flair_cadec_v1.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "xONJpNLKtGie"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import os\n",
        "import pickle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nw6RM8uotGih",
        "outputId": "b7a2a9db-9c2f-4db3-f543-cc4016a19974"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\")\n",
        "\n",
        "os.chdir('/content/drive/MyDrive/SI 630 project')\n",
        "# os.listdir('.')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P9LwpBadtGih",
        "outputId": "fdc4aa2a-bd0b-42e2-974b-e9048f9b647f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['sct',\n",
              " 'text',\n",
              " 'original - unedited version of edited files',\n",
              " 'meddra - unedited version of edited files',\n",
              " 'sct - unedited version of edited files',\n",
              " 'original',\n",
              " 'meddra']"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "os.listdir('datasets/CADEC/CADEC.v2/cadec')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "QKGsELuHb6NP"
      },
      "outputs": [],
      "source": [
        "suf = '_v1'\n",
        "\n",
        "data_save_dir = 'data'+suf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "BIUl2-W9tGii",
        "outputId": "8676898f-8933-41ab-bdf8-3e9e840b756b"
      },
      "outputs": [
        {
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              "      <th></th>\n",
              "      <th>ner</th>\n",
              "      <th>meddra</th>\n",
              "      <th>counts_exploded</th>\n",
              "      <th>counts</th>\n",
              "      <th>counts_exploded_rank</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Drug</td>\n",
              "      <td>Lipitor</td>\n",
              "      <td>1081</td>\n",
              "      <td>1073</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ADR</td>\n",
              "      <td>CONCEPT_LESS</td>\n",
              "      <td>1016</td>\n",
              "      <td>266</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ADR</td>\n",
              "      <td>Severe_pain</td>\n",
              "      <td>722</td>\n",
              "      <td>247</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ADR</td>\n",
              "      <td>Myalgia</td>\n",
              "      <td>619</td>\n",
              "      <td>305</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ADR</td>\n",
              "      <td>Pain</td>\n",
              "      <td>292</td>\n",
              "      <td>260</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1294</th>\n",
              "      <td>Finding</td>\n",
              "      <td>Senility</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1295</th>\n",
              "      <td>Drug</td>\n",
              "      <td>Seroquel</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1296</th>\n",
              "      <td>Drug</td>\n",
              "      <td>Solaraze</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1297</th>\n",
              "      <td>Drug</td>\n",
              "      <td>Tagamet</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1298</th>\n",
              "      <td>Drug</td>\n",
              "      <td>testosterone</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1233</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1299 rows × 5 columns</p>\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ],
            "text/plain": [
              "          ner        meddra  counts_exploded  counts  counts_exploded_rank\n",
              "0        Drug       Lipitor             1081    1073                     1\n",
              "1         ADR  CONCEPT_LESS             1016     266                     2\n",
              "2         ADR   Severe_pain              722     247                     3\n",
              "3         ADR       Myalgia              619     305                     4\n",
              "4         ADR          Pain              292     260                     5\n",
              "...       ...           ...              ...     ...                   ...\n",
              "1294  Finding      Senility                1       1                  1233\n",
              "1295     Drug      Seroquel                1       1                  1233\n",
              "1296     Drug      Solaraze                1       1                  1233\n",
              "1297     Drug       Tagamet                1       1                  1233\n",
              "1298     Drug  testosterone                1       1                  1233\n",
              "\n",
              "[1299 rows x 5 columns]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "meddra_counts = pd.read_csv(data_save_dir+'/meddra_counts.csv')\n",
        "meddra_counts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
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              "      <th>1</th>\n",
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              "      <td>CONCEPT_LESS</td>\n",
              "      <td>1016</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ADR</td>\n",
              "      <td>Severe_pain</td>\n",
              "      <td>722</td>\n",
              "      <td>247</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ADR</td>\n",
              "      <td>Myalgia</td>\n",
              "      <td>619</td>\n",
              "      <td>305</td>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ADR</td>\n",
              "      <td>Pain</td>\n",
              "      <td>292</td>\n",
              "      <td>260</td>\n",
              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>ADR</td>\n",
              "      <td>Arthralgia</td>\n",
              "      <td>280</td>\n",
              "      <td>163</td>\n",
              "      <td>6</td>\n",
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              "    <tr>\n",
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              "    <tr>\n",
              "      <th>1268</th>\n",
              "      <td>ADR</td>\n",
              "      <td>Labyrinthitis</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1269</th>\n",
              "      <td>ADR</td>\n",
              "      <td>Dysphagia</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1275</th>\n",
              "      <td>ADR</td>\n",
              "      <td>Lupus_erythematosus</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1278</th>\n",
              "      <td>ADR</td>\n",
              "      <td>Cataract</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1283</th>\n",
              "      <td>ADR</td>\n",
              "      <td>Crying_associated_with_mood</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1233</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>800 rows × 5 columns</p>\n",
              "</div>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ],
            "text/plain": [
              "      ner                       meddra  counts_exploded  counts  \\\n",
              "1     ADR                 CONCEPT_LESS             1016     266   \n",
              "2     ADR                  Severe_pain              722     247   \n",
              "3     ADR                      Myalgia              619     305   \n",
              "4     ADR                         Pain              292     260   \n",
              "5     ADR                   Arthralgia              280     163   \n",
              "...   ...                          ...              ...     ...   \n",
              "1268  ADR                Labyrinthitis                1       1   \n",
              "1269  ADR                    Dysphagia                1       1   \n",
              "1275  ADR          Lupus_erythematosus                1       1   \n",
              "1278  ADR                     Cataract                1       1   \n",
              "1283  ADR  Crying_associated_with_mood                1       1   \n",
              "\n",
              "      counts_exploded_rank  \n",
              "1                        2  \n",
              "2                        3  \n",
              "3                        4  \n",
              "4                        5  \n",
              "5                        6  \n",
              "...                    ...  \n",
              "1268                  1233  \n",
              "1269                  1233  \n",
              "1275                  1233  \n",
              "1278                  1233  \n",
              "1283                  1233  \n",
              "\n",
              "[800 rows x 5 columns]"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "meddra_counts.loc[meddra_counts.ner == 'ADR',:]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hAGPini7tGii",
        "outputId": "a74e8572-609c-4c60-9757-39747187bbbd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[Sentence[117]: \"i feel a bit drowsy & have a little blurred vision , so far no gastric problems . ive been on arthrotec 50 for over 10 years on and off , only taking it when i needed it . due to my arthritis getting progressively worse , to the point where i am in tears with the agony , gp ' s started me on 75 twice a day and i have to take it . every day for the next month to see how i get on , here goes . so far its been very good , pains almost gone , but i feel a bit weird , didnt have that when on 50 .\" → [\"i\"/O/O, \"feel\"/O/O, \"a\"/O/O, \"bit\"/B-ADR/B-Drowsy, \"drowsy\"/E-ADR/E-Drowsy, \"&\"/O/O, \"have\"/O/O, \"a\"/O/O, \"little\"/B-ADR/B-Blurred_vision_-_hazy, \"blurred\"/I-ADR/I-Blurred_vision_-_hazy, \"vision\"/E-ADR/E-Blurred_vision_-_hazy, \",\"/O/O, \"so\"/O/O, \"far\"/O/O, \"no\"/O/O, \"gastric\"/B-ADR/B-Excessive_upper_gastrointestinal_gas, \"problems\"/E-ADR/E-Excessive_upper_gastrointestinal_gas, \".\"/O/O, \"ive\"/O/O, \"been\"/O/O, \"on\"/O/O, \"arthrotec\"/B-Drug/B-Arthrotec, \"50\"/O/O, \"for\"/O/O, \"over\"/O/O, \"10\"/O/O, \"years\"/O/O, \"on\"/O/O, \"and\"/O/O, \"off\"/O/O, \",\"/O/O, \"only\"/O/O, \"taking\"/O/O, \"it\"/O/O, \"when\"/O/O, \"i\"/O/O, \"needed\"/O/O, \"it\"/O/O, \".\"/O/O, \"due\"/O/O, \"to\"/O/O, \"my\"/O/O, \"arthritis\"/B-Disease/B-Arthritis, \"getting\"/O/O, \"progressively\"/O/O, \"worse\"/O/O, \",\"/O/O, \"to\"/O/O, \"the\"/O/O, \"point\"/O/O, \"where\"/O/O, \"i\"/O/O, \"am\"/O/O, \"in\"/O/O, \"tears\"/O/O, \"with\"/O/O, \"the\"/O/O, \"agony\"/B-Symptom/B-Agony, \",\"/O/O, \"gp\"/O/O, \"'\"/O/O, \"s\"/O/O, \"started\"/O/O, \"me\"/O/O, \"on\"/O/O, \"75\"/O/O, \"twice\"/O/O, \"a\"/O/O, \"day\"/O/O, \"and\"/O/O, \"i\"/O/O, \"have\"/O/O, \"to\"/O/O, \"take\"/O/O, \"it\"/O/O, \".\"/O/O, \"every\"/O/O, \"day\"/O/O, \"for\"/O/O, \"the\"/O/O, \"next\"/O/O, \"month\"/O/O, \"to\"/O/O, \"see\"/O/O, \"how\"/O/O, \"i\"/O/O, \"get\"/O/O, \"on\"/O/O, \",\"/O/O, \"here\"/O/O, \"goes\"/O/O, \".\"/O/O, \"so\"/O/O, \"far\"/O/O, \"its\"/O/O, \"been\"/O/O, \"very\"/O/O, \"good\"/O/O, \",\"/O/O, \"pains\"/B-Symptom/B-Pain, \"almost\"/O/O, \"gone\"/O/O, \",\"/O/O, \"but\"/O/O, \"i\"/O/O, \"feel\"/B-ADR/B-Malaise, \"a\"/I-ADR/I-Malaise, \"bit\"/I-ADR/I-Malaise, \"weird\"/E-ADR/E-Malaise, \",\"/O/O, \"didnt\"/O/O, \"have\"/O/O, \"that\"/O/O, \"when\"/O/O, \"on\"/O/O, \"50\"/O/O, \".\"/O/O],\n",
              " Sentence[39]: \"hunger pangs . brilliant , i have a new lease of life , i walk up & down steps properly , no longer sideways like a toddler , hip pain as gone other than if i jar it .\" → [\"hunger\"/B-ADR/B-Hunger_pain, \"pangs\"/E-ADR/E-Hunger_pain, \".\"/O/O, \"brilliant\"/O/O, \",\"/O/O, \"i\"/O/O, \"have\"/O/O, \"a\"/O/O, \"new\"/O/O, \"lease\"/O/O, \"of\"/O/O, \"life\"/O/O, \",\"/O/O, \"i\"/O/O, \"walk\"/B-Symptom/B-CONCEPT_LESS, \"up\"/I-Symptom/I-CONCEPT_LESS, \"&\"/I-Symptom/I-CONCEPT_LESS, \"down\"/I-Symptom/I-CONCEPT_LESS, \"steps\"/I-Symptom/I-CONCEPT_LESS, \"properly\"/O/O, \",\"/O/O, \"no\"/O/O, \"longer\"/O/O, \"sideways\"/E-Symptom/E-CONCEPT_LESS, \"like\"/O/O, \"a\"/O/O, \"toddler\"/O/O, \",\"/O/O, \"hip\"/B-Symptom/B-Hip_pain, \"pain\"/E-Symptom/E-Hip_pain, \"as\"/O/O, \"gone\"/O/O, \"other\"/O/O, \"than\"/O/O, \"if\"/O/O, \"i\"/O/O, \"jar\"/O/O, \"it\"/O/O, \".\"/O/O],\n",
              " Sentence[51]: \"no side effects for the first two months . then vaginal bleeding 2 wks after menstral cycle . stomach pain . canker sores in my mouth . headache . been off for 1 week still have bleeding . helped my pain alot . too scared to take this drug again .\" → [\"no\"/O/O, \"side\"/O/O, \"effects\"/O/O, \"for\"/O/O, \"the\"/O/O, \"first\"/O/O, \"two\"/O/O, \"months\"/O/O, \".\"/O/O, \"then\"/O/O, \"vaginal\"/B-ADR/B-Vaginal_bleeding, \"bleeding\"/E-ADR/E-Vaginal_bleeding, \"2\"/O/O, \"wks\"/O/O, \"after\"/O/O, \"menstral\"/O/O, \"cycle\"/O/O, \".\"/O/O, \"stomach\"/B-ADR/B-Stomach_ache, \"pain\"/E-ADR/E-Stomach_ache, \".\"/O/O, \"canker\"/B-ADR/B-Aphthous_ulcer, \"sores\"/E-ADR/E-Aphthous_ulcer, \"in\"/O/O, \"my\"/O/O, \"mouth\"/O/O, \".\"/O/O, \"headache\"/B-ADR/B-Headache, \".\"/O/O, \"been\"/O/O, \"off\"/O/O, \"for\"/O/O, \"1\"/O/O, \"week\"/O/O, \"still\"/O/O, \"have\"/O/O, \"bleeding\"/O/O, \".\"/O/O, \"helped\"/O/O, \"my\"/O/O, \"pain\"/B-Symptom/B-Pain, \"alot\"/O/O, \".\"/O/O, \"too\"/O/O, \"scared\"/O/O, \"to\"/O/O, \"take\"/O/O, \"this\"/O/O, \"drug\"/O/O, \"again\"/O/O, \".\"/O/O],\n",
