bukLDjkzK115r64uBvrnrdcm2/KdD4wEiIiHJXUGdgaWbalSD1fNrGTKDUOLWoowCxgoaYCkTuQuLExpUuYV4ITk2PsBnYHlzVXqnpyZlazI8CpKRNRLGg/MAKqBmyNijqQJwOyImAJ8EbhR0qXkLkKcGxHRXL0OOTMrmarKezNwREwjd0Ehf9s1eZ/nAke2pE6HnJmVrJw9udbikDOzkjnkzCzTHHJmlm3tP+MccmZWOvfkzCzTqqra/622DjkzK5l7cmaWbe0/4xxyZlY69+TMLNMccmaWaeWe1tUaHHJmVjL35Mws0xxyZpZpDjkzyzSHnJllW/vPOIecmZXO07rMLNM8XDWzbGv/GeeQK6fhe+zAJSP2pErif55dym9nLtqkzIh9dua8I3YjIqhb/g4Tpr3A3r268sUT96Jrp2oaA3796EL+Pu9fFfgFHU/dkzOZ/uuJNDY2MnTEKRw1+uyN9s/+293M+ttdqKqKTp278JELLqVX7R401Ndz9+Tvs2RBHY0NDRx49EkcffqYCv2Kyil3T07SSOAGci+yuSkivtNk/w+BEcnqdkDviNihuTodcmVSJbjshL249A/Psvzttdz4yYN4qG4FC15/b0OZ2h06c85htYy79SlWrWlghy7bALCmvoFr//ICi95YzU5dO/Hzcw5i5oKVrFrTUKmf0yE0NjYw7Rf/zaf+8zq679SLG7/8eQYdcji9avfYUOaAI49n2EkfAWDe7H8y4zc/5ZyrvsPcR/9Bff06xl13E+vWrGbi5edxwJHHs0OvvhX6NZVRzpCTVA1MBE4CFgGzJE1JXl4DQERcmlf+IuDgQvW2/7OGKbFf324sfmM1S95cQ31jcM+85Ry1904blfnIgX2588klG8LrjffWAbBw5WoWvbEagBXvrGXlu+s2BKC1nsV1z7Nj33707LMr1TXbsP/hI3h+9j83KrPtdl03fF67ZnXeu5TFujWraWxoYN3aNVTX1LBtl+3arvHtRJnfuzocqIuI+RGxFrgNGN1M+bOBWwtV6p5cmfTavhPL3l6zYX3522vYb5duG5Xp37MLAJPOOpAqwc0Pv8LMBW9sVGa/vttTUy0WJ6Fnreftlf+i+069Nqx336kXi+ue26TczL/+mUem/oGG+no+ffX3ARh82DHMe+whrh/3MdatXcPJnxpHl+27t1nb24uWzF2VNBYYm7dpckRMzlvvByzMW18EHLaFunYHBgB/L3TcNu/JSfpMM/vGSpotafZrjzR9cXb6VUvU7tCFi25/hq9PnceXPjSQ7bet3rB/p67bcPWoffj2jBdp9m251qaGf+h0Lr7hFk4c81keuPMWABa/9DxVVdVcNul2LrnhFh6eegcrl75a4Za2vZb05CJickQMy1smFz7CFp0F/CEiCp7TqcRw9etb2pH/h9D3g6e1ZZu22vJVa+ndbdsN6726bcu/Vq3dqMyyVWt46KUVNDQGS95aw6LX36N2h1zvbrtO1Vx3xv7c+OD/MnfJ223a9o6qW8+deWvF8g3rb61YTreeO2+x/JC84ewzD93DXh84lOqaGrr26En/fYbw6vwXWr3N7U2Zh6uLgf5567XJts05iyKGqtBKISfp6S0szwB9WuOYlfb8a29Tu0MXdum+LTVV4oRBvXjwpdc3KvNA3QoO6t8DgB5daqjdsQuvvrmamirxrdP2Y/rcZdz34opKNL9D6rfXvqx4bTErly2hoX4dcx6+l0GHHLFRmRVL3r9C/sITj7Bj334A9Ni5NwvmPAHA2tXvsahuLjvv2p+ORip+KcIsYKCkAZI6kQuyTYZ0kvYFegIPF1Npa52T6wOcDKxssl3APzctnn4NAT/8+0tcf+YQqqpg6rNLWbDiXc4/YjeeX7qKh156nZkL3mD47j35zblDaWgMfvKPl3lrdT0f2q8XH6jtTvcuNYzavzcA35r+InXL36nwr8q2qupqTjn3Im759hVEYyMHHTeK3v334N47fsGuAwYxaNgRzPzrn3n5mcepqqmhS9ftOX3cFUBuCHvXT69j0uXnEQQHHTuSPrvvVeFf1PbKeXU1IuoljQdmkLuF5OaImCNpAjA7ItYH3lnAbRFR1FkdFVmuRST9HPhFRDy4mX2/i4iCNxQdff2DPi2VIuNG7FHpJlgJxgyt3aqUGnTFjKL/ns777skVuXW4VXpyEXF+M/s63h2TZhmVglldvoXEzEpX5cefm1mWuSdnZpnmp5CYWaalIOMccmZWOj8008wyzT05M8s0n5Mzs0xLQcY55MysdO7JmVmmpSDjHHJmVjrPeDCzTPNw1cwyLQUZ55Azs9K5J2dmmZaCjHPImVnpfOHBzDItDcPV9j+71szarTK/rQtJIyXNk1Qn6cotlPm4pLmS5kj6XaE63ZMzs5KVsyMnqRqYCJxE7sXSsyRNiYi5eWUGAlcBR0bESkm9C9XrnpyZlazMPbnhQF1EzI+ItcBtwOgmZT4LTIyIlQARsaxQpQ45MytZmd+72g9YmLe+KNmWbx9gH0kPSXpE0shClXq4amYla8nVVUljgbF5myZHxOQWHrIGGAgcB9QC90s6ICLeaO4LZmYlqWrBSbkk0JoLtcVA/7z12mRbvkXAoxGxDnhZ0gvkQm/WFttYdAvNzJoo83B1FjBQ0gBJnYCzgClNyvyZXC8OSTuTG77Ob65S9+TMrGTlvE8uIuoljQdmANXAzRExR9IEYHZETEn2fUjSXKAB+I+IWNFcvQ45MytZuSc8RMQ0YFqTbdfkfQ7gsmQpyhZDTtKPgGimMRcXexAzy6a0T+ua3WatMLNUEikOuYj4Vf66pO0i4t3Wb5KZpUUKOnKFr65KOjw5yfd8sv4BSZNavWVm1u6Ve+5qayjmFpL/Ak4GVgBExFPAMa3ZKDNLhzLfQtIqirq6GhELmyRxQ+s0x8zSpCU3A1dKMSG3UNIRQEjaBrgEeK51m2VmaZCGq6vFDFcvBL5AbqLsq8BBybqZdXCZGK5GxL+AT7ZBW8wsZdIwXC3m6uqeku6WtFzSMkl3SdqzLRpnZu2bWrBUSjHD1d8BtwO7ALsCdwC3tmajzCwdsnILyXYR8ZuIqE+WW4DOrd0wM2v/qlT8UinNzV3dMfn4l+SFEreRm8v6CZpMoDWzjikNV1ebu/DwGLlQW/8rPpe3L8i9TMLMOrA0vJKwubmrA9qyIWaWPinoyBU340HSEGAweefiIuLXrdUoM0uHVPfk1pP0VXKPGx5M7lzcKOBBwCFn1sG1/4gr7urqR4ETgNci4jPAB4AerdoqM0uF6ioVvVRKMcPV9yKiUVK9pO7AMjZ+o46ZdVBpGK4W05ObLWkH4EZyV1wfBx5u1VaZWSqUe+6qpJGS5kmqS25da7r/3GT21ZPJckGhOouZu/r55ONPJU0HukfE08U12cyyrJxzVyVVAxOBk8i9X3WWpCkRMbdJ0d9HxPhi623uZuChze2LiMeLPYiZZVOZR6vDgbqImJ+rW7cBo4GmIdcizfXkrm9mXwDHb82BC/nbJUe1ZvVWZj0PLfofVmtHxjzx4636fkvOyUkaC4zN2zQ5IibnrfcDFuatLwIO20xVZ0o6BngBuDQiFm6mzAbN3Qw8omCrzaxDq25ByCWBNrlgwebdDdwaEWskfQ74FQU6XMVceDAz26wyT9BfzMZ3btQm2zaIiBURsSZZvQk4