              " Sentence[68]: \"1st pill taken with food , a few hours after i experienced shortness of breath , a sense of depression , cramping , upset stomache will stop taking immediately . honestly can not recommend this drug , i am still experiencing side effects just after 1 pill and im supposed to take twice / day 75mg . i just cant keep going have to try something else .\" → [\"1st\"/O/O, \"pill\"/O/O, \"taken\"/O/O, \"with\"/O/O, \"food\"/O/O, \",\"/O/O, \"a\"/O/O, \"few\"/O/O, \"hours\"/O/O, \"after\"/O/O, \"i\"/O/O, \"experienced\"/O/O, \"shortness\"/B-ADR/B-Dyspnoea, \"of\"/I-ADR/I-Dyspnoea, \"breath\"/E-ADR/E-Dyspnoea, \",\"/O/O, \"a\"/O/O, \"sense\"/O/O, \"of\"/O/O, \"depression\"/B-ADR/B-Depression, \",\"/O/O, \"cramping\"/B-ADR/B-Muscle_cramp, \",\"/O/O, \"upset\"/B-ADR/B-Upset_stomach, \"stomache\"/E-ADR/E-Upset_stomach, \"will\"/O/O, \"stop\"/O/O, \"taking\"/O/O, \"immediately\"/O/O, \".\"/O/O, \"honestly\"/O/O, \"can\"/O/O, \"not\"/O/O, \"recommend\"/O/O, \"this\"/O/O, \"drug\"/O/O, \",\"/O/O, \"i\"/O/O, \"am\"/O/O, \"still\"/O/O, \"experiencing\"/O/O, \"side\"/O/O, \"effects\"/O/O, \"just\"/O/O, \"after\"/O/O, \"1\"/O/O, \"pill\"/O/O, \"and\"/O/O, \"im\"/O/O, \"supposed\"/O/O, \"to\"/O/O, \"take\"/O/O, \"twice\"/O/O, \"/\"/O/O, \"day\"/O/O, \"75mg\"/O/O, \".\"/O/O, \"i\"/O/O, \"just\"/O/O, \"cant\"/O/O, \"keep\"/O/O, \"going\"/O/O, \"have\"/O/O, \"to\"/O/O, \"try\"/O/O, \"something\"/O/O, \"else\"/O/O, \".\"/O/O],\n",
              " Sentence[50]: \"i have had no side effects been taking arthrotec a little over a year , have not noticed any side effects . it does help alot i noticed that when there are times when i forget to take it i cant stand or walk for any lengths of time .\" → [\"i\"/O/O, \"have\"/O/O, \"had\"/O/O, \"no\"/O/O, \"side\"/O/O, \"effects\"/O/O, \"been\"/O/O, \"taking\"/O/O, \"arthrotec\"/B-Drug/B-Arthrotec, \"a\"/O/O, \"little\"/O/O, \"over\"/O/O, \"a\"/O/O, \"year\"/O/O, \",\"/O/O, \"have\"/O/O, \"not\"/O/O, \"noticed\"/O/O, \"any\"/O/O, \"side\"/O/O, \"effects\"/O/O, \".\"/O/O, \"it\"/O/O, \"does\"/O/O, \"help\"/O/O, \"alot\"/O/O, \"i\"/O/O, \"noticed\"/O/O, \"that\"/O/O, \"when\"/O/O, \"there\"/O/O, \"are\"/O/O, \"times\"/O/O, \"when\"/O/O, \"i\"/O/O, \"forget\"/O/O, \"to\"/O/O, \"take\"/O/O, \"it\"/O/O, \"i\"/B-Symptom/B-Reduced_mobility, \"cant\"/I-Symptom/I-Reduced_mobility, \"stand\"/I-Symptom/I-Reduced_mobility, \"or\"/I-Symptom/I-Reduced_mobility, \"walk\"/I-Symptom/I-Reduced_mobility, \"for\"/I-Symptom/I-Reduced_mobility, \"any\"/I-Symptom/I-Reduced_mobility, \"lengths\"/I-Symptom/I-Reduced_mobility, \"of\"/I-Symptom/I-Reduced_mobility, \"time\"/E-Symptom/E-Reduced_mobility, \".\"/O/O],\n",
              " Sentence[89]: \"it seems as if bleeding forever is a side effect but doctors tell you that its not the drug . i have been on arthotec for three months i still have pain and stiffness . at first week into this drug i was in so much pain i sat on the floor at work and thought i was gaving a baby . i always feel weak and almost fainted a bunch of times . consider somthing that works i am switching today as this isnt in my head .\" → [\"it\"/O/O, \"seems\"/O/O, \"as\"/O/O, \"if\"/O/O, \"bleeding\"/B-ADR/B-Bleeding, \"forever\"/O/O, \"is\"/O/O, \"a\"/O/O, \"side\"/O/O, \"effect\"/O/O, \"but\"/O/O, \"doctors\"/O/O, \"tell\"/O/O, \"you\"/O/O, \"that\"/O/O, \"its\"/O/O, \"not\"/O/O, \"the\"/O/O, \"drug\"/O/O, \".\"/O/O, \"i\"/O/O, \"have\"/O/O, \"been\"/O/O, \"on\"/O/O, \"arthotec\"/B-Drug/B-Arthrotec, \"for\"/O/O, \"three\"/O/O, \"months\"/O/O, \"i\"/O/O, \"still\"/O/O, \"have\"/O/O, \"pain\"/B-Symptom/B-Pain, \"and\"/I-Symptom/I-Pain, \"stiffness\"/E-Symptom/E-Pain, \".\"/O/O, \"at\"/O/O, \"first\"/O/O, \"week\"/O/O, \"into\"/O/O, \"this\"/O/O, \"drug\"/O/O, \"i\"/O/O, \"was\"/O/O, \"in\"/O/O, \"so\"/B-ADR/B-Pain, \"much\"/I-ADR/I-Pain, \"pain\"/E-ADR/E-Pain, \"i\"/O/O, \"sat\"/O/O, \"on\"/O/O, \"the\"/O/O, \"floor\"/O/O, \"at\"/O/O, \"work\"/O/O, \"and\"/O/O, \"thought\"/O/O, \"i\"/O/O, \"was\"/O/O, \"gaving\"/O/O, \"a\"/O/O, \"baby\"/O/O, \".\"/O/O, \"i\"/O/O, \"always\"/O/O, \"feel\"/B-ADR/B-Malaise_and_fatigue, \"weak\"/I-ADR/I-Malaise_and_fatigue, \"and\"/I-ADR/I-Malaise_and_fatigue, \"almost\"/I-ADR/I-Malaise_and_fatigue, \"fainted\"/E-ADR/E-Malaise_and_fatigue, \"a\"/O/O, \"bunch\"/O/O, \"of\"/O/O, \"times\"/O/O, \".\"/O/O, \"consider\"/O/O, \"somthing\"/O/O, \"that\"/O/O, \"works\"/O/O, \"i\"/O/O, \"am\"/O/O, \"switching\"/O/O, \"today\"/O/O, \"as\"/O/O, \"this\"/O/O, \"isnt\"/O/O, \"in\"/O/O, \"my\"/O/O, \"head\"/O/O, \".\"/O/O],\n",
              " Sentence[56]: \"some diarrhea , some constipation , fatigue . it works very well for the heel ; i couldnt walk on it very well before taking it . i take 75mg . twice a day . also have severe osteoarthritis in the knees and hands , but it doesnt seem to relieve that pain at all .\" → [\"some\"/O/O, \"diarrhea\"/B-ADR/B-Diarrhoea, \",\"/O/O, \"some\"/O/O, \"constipation\"/B-ADR/B-Constipation, \",\"/O/O, \"fatigue\"/B-ADR/B-Fatigue, \".\"/O/O, \"it\"/O/O, \"works\"/O/O, \"very\"/O/O, \"well\"/O/O, \"for\"/O/O, \"the\"/B-Symptom/B-Walking_disability, \"heel\"/I-Symptom/I-Walking_disability, \";\"/O/O, \"i\"/I-Symptom/I-Walking_disability, \"couldnt\"/I-Symptom/I-Walking_disability, \"walk\"/I-Symptom/I-Walking_disability, \"on\"/I-Symptom/I-Walking_disability, \"it\"/O/O, \"very\"/I-Symptom/I-Walking_disability, \"well\"/E-Symptom/E-Walking_disability, \"before\"/O/O, \"taking\"/O/O, \"it\"/O/O, \".\"/O/O, \"i\"/O/O, \"take\"/O/O, \"75mg\"/O/O, \".\"/O/O, \"twice\"/O/O, \"a\"/O/O, \"day\"/O/O, \".\"/O/O, \"also\"/O/O, \"have\"/O/O, \"severe\"/B-Disease/B-Osteoarthritis_of_knee/B-Osteoarthrosis_of_hand, \"osteoarthritis\"/I-Disease/I-Osteoarthritis_of_knee/I-Osteoarthrosis_of_hand, \"in\"/I-Disease/I-Osteoarthritis_of_knee/I-Osteoarthrosis_of_hand, \"the\"/I-Disease/I-Osteoarthritis_of_knee/I-Osteoarthrosis_of_hand, \"knees\"/E-Disease/E-Osteoarthritis_of_knee, \"and\"/O/O, \"hands\"/E-Disease/E-Osteoarthrosis_of_hand, \",\"/O/O, \"but\"/O/O, \"it\"/O/O, \"doesnt\"/O/O, \"seem\"/O/O, \"to\"/O/O, \"relieve\"/O/O, \"that\"/O/O, \"pain\"/B-Symptom/B-Pain, \"at\"/O/O, \"all\"/O/O, \".\"/O/O],\n",
              " Sentence[84]: \"ive been on 75mg twice daily for 13 days , with recent body swelling , face , wrists , abdomen , thighs - clothes fit tight . insomina is much worse . i have forgetfulnes and confussion - really bad - almost dangerous . have felt \" empty stomach \" feeling , but not bothersome . have had more loose stools , but tolerable . im looking for a more natural therapy , checking into ha ( hyaluronic acid ) to replace arthrotec .\" → [\"ive\"/O/O, \"been\"/O/O, \"on\"/O/O, \"75mg\"/O/O, \"twice\"/O/O, \"daily\"/O/O, \"for\"/O/O, \"13\"/O/O, \"days\"/O/O, \",\"/O/O, \"with\"/O/O, \"recent\"/O/O, \"body\"/B-ADR/B-Swelling_of_body_region, \"swelling\"/B-ADR/E-ADR/B-Abdominal_swelling/B-Facial_swelling/E-Swelling_of_body_region/B-Swelling_of_lower_limb/B-Swelling_of_wrist_joint, \",\"/O/O, \"face\"/E-ADR/E-Facial_swelling, \",\"/O/O, \"wrists\"/E-ADR/E-Swelling_of_wrist_joint, \",\"/O/O, \"abdomen\"/E-ADR/E-Abdominal_swelling, \",\"/O/O, \"thighs\"/E-ADR/E-Swelling_of_lower_limb, \"-\"/O/O, \"clothes\"/O/O, \"fit\"/O/O, \"tight\"/O/O, \".\"/O/O, \"insomina\"/B-ADR/B-Insomnia, \"is\"/O/O, \"much\"/O/O, \"worse\"/O/O, \".\"/O/O, \"i\"/O/O, \"have\"/O/O, \"forgetfulnes\"/B-ADR/B-Forgetful, \"and\"/I-ADR/I-Forgetful, \"confussion\"/E-ADR/E-Forgetful, \"-\"/O/O, \"really\"/O/O, \"bad\"/O/O, \"-\"/O/O, \"almost\"/O/O, \"dangerous\"/O/O, \".\"/O/O, \"have\"/O/O, \"felt\"/O/O, \"\"\"/B-ADR/B-Feeling_empty, \"empty\"/I-ADR/I-Feeling_empty, \"stomach\"/I-ADR/I-Feeling_empty, \"\"\"/I-ADR/I-Feeling_empty, \"feeling\"/E-ADR/E-Feeling_empty, \",\"/O/O, \"but\"/O/O, \"not\"/O/O, \"bothersome\"/O/O, \".\"/O/O, \"have\"/O/O, \"had\"/O/O, \"more\"/O/O, \"loose\"/B-ADR/B-Loose_stool, \"stools\"/E-ADR/E-Loose_stool, \",\"/O/O, \"but\"/O/O, \"tolerable\"/O/O, \".\"/O/O, \"im\"/O/O, \"looking\"/O/O, \"for\"/O/O, \"a\"/O/O, \"more\"/O/O, \"natural\"/O/O, \"therapy\"/O/O, \",\"/O/O, \"checking\"/O/O, \"into\"/O/O, \"ha\"/B-Drug/B-CONCEPT_LESS, \"(\"/I-Drug/I-CONCEPT_LESS, \"hyaluronic\"/I-Drug/I-CONCEPT_LESS, \"acid\"/I-Drug/I-CONCEPT_LESS, \")\"/E-Drug/E-CONCEPT_LESS, \"to\"/O/O, \"replace\"/O/O, \"arthrotec\"/B-Drug/B-Arthrotec, \".\"/O/O],\n",
              " Sentence[68]: \"none that i can think of . i recently had bilateral knee replacement . prior to that , arthrotec was the only drug that helped . i tried every drug on the market , but , to no avail . i took 50 mg . 3 times a day of arthrotec and was almost pain free . i highly recommend it to anyone that suffers from arthritis .\" → [\"none\"/O/O, \"that\"/O/O, \"i\"/O/O, \"can\"/O/O, \"think\"/O/O, \"of\"/O/O, \".\"/O/O, \"i\"/O/O, \"recently\"/O/O, \"had\"/O/O, \"bilateral\"/O/O, \"knee\"/O/O, \"replacement\"/O/O, \".\"/O/O, \"prior\"/O/O, \"to\"/O/O, \"that\"/O/O, \",\"/O/O, \"arthrotec\"/B-Drug/B-Arthrotec, \"was\"/O/O, \"the\"/O/O, \"only\"/O/O, \"drug\"/O/O, \"that\"/O/O, \"helped\"/O/O, \".\"/O/O, \"i\"/O/O, \"tried\"/O/O, \"every\"/O/O, \"drug\"/O/O, \"on\"/O/O, \"the\"/O/O, \"market\"/O/O, \",\"/O/O, \"but\"/O/O, \",\"/O/O, \"to\"/O/O, \"no\"/O/O, \"avail\"/O/O, \".\"/O/O, \"i\"/O/O, \"took\"/O/O, \"50\"/O/O, \"mg\"/O/O, \".\"/O/O, \"3\"/O/O, \"times\"/O/O, \"a\"/O/O, \"day\"/O/O, \"of\"/O/O, \"arthrotec\"/B-Drug/B-Arthrotec, \"and\"/O/O, \"was\"/O/O, \"almost\"/O/O, \"pain\"/B-Symptom/B-Pain, \"free\"/O/O, \".\"/O/O, \"i\"/O/O, \"highly\"/O/O, \"recommend\"/O/O, \"it\"/O/O, \"to\"/O/O, \"anyone\"/O/O, \"that\"/O/O, \"suffers\"/O/O, \"from\"/O/O, \"arthritis\"/B-Finding/B-Arthritis, \".\"/O/O],\n",
              " Sentence[28]: \"hospitalization due extreme rectal bleed that required blood transfusion . i was extremely sick and initially felt i might not survive . should be removed from market .\" → [\"hospitalization\"/O/O, \"due\"/O/O, \"extreme\"/O/O, \"rectal\"/B-ADR/B-Rectal_haemorrhage, \"bleed\"/E-ADR/E-Rectal_haemorrhage, \"that\"/O/O, \"required\"/O/O, \"blood\"/O/O, \"transfusion\"/O/O, \".\"/O/O, \"i\"/O/O, \"was\"/O/O, \"extremely\"/B-ADR/B-Generally_unwell, \"sick\"/E-ADR/E-Generally_unwell, \"and\"/O/O, \"initially\"/O/O, \"felt\"/B-ADR/B-CONCEPT_LESS, \"i\"/I-ADR/I-CONCEPT_LESS, \"might\"/I-ADR/I-CONCEPT_LESS, \"not\"/I-ADR/I-CONCEPT_LESS, \"survive\"/E-ADR/E-CONCEPT_LESS, \".\"/O/O, \"should\"/O/O, \"be\"/O/O, \"removed\"/O/O, \"from\"/O/O, \"market\"/O/O, \".\"/O/O]]"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "with open(data_save_dir+'/cadec_flair_objects.pkl', 'rb') as f:\n",
        "    docs = pickle.load(f)\n",
        "docs[:10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sW10pPAPtGij",
        "outputId": "19cf697a-9d09-4ceb-cc80-f2be36fac686"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1186"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(docs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EMT5TsNStGij",
        "outputId": "7b7e6c03-7079-4d95-8c08-5747fc728457"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Sentence[117]: \"i feel a bit drowsy & have a little blurred vision , so far no gastric problems . ive been on arthrotec 50 for over 10 years on and off , only taking it when i needed it . due to my arthritis getting progressively worse , to the point where i am in tears with the agony , gp ' s started me on 75 twice a day and i have to take it . every day for the next month to see how i get on , here goes . so far its been very good , pains almost gone , but i feel a bit weird , didnt have that when on 50 .\" → [\"i\"/O/O, \"feel\"/O/O, \"a\"/O/O, \"bit\"/B-ADR/B-Drowsy, \"drowsy\"/E-ADR/E-Drowsy, \"&\"/O/O, \"have\"/O/O, \"a\"/O/O, \"little\"/B-ADR/B-Blurred_vision_-_hazy, \"blurred\"/I-ADR/I-Blurred_vision_-_hazy, \"vision\"/E-ADR/E-Blurred_vision_-_hazy, \",\"/O/O, \"so\"/O/O, \"far\"/O/O, \"no\"/O/O, \"gastric\"/B-ADR/B-Excessive_upper_gastrointestinal_gas, \"problems\"/E-ADR/E-Excessive_upper_gastrointestinal_gas, \".\"/O/O, \"ive\"/O/O, \"been\"/O/O, \"on\"/O/O, \"arthrotec\"/B-Drug/B-Arthrotec, \"50\"/O/O, \"for\"/O/O, \"over\"/O/O, \"10\"/O/O, \"years\"/O/O, \"on\"/O/O, \"and\"/O/O, \"off\"/O/O, \",\"/O/O, \"only\"/O/O, \"taking\"/O/O, \"it\"/O/O, \"when\"/O/O, \"i\"/O/O, \"needed\"/O/O, \"it\"/O/O, \".\"/O/O, \"due\"/O/O, \"to\"/O/O, \"my\"/O/O, \"arthritis\"/B-Disease/B-Arthritis, \"getting\"/O/O, \"progressively\"/O/O, \"worse\"/O/O, \",\"/O/O, \"to\"/O/O, \"the\"/O/O, \"point\"/O/O, \"where\"/O/O, \"i\"/O/O, \"am\"/O/O, \"in\"/O/O, \"tears\"/O/O, \"with\"/O/O, \"the\"/O/O, \"agony\"/B-Symptom/B-Agony, \",\"/O/O, \"gp\"/O/O, \"'\"/O/O, \"s\"/O/O, \"started\"/O/O, \"me\"/O/O, \"on\"/O/O, \"75\"/O/O, \"twice\"/O/O, \"a\"/O/O, \"day\"/O/O, \"and\"/O/O, \"i\"/O/O, \"have\"/O/O, \"to\"/O/O, \"take\"/O/O, \"it\"/O/O, \".\"/O/O, \"every\"/O/O, \"day\"/O/O, \"for\"/O/O, \"the\"/O/O, \"next\"/O/O, \"month\"/O/O, \"to\"/O/O, \"see\"/O/O, \"how\"/O/O, \"i\"/O/O, \"get\"/O/O, \"on\"/O/O, \",\"/O/O, \"here\"/O/O, \"goes\"/O/O, \".\"/O/O, \"so\"/O/O, \"far\"/O/O, \"its\"/O/O, \"been\"/O/O, \"very\"/O/O, \"good\"/O/O, \",\"/O/O, \"pains\"/B-Symptom/B-Pain, \"almost\"/O/O, \"gone\"/O/O, \",\"/O/O, \"but\"/O/O, \"i\"/O/O, \"feel\"/B-ADR/B-Malaise, \"a\"/I-ADR/I-Malaise, \"bit\"/I-ADR/I-Malaise, \"weird\"/E-ADR/E-Malaise, \",\"/O/O, \"didnt\"/O/O, \"have\"/O/O, \"that\"/O/O, \"when\"/O/O, \"on\"/O/O, \"50\"/O/O, \".\"/O/O]"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "temp = docs[0]\n",
        "temp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2EeTdfHstGij",
        "outputId": "50a8db81-41c0-42d9-ca5e-31eeeb7ea3ad"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'ner': ['Token[0]: \"i\"'/'O' (1.0),\n",
              "  'Token[1]: \"feel\"'/'O' (1.0),\n",
              "  'Token[2]: \"a\"'/'O' (1.0),\n",
              "  'Token[3]: \"bit\"'/'B-ADR' (1.0),\n",
              "  'Token[4]: \"drowsy\"'/'E-ADR' (1.0),\n",
              "  'Token[5]: \"&\"'/'O' (1.0),\n",
              "  'Token[6]: \"have\"'/'O' (1.0),\n",
              "  'Token[7]: \"a\"'/'O' (1.0),\n",
              "  'Token[8]: \"little\"'/'B-ADR' (1.0),\n",
              "  'Token[9]: \"blurred\"'/'I-ADR' (1.0),\n",
              "  'Token[10]: \"vision\"'/'E-ADR' (1.0),\n",
              "  'Token[11]: \",\"'/'O' (1.0),\n",
              "  'Token[12]: \"so\"'/'O' (1.0),\n",
              "  'Token[13]: \"far\"'/'O' (1.0),\n",
              "  'Token[14]: \"no\"'/'O' (1.0),\n",
              "  'Token[15]: \"gastric\"'/'B-ADR' (1.0),\n",
              "  'Token[16]: \"problems\"'/'E-ADR' (1.0),\n",
              "  'Token[17]: \".\"'/'O' (1.0),\n",
              "  'Token[18]: \"ive\"'/'O' (1.0),\n",
              "  'Token[19]: \"been\"'/'O' (1.0),\n",
              "  'Token[20]: \"on\"'/'O' (1.0),\n",
              "  'Token[21]: \"arthrotec\"'/'B-Drug' (1.0),\n",
              "  'Token[22]: \"50\"'/'O' (1.0),\n",
              "  'Token[23]: \"for\"'/'O' (1.0),\n",
              "  'Token[24]: \"over\"'/'O' (1.0),\n",
              "  'Token[25]: \"10\"'/'O' (1.0),\n",
              "  'Token[26]: \"years\"'/'O' (1.0),\n",
              "  'Token[27]: \"on\"'/'O' (1.0),\n",
              "  'Token[28]: \"and\"'/'O' (1.0),\n",
              "  'Token[29]: \"off\"'/'O' (1.0),\n",
              "  'Token[30]: \",\"'/'O' (1.0),\n",
              "  'Token[31]: \"only\"'/'O' (1.0),\n",
              "  'Token[32]: \"taking\"'/'O' (1.0),\n",
              "  'Token[33]: \"it\"'/'O' (1.0),\n",
              "  'Token[34]: \"when\"'/'O' (1.0),\n",
              "  'Token[35]: \"i\"'/'O' (1.0),\n",
              "  'Token[36]: \"needed\"'/'O' (1.0),\n",
              "  'Token[37]: \"it\"'/'O' (1.0),\n",
              "  'Token[38]: \".\"'/'O' (1.0),\n",
              "  'Token[39]: \"due\"'/'O' (1.0),\n",
              "  'Token[40]: \"to\"'/'O' (1.0),\n",
              "  'Token[41]: \"my\"'/'O' (1.0),\n",
              "  'Token[42]: \"arthritis\"'/'B-Disease' (1.0),\n",
              "  'Token[43]: \"getting\"'/'O' (1.0),\n",
              "  'Token[44]: \"progressively\"'/'O' (1.0),\n",
              "  'Token[45]: \"worse\"'/'O' (1.0),\n",
              "  'Token[46]: \",\"'/'O' (1.0),\n",
              "  'Token[47]: \"to\"'/'O' (1.0),\n",
              "  'Token[48]: \"the\"'/'O' (1.0),\n",
              "  'Token[49]: \"point\"'/'O' (1.0),\n",
              "  'Token[50]: \"where\"'/'O' (1.0),\n",
              "  'Token[51]: \"i\"'/'O' (1.0),\n",
              "  'Token[52]: \"am\"'/'O' (1.0),\n",
              "  'Token[53]: \"in\"'/'O' (1.0),\n",
              "  'Token[54]: \"tears\"'/'O' (1.0),\n",
              "  'Token[55]: \"with\"'/'O' (1.0),\n",
              "  'Token[56]: \"the\"'/'O' (1.0),\n",
              "  'Token[57]: \"agony\"'/'B-Symptom' (1.0),\n",
              "  'Token[58]: \",\"'/'O' (1.0),\n",
              "  'Token[59]: \"gp\"'/'O' (1.0),\n",
              "  'Token[60]: \"'\"'/'O' (1.0),\n",
              "  'Token[61]: \"s\"'/'O' (1.0),\n",
              "  'Token[62]: \"started\"'/'O' (1.0),\n",
              "  'Token[63]: \"me\"'/'O' (1.0),\n",
              "  'Token[64]: \"on\"'/'O' (1.0),\n",
              "  'Token[65]: \"75\"'/'O' (1.0),\n",
              "  'Token[66]: \"twice\"'/'O' (1.0),\n",
              "  'Token[67]: \"a\"'/'O' (1.0),\n",
              "  'Token[68]: \"day\"'/'O' (1.0),\n",
              "  'Token[69]: \"and\"'/'O' (1.0),\n",
              "  'Token[70]: \"i\"'/'O' (1.0),\n",
              "  'Token[71]: \"have\"'/'O' (1.0),\n",
              "  'Token[72]: \"to\"'/'O' (1.0),\n",
              "  'Token[73]: \"take\"'/'O' (1.0),\n",
              "  'Token[74]: \"it\"'/'O' (1.0),\n",
              "  'Token[75]: \".\"'/'O' (1.0),\n",
              "  'Token[76]: \"every\"'/'O' (1.0),\n",
              "  'Token[77]: \"day\"'/'O' (1.0),\n",
              "  'Token[78]: \"for\"'/'O' (1.0),\n",
              "  'Token[79]: \"the\"'/'O' (1.0),\n",
              "  'Token[80]: \"next\"'/'O' (1.0),\n",
              "  'Token[81]: \"month\"'/'O' (1.0),\n",
              "  'Token[82]: \"to\"'/'O' (1.0),\n",
              "  'Token[83]: \"see\"'/'O' (1.0),\n",
              "  'Token[84]: \"how\"'/'O' (1.0),\n",
              "  'Token[85]: \"i\"'/'O' (1.0),\n",
              "  'Token[86]: \"get\"'/'O' (1.0),\n",
              "  'Token[87]: \"on\"'/'O' (1.0),\n",
              "  'Token[88]: \",\"'/'O' (1.0),\n",
              "  'Token[89]: \"here\"'/'O' (1.0),\n",
              "  'Token[90]: \"goes\"'/'O' (1.0),\n",
              "  'Token[91]: \".\"'/'O' (1.0),\n",
              "  'Token[92]: \"so\"'/'O' (1.0),\n",
              "  'Token[93]: \"far\"'/'O' (1.0),\n",
              "  'Token[94]: \"its\"'/'O' (1.0),\n",
              "  'Token[95]: \"been\"'/'O' (1.0),\n",
              "  'Token[96]: \"very\"'/'O' (1.0),\n",
              "  'Token[97]: \"good\"'/'O' (1.0),\n",
              "  'Token[98]: \",\"'/'O' (1.0),\n",
              "  'Token[99]: \"pains\"'/'B-Symptom' (1.0),\n",
              "  'Token[100]: \"almost\"'/'O' (1.0),\n",
              "  'Token[101]: \"gone\"'/'O' (1.0),\n",
              "  'Token[102]: \",\"'/'O' (1.0),\n",
              "  'Token[103]: \"but\"'/'O' (1.0),\n",
              "  'Token[104]: \"i\"'/'O' (1.0),\n",
              "  'Token[105]: \"feel\"'/'B-ADR' (1.0),\n",
              "  'Token[106]: \"a\"'/'I-ADR' (1.0),\n",
              "  'Token[107]: \"bit\"'/'I-ADR' (1.0),\n",
              "  'Token[108]: \"weird\"'/'E-ADR' (1.0),\n",
              "  'Token[109]: \",\"'/'O' (1.0),\n",
              "  'Token[110]: \"didnt\"'/'O' (1.0),\n",
              "  'Token[111]: \"have\"'/'O' (1.0),\n",
              "  'Token[112]: \"that\"'/'O' (1.0),\n",
              "  'Token[113]: \"when\"'/'O' (1.0),\n",
              "  'Token[114]: \"on\"'/'O' (1.0),\n",
              "  'Token[115]: \"50\"'/'O' (1.0),\n",
              "  'Token[116]: \".\"'/'O' (1.0)],\n",
              " 'meddra': ['Token[0]: \"i\"'/'O' (1.0),\n",
              "  'Token[1]: \"feel\"'/'O' (1.0),\n",
              "  'Token[2]: \"a\"'/'O' (1.0),\n",
              "  'Token[3]: \"bit\"'/'B-Drowsy' (1.0),\n",
              "  'Token[4]: \"drowsy\"'/'E-Drowsy' (1.0),\n",
              "  'Token[5]: \"&\"'/'O' (1.0),\n",
              "  'Token[6]: \"have\"'/'O' (1.0),\n",
              "  'Token[7]: \"a\"'/'O' (1.0),\n",
              "  'Token[8]: \"little\"'/'B-Blurred_vision_-_hazy' (1.0),\n",
              "  'Token[9]: \"blurred\"'/'I-Blurred_vision_-_hazy' (1.0),\n",
              "  'Token[10]: \"vision\"'/'E-Blurred_vision_-_hazy' (1.0),\n",
              "  'Token[11]: \",\"'/'O' (1.0),\n",
              "  'Token[12]: \"so\"'/'O' (1.0),\n",
              "  'Token[13]: \"far\"'/'O' (1.0),\n",
              "  'Token[14]: \"no\"'/'O' (1.0),\n",
              "  'Token[15]: \"gastric\"'/'B-Excessive_upper_gastrointestinal_gas' (1.0),\n",
              "  'Token[16]: \"problems\"'/'E-Excessive_upper_gastrointestinal_gas' (1.0),\n",
              "  'Token[17]: \".\"'/'O' (1.0),\n",
              "  'Token[18]: \"ive\"'/'O' (1.0),\n",
              "  'Token[19]: \"been\"'/'O' (1.0),\n",
              "  'Token[20]: \"on\"'/'O' (1.0),\n",
              "  'Token[21]: \"arthrotec\"'/'B-Arthrotec' (1.0),\n",
              "  'Token[22]: \"50\"'/'O' (1.0),\n",
              "  'Token[23]: \"for\"'/'O' (1.0),\n",
              "  'Token[24]: \"over\"'/'O' (1.0),\n",
              "  'Token[25]: \"10\"'/'O' (1.0),\n",
              "  'Token[26]: \"years\"'/'O' (1.0),\n",
              "  'Token[27]: \"on\"'/'O' (1.0),\n",
              "  'Token[28]: \"and\"'/'O' (1.0),\n",
              "  'Token[29]: \"off\"'/'O' (1.0),\n",
              "  'Token[30]: \",\"'/'O' (1.0),\n",
              "  'Token[31]: \"only\"'/'O' (1.0),\n",
              "  'Token[32]: \"taking\"'/'O' (1.0),\n",
              "  'Token[33]: \"it\"'/'O' (1.0),\n",
              "  'Token[34]: \"when\"'/'O' (1.0),\n",
              "  'Token[35]: \"i\"'/'O' (1.0),\n",
              "  'Token[36]: \"needed\"'/'O' (1.0),\n",
              "  'Token[37]: \"it\"'/'O' (1.0),\n",
              "  'Token[38]: \".\"'/'O' (1.0),\n",
              "  'Token[39]: \"due\"'/'O' (1.0),\n",
              "  'Token[40]: \"to\"'/'O' (1.0),\n",
              "  'Token[41]: \"my\"'/'O' (1.0),\n",
              "  'Token[42]: \"arthritis\"'/'B-Arthritis' (1.0),\n",
              "  'Token[43]: \"getting\"'/'O' (1.0),\n",
              "  'Token[44]: \"progressively\"'/'O' (1.0),\n",
              "  'Token[45]: \"worse\"'/'O' (1.0),\n",
              "  'Token[46]: \",\"'/'O' (1.0),\n",
              "  'Token[47]: \"to\"'/'O' (1.0),\n",
              "  'Token[48]: \"the\"'/'O' (1.0),\n",
              "  'Token[49]: \"point\"'/'O' (1.0),\n",
              "  'Token[50]: \"where\"'/'O' (1.0),\n",
              "  'Token[51]: \"i\"'/'O' (1.0),\n",
              "  'Token[52]: \"am\"'/'O' (1.0),\n",
              "  'Token[53]: \"in\"'/'O' (1.0),\n",
              "  'Token[54]: \"tears\"'/'O' (1.0),\n",
              "  'Token[55]: \"with\"'/'O' (1.0),\n",
              "  'Token[56]: \"the\"'/'O' (1.0),\n",
              "  'Token[57]: \"agony\"'/'B-Agony' (1.0),\n",
              "  'Token[58]: \",\"'/'O' (1.0),\n",
              "  'Token[59]: \"gp\"'/'O' (1.0),\n",
              "  'Token[60]: \"'\"'/'O' (1.0),\n",
              "  'Token[61]: \"s\"'/'O' (1.0),\n",
              "  'Token[62]: \"started\"'/'O' (1.0),\n",
              "  'Token[63]: \"me\"'/'O' (1.0),\n",
              "  'Token[64]: \"on\"'/'O' (1.0),\n",
              "  'Token[65]: \"75\"'/'O' (1.0),\n",
              "  'Token[66]: \"twice\"'/'O' (1.0),\n",
              "  'Token[67]: \"a\"'/'O' (1.0),\n",
              "  'Token[68]: \"day\"'/'O' (1.0),\n",
              "  'Token[69]: \"and\"'/'O' (1.0),\n",
              "  'Token[70]: \"i\"'/'O' (1.0),\n",
              "  'Token[71]: \"have\"'/'O' (1.0),\n",
              "  'Token[72]: \"to\"'/'O' (1.0),\n",
              "  'Token[73]: \"take\"'/'O' (1.0),\n",
              "  'Token[74]: \"it\"'/'O' (1.0),\n",
              "  'Token[75]: \".\"'/'O' (1.0),\n",
              "  'Token[76]: \"every\"'/'O' (1.0),\n",
              "  'Token[77]: \"day\"'/'O' (1.0),\n",
              "  'Token[78]: \"for\"'/'O' (1.0),\n",
              "  'Token[79]: \"the\"'/'O' (1.0),\n",
              "  'Token[80]: \"next\"'/'O' (1.0),\n",
              "  'Token[81]: \"month\"'/'O' (1.0),\n",
              "  'Token[82]: \"to\"'/'O' (1.0),\n",
              "  'Token[83]: \"see\"'/'O' (1.0),\n",
              "  'Token[84]: \"how\"'/'O' (1.0),\n",
              "  'Token[85]: \"i\"'/'O' (1.0),\n",
              "  'Token[86]: \"get\"'/'O' (1.0),\n",
              "  'Token[87]: \"on\"'/'O' (1.0),\n",
              "  'Token[88]: \",\"'/'O' (1.0),\n",
              "  'Token[89]: \"here\"'/'O' (1.0),\n",
              "  'Token[90]: \"goes\"'/'O' (1.0),\n",
              "  'Token[91]: \".\"'/'O' (1.0),\n",
              "  'Token[92]: \"so\"'/'O' (1.0),\n",
              "  'Token[93]: \"far\"'/'O' (1.0),\n",
              "  'Token[94]: \"its\"'/'O' (1.0),\n",
              "  'Token[95]: \"been\"'/'O' (1.0),\n",