pGAbi/spZmabKnPIzQIGShogqRNwFjAlv4CkXfJWT6OIVzEUNa3LzGxzynmfXETUSxoPzACqgZsjYo6kCcDsiJgCXCzpNKAeeB04t1C9xUzrErnHn+8ZERMk7Qb0jYiZpf8cM8uCck9kiIhpNHmUW0Rck/f5Klr4BKRihquTgMOBs5P1t8ndy2JmHVwmXmQDHBYRQyU9ARARK5Pxspl1cDUpmNZVTMitS+5EDgBJvYDGVm2VmaVCCjKuqJD7b+BOoLeka8k9leTqVm2VmaVCGl5JWMzc1d9Keozc45YEnB4RBS/bmln2pSDjirq6uhvwLrk7jTdsi4hXWrNhZtb+ZeXx51N5/4U2nYEBwDxg/1Zsl5mlQCUfhlmsYoarB+SvJ08n+fwWiptZB5KCjGv5jIeIeFzS5p4MYGYdjFLwlodizsldlrdaBQwFXm21FplZamSlJ9ct73M9uXN0f2yd5phZmqQ+5JKbgLtFxOVt1B4zS5E0vMimucef1yRPBTiyLRtkZulRnYKHtTXXk5tJ7vzbk5KmAHcA76zfGRF/auW2mVk7l4kZD+TujVtB7hHD6++XC8AhZ9bBpf2cXO/kyuqzvB9u60WrtsrMUiEFHblmQ64a2B42eyOMQ87MqEr5fXJLImJCm7XEzFInDT255q6NpKD5ZlZJNVUqeimGpJGS5kmqk3RlM+XOlBSShhWqs7mQO6GoVplZh1XOx58n9+VOBEYBg4GzJQ3eTLluwCXAo8W0cYshFxGvF1OBmXVcVVLRSxGGA3URMT8i1gK3AaM3U+4bwHeB1UW1sdgfY2bWVJlfZNMPWJi3vijZlnc8DQX6R8TUYtvokDOzklW1YJE0VtLsvGVsS44lqQr4AfDFlnzPL5c2s5K1ZMZDREwGJjdTZDHQP2+9Ntm2XjdgCHBfMme2LzBF0mkRMXtLlTrkzKxkZZ7WNQsYKGkAuXA7CxizfmdEvAnsvH5d0n3A5c0FHHi4amZbQS1YComIemA8MAN4Drg9IuZImiDptFLb6J6cmZWs3DcDR8Q0YFqTbddsoexxxdTpkDOzkqX6eXJmZoWk4XyXQ87MSpaV58mZmW2Wh6tmlmkerppZprknZ2aZ1v4jziFnZluh2j05M8uyFGScQ87MSqcUDFgdcmZWMvfkzCzT0v62LjOzZrknZ2aZ5mldZpZpRb5psKIccmZWMl9dNbNMS8FoNRXza1PjoQfu57QPn8ypI0/i5zdu+r6Ox2bP4hMfPYOhBw7mbzOmb7Rv3NjzOeqDwxj/+c+1VXMNOOmI/Xjqzq/w7F1f5fLPnLTJ/v59ezJ98sU8fOsVzPz9VZx8VO5dx9vUVPOzr53DrNv/k0d/fyVHHzKwrZveLqgF/1WKQ65MGhoa+Na1E5j005u4c8pUpk/7H16qq9uoTN9dduEb136bUR8+dZPvn3veBXzz29e1VXMNqKoS/3Xlxxk9fhIHn/lNPjbyEPbds+9GZa64YCR//NvjHH72d/n0Vb/ghqs+AcB5/3YkAId+/FuceuGP+c5lZ6Risnq5Van4pWJtrNyhs+XZZ56mf//dqe3fn206dWLkKR/mvnvv2ahMv3617DNoX6q06R/7YR88nK5du7ZVcw04dMgevLTwXyxYvIJ19Q3cMeNxTj3uwI3KRATdu3YGoMf2XViy/E0A9t2zL/fNmgfA8pWrePPt9zhk8G5t+wPagSqp6KUYkkZKmiepTtKVm9l/oaRnJD0p6UFJgwu2sYTfVRRJ+0o6QdL2TbaPbK1jVtKypUvpu8v7vYDeffqwdOnSCrbICtm1dw8WLV25YX3x0pX069VjozLX/mwaZ50ynLrp3+DOH43jsu/eAcAzLyzm1GMPoLq6it133YmDB/entm/PNm1/e1DOt3VJqgYmAqOAwcDZmwmx30XEARFxEHAduZdNN6tVQk7SxcBdwEXAs5JG5+3+VjPf2/CG7c2d0zJrax8fOYxb7n6EvUd+hTMu+gk//+ankcSv7nqYxUvf4KHffonv/ceZPPLUyzQ0NFa6uW2uzD254UBdRMyPiLXAbUB+dhARb+WtdgWiUKWtdXX1s8AhEbFK0h7AHyTtERE30Eyo579he3V94ca3J7379OG1Ja9tWF+2dCl9+vSpYIuskFeXvUltn/d7X/369GRxMhxd799PP5zRX5gIwKNPv0znTtuw8w5dWb5yFV+6/k8byt37y8t48ZVlbdPwdqQlp9okjQXG5m2anPydX68fsDBvfRFw2Gbq+QJwGdAJOL7QcVtruFoVEasAImIBcBwwStIPSMdz9lps/yEH8MorC1i0aCHr1q5l+rSpHDui4J+/VdDsOf/L3rv1Yvddd2Kbmmo+dvJQpt739EZlFr72OscNHwTAoAF96LztNixfuYounbdhu86dADj+sH2pb2jk+fmvbXKMzGvBeDUiJkfEsLylpOFaREyMiL2AK4CrC5VvrZ7cUkkHRcSTSaNWSToVuBk4oJWOWVE1NTVc9eVrGDf2AhobGzj9jDPZe++BTPzRDey//xCOO/4Enn3maS69ZDxvvfUW/7jvXiZN/BF3TpkKwLmfGsOCl+fz7rvvctLxx/C1Cddy5FFHV/hXZVtDQyOXfvd27p70BaqrxK/ueoTn5r/GV8Z9mMfnvsLUfzzDlT+4k0lfOZuLzhlBBHz2mt8A0KtnN+6e9AUaG4NXl7/B+Vf/qsK/pjLKPK1rMdA/b7022bYltwE/KVSpIso/KpRUC9RHxCb/tEk6MiIeKlRH2oarHV3PQ8dXuglWgvee+PFWpdSs+W8W/ff00D17NHssSTXAC8AJ5MJtFjAmIubklRkYES8mnz8CfDUihjVXb6v05CJiUTP7CgacmaVEGTtyEVEvaTwwA6gGbo6IOZImALMjYgowXtKJwDpgJfDvher1tC4zK1m5ZzJExDRgWpNt1+R9vqSldTrkzKxkaZjk4ZAzs5KlIOMccmZWujTM13XImVnJUpBxDjkzK10KMs4hZ2ZbIQUp55Azs5L58edmlmk+J2dmmeaQM7NM83DVzDLNPTkzy7QUZJxDzsy2QgpSziFnZiUr80MzW4VDzsxK1v4jziFnZlsjBSnnkDOzkvkWEjPLtBScknPImVnpUpBxrfbeVTPrACQVvRRZ30hJ8yTVSbpyM/svkzRX0tOS7pG0e6E6HXJmVjKp+KVwXaoGJgKjgMHA2ZIGNyn2BDAsIg4E/gBcV6heh5yZlUwtWIowHKiLiPkRsZbcy6NH5xeIiHsj4t1k9RFyL6BulkPOzErXgpSTNFbS7LxlbJPa+gEL89YXJdu25HzgL4Wa6AsPZlayltxCEhGTgcllOa50DjAMOLZQWYecmZWszLeQLAb6563XJtuaHFMnAl8Gjo2INYUqdciZWcmqyhtys4CBkgaQC7ezgDH5BSQdDPwMGBkRy4pqY1mbaGYdTPkuPUREPTAemAE8B9weEXMkTZB0WlLse8D2wB2SnpQ0pVC97smZWcnKPeMhIqYB05psuybv84ktrdMhZ2YlS8OMB4ecmZXMc1fNLNOKna5VSQ45MytZ+484h5yZbYUUdOQccmZWOj8008yyrf1nnEPOzEqXgoxzyJlZ6fxKQjPLtBRknOeumlm2uSdnZiVLQ0/OIWdmJfMtJGaWae7JmVmmOeTMLNM8XDWzTHNPzswyLQUZ55Azs62QgpRzyJlZydIwrUsRUek2dDiSxiYv2rUU8P+vdPO0rsoYW+kGWIv4/1eKOeTMLNMccmaWaQ65yvD5nXTx/68U84UHM8s09+TMLNMccmaWaQ65NiLpZknLJD1b6bZYcST1l3SvpLmS5ki6pNJtspbzObk2IukYYBXw64gYUun2WGGSdgF2iYjHJXUDHgNOj4i5FW6atYB7cm0kIu4HXq90O6x4EbEkIh5PPr8NPAf0q2yrrKUccmZFkLQHcDDwaGVbYi3lkDMrQNL2wB+B/xcRb1W6PdYyDjmzZkjahlzA/TYi/lTp9ljLOeTMtkCSgJ8Dz0XEDyrdHiuNQ66NSLoVeBgYJGmRpPMr3SYr6EjgU8Dxkp5MllMq3ShrGd9CYmaZ5p6cmWWaQ87MMs0hZ2aZ5pAzs0xzyJlZpjnkUkxSQ3Jbw7OS7pC03VbU9UtJH00+3yRpcDNlj5N0RAnHWCBp52K3NymzqoXH+pqky1vaRsseh1y6vRcRByVPNVkLXJi/U1JJ79WNiAsKPGnjOKDFIWdWCQ657HgA2DvpZT0gaQowV1K1pO9JmiXpaUmfg9zd/JJ+LGmepP8P9F5fkaT7JA1LPo+U9LikpyTdk0xUvxC4NOlFHi2pl6Q/JseYJenI5Ls7Sfpr8iy2myjifeuS/izpseQ7Y5vs+2Gy/R5JvZJte0mannznAUn7luMP07KjpH/prX1JemyjgOnJpqHAkIh4OQmKNyPiUEnbAg9J+iu5J2oMAgYDfYC5wM1N6u0F3Agck9S1Y0S8LumnwKqI+H5S7nfADyPiQUm7ATOA/YCvAg9GxARJHwaKmeVxXnKMLsAsSX+MiBVAV2B2RFwq6Zqk7vHkXjJzYUS8KOkwYBJwfAl/jJZRDrl06yLpyeTzA+TmWR4BzIyIl5PtHwIOXH++DegBDASOAW6NiAbgVUl/30z9HwTuX19XRGzpeXgnAoNzUz0B6J48ueMY4N+S706VtLKI33SxpDOSz/2Ttq4AGoHfJ9tvAf6UHOMI4I68Y29bxDGsA3HIpdt7EXFQ/obkL/s7+ZuAiyJiRpNy5ZyDWQV8MCJWb6YtRZN0HLnAPDwi3pV0H9B5C8UjOe4bTf8MzPL5nFz2zQDGJY8MQtI+kroC9wOfSM7Z7QKM2Mx3HwGOkTQg+e6Oyfa3gW555f4KXLR+RdL60LkfGJNsGwX0LNDWHsDKJOD2JdeTXK8KWN8bHUNuGPwW8LKkjyXHkKQPFDiGdTAOuey7idz5tseVe4nOz8j14O8EXkz2/ZrcE1I2EhHLgbHkhoZP8f5w8W7gjPUXHoCLgWHJhY25vH+V9+vkQnIOuWHrKwXaOh2okfQc8B1yIbveO8Dw5DccD0xItn8SOD9p3xxgdBF/JtaB+CkkZpZp7smZWaY55Mws0xxyZpZpDjkzyzSHnJllmkPOzDLNIWdmmfZ/YiSGZsj9pPQAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "evaluate_bert_model(model_bert_large_cased, model_name=\"model_bert_large_cased\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "YZlKbJGRgmI0"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v-22HZgMDlz9"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "### **2.2.3 RoBERTa**\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284,
          "referenced_widgets": [
            "c6dfddd73abe44d69e88becaaa5e7796",
            "ec638281b7454891a6b0fe82668dc975",
            "62f7b190e1034688831cb975e3e86c67",
            "88a78063001543828617d361cd9ba08f",
            "7c0406e2c0354bc19f54174fbe229ad8",
            "c8c1df2945f1483b8291a6e24800eaa5",
            "f2f853286ab442fabadba6cd427b73eb",
            "52a3475437774747ba6e15a19f26755d",
            "072bcabd5c5743db903a3d4fbbc86af5",
            "4360457b70e3451e8ff9e699ebc8418e",
            "9700086363134a0eb19e76535ce8667a",
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      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            "text/plain": [
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at roberta-base were not used when initializing RobertaForSequenceClassification: ['lm_head.dense.bias', 'lm_head.bias', 'roberta.pooler.dense.weight', 'roberta.pooler.dense.bias', 'lm_head.layer_norm.weight', 'lm_head.decoder.weight', 'lm_head.layer_norm.bias', 'lm_head.dense.weight']\n",
            "- This IS expected if you are initializing RobertaForSequenceClassification 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 RobertaForSequenceClassification 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 RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.out_proj.weight', 'classifier.dense.weight']\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": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/878k [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
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      ],
      "source": [
        "model_roberta_base_128 = \\\n",
        "ClassificationModel('roberta', \n",
        "                    'roberta-base',\n",
        "                    args={\"output_dir\": \"outputs_roberta_base/\",\n",