              "  'Token[96]: \"very\"'/'O' (1.0),\n",
              "  'Token[97]: \"good\"'/'O' (1.0),\n",
              "  'Token[98]: \",\"'/'O' (1.0),\n",
              "  'Token[99]: \"pains\"'/'B-Pain' (1.0),\n",
              "  'Token[100]: \"almost\"'/'O' (1.0),\n",
              "  'Token[101]: \"gone\"'/'O' (1.0),\n",
              "  'Token[102]: \",\"'/'O' (1.0),\n",
              "  'Token[103]: \"but\"'/'O' (1.0),\n",
              "  'Token[104]: \"i\"'/'O' (1.0),\n",
              "  'Token[105]: \"feel\"'/'B-Malaise' (1.0),\n",
              "  'Token[106]: \"a\"'/'I-Malaise' (1.0),\n",
              "  'Token[107]: \"bit\"'/'I-Malaise' (1.0),\n",
              "  'Token[108]: \"weird\"'/'E-Malaise' (1.0),\n",
              "  'Token[109]: \",\"'/'O' (1.0),\n",
              "  'Token[110]: \"didnt\"'/'O' (1.0),\n",
              "  'Token[111]: \"have\"'/'O' (1.0),\n",
              "  'Token[112]: \"that\"'/'O' (1.0),\n",
              "  'Token[113]: \"when\"'/'O' (1.0),\n",
              "  'Token[114]: \"on\"'/'O' (1.0),\n",
              "  'Token[115]: \"50\"'/'O' (1.0),\n",
              "  'Token[116]: \".\"'/'O' (1.0)]}"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "temp.annotation_layers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "4CsGKKB6tGik"
      },
      "outputs": [],
      "source": [
        "\n",
        "# define your NER tags\n",
        "label_dict = {\n",
        "    'ADR': 'ADR',\n",
        "    'Drug': 'Drug',\n",
        "    'Finding': 'Finding',\n",
        "    'Symptom': 'Symptom',\n",
        "    'Disease': 'Disease'\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QmNsy8AltGik",
        "outputId": "4968cae4-7e95-4bd5-8ba2-af8c8b383a46"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 01:37:02,589 Filtering empty sentences\n",
            "2023-04-21 01:37:02,611 Corpus: 948 train + 119 dev + 119 test sentences\n",
            "2023-04-21 01:37:02,612 Computing label dictionary. Progress:\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "948it [00:00, 6599.81it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 01:37:02,762 Dictionary created for label 'ner' with 17 values: O (seen 79057 times), B-ADR (seen 4712 times), I-ADR (seen 4449 times), E-ADR (seen 3506 times), B-Drug (seen 1396 times), B-Finding (seen 309 times), B-Disease (seen 231 times), B-Symptom (seen 222 times), E-Finding (seen 200 times), I-Finding (seen 149 times), E-Disease (seen 119 times), E-Symptom (seen 114 times), E-Drug (seen 111 times), I-Symptom (seen 104 times), I-Drug (seen 50 times), I-Disease (seen 50 times)\n",
            "False\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 01:37:11,209 SequenceTagger predicts: Dictionary with 17 tags: <unk>, O, B-ADR, I-ADR, E-ADR, B-Drug, B-Finding, B-Disease, B-Symptom, E-Finding, I-Finding, E-Disease, E-Symptom, E-Drug, I-Symptom, I-Drug, I-Disease\n",
            "True\n",
            "False\n"
          ]
        }
      ],
      "source": [
        "from flair.data import Corpus\n",
        "\n",
        "from flair.embeddings import WordEmbeddings, FlairEmbeddings, StackedEmbeddings, TransformerWordEmbeddings\n",
        "from flair.models import SequenceTagger\n",
        "from flair.trainers import ModelTrainer\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from torch.optim import AdamW, SGD\n",
        "\n",
        "from flair.visual.training_curves import Plotter\n",
        "plotter = Plotter()\n",
        "\n",
        "\n",
        "\n",
        "train_docs, test_docs = train_test_split(docs, test_size=0.2, random_state=321)\n",
        "dev_docs, test_docs = train_test_split(test_docs, test_size=0.5, random_state=123)\n",
        "\n",
        "corpus = Corpus(train=train_docs, dev=dev_docs, test=test_docs) # Corpus: 948 train + 119 dev + 119 test sentences\n",
        "corpus.filter_empty_sentences()\n",
        "# # create a tag dictionary\n",
        "labels = ['ADR', 'Drug', 'Finding', 'Symptom', 'Disease']\n",
        "# tag_dictionary = Dictionary(add_unk=True)\n",
        "# for label in labels:\n",
        "#     tag_dictionary.add_item(label)\n",
        "tag_dictionary = corpus.make_label_dictionary(label_type='ner')\n",
        "print(tag_dictionary.span_labels)\n",
        "\n",
        "# create a stacked embedding using GloVe and Flair embeddings\n",
        "stacked_embeddings = StackedEmbeddings([\n",
        "    WordEmbeddings('glove',\n",
        "                   force_cpu=False,\n",
        "                #    embedding_length=1024,\n",
        "                  #  fine_tune=True,\n",
        "                   ),\n",
        "    # TransformerWordEmbeddings('emilyalsentzer/Bio_ClinicalBERT', layers=\"-1\",\n",
        "    #                                    subtoken_pooling=\"first\",\n",
        "    #                                    is_document_embedding=True,\n",
        "    #                                    fine_tune=True,\n",
        "    #                                    use_context=True,\n",
        "    #                                    max_seq_length=1024,\n",
        "    #                                    ),\n",
        "    FlairEmbeddings('news-forward',\n",
        "                    # fine_tune=True\n",
        "                    ),\n",
        "    FlairEmbeddings('news-backward',\n",
        "                    # fine_tune=True\n",
        "                    ),\n",
        "])\n",
        "# https://github.com/flairNLP/flair/blob/master/resources/docs/embeddings/FLAIR_EMBEDDINGS.md\n",
        "\n",
        "# create a SequenceTagger object and specify the tag dictionary and embeddings to use\n",
        "tagger = SequenceTagger(hidden_size=256,\n",
        "                        embeddings=stacked_embeddings,\n",
        "                        tag_dictionary=tag_dictionary,\n",
        "                        tag_type='ner',\n",
        "                        # use_crf=False,\n",
        "                        # use_rnn=False,\n",
        "                        # reproject_embeddings=False,\n",
        "                        )\n",
        "tagger.predict_spans = False\n",
        "\n",
        "print(tagger._determine_if_span_prediction_problem(tagger.label_dictionary))\n",
        "print(tagger.predict_spans)\n",
        "\n",
        "# https://github.com/flairNLP/flair/blob/master/flair/trainers/trainer.py\n",
        "# create a ModelTrainer object and train the tagger\n",
        "trainer = ModelTrainer(tagger, corpus)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "fh0zU2jHAA4J"
      },
      "outputs": [],
      "source": [
        "# !tensorboard --logdir=runs\n",
        "# http://localhost:6006/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UKTMzxV8zF9o",
        "outputId": "506e0d69-261c-4b04-a29d-468be13c8739"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 01:37:11,468 tensorboard logging path is runs/tb_logs_v1\n",
            "2023-04-21 01:37:11,480 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:37:11,481 Model: \"SequenceTagger(\n",
            "  (embeddings): StackedEmbeddings(\n",
            "    (list_embedding_0): WordEmbeddings(\n",
            "      'glove'\n",
            "      (embedding): Embedding(400001, 100)\n",
            "    )\n",
            "    (list_embedding_1): FlairEmbeddings(\n",
            "      (lm): LanguageModel(\n",
            "        (drop): Dropout(p=0.05, inplace=False)\n",
            "        (encoder): Embedding(300, 100)\n",
            "        (rnn): LSTM(100, 2048)\n",
            "      )\n",
            "    )\n",
            "    (list_embedding_2): FlairEmbeddings(\n",
            "      (lm): LanguageModel(\n",
            "        (drop): Dropout(p=0.05, inplace=False)\n",
            "        (encoder): Embedding(300, 100)\n",
            "        (rnn): LSTM(100, 2048)\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (word_dropout): WordDropout(p=0.05)\n",
            "  (locked_dropout): LockedDropout(p=0.5)\n",
            "  (embedding2nn): Linear(in_features=4196, out_features=4196, bias=True)\n",
            "  (rnn): LSTM(4196, 256, batch_first=True, bidirectional=True)\n",
            "  (linear): Linear(in_features=512, out_features=19, bias=True)\n",
            "  (loss_function): ViterbiLoss()\n",
            "  (crf): CRF()\n",
            ")\"\n",
            "2023-04-21 01:37:11,483 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:37:11,484 Corpus: \"Corpus: 948 train + 119 dev + 119 test sentences\"\n",
            "2023-04-21 01:37:11,486 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:37:11,487 Parameters:\n",
            "2023-04-21 01:37:11,489  - learning_rate: \"0.100000\"\n",
            "2023-04-21 01:37:11,490  - mini_batch_size: \"12\"\n",
            "2023-04-21 01:37:11,492  - patience: \"1\"\n",
            "2023-04-21 01:37:11,493  - anneal_factor: \"0.5\"\n",
            "2023-04-21 01:37:11,495  - max_epochs: \"100\"\n",
            "2023-04-21 01:37:11,497  - shuffle: \"True\"\n",
            "2023-04-21 01:37:11,498  - train_with_dev: \"False\"\n",
            "2023-04-21 01:37:11,500  - batch_growth_annealing: \"False\"\n",
            "2023-04-21 01:37:11,501 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:37:11,503 Model training base path: \"flair_ner_model_v1\"\n",
            "2023-04-21 01:37:11,504 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:37:11,505 Device: cuda:0\n",
            "2023-04-21 01:37:11,506 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:37:11,507 Embeddings storage mode: gpu\n",
            "2023-04-21 01:37:11,509 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:37:14,547 epoch 1 - iter 7/79 - loss 3.43966220 - time (sec): 3.04 - samples/sec: 3010.03 - lr: 0.100000\n",
            "2023-04-21 01:37:16,876 epoch 1 - iter 14/79 - loss 2.80625205 - time (sec): 5.37 - samples/sec: 3047.72 - lr: 0.100000\n",
            "2023-04-21 01:37:19,640 epoch 1 - iter 21/79 - loss 2.67319299 - time (sec): 8.13 - samples/sec: 3005.11 - lr: 0.100000\n",
            "2023-04-21 01:37:22,285 epoch 1 - iter 28/79 - loss 2.52823098 - time (sec): 10.78 - samples/sec: 3025.77 - lr: 0.100000\n",
            "2023-04-21 01:37:25,050 epoch 1 - iter 35/79 - loss 2.42297634 - time (sec): 13.54 - samples/sec: 3048.89 - lr: 0.100000\n",
            "2023-04-21 01:37:27,758 epoch 1 - iter 42/79 - loss 2.60474561 - time (sec): 16.25 - samples/sec: 3045.25 - lr: 0.100000\n",
            "2023-04-21 01:37:30,458 epoch 1 - iter 49/79 - loss 2.60485300 - time (sec): 18.95 - samples/sec: 3054.83 - lr: 0.100000\n",
            "2023-04-21 01:37:34,305 epoch 1 - iter 56/79 - loss 2.50025391 - time (sec): 22.79 - samples/sec: 2919.41 - lr: 0.100000\n",
            "2023-04-21 01:37:36,658 epoch 1 - iter 63/79 - loss 2.43013997 - time (sec): 25.15 - samples/sec: 2978.05 - lr: 0.100000\n",
            "2023-04-21 01:37:39,675 epoch 1 - iter 70/79 - loss 2.46221956 - time (sec): 28.16 - samples/sec: 2953.19 - lr: 0.100000\n",
            "2023-04-21 01:37:43,597 epoch 1 - iter 77/79 - loss 2.46783093 - time (sec): 32.09 - samples/sec: 2864.75 - lr: 0.100000\n",
            "2023-04-21 01:37:45,155 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:37:45,156 EPOCH 1 done: loss 2.4695 - lr 0.100000\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 79/79 [00:21<00:00,  3.72it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 01:38:06,496 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 01:38:08,775 TRAIN : loss 1.620639681816101 - f1-score (weighted avg)  0.065\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 10/10 [00:04<00:00,  2.32it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 01:38:13,449 Evaluating as a multi-label problem: False\n",
            "2023-04-21 01:38:13,514 DEV : loss 1.6220180988311768 - f1-score (weighted avg)  0.0714\n",
            "2023-04-21 01:38:13,524 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 01:38:13,528 saving best model\n",
            "2023-04-21 01:38:15,540 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:38:17,184 epoch 2 - iter 7/79 - loss 2.07842031 - time (sec): 1.64 - samples/sec: 5203.09 - lr: 0.100000\n",
            "2023-04-21 01:38:18,703 epoch 2 - iter 14/79 - loss 2.18068577 - time (sec): 3.16 - samples/sec: 5121.74 - lr: 0.100000\n",
            "2023-04-21 01:38:20,270 epoch 2 - iter 21/79 - loss 2.23710320 - time (sec): 4.73 - samples/sec: 5216.34 - lr: 0.100000\n",
            "2023-04-21 01:38:22,288 epoch 2 - iter 28/79 - loss 2.39456664 - time (sec): 6.75 - samples/sec: 4949.30 - lr: 0.100000\n",
            "2023-04-21 01:38:24,392 epoch 2 - iter 35/79 - loss 2.22319636 - time (sec): 8.85 - samples/sec: 4856.75 - lr: 0.100000\n",
            "2023-04-21 01:38:26,266 epoch 2 - iter 42/79 - loss 2.22414489 - time (sec): 10.72 - samples/sec: 4814.51 - lr: 0.100000\n",
            "2023-04-21 01:38:28,037 epoch 2 - iter 49/79 - loss 2.25733133 - time (sec): 12.50 - samples/sec: 4761.34 - lr: 0.100000\n",
            "2023-04-21 01:38:29,716 epoch 2 - iter 56/79 - loss 2.25672610 - time (sec): 14.17 - samples/sec: 4723.58 - lr: 0.100000\n",
            "2023-04-21 01:38:32,072 epoch 2 - iter 63/79 - loss 2.18043533 - time (sec): 16.53 - samples/sec: 4617.10 - lr: 0.100000\n",
            "2023-04-21 01:38:33,972 epoch 2 - iter 70/79 - loss 2.16674612 - time (sec): 18.43 - samples/sec: 4610.24 - lr: 0.100000\n",
            "2023-04-21 01:38:35,384 epoch 2 - iter 77/79 - loss 2.22499754 - time (sec): 19.84 - samples/sec: 4674.54 - lr: 0.100000\n",
            "2023-04-21 01:38:35,869 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:38:35,871 EPOCH 2 done: loss 2.2140 - lr 0.100000\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 79/79 [00:22<00:00,  3.48it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 01:38:58,673 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 01:39:00,939 TRAIN : loss 1.7005283832550049 - f1-score (weighted avg)  0.232\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 10/10 [00:02<00:00,  3.57it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 01:39:03,817 Evaluating as a multi-label problem: False\n",
            "2023-04-21 01:39:03,884 DEV : loss 1.6920753717422485 - f1-score (weighted avg)  0.223\n",
            "2023-04-21 01:39:03,893 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 01:39:03,897 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 01:39:05,564 epoch 3 - iter 7/79 - loss 2.92270685 - time (sec): 1.66 - samples/sec: 4730.42 - lr: 0.100000\n",
            "2023-04-21 01:39:07,158 epoch 3 - iter 14/79 - loss 2.52563672 - time (sec): 3.26 - samples/sec: 5065.70 - lr: 0.100000\n",
            "2023-04-21 01:39:08,860 epoch 3 - iter 21/79 - loss 2.48414391 - time (sec): 4.96 - samples/sec: 4950.24 - lr: 0.100000\n",
            "2023-04-21 01:39:10,359 epoch 3 - iter 28/79 - loss 2.30927048 - time (sec): 6.46 - samples/sec: 5045.34 - lr: 0.100000\n",
            "2023-04-21 01:39:12,255 epoch 3 - iter 35/79 - loss 2.16882463 - time (sec): 8.36 - samples/sec: 4925.97 - lr: 0.100000\n",
            "2023-04-21 01:39:13,914 epoch 3 - iter 42/79 - loss 2.10839802 - time (sec): 10.02 - samples/sec: 4943.27 - lr: 0.100000\n",
            "2023-04-21 01:39:15,357 epoch 3 - iter 49/79 - loss 2.09331312 - time (sec): 11.46 - samples/sec: 4990.36 - lr: 0.100000\n",
            "2023-04-21 01:39:16,760 epoch 3 - iter 56/79 - loss 2.08952814 - time (sec): 12.86 - samples/sec: 5137.66 - lr: 0.100000\n",
            "2023-04-21 01:39:18,427 epoch 3 - iter 63/79 - loss 2.08166051 - time (sec): 14.53 - samples/sec: 5124.51 - lr: 0.100000\n",
            "2023-04-21 01:39:20,378 epoch 3 - iter 70/79 - loss 2.08060340 - time (sec): 16.48 - samples/sec: 5071.94 - lr: 0.100000\n",
            "2023-04-21 01:39:22,621 epoch 3 - iter 77/79 - loss 2.09100561 - time (sec): 18.72 - samples/sec: 4964.45 - lr: 0.100000\n",
            "2023-04-21 01:39:22,979 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:39:22,981 EPOCH 3 done: loss 2.1031 - lr 0.100000\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 79/79 [00:21<00:00,  3.72it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 01:39:44,376 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 01:39:46,673 TRAIN : loss 2.2053091526031494 - f1-score (weighted avg)  0.0213\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 10/10 [00:03<00:00,  2.59it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 01:39:50,610 Evaluating as a multi-label problem: False\n",
            "2023-04-21 01:39:50,674 DEV : loss 2.2307281494140625 - f1-score (weighted avg)  0.0171\n",
            "2023-04-21 01:39:50,684 Epoch     3: reducing learning rate of group 0 to 5.0000e-02.\n",
            "2023-04-21 01:39:50,685 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 01:39:50,689 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 01:39:52,389 epoch 4 - iter 7/79 - loss 2.12729465 - time (sec): 1.70 - samples/sec: 5125.77 - lr: 0.050000\n",
            "2023-04-21 01:39:54,618 epoch 4 - iter 14/79 - loss 2.00272651 - time (sec): 3.93 - samples/sec: 4603.68 - lr: 0.050000\n",
            "2023-04-21 01:39:55,956 epoch 4 - iter 21/79 - loss 2.06955041 - time (sec): 5.27 - samples/sec: 4831.97 - lr: 0.050000\n",
            "2023-04-21 01:39:57,538 epoch 4 - iter 28/79 - loss 1.95443819 - time (sec): 6.85 - samples/sec: 4911.61 - lr: 0.050000\n",
            "2023-04-21 01:39:59,315 epoch 4 - iter 35/79 - loss 1.98413529 - time (sec): 8.62 - samples/sec: 4787.99 - lr: 0.050000\n",
            "2023-04-21 01:40:01,359 epoch 4 - iter 42/79 - loss 1.90862448 - time (sec): 10.67 - samples/sec: 4711.02 - lr: 0.050000\n",
            "2023-04-21 01:40:03,219 epoch 4 - iter 49/79 - loss 1.90954704 - time (sec): 12.53 - samples/sec: 4715.62 - lr: 0.050000\n",
            "2023-04-21 01:40:04,738 epoch 4 - iter 56/79 - loss 1.84428117 - time (sec): 14.05 - samples/sec: 4814.35 - lr: 0.050000\n",
            "2023-04-21 01:40:06,452 epoch 4 - iter 63/79 - loss 1.79227424 - time (sec): 15.76 - samples/sec: 4812.68 - lr: 0.050000\n",
            "2023-04-21 01:40:08,014 epoch 4 - iter 70/79 - loss 1.76755631 - time (sec): 17.32 - samples/sec: 4815.96 - lr: 0.050000\n",
            "2023-04-21 01:40:09,970 epoch 4 - iter 77/79 - loss 1.75585139 - time (sec): 19.28 - samples/sec: 4797.20 - lr: 0.050000\n",
            "2023-04-21 01:40:10,481 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:40:10,483 EPOCH 4 done: loss 1.7678 - lr 0.050000\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 79/79 [00:21<00:00,  3.66it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 01:40:32,266 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 01:40:35,890 TRAIN : loss 1.8077532052993774 - f1-score (weighted avg)  0.1056\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 10/10 [00:02<00:00,  3.58it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 01:40:38,774 Evaluating as a multi-label problem: False\n",
            "2023-04-21 01:40:38,854 DEV : loss 1.8756130933761597 - f1-score (weighted avg)  0.1\n",
            "2023-04-21 01:40:38,863 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 01:40:38,867 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 01:40:40,662 epoch 5 - iter 7/79 - loss 1.40240675 - time (sec): 1.79 - samples/sec: 4908.87 - lr: 0.050000\n",
            "2023-04-21 01:40:42,417 epoch 5 - iter 14/79 - loss 1.34595637 - time (sec): 3.55 - samples/sec: 5168.86 - lr: 0.050000\n",
            "2023-04-21 01:40:44,281 epoch 5 - iter 21/79 - loss 1.38305373 - time (sec): 5.41 - samples/sec: 5020.50 - lr: 0.050000\n",
            "2023-04-21 01:40:46,339 epoch 5 - iter 28/79 - loss 1.32497640 - time (sec): 7.47 - samples/sec: 4936.09 - lr: 0.050000\n",
            "2023-04-21 01:40:47,886 epoch 5 - iter 35/79 - loss 1.34061295 - time (sec): 9.02 - samples/sec: 4941.02 - lr: 0.050000\n",
            "2023-04-21 01:40:49,480 epoch 5 - iter 42/79 - loss 1.32863911 - time (sec): 10.61 - samples/sec: 4970.74 - lr: 0.050000\n",
            "2023-04-21 01:40:51,836 epoch 5 - iter 49/79 - loss 1.34346558 - time (sec): 12.97 - samples/sec: 4796.82 - lr: 0.050000\n",
            "2023-04-21 01:40:53,172 epoch 5 - iter 56/79 - loss 1.35208768 - time (sec): 14.30 - samples/sec: 4857.71 - lr: 0.050000\n",
            "2023-04-21 01:40:54,560 epoch 5 - iter 63/79 - loss 1.34430379 - time (sec): 15.69 - samples/sec: 4905.28 - lr: 0.050000\n",
            "2023-04-21 01:40:56,005 epoch 5 - iter 70/79 - loss 1.34116476 - time (sec): 17.14 - samples/sec: 4941.82 - lr: 0.050000\n",
            "2023-04-21 01:40:57,652 epoch 5 - iter 77/79 - loss 1.33204488 - time (sec): 18.78 - samples/sec: 4930.14 - lr: 0.050000\n",
            "2023-04-21 01:40:58,128 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:40:58,130 EPOCH 5 done: loss 1.3271 - lr 0.050000\n"
          ]
        },
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            "2023-04-21 01:41:19,525 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
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            "\n"
          ]
        },
        {
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          "text": [
            "2023-04-21 01:41:21,724 TRAIN : loss 1.1516971588134766 - f1-score (weighted avg)  0.0945\n"
          ]
        },
        {
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          "text": [
            "2023-04-21 01:41:24,534 Evaluating as a multi-label problem: False\n",
            "2023-04-21 01:41:24,602 DEV : loss 1.154456377029419 - f1-score (weighted avg)  0.0945\n",
            "2023-04-21 01:41:24,612 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 01:41:24,616 saving best model\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 01:41:26,692 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:41:28,215 epoch 6 - iter 7/79 - loss 1.36199883 - time (sec): 1.52 - samples/sec: 5096.18 - lr: 0.050000\n",
            "2023-04-21 01:41:29,927 epoch 6 - iter 14/79 - loss 1.34050180 - time (sec): 3.23 - samples/sec: 5142.03 - lr: 0.050000\n",
            "2023-04-21 01:41:31,726 epoch 6 - iter 21/79 - loss 1.27342608 - time (sec): 5.03 - samples/sec: 5235.01 - lr: 0.050000\n",
            "2023-04-21 01:41:33,452 epoch 6 - iter 28/79 - loss 1.26511165 - time (sec): 6.76 - samples/sec: 5208.46 - lr: 0.050000\n",
            "2023-04-21 01:41:35,328 epoch 6 - iter 35/79 - loss 1.24188278 - time (sec): 8.63 - samples/sec: 5047.17 - lr: 0.050000\n",
            "2023-04-21 01:41:37,532 epoch 6 - iter 42/79 - loss 1.20265371 - time (sec): 10.84 - samples/sec: 4898.51 - lr: 0.050000\n",
            "2023-04-21 01:41:39,225 epoch 6 - iter 49/79 - loss 1.17908861 - time (sec): 12.53 - samples/sec: 4880.09 - lr: 0.050000\n",
            "2023-04-21 01:41:40,849 epoch 6 - iter 56/79 - loss 1.16093649 - time (sec): 14.15 - samples/sec: 4800.09 - lr: 0.050000\n",
            "2023-04-21 01:41:42,835 epoch 6 - iter 63/79 - loss 1.17236619 - time (sec): 16.14 - samples/sec: 4710.46 - lr: 0.050000\n",
            "2023-04-21 01:41:44,649 epoch 6 - iter 70/79 - loss 1.15419452 - time (sec): 17.95 - samples/sec: 4717.81 - lr: 0.050000\n",
            "2023-04-21 01:41:46,228 epoch 6 - iter 77/79 - loss 1.14454721 - time (sec): 19.53 - samples/sec: 4749.60 - lr: 0.050000\n",
            "2023-04-21 01:41:46,593 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:41:46,594 EPOCH 6 done: loss 1.1407 - lr 0.050000\n"
          ]
        },
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          "text": [
            "2023-04-21 01:42:08,002 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
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            "\n"
          ]
        },
        {
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          "text": [
            "2023-04-21 01:42:11,494 TRAIN : loss 0.8717836141586304 - f1-score (weighted avg)  0.1639\n"
          ]
        },
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          "text": [
            "2023-04-21 01:42:14,341 Evaluating as a multi-label problem: False\n",
            "2023-04-21 01:42:14,408 DEV : loss 0.9019215106964111 - f1-score (weighted avg)  0.1771\n",
            "2023-04-21 01:42:14,418 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 01:42:14,423 saving best model\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 01:42:16,506 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:42:18,059 epoch 7 - iter 7/79 - loss 1.39554461 - time (sec): 1.54 - samples/sec: 5695.56 - lr: 0.050000\n",
            "2023-04-21 01:42:19,736 epoch 7 - iter 14/79 - loss 1.27425625 - time (sec): 3.22 - samples/sec: 5325.55 - lr: 0.050000\n",
            "2023-04-21 01:42:21,527 epoch 7 - iter 21/79 - loss 1.17008782 - time (sec): 5.01 - samples/sec: 5090.25 - lr: 0.050000\n",
            "2023-04-21 01:42:23,552 epoch 7 - iter 28/79 - loss 1.21190576 - time (sec): 7.03 - samples/sec: 5100.97 - lr: 0.050000\n",
            "2023-04-21 01:42:25,231 epoch 7 - iter 35/79 - loss 1.23173015 - time (sec): 8.71 - samples/sec: 5140.94 - lr: 0.050000\n",
            "2023-04-21 01:42:26,896 epoch 7 - iter 42/79 - loss 1.21721352 - time (sec): 10.38 - samples/sec: 5078.06 - lr: 0.050000\n",
            "2023-04-21 01:42:28,409 epoch 7 - iter 49/79 - loss 1.24819598 - time (sec): 11.89 - samples/sec: 5063.14 - lr: 0.050000\n",
            "2023-04-21 01:42:30,362 epoch 7 - iter 56/79 - loss 1.24749863 - time (sec): 13.84 - samples/sec: 4968.55 - lr: 0.050000\n",
            "2023-04-21 01:42:31,945 epoch 7 - iter 63/79 - loss 1.24336973 - time (sec): 15.42 - samples/sec: 4927.54 - lr: 0.050000\n",