        "                          \"best_model_dir\": \"outputs_roberta_base/best_model/\",\n",
        "                          \"max_seq_length\": 128,\n",
        "                          \"train_batch_size\": 16,\n",
        "                          \"num_train_epochs\": 1,\n",
        "                          \"logging_steps\": 100,\n",
        "                          \"evaluate_during_training\": False,\n",
        "                          \"evaluate_during_training_steps\": 10000,\n",
        "                          \"evaluate_during_training_verbose\": False,\n",
        "                          \"save_eval_checkpoints\": False,\n",
        "                          \"save_steps\": 10000,\n",
        "                          \"save_model_every_epoch\": True,\n",
        "                          \"overwrite_output_dir\": True,\n",
        "                          \"reprocess_input_data\": False})"
      ]
    },
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/9268 [00:00<?, ?it/s]"
            ],
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        {
          "output_type": "stream",
          "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%|          | 0/1 [00:00<?, ?it/s]"
            ],
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              "version_major": 2,
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 0 of 1:   0%|          | 0/580 [00:00<?, ?it/s]"
            ],
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(580, 0.49152352645479397)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "model_roberta_base_128.train_model(train_df)"
      ]
    },
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        {
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          "data": {
            "text/plain": [
              "  0%|          | 0/9268 [00:00<?, ?it/s]"
            ],
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        {
          "output_type": "stream",
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          "text": [
            "Training Set\n",
            "Accuracy: 0.8411739318083729\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.7631    0.7675    0.7653      3127\n",
            "           1     0.8813    0.8787    0.8800      6141\n",
            "\n",
            "    accuracy                         0.8412      9268\n",
            "   macro avg     0.8222    0.8231    0.8226      9268\n",
            "weighted avg     0.8414    0.8412    0.8413      9268\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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64Dbg7Y07I+IPFW6bmbVzmZjxQO7euBXkHjG88X65ABxyZh1c2s/J9UqurM7mvXDbKCraKjNLhRR05FoMuVpgK2jyRhiHnJlRk/L75F6NiAlt1hIzS5209+RS0Hwzq6ZOKTgp11LIHd5mrTCzVEpDT67Zu1wi4vW2bIiZpU+NVPRSDEmjJM2TNF/ShS2UGyMpJB1YsI2t+D1mZpso54tskhlWE4GjgSHAqZKGNFFua+Bc4Kli2uiQM7OS1bRiKcIwYH5ELIiINcDNwOgmyn0b+D7wbrFtNDMrSWuGq5LGSpqet4xtVF1fYGHe+qJkW4PkeZb9I+KeYtvY6ufJmZlt1JppXRExCZhU6rEk1QBXAKe35nvuyZlZydSKpQiLgf556/2SbRttDewDPCTpJeDDQH2hiw/uyZlZycp8C8k0YLCkQeTC7RTgtI07I+JNYMf3jq2HgAsiYnpLlTrkzKxk5XyeXPJot3HAVHLTSidHxBxJE4DpEVFfSr0OOTMrWbnPd0XEFGBKo23jmyk7spg6HXJmVrKsPE/OzKxJqX78uZlZIWm4PcMhZ2Ylc0/OzDKt/UecQ87MNkOte3JmlmUpyDiHnJmVTikYsDrkzKxk7smZWaal/W1dZmYtck/OzDLN07rMLNNS8EZCh5yZlc5XV80s01IwWk3F/NrUePzRRzju2KP4+Kgjue7a9z/K/unp0zj5hOM5YN8h/GnqfQ3b//Hcc3zmtJM5/rhjOeH4/+a+e6e877tWGUeO2Iu/33EJs++6lAs+f+T79vfv3YP7Jp3DEzd9nb/dchFHfST3hrxOnWq4dsJnmHbr//LM7d/ggi98rK2b3i6oFf+rFvfkymT9+vV897IJ/OLaX1FXV8dpJ5/AyI8exq677dZQpnefPnz7ssu5/teTN/lu125d+c7l32fAgIEsW7aUU08cw4iDP8I222zT1j+jQ6mpET++8CSOPfMqFi99g8d++zX++PCz/GPBkoYyX//SKG7/0wyuve0x9tylN3f+7Ez2PPZSxhxxAFt06