            "2023-04-21 01:42:33,744 epoch 7 - iter 70/79 - loss 1.22879091 - time (sec): 17.22 - samples/sec: 4854.08 - lr: 0.050000\n",
            "2023-04-21 01:42:35,803 epoch 7 - iter 77/79 - loss 1.20316355 - time (sec): 19.28 - samples/sec: 4784.80 - lr: 0.050000\n",
            "2023-04-21 01:42:36,288 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:42:36,289 EPOCH 7 done: loss 1.1960 - lr 0.050000\n"
          ]
        },
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          "text": [
            "2023-04-21 01:42:57,716 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
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          "output_type": "stream",
          "text": [
            "2023-04-21 01:42:59,955 TRAIN : loss 0.6627253890037537 - f1-score (weighted avg)  0.3197\n"
          ]
        },
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          "output_type": "stream",
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          "text": [
            "2023-04-21 01:43:02,832 Evaluating as a multi-label problem: False\n",
            "2023-04-21 01:43:02,897 DEV : loss 0.7184054255485535 - f1-score (weighted avg)  0.313\n",
            "2023-04-21 01:43:02,913 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 01:43:02,918 saving best model\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 01:43:04,992 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:43:06,423 epoch 8 - iter 7/79 - loss 1.29250986 - time (sec): 1.41 - samples/sec: 5227.76 - lr: 0.050000\n",
            "2023-04-21 01:43:08,409 epoch 8 - iter 14/79 - loss 1.24608116 - time (sec): 3.40 - samples/sec: 4973.91 - lr: 0.050000\n",
            "2023-04-21 01:43:09,955 epoch 8 - iter 21/79 - loss 1.25182123 - time (sec): 4.95 - samples/sec: 5053.05 - lr: 0.050000\n",
            "2023-04-21 01:43:11,493 epoch 8 - iter 28/79 - loss 1.21233272 - time (sec): 6.48 - samples/sec: 5084.84 - lr: 0.050000\n",
            "2023-04-21 01:43:13,539 epoch 8 - iter 35/79 - loss 1.15628299 - time (sec): 8.53 - samples/sec: 4919.98 - lr: 0.050000\n",
            "2023-04-21 01:43:14,970 epoch 8 - iter 42/79 - loss 1.17207685 - time (sec): 9.96 - samples/sec: 4880.01 - lr: 0.050000\n",
            "2023-04-21 01:43:16,902 epoch 8 - iter 49/79 - loss 1.15251194 - time (sec): 11.89 - samples/sec: 4800.17 - lr: 0.050000\n",
            "2023-04-21 01:43:18,662 epoch 8 - iter 56/79 - loss 1.10182703 - time (sec): 13.65 - samples/sec: 4798.81 - lr: 0.050000\n",
            "2023-04-21 01:43:20,332 epoch 8 - iter 63/79 - loss 1.09499766 - time (sec): 15.32 - samples/sec: 4829.32 - lr: 0.050000\n",
            "2023-04-21 01:43:22,014 epoch 8 - iter 70/79 - loss 1.10058970 - time (sec): 17.01 - samples/sec: 4870.46 - lr: 0.050000\n",
            "2023-04-21 01:43:24,365 epoch 8 - iter 77/79 - loss 1.09770785 - time (sec): 19.36 - samples/sec: 4790.72 - lr: 0.050000\n",
            "2023-04-21 01:43:24,780 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:43:24,782 EPOCH 8 done: loss 1.1050 - lr 0.050000\n"
          ]
        },
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          "text": [
            "2023-04-21 01:43:48,056 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
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          "output_type": "stream",
          "text": [
            "2023-04-21 01:43:51,795 TRAIN : loss 1.5408475399017334 - f1-score (weighted avg)  0.0843\n"
          ]
        },
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          "output_type": "stream",
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          "output_type": "stream",
          "text": [
            "2023-04-21 01:43:54,673 Evaluating as a multi-label problem: False\n",
            "2023-04-21 01:43:54,746 DEV : loss 1.6126140356063843 - f1-score (weighted avg)  0.0731\n",
            "2023-04-21 01:43:54,755 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 01:43:54,759 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
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            "\n"
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        {
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          "text": [
            "2023-04-21 01:43:56,472 epoch 9 - iter 7/79 - loss 1.45135394 - time (sec): 1.71 - samples/sec: 4515.23 - lr: 0.050000\n",
            "2023-04-21 01:43:58,106 epoch 9 - iter 14/79 - loss 1.23748734 - time (sec): 3.34 - samples/sec: 4761.79 - lr: 0.050000\n",
            "2023-04-21 01:44:00,328 epoch 9 - iter 21/79 - loss 1.34387326 - time (sec): 5.57 - samples/sec: 4599.77 - lr: 0.050000\n",
            "2023-04-21 01:44:02,335 epoch 9 - iter 28/79 - loss 1.39399307 - time (sec): 7.57 - samples/sec: 4529.70 - lr: 0.050000\n",
            "2023-04-21 01:44:04,015 epoch 9 - iter 35/79 - loss 1.35922565 - time (sec): 9.25 - samples/sec: 4561.84 - lr: 0.050000\n",
            "2023-04-21 01:44:05,512 epoch 9 - iter 42/79 - loss 1.37583334 - time (sec): 10.75 - samples/sec: 4613.42 - lr: 0.050000\n",
            "2023-04-21 01:44:07,317 epoch 9 - iter 49/79 - loss 1.36466593 - time (sec): 12.56 - samples/sec: 4645.50 - lr: 0.050000\n",
            "2023-04-21 01:44:08,922 epoch 9 - iter 56/79 - loss 1.33947834 - time (sec): 14.16 - samples/sec: 4728.78 - lr: 0.050000\n",
            "2023-04-21 01:44:10,548 epoch 9 - iter 63/79 - loss 1.32491563 - time (sec): 15.79 - samples/sec: 4699.84 - lr: 0.050000\n",
            "2023-04-21 01:44:12,267 epoch 9 - iter 70/79 - loss 1.32051069 - time (sec): 17.51 - samples/sec: 4736.55 - lr: 0.050000\n",
            "2023-04-21 01:44:14,198 epoch 9 - iter 77/79 - loss 1.29249957 - time (sec): 19.44 - samples/sec: 4741.23 - lr: 0.050000\n",
            "2023-04-21 01:44:14,705 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:44:14,706 EPOCH 9 done: loss 1.2903 - lr 0.050000\n"
          ]
        },
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          "output_type": "stream",
          "text": [
            "2023-04-21 01:44:36,218 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
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          "text": [
            "2023-04-21 01:44:38,500 TRAIN : loss 0.7558526396751404 - f1-score (weighted avg)  0.2692\n"
          ]
        },
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          "output_type": "stream",
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          "output_type": "stream",
          "text": [
            "2023-04-21 01:44:41,295 Evaluating as a multi-label problem: False\n",
            "2023-04-21 01:44:41,359 DEV : loss 0.7895233035087585 - f1-score (weighted avg)  0.2816\n",
            "2023-04-21 01:44:41,369 Epoch     9: reducing learning rate of group 0 to 2.5000e-02.\n",
            "2023-04-21 01:44:41,371 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 01:44:41,375 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 01:44:43,009 epoch 10 - iter 7/79 - loss 1.02457327 - time (sec): 1.63 - samples/sec: 4994.73 - lr: 0.025000\n",
            "2023-04-21 01:44:44,687 epoch 10 - iter 14/79 - loss 1.01007959 - time (sec): 3.31 - samples/sec: 4919.21 - lr: 0.025000\n",
            "2023-04-21 01:44:46,598 epoch 10 - iter 21/79 - loss 1.01769870 - time (sec): 5.22 - samples/sec: 4810.74 - lr: 0.025000\n",
            "2023-04-21 01:44:48,305 epoch 10 - iter 28/79 - loss 1.02237230 - time (sec): 6.93 - samples/sec: 4802.17 - lr: 0.025000\n",
            "2023-04-21 01:44:50,039 epoch 10 - iter 35/79 - loss 0.99598757 - time (sec): 8.66 - samples/sec: 4858.21 - lr: 0.025000\n",
            "2023-04-21 01:44:51,543 epoch 10 - iter 42/79 - loss 0.97670677 - time (sec): 10.17 - samples/sec: 4854.89 - lr: 0.025000\n",
            "2023-04-21 01:44:53,149 epoch 10 - iter 49/79 - loss 0.94800114 - time (sec): 11.77 - samples/sec: 4888.82 - lr: 0.025000\n",
            "2023-04-21 01:44:55,502 epoch 10 - iter 56/79 - loss 0.91869466 - time (sec): 14.13 - samples/sec: 4750.37 - lr: 0.025000\n",
            "2023-04-21 01:44:57,115 epoch 10 - iter 63/79 - loss 0.90820196 - time (sec): 15.74 - samples/sec: 4800.68 - lr: 0.025000\n",
            "2023-04-21 01:44:58,984 epoch 10 - iter 70/79 - loss 0.90886372 - time (sec): 17.61 - samples/sec: 4765.00 - lr: 0.025000\n",
            "2023-04-21 01:45:00,908 epoch 10 - iter 77/79 - loss 0.89865267 - time (sec): 19.53 - samples/sec: 4715.69 - lr: 0.025000\n",
            "2023-04-21 01:45:01,435 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:45:01,437 EPOCH 10 done: loss 0.8977 - lr 0.025000\n"
          ]
        },
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          "output_type": "stream",
          "text": [
            "2023-04-21 01:45:22,857 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
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          "output_type": "stream",
          "text": [
            "2023-04-21 01:45:26,388 TRAIN : loss 0.6128535270690918 - f1-score (weighted avg)  0.2919\n"
          ]
        },
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        },
        {
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          "output_type": "stream",
          "text": [
            "2023-04-21 01:45:29,169 Evaluating as a multi-label problem: False\n",
            "2023-04-21 01:45:29,236 DEV : loss 0.6277104020118713 - f1-score (weighted avg)  0.3098\n",
            "2023-04-21 01:45:29,245 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 01:45:29,249 saving best model\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
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          "text": [
            "2023-04-21 01:45:31,348 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:45:33,563 epoch 11 - iter 7/79 - loss 0.65287012 - time (sec): 2.21 - samples/sec: 3626.53 - lr: 0.025000\n",
            "2023-04-21 01:45:35,362 epoch 11 - iter 14/79 - loss 0.69423417 - time (sec): 4.01 - samples/sec: 4092.96 - lr: 0.025000\n",
            "2023-04-21 01:45:36,970 epoch 11 - iter 21/79 - loss 0.71226878 - time (sec): 5.62 - samples/sec: 4463.24 - lr: 0.025000\n",
            "2023-04-21 01:45:38,639 epoch 11 - iter 28/79 - loss 0.70253258 - time (sec): 7.29 - samples/sec: 4518.93 - lr: 0.025000\n",
            "2023-04-21 01:45:40,129 epoch 11 - iter 35/79 - loss 0.70208274 - time (sec): 8.78 - samples/sec: 4552.98 - lr: 0.025000\n",
            "2023-04-21 01:45:41,752 epoch 11 - iter 42/79 - loss 0.69937471 - time (sec): 10.40 - samples/sec: 4620.51 - lr: 0.025000\n",
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            "2023-04-21 01:45:47,419 epoch 11 - iter 63/79 - loss 0.68773485 - time (sec): 16.07 - samples/sec: 4725.31 - lr: 0.025000\n",
            "2023-04-21 01:45:49,205 epoch 11 - iter 70/79 - loss 0.69095453 - time (sec): 17.86 - samples/sec: 4681.93 - lr: 0.025000\n",
            "2023-04-21 01:45:50,903 epoch 11 - iter 77/79 - loss 0.69382907 - time (sec): 19.55 - samples/sec: 4713.48 - lr: 0.025000\n",
            "2023-04-21 01:45:51,427 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:45:51,429 EPOCH 11 done: loss 0.7028 - lr 0.025000\n"
          ]
        },
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            "2023-04-21 01:46:12,682 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
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            "\n"
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        {
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          "text": [
            "2023-04-21 01:46:14,944 TRAIN : loss 0.8146184086799622 - f1-score (weighted avg)  0.0879\n"
          ]
        },
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            "2023-04-21 01:46:17,795 Evaluating as a multi-label problem: False\n",
            "2023-04-21 01:46:17,859 DEV : loss 0.8435848951339722 - f1-score (weighted avg)  0.0906\n",
            "2023-04-21 01:46:17,868 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 01:46:17,873 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
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          "output_type": "stream",
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            "2023-04-21 01:46:19,322 epoch 12 - iter 7/79 - loss 0.74544615 - time (sec): 1.45 - samples/sec: 5357.14 - lr: 0.025000\n",
            "2023-04-21 01:46:21,075 epoch 12 - iter 14/79 - loss 0.71992943 - time (sec): 3.20 - samples/sec: 5252.92 - lr: 0.025000\n",
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            "2023-04-21 01:46:24,555 epoch 12 - iter 28/79 - loss 0.71905591 - time (sec): 6.68 - samples/sec: 5009.71 - lr: 0.025000\n",
            "2023-04-21 01:46:26,265 epoch 12 - iter 35/79 - loss 0.71132600 - time (sec): 8.39 - samples/sec: 4981.32 - lr: 0.025000\n",
            "2023-04-21 01:46:28,023 epoch 12 - iter 42/79 - loss 0.69332719 - time (sec): 10.15 - samples/sec: 4897.37 - lr: 0.025000\n",