cTQk75Lt66deeb2b3DrvdN5+dWO9U52n5PrQGY/O4v+/QfQr3/uPRyjjjmWhx58YJOQ69u3HwA12rQDPXDgoIbPvXrVsf3227Ny5esOuQobus9A/rnwNV5avAKA26bO4OMj990k5CKCbbp3BWDbrbrx6vI3c9sJtuzahdraGrpt0YU1a9fz77eLeg1opqTh6mrFhquS9pR0uKStGm0fValjVtOypUvp3ad3w3qvujqWLl3a6nqenTWLtevW0r//zuVsnjVhp17bsmjpyob1xUtX0rfntpuUuewXUzjlmGHMv+/b3PGzMzn/+7cB8Ic/P8Pqd9fw4p8u4/l7J/DjGx5g5Vur27T97UGZ39ZVERUJOUnnAHcBZwOzJeW/Bfu7LXyv4eWzTZ3Tyrrly5dx8UVfY8J3LqemxqdL24OTRh3Ib+5+kt1GXcLxZ1/Ddd/5LJIYuvdA1q/fwC4fu5i9jr2Ucz9zGAP77lDt5ra51rxcuhiSRkmaJ2m+pAub2H+GpGclzZT0mKQhheqs1HD1y8CHImKVpIHA7yUNjIif0EKo57989t11RIXaVhG96upY8up7w5xlS5dSV1dX9PdXrVrFuDO/wtnnnMe+++1fiSZaI68se5N+dT0a1vvW9WBxMhzd6HOfOIjRZ00E4KlZL9K1S2d23K47Jx19IPf/dS7r1m1g+cpVPDFzAR8asnPD0LejKGcPTVItMBE4ElgETJNUHxFz84r9LiJ+npQ/jtzLplscHVaqu1ATEasAIuIlYCRwtKQrSMdz9lpt730+wMsvv8SiRQtZu2YN9025h0M/elhR3127Zg3nnXMW/33caI48KpOj+XZp+px/sdvOPRmw0w507lTLiUcdwD0PzdqkzMIlrzNy2B4A7DGojq5bdGb5ylUsWvI6I4fmtm/ZtQvD9h3IvJdaf3oi9co7Xh0GzI+IBRGxBrgZyB8FEhFv5a12h8KdoUr15JZK2j8iZiYNWyXp48Bk4AMVOmZVderUiYsuHs+ZY7/Ehg3r+cTxY9htt8FM/NlP2HvvfRh52OHMfnYW5507jrfeeouHH3qQqyf+jDvq72Hq1HuZ8fR03nzjDervvAOACZd9jz332qvKvyrb1q/fwHnfv5W7rz6L2hpx/V1P8tyCJVxy5rHMmPsy9zz8LBdecQdXX3IqZ3/6o0TAl8ffCMDPb3mESd/6NE///mIkuPGuJ5n9witV/kVtrzUXHiSNBcbmbZqUjN426gsszFtfBAxvop6zgPOBLkDBnoQiyj8qlNQPWBcRS5rYd3BEPF6ojrQNVzu6HkPHVbsJVoJ3nrlqs0ZW0xa8WfTf06G7bNvisSSdAIyKiC8l658BhkdEk//nknQacFREfK6leivSk4uIRS3sKxhwZpYS5T35tBjon7feL9nWnJuBawpV6kt4ZlayMs94mAYMljRIUhfgFKB+k+NJg/NWjwVeKFSpbwY2s5KV817giFgnaRwwFagFJkfEHEkTgOkRUQ+Mk3QEsBZYCbQ4VAWHnJlthnLfKhERU4ApjbaNz/t8bmvrdMiZWcn8cmkzy7QUZJxDzsxKl4KMc8iZ2WZIQco55MysZH78uZllms/JmVmmOeTMLNM8XDWzTHNPzswyLQUZ55Azs82QgpRzyJlZydLwti6HnJmVrP1HnEPOzDZHClLOIWdmJfMtJGaWaSk4JeeQM7PSpSDjHHJmVjo/NNPMMi0FGeeQM7PSpSDj/EpCM9sMasVSTHXSKEnzJM2XdGET+8+XNFfSLEkPSBpQqE6HnJmVrJzvXZVUC0wEjgaGAKdKGtKo2DPAgRGxL/B74AeF6nXImVnJpOKXIgwD5kfEgohYA9wMjM4vEBEPRsTqZPVJoF+hSh1yZlayGhW/SBoraXreMrZRdX2BhXnri5JtzfkicG+hNvrCg5lthuIvPUTEJGBSWY4qfRo4EDi0UFmHnJmVrMy3kCwG+uet90u2NTqmjgAuBg6NiP8UqtTDVTMrWZkvrk4DBksaJKkLcApQv8nxpA8CvwCOi4hlxVTqnpyZlaycPbmIWCdpHDAVqAUmR8QcSROA6RFRD/wfsBVwWzLb4uWIOK6leh1yZlayck/riogpwJRG28bnfT6itXU65MysZGmY8eCQM7OSee6qmWWaH5ppZtnW/jPOIWdmpUtBxjnkzKx0fiWhmWVaCjLOMx7MLNvckzOzkqWhJ+eQM7OS+RYSM8s09+TMLNMccmaWaR6umlmmuSdnZpmWgoxzyJnZZkhByjnkzKxkaZjWpYiodhs6HEljkzcXWQr4v1e6eVpXdTR+36S1b/7vlWIOOTPLNIecmWWaQ646fH4nXfzfK8V84cHMMs09OTPLNIecmWWaQ66NSJosaZmk2dVuixVHUn9JD0qaK2mOpHOr3SZrPZ+TayOSDgFWATdExD7Vbo8VJqkP0CciZkjaGnga+EREzK1y06wV3JNrIxHxCPB6tdthxYuIVyNiRvL538BzQN/qtspayyFnVgRJA4EPAk9VtyXWWg45swIkbQXcDnw1It6qdnusdRxyZi2Q1JlcwP02Iv5Q7fZY6znkzJohScB1wHMRcUW122Olcci1EUk3AU8Ae0haJOmL1W6TFXQw8BngMEkzk+WYajfKWse3kJhZprknZ2aZ5pAzs0xzyJlZpjnkzCzTHHJmlmkOuRSTtD65rWG2pNskbbkZdf1a0gnJ519KGtJC2ZGSRpRwjJck7Vjs9kZlVrXyWN+UdEFr22jZ45BLt3ciYv/kqSZrgDPyd0oq6b26EfGlAk/aGAm0OuTMqsEhlx2PArslvaxHJdUDcyXVSvo/SdMkzZL0FcjdzS/pKknzJP0Z6LWxIkkPSTow+TxK0gxJf5f0QDJR/QzgvKQX+V+Sekq6PTnGNEkHJ9/dQdL9ybPYfkkR71uXdKekp5PvjG2078pk+wOSeibbdpV0X/KdRyXtWY4/TMuOkv6lt/Yl6bEdDdyXbDoA2CciXkyC4s2IGCppC+BxSfeTe6LGHsAQoA6YC0xuVG9P4FrgkKSu7SPidUk/B1ZFxA+Tcr8DroyIxyTtDEwF9gIuBR6LiAmSjgWKmeXxheQY3YBpkm6PiBVAd2B6RJwnaXxS9zhyL5k5IyJekDQcuBo4rIQ/Rssoh1y6dZM0M/n8KLl5liOAv0XEi8n2jwH7bjzfBmwLDAYOAW6KiPXAK5L+0kT9HwYe2VhXRDT3PLwjgCG5qZ4AbJM8ueMQ4JPJd++RtLKI33SOpOOTz/2Ttq4ANgC3JNt/A/whOcYI4La8Y29RxDGsA3HIpds7EbF//obkL/vb+ZuAsyNiaqNy5ZyDWQN8OCLebaItRZM0klxgHhQRqyU9BHRtpngkx32j8Z+BWT6fk8u+qcCZySODkLS7pO7AI8DJyTm7PsBHm/juk8AhkgYl390+2f5vYOu8cvcDZ29ckbQxdB4BTku2HQ30KNDWbYGVScDtSa4nuVENsLE3ehq5YfBbwIuSTkyOIUn7FTiGdTAOuez7JbnzbTOUe4nOL8j14O8AXkj23UDuCSmbiIjlwFhyQ8O/895w8W7g+I0XHoBzgAOTCxtzee8q77fIheQccsPWlwu09T6gk6TngO+RC9mN3gaGJb/hMGBCsv1TwBeT9s0BRhfxZ2IdiJ9CYmaZ5p6cmWWaQ87MMs0hZ2aZ5pAzs0xzyJlZpjnkzCzTHHJmlmn/HxJ7fHi9UH9ZAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3972 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "828af3841870461abc7cf81918b2affa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/497 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0b00e891cfdd42a3afac2b867fae00e5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation Set\n",
            "Accuracy: 0.803877139979859\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.6888    0.7078    0.6982      1273\n",
            "           1     0.8604    0.8492    0.8547      2699\n",
            "\n",
            "    accuracy                         0.8039      3972\n",
            "   macro avg     0.7746    0.7785    0.7765      3972\n",
            "weighted avg     0.8054    0.8039    0.8046      3972\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "evaluate_bert_model(model_roberta_base_128, model_name=\"model_roberta_base_128\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "fYGd63rD3ghn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_roberta_base_64 = \\\n",
        "ClassificationModel('roberta', \n",
        "                    'roberta-base',\n",
        "                    args={\"output_dir\": \"outputs_roberta_base/\",\n",
        "                          \"best_model_dir\": \"outputs_roberta_base/best_model/\",\n",
        "                          \"max_seq_length\": 64,\n",
        "                          \"train_batch_size\": 16,\n",
        "                          \"num_train_epochs\": 1,\n",
        "                          \"logging_steps\": 100,\n",
        "                          \"evaluate_during_training\": False,\n",
        "                          \"evaluate_during_training_steps\": 10000,\n",
        "                          \"evaluate_during_training_verbose\": False,\n",
        "                          \"save_eval_checkpoints\": False,\n",
        "                          \"save_steps\": 10000,\n",
        "                          \"save_model_every_epoch\": True,\n",
        "                          \"overwrite_output_dir\": True,\n",
        "                          \"reprocess_input_data\": False})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-o4nERdd3iBc",
        "outputId": "8db4b539-ca8a-44a2-c851-dd78d09bb9f8"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at roberta-base were not used when initializing RobertaForSequenceClassification: ['lm_head.dense.bias', 'lm_head.bias', 'roberta.pooler.dense.weight', 'roberta.pooler.dense.bias', 'lm_head.layer_norm.weight', 'lm_head.decoder.weight', 'lm_head.layer_norm.bias', 'lm_head.dense.weight']\n",
            "- This IS expected if you are initializing RobertaForSequenceClassification 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 RobertaForSequenceClassification 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 RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.out_proj.weight', 'classifier.dense.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": [
        "model_roberta_base_64.train_model(train_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153,
          "referenced_widgets": [
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            "7c6060cee654402a831e657429528d67"
          ]
        },
        "id": "8XzAVZda3yD_",