            "2023-04-21 01:46:30,396 epoch 12 - iter 49/79 - loss 0.67508285 - time (sec): 12.52 - samples/sec: 4741.90 - lr: 0.025000\n",
            "2023-04-21 01:46:32,067 epoch 12 - iter 56/79 - loss 0.67705970 - time (sec): 14.19 - samples/sec: 4751.34 - lr: 0.025000\n",
            "2023-04-21 01:46:33,587 epoch 12 - iter 63/79 - loss 0.67614729 - time (sec): 15.71 - samples/sec: 4817.23 - lr: 0.025000\n",
            "2023-04-21 01:46:35,581 epoch 12 - iter 70/79 - loss 0.66323994 - time (sec): 17.71 - samples/sec: 4785.40 - lr: 0.025000\n",
            "2023-04-21 01:46:37,151 epoch 12 - iter 77/79 - loss 0.66756235 - time (sec): 19.28 - samples/sec: 4787.35 - lr: 0.025000\n",
            "2023-04-21 01:46:37,580 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:46:37,581 EPOCH 12 done: loss 0.6684 - lr 0.025000\n"
          ]
        },
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            "2023-04-21 01:47:00,192 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
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            "\n"
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        {
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          "text": [
            "2023-04-21 01:47:02,475 TRAIN : loss 0.4492488205432892 - f1-score (weighted avg)  0.3303\n"
          ]
        },
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          "text": [
            "2023-04-21 01:47:05,255 Evaluating as a multi-label problem: False\n",
            "2023-04-21 01:47:05,324 DEV : loss 0.49424412846565247 - f1-score (weighted avg)  0.3193\n",
            "2023-04-21 01:47:05,334 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 01:47:05,338 saving best model\n"
          ]
        },
        {
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            "2023-04-21 01:47:07,431 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:47:09,144 epoch 13 - iter 7/79 - loss 0.49939300 - time (sec): 1.71 - samples/sec: 5177.06 - lr: 0.025000\n",
            "2023-04-21 01:47:10,774 epoch 13 - iter 14/79 - loss 0.63809966 - time (sec): 3.34 - samples/sec: 5176.36 - lr: 0.025000\n",
            "2023-04-21 01:47:12,724 epoch 13 - iter 21/79 - loss 0.70484954 - time (sec): 5.29 - samples/sec: 4798.37 - lr: 0.025000\n",
            "2023-04-21 01:47:14,354 epoch 13 - iter 28/79 - loss 0.70807008 - time (sec): 6.92 - samples/sec: 4813.43 - lr: 0.025000\n",
            "2023-04-21 01:47:16,050 epoch 13 - iter 35/79 - loss 0.72775805 - time (sec): 8.62 - samples/sec: 4729.12 - lr: 0.025000\n",
            "2023-04-21 01:47:17,656 epoch 13 - iter 42/79 - loss 0.72262142 - time (sec): 10.22 - samples/sec: 4662.58 - lr: 0.025000\n",
            "2023-04-21 01:47:19,532 epoch 13 - iter 49/79 - loss 0.71798235 - time (sec): 12.10 - samples/sec: 4706.78 - lr: 0.025000\n",
            "2023-04-21 01:47:21,575 epoch 13 - iter 56/79 - loss 0.71925208 - time (sec): 14.14 - samples/sec: 4744.95 - lr: 0.025000\n",
            "2023-04-21 01:47:23,460 epoch 13 - iter 63/79 - loss 0.70302050 - time (sec): 16.03 - samples/sec: 4781.35 - lr: 0.025000\n",
            "2023-04-21 01:47:25,199 epoch 13 - iter 70/79 - loss 0.69025178 - time (sec): 17.77 - samples/sec: 4771.08 - lr: 0.025000\n",
            "2023-04-21 01:47:26,852 epoch 13 - iter 77/79 - loss 0.67966541 - time (sec): 19.42 - samples/sec: 4785.66 - lr: 0.025000\n",
            "2023-04-21 01:47:27,221 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:47:27,223 EPOCH 13 done: loss 0.6767 - lr 0.025000\n"
          ]
        },
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            "2023-04-21 01:47:48,631 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
          ]
        },
        {
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          "text": [
            "2023-04-21 01:47:52,204 TRAIN : loss 0.5255668759346008 - f1-score (weighted avg)  0.2774\n"
          ]
        },
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          "text": [
            "2023-04-21 01:47:55,020 Evaluating as a multi-label problem: False\n",
            "2023-04-21 01:47:55,088 DEV : loss 0.5515778064727783 - f1-score (weighted avg)  0.2925\n",
            "2023-04-21 01:47:55,097 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 01:47:55,102 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 01:47:56,581 epoch 14 - iter 7/79 - loss 0.56865980 - time (sec): 1.48 - samples/sec: 5469.14 - lr: 0.025000\n",
            "2023-04-21 01:47:58,131 epoch 14 - iter 14/79 - loss 0.60008032 - time (sec): 3.03 - samples/sec: 5254.24 - lr: 0.025000\n",
            "2023-04-21 01:47:59,473 epoch 14 - iter 21/79 - loss 0.60682512 - time (sec): 4.37 - samples/sec: 5321.81 - lr: 0.025000\n",
            "2023-04-21 01:48:01,284 epoch 14 - iter 28/79 - loss 0.60684850 - time (sec): 6.18 - samples/sec: 5187.46 - lr: 0.025000\n",
            "2023-04-21 01:48:02,980 epoch 14 - iter 35/79 - loss 0.61899182 - time (sec): 7.88 - samples/sec: 5172.20 - lr: 0.025000\n",
            "2023-04-21 01:48:04,858 epoch 14 - iter 42/79 - loss 0.63386891 - time (sec): 9.76 - samples/sec: 5125.26 - lr: 0.025000\n",
            "2023-04-21 01:48:06,534 epoch 14 - iter 49/79 - loss 0.62446266 - time (sec): 11.43 - samples/sec: 5130.63 - lr: 0.025000\n",
            "2023-04-21 01:48:08,535 epoch 14 - iter 56/79 - loss 0.62048188 - time (sec): 13.43 - samples/sec: 4971.84 - lr: 0.025000\n",
            "2023-04-21 01:48:10,075 epoch 14 - iter 63/79 - loss 0.62714696 - time (sec): 14.97 - samples/sec: 5013.28 - lr: 0.025000\n",
            "2023-04-21 01:48:12,724 epoch 14 - iter 70/79 - loss 0.63214505 - time (sec): 17.62 - samples/sec: 4792.60 - lr: 0.025000\n",
            "2023-04-21 01:48:14,394 epoch 14 - iter 77/79 - loss 0.63926769 - time (sec): 19.29 - samples/sec: 4811.37 - lr: 0.025000\n",
            "2023-04-21 01:48:14,746 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:48:14,748 EPOCH 14 done: loss 0.6398 - lr 0.025000\n"
          ]
        },
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          "text": [
            "2023-04-21 01:48:36,160 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
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            "\n"
          ]
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        {
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          "text": [
            "2023-04-21 01:48:38,448 TRAIN : loss 0.45173633098602295 - f1-score (weighted avg)  0.3171\n"
          ]
        },
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          "text": [
            "2023-04-21 01:48:41,281 Evaluating as a multi-label problem: False\n",
            "2023-04-21 01:48:41,348 DEV : loss 0.45647749304771423 - f1-score (weighted avg)  0.3354\n",
            "2023-04-21 01:48:41,357 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 01:48:41,362 saving best model\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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            "2023-04-21 01:48:43,466 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:48:45,298 epoch 15 - iter 7/79 - loss 0.58574968 - time (sec): 1.83 - samples/sec: 4784.74 - lr: 0.025000\n",
            "2023-04-21 01:48:46,890 epoch 15 - iter 14/79 - loss 0.59432464 - time (sec): 3.42 - samples/sec: 4890.95 - lr: 0.025000\n",
            "2023-04-21 01:48:48,762 epoch 15 - iter 21/79 - loss 0.59757923 - time (sec): 5.29 - samples/sec: 4809.18 - lr: 0.025000\n",
            "2023-04-21 01:48:50,545 epoch 15 - iter 28/79 - loss 0.60003284 - time (sec): 7.08 - samples/sec: 4814.88 - lr: 0.025000\n",
            "2023-04-21 01:48:52,607 epoch 15 - iter 35/79 - loss 0.60942705 - time (sec): 9.14 - samples/sec: 4668.52 - lr: 0.025000\n",
            "2023-04-21 01:48:54,595 epoch 15 - iter 42/79 - loss 0.61856047 - time (sec): 11.13 - samples/sec: 4631.35 - lr: 0.025000\n",
            "2023-04-21 01:48:56,438 epoch 15 - iter 49/79 - loss 0.62211663 - time (sec): 12.97 - samples/sec: 4590.90 - lr: 0.025000\n",
            "2023-04-21 01:48:57,878 epoch 15 - iter 56/79 - loss 0.63160633 - time (sec): 14.41 - samples/sec: 4650.07 - lr: 0.025000\n",
            "2023-04-21 01:48:59,314 epoch 15 - iter 63/79 - loss 0.63108467 - time (sec): 15.85 - samples/sec: 4760.26 - lr: 0.025000\n",
            "2023-04-21 01:49:01,270 epoch 15 - iter 70/79 - loss 0.64438144 - time (sec): 17.80 - samples/sec: 4719.29 - lr: 0.025000\n",
            "2023-04-21 01:49:03,283 epoch 15 - iter 77/79 - loss 0.63713241 - time (sec): 19.81 - samples/sec: 4656.59 - lr: 0.025000\n",
            "2023-04-21 01:49:03,793 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:49:03,794 EPOCH 15 done: loss 0.6338 - lr 0.025000\n"
          ]
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          "text": [
            "2023-04-21 01:49:26,430 Evaluating as a multi-label problem: True\n",
            "2023-04-21 01:49:28,722 TRAIN : loss 0.44462481141090393 - f1-score (weighted avg)  0.3421\n"
          ]
        },
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          "text": [
            "2023-04-21 01:49:31,545 Evaluating as a multi-label problem: False\n",
            "2023-04-21 01:49:31,618 DEV : loss 0.45877110958099365 - f1-score (weighted avg)  0.3368\n",
            "2023-04-21 01:49:31,628 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 01:49:31,633 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
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          "output_type": "stream",
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          "text": [
            "2023-04-21 01:49:33,098 epoch 16 - iter 7/79 - loss 0.59559626 - time (sec): 1.46 - samples/sec: 5283.56 - lr: 0.025000\n",
            "2023-04-21 01:49:34,514 epoch 16 - iter 14/79 - loss 0.61984577 - time (sec): 2.88 - samples/sec: 5299.71 - lr: 0.025000\n",
            "2023-04-21 01:49:36,888 epoch 16 - iter 21/79 - loss 0.63568487 - time (sec): 5.25 - samples/sec: 4810.33 - lr: 0.025000\n",
            "2023-04-21 01:49:38,653 epoch 16 - iter 28/79 - loss 0.63096337 - time (sec): 7.02 - samples/sec: 4706.98 - lr: 0.025000\n",
            "2023-04-21 01:49:40,437 epoch 16 - iter 35/79 - loss 0.63272144 - time (sec): 8.80 - samples/sec: 4790.73 - lr: 0.025000\n",
            "2023-04-21 01:49:42,246 epoch 16 - iter 42/79 - loss 0.64439573 - time (sec): 10.61 - samples/sec: 4791.60 - lr: 0.025000\n",
            "2023-04-21 01:49:43,948 epoch 16 - iter 49/79 - loss 0.64637692 - time (sec): 12.31 - samples/sec: 4796.15 - lr: 0.025000\n",
            "2023-04-21 01:49:46,116 epoch 16 - iter 56/79 - loss 0.64288005 - time (sec): 14.48 - samples/sec: 4690.06 - lr: 0.025000\n",
            "2023-04-21 01:49:47,700 epoch 16 - iter 63/79 - loss 0.64244004 - time (sec): 16.07 - samples/sec: 4734.94 - lr: 0.025000\n",
            "2023-04-21 01:49:49,386 epoch 16 - iter 70/79 - loss 0.63799214 - time (sec): 17.75 - samples/sec: 4798.50 - lr: 0.025000\n",
            "2023-04-21 01:49:50,893 epoch 16 - iter 77/79 - loss 0.62986899 - time (sec): 19.26 - samples/sec: 4814.64 - lr: 0.025000\n",
            "2023-04-21 01:49:51,234 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:49:51,236 EPOCH 16 done: loss 0.6311 - lr 0.025000\n"
          ]
        },
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          "text": [
            "2023-04-21 01:50:12,675 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
          ]
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        {
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          "text": [
            "2023-04-21 01:50:14,954 TRAIN : loss 0.631736159324646 - f1-score (weighted avg)  0.1055\n"
          ]
        },
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          "text": [
            "2023-04-21 01:50:17,759 Evaluating as a multi-label problem: False\n",
            "2023-04-21 01:50:17,825 DEV : loss 0.6615258455276489 - f1-score (weighted avg)  0.1243\n",
            "2023-04-21 01:50:17,834 Epoch    16: reducing learning rate of group 0 to 1.2500e-02.\n",
            "2023-04-21 01:50:17,836 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 01:50:17,850 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
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          "output_type": "stream",
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          "text": [
            "2023-04-21 01:50:19,467 epoch 17 - iter 7/79 - loss 0.59108344 - time (sec): 1.61 - samples/sec: 5312.04 - lr: 0.012500\n",
            "2023-04-21 01:50:21,057 epoch 17 - iter 14/79 - loss 0.59307355 - time (sec): 3.20 - samples/sec: 5301.52 - lr: 0.012500\n",
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            "2023-04-21 01:50:26,711 epoch 17 - iter 35/79 - loss 0.59269831 - time (sec): 8.86 - samples/sec: 4908.27 - lr: 0.012500\n",