        "outputId": "1349b534-7073-4589-e26c-b9e6ab2d00f9"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "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%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "466a3ab4a3dd49e4b56cbb781333b123"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 0 of 1:   0%|          | 0/580 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4d3b2aa19e8d4ab399bb766098bb7858"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(580, 0.5056736419940817)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate_bert_model(model_roberta_base_64, model_name=\"model_roberta_base_64\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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            "e0e73b6db49643a7a5cd72432120e795"
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        },
        "id": "Kuv_dowr3yNI",
        "outputId": "49abec02-0d43-4ee8-fd9d-df4312c2d265"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/9268 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fce8eb0ad318443fb2615c2985bd1b78"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1159 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4bb19ce537d84102b8f32fe3486048d3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Set\n",
            "Accuracy: 0.8361027190332326\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.7645    0.7432    0.7537      3127\n",
            "           1     0.8711    0.8834    0.8772      6141\n",
            "\n",
            "    accuracy                         0.8361      9268\n",
            "   macro avg     0.8178    0.8133    0.8154      9268\n",
            "weighted avg     0.8351    0.8361    0.8355      9268\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3972 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dfb1d1472fc347ffbb2456344fb26f98"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/497 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cf1ab6b68abc49ab98a3aaad8d05cf28"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation Set\n",
            "Accuracy: 0.7998489425981873\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.6906    0.6803    0.6854      1273\n",
            "           1     0.8503    0.8562    0.8532      2699\n",
            "\n",
            "    accuracy                         0.7998      3972\n",
            "   macro avg     0.7704    0.7683    0.7693      3972\n",
            "weighted avg     0.7991    0.7998    0.7994      3972\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "D8p14ia938Jh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_roberta_base_32 = \\\n",
        "ClassificationModel('roberta', \n",
        "                    'roberta-base',\n",
        "                    args={\"output_dir\": \"outputs_roberta_base/\",\n",
        "                          \"best_model_dir\": \"outputs_roberta_base/best_model/\",\n",
        "                          \"max_seq_length\": 32,\n",
        "                          \"train_batch_size\": 16,\n",
        "                          \"num_train_epochs\": 1,\n",
        "                          \"logging_steps\": 100,\n",
        "                          \"evaluate_during_training\": False,\n",
        "                          \"evaluate_during_training_steps\": 10000,\n",
        "                          \"evaluate_during_training_verbose\": False,\n",
        "                          \"save_eval_checkpoints\": False,\n",
        "                          \"save_steps\": 10000,\n",
        "                          \"save_model_every_epoch\": True,\n",
        "                          \"overwrite_output_dir\": True,\n",
        "                          \"reprocess_input_data\": False})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nG97BEuZ3iJ4",
        "outputId": "13ed6d3d-ea58-46e8-eae1-39da82cd2644"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at roberta-base were not used when initializing RobertaForSequenceClassification: ['lm_head.dense.bias', 'lm_head.bias', 'roberta.pooler.dense.weight', 'roberta.pooler.dense.bias', 'lm_head.layer_norm.weight', 'lm_head.decoder.weight', 'lm_head.layer_norm.bias', 'lm_head.dense.weight']\n",
            "- This IS expected if you are initializing RobertaForSequenceClassification 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 RobertaForSequenceClassification 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 RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.out_proj.weight', 'classifier.dense.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": [
        "model_roberta_base_32.train_model(train_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153,
          "referenced_widgets": [
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            "482b19ba21a446fb8650ebb19f4957a8",
            "8ae978fcdc66490fa70cd25e3bea1044"
          ]
        },
        "id": "_T_W0x2A30pL",
        "outputId": "0cd23f54-608d-4ffb-9ca5-af76ce853bc1"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "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%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9a9d4f9fbf2443ceaeb414dfba850c36"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 0 of 1:   0%|          | 0/580 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c9139f09051d4ec2a6ffbff7590c3296"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(580, 0.5148272152604728)"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate_bert_model(model_roberta_base_32, model_name=\"model_roberta_base_32\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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        },
        "id": "bN82Q68s4AIJ",
        "outputId": "d0266f5b-48e7-4da5-b024-a4bee162cd69"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/9268 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "87b9ca2c08c6407b9f79bd49f8cffea9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1159 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "55d7b51b98ba4aed8ca65d0d6434c372"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Set\n",
            "Accuracy: 0.8291972378075098\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.7793    0.6888    0.7313      3127\n",
            "           1     0.8504    0.9007    0.8748      6141\n",
            "\n",
            "    accuracy                         0.8292      9268\n",
            "   macro avg     0.8149    0.7948    0.8030      9268\n",
            "weighted avg     0.8264    0.8292    0.8264      9268\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3972 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3ba5226e3ab24ef988ac513f6b923dd3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/497 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5915ab8ac4f44069a4a2a8d6c3ee5369"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation Set\n",
            "Accuracy: 0.7849949647532729\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.6849    0.6096    0.6451      1273\n",
            "           1     0.8249    0.8677    0.8458      2699\n",
            "\n",
            "    accuracy                         0.7850      3972\n",
            "   macro avg     0.7549    0.7387    0.7454      3972\n",
            "weighted avg     0.7801    0.7850    0.7815      3972\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "olidTask_snlp(SimpleTransformer_models)",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    },
    "widgets": {
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            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
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