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            "2023-04-21 01:50:31,645 epoch 17 - iter 56/79 - loss 0.55866121 - time (sec): 13.79 - samples/sec: 4823.62 - lr: 0.012500\n",
            "2023-04-21 01:50:33,361 epoch 17 - iter 63/79 - loss 0.55211619 - time (sec): 15.51 - samples/sec: 4850.61 - lr: 0.012500\n",
            "2023-04-21 01:50:35,153 epoch 17 - iter 70/79 - loss 0.54844920 - time (sec): 17.30 - samples/sec: 4867.33 - lr: 0.012500\n",
            "2023-04-21 01:50:37,083 epoch 17 - iter 77/79 - loss 0.54429074 - time (sec): 19.23 - samples/sec: 4820.89 - lr: 0.012500\n",
            "2023-04-21 01:50:37,524 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:50:37,526 EPOCH 17 done: loss 0.5432 - lr 0.012500\n"
          ]
        },
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            "2023-04-21 01:51:00,081 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
          ]
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        {
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          "text": [
            "2023-04-21 01:51:02,359 TRAIN : loss 0.38410213589668274 - f1-score (weighted avg)  0.3283\n"
          ]
        },
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          "text": [
            "2023-04-21 01:51:05,186 Evaluating as a multi-label problem: False\n",
            "2023-04-21 01:51:05,255 DEV : loss 0.4051736891269684 - f1-score (weighted avg)  0.3394\n",
            "2023-04-21 01:51:05,265 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 01:51:05,269 saving best model\n"
          ]
        },
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            "2023-04-21 01:51:07,380 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:51:08,837 epoch 18 - iter 7/79 - loss 0.52628897 - time (sec): 1.45 - samples/sec: 5176.23 - lr: 0.012500\n",
            "2023-04-21 01:51:11,021 epoch 18 - iter 14/79 - loss 0.51689308 - time (sec): 3.64 - samples/sec: 4282.56 - lr: 0.012500\n",
            "2023-04-21 01:51:12,564 epoch 18 - iter 21/79 - loss 0.52433285 - time (sec): 5.18 - samples/sec: 4646.08 - lr: 0.012500\n",
            "2023-04-21 01:51:14,052 epoch 18 - iter 28/79 - loss 0.53246114 - time (sec): 6.67 - samples/sec: 4770.31 - lr: 0.012500\n",
            "2023-04-21 01:51:15,821 epoch 18 - iter 35/79 - loss 0.54323964 - time (sec): 8.44 - samples/sec: 4794.00 - lr: 0.012500\n",
            "2023-04-21 01:51:17,791 epoch 18 - iter 42/79 - loss 0.54825911 - time (sec): 10.41 - samples/sec: 4754.62 - lr: 0.012500\n",
            "2023-04-21 01:51:20,213 epoch 18 - iter 49/79 - loss 0.53247927 - time (sec): 12.83 - samples/sec: 4621.91 - lr: 0.012500\n",
            "2023-04-21 01:51:21,710 epoch 18 - iter 56/79 - loss 0.52571288 - time (sec): 14.33 - samples/sec: 4717.96 - lr: 0.012500\n",
            "2023-04-21 01:51:23,292 epoch 18 - iter 63/79 - loss 0.52364630 - time (sec): 15.91 - samples/sec: 4779.23 - lr: 0.012500\n",
            "2023-04-21 01:51:24,907 epoch 18 - iter 70/79 - loss 0.51368744 - time (sec): 17.52 - samples/sec: 4796.75 - lr: 0.012500\n",
            "2023-04-21 01:51:26,718 epoch 18 - iter 77/79 - loss 0.51177895 - time (sec): 19.34 - samples/sec: 4800.80 - lr: 0.012500\n",
            "2023-04-21 01:51:27,152 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:51:27,154 EPOCH 18 done: loss 0.5076 - lr 0.012500\n"
          ]
        },
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            "2023-04-21 01:51:48,545 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
          ]
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          "text": [
            "2023-04-21 01:51:52,081 TRAIN : loss 0.3809095323085785 - f1-score (weighted avg)  0.2977\n"
          ]
        },
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          "text": [
            "2023-04-21 01:51:54,889 Evaluating as a multi-label problem: False\n",
            "2023-04-21 01:51:54,960 DEV : loss 0.3844245374202728 - f1-score (weighted avg)  0.3109\n",
            "2023-04-21 01:51:54,969 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 01:51:54,973 saving best model\n"
          ]
        },
        {
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          "output_type": "stream",
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            "2023-04-21 01:51:57,069 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:51:58,826 epoch 19 - iter 7/79 - loss 0.44335154 - time (sec): 1.75 - samples/sec: 4974.02 - lr: 0.012500\n",
            "2023-04-21 01:52:00,287 epoch 19 - iter 14/79 - loss 0.45667725 - time (sec): 3.22 - samples/sec: 5100.35 - lr: 0.012500\n",
            "2023-04-21 01:52:02,152 epoch 19 - iter 21/79 - loss 0.46541431 - time (sec): 5.08 - samples/sec: 4668.43 - lr: 0.012500\n",
            "2023-04-21 01:52:03,797 epoch 19 - iter 28/79 - loss 0.46273756 - time (sec): 6.73 - samples/sec: 4779.02 - lr: 0.012500\n",
            "2023-04-21 01:52:05,667 epoch 19 - iter 35/79 - loss 0.47251472 - time (sec): 8.60 - samples/sec: 4686.32 - lr: 0.012500\n",
            "2023-04-21 01:52:07,521 epoch 19 - iter 42/79 - loss 0.46895600 - time (sec): 10.45 - samples/sec: 4660.97 - lr: 0.012500\n",
            "2023-04-21 01:52:09,057 epoch 19 - iter 49/79 - loss 0.47510507 - time (sec): 11.99 - samples/sec: 4690.76 - lr: 0.012500\n",
            "2023-04-21 01:52:11,608 epoch 19 - iter 56/79 - loss 0.47735245 - time (sec): 14.54 - samples/sec: 4572.59 - lr: 0.012500\n",
            "2023-04-21 01:52:13,390 epoch 19 - iter 63/79 - loss 0.48088022 - time (sec): 16.32 - samples/sec: 4592.01 - lr: 0.012500\n",
            "2023-04-21 01:52:15,050 epoch 19 - iter 70/79 - loss 0.47714995 - time (sec): 17.98 - samples/sec: 4647.32 - lr: 0.012500\n",
            "2023-04-21 01:52:17,053 epoch 19 - iter 77/79 - loss 0.46821159 - time (sec): 19.98 - samples/sec: 4622.98 - lr: 0.012500\n",
            "2023-04-21 01:52:17,374 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:52:17,376 EPOCH 19 done: loss 0.4674 - lr 0.012500\n"
          ]
        },
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            "2023-04-21 01:52:38,796 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
          ]
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        {
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          "text": [
            "2023-04-21 01:52:41,090 TRAIN : loss 0.32065215706825256 - f1-score (weighted avg)  0.3163\n"
          ]
        },
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          "text": [
            "2023-04-21 01:52:43,951 Evaluating as a multi-label problem: False\n",
            "2023-04-21 01:52:44,020 DEV : loss 0.33562713861465454 - f1-score (weighted avg)  0.3071\n",
            "2023-04-21 01:52:44,030 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 01:52:44,034 saving best model\n"
          ]
        },
        {
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          "output_type": "stream",
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            "2023-04-21 01:52:46,147 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:52:47,749 epoch 20 - iter 7/79 - loss 0.47917691 - time (sec): 1.60 - samples/sec: 4844.43 - lr: 0.012500\n",
            "2023-04-21 01:52:49,582 epoch 20 - iter 14/79 - loss 0.49961809 - time (sec): 3.43 - samples/sec: 4870.86 - lr: 0.012500\n",
            "2023-04-21 01:52:51,321 epoch 20 - iter 21/79 - loss 0.48384501 - time (sec): 5.17 - samples/sec: 5013.72 - lr: 0.012500\n",
            "2023-04-21 01:52:53,082 epoch 20 - iter 28/79 - loss 0.48517420 - time (sec): 6.93 - samples/sec: 5002.89 - lr: 0.012500\n",
            "2023-04-21 01:52:54,464 epoch 20 - iter 35/79 - loss 0.48132225 - time (sec): 8.32 - samples/sec: 5071.55 - lr: 0.012500\n",
            "2023-04-21 01:52:56,332 epoch 20 - iter 42/79 - loss 0.47853189 - time (sec): 10.18 - samples/sec: 5036.68 - lr: 0.012500\n",
            "2023-04-21 01:52:58,116 epoch 20 - iter 49/79 - loss 0.47844056 - time (sec): 11.97 - samples/sec: 4998.51 - lr: 0.012500\n",
            "2023-04-21 01:52:59,498 epoch 20 - iter 56/79 - loss 0.48146292 - time (sec): 13.35 - samples/sec: 5041.12 - lr: 0.012500\n",
            "2023-04-21 01:53:01,325 epoch 20 - iter 63/79 - loss 0.47606542 - time (sec): 15.18 - samples/sec: 4982.43 - lr: 0.012500\n",
            "2023-04-21 01:53:03,439 epoch 20 - iter 70/79 - loss 0.47027764 - time (sec): 17.29 - samples/sec: 4867.44 - lr: 0.012500\n",
            "2023-04-21 01:53:05,131 epoch 20 - iter 77/79 - loss 0.47136219 - time (sec): 18.98 - samples/sec: 4862.65 - lr: 0.012500\n",
            "2023-04-21 01:53:05,545 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:53:05,547 EPOCH 20 done: loss 0.4713 - lr 0.012500\n"
          ]
        },
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          "text": [
            "2023-04-21 01:53:28,140 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
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            "\n"
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        {
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          "text": [
            "2023-04-21 01:53:30,467 TRAIN : loss 0.3540274202823639 - f1-score (weighted avg)  0.3176\n"
          ]
        },
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          "text": [
            "2023-04-21 01:53:33,273 Evaluating as a multi-label problem: False\n",
            "2023-04-21 01:53:33,342 DEV : loss 0.3610313832759857 - f1-score (weighted avg)  0.3231\n",
            "2023-04-21 01:53:33,352 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 01:53:33,356 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 01:53:35,175 epoch 21 - iter 7/79 - loss 0.43701777 - time (sec): 1.82 - samples/sec: 4574.66 - lr: 0.012500\n",
            "2023-04-21 01:53:37,000 epoch 21 - iter 14/79 - loss 0.46208891 - time (sec): 3.64 - samples/sec: 4674.92 - lr: 0.012500\n",
            "2023-04-21 01:53:38,705 epoch 21 - iter 21/79 - loss 0.44794048 - time (sec): 5.35 - samples/sec: 4749.44 - lr: 0.012500\n",
            "2023-04-21 01:53:40,295 epoch 21 - iter 28/79 - loss 0.44918776 - time (sec): 6.94 - samples/sec: 4933.27 - lr: 0.012500\n",
            "2023-04-21 01:53:41,905 epoch 21 - iter 35/79 - loss 0.44921540 - time (sec): 8.55 - samples/sec: 4973.22 - lr: 0.012500\n",
            "2023-04-21 01:53:43,482 epoch 21 - iter 42/79 - loss 0.45266293 - time (sec): 10.12 - samples/sec: 4956.10 - lr: 0.012500\n",
            "2023-04-21 01:53:45,127 epoch 21 - iter 49/79 - loss 0.46310382 - time (sec): 11.77 - samples/sec: 4991.74 - lr: 0.012500\n",
            "2023-04-21 01:53:46,902 epoch 21 - iter 56/79 - loss 0.45650510 - time (sec): 13.54 - samples/sec: 4983.22 - lr: 0.012500\n",
            "2023-04-21 01:53:49,217 epoch 21 - iter 63/79 - loss 0.45467764 - time (sec): 15.86 - samples/sec: 4845.00 - lr: 0.012500\n",
            "2023-04-21 01:53:51,032 epoch 21 - iter 70/79 - loss 0.45491352 - time (sec): 17.67 - samples/sec: 4815.54 - lr: 0.012500\n",
            "2023-04-21 01:53:52,563 epoch 21 - iter 77/79 - loss 0.45775644 - time (sec): 19.21 - samples/sec: 4818.10 - lr: 0.012500\n",
            "2023-04-21 01:53:53,081 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:53:53,083 EPOCH 21 done: loss 0.4588 - lr 0.012500\n"
          ]
        },
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          "text": [
            "2023-04-21 01:54:14,466 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
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          "output_type": "stream",
          "text": [
            "\n"
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        {
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          "text": [
            "2023-04-21 01:54:18,038 TRAIN : loss 0.3453005254268646 - f1-score (weighted avg)  0.293\n"
          ]
        },
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          "text": [
            "2023-04-21 01:54:20,829 Evaluating as a multi-label problem: False\n",
            "2023-04-21 01:54:20,896 DEV : loss 0.3505595624446869 - f1-score (weighted avg)  0.2961\n",
            "2023-04-21 01:54:20,907 Epoch    21: reducing learning rate of group 0 to 6.2500e-03.\n",
            "2023-04-21 01:54:20,909 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 01:54:20,912 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
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          "output_type": "stream",
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          "text": [
            "2023-04-21 01:54:22,374 epoch 22 - iter 7/79 - loss 0.44933411 - time (sec): 1.46 - samples/sec: 5497.95 - lr: 0.006250\n",
            "2023-04-21 01:54:24,079 epoch 22 - iter 14/79 - loss 0.45198079 - time (sec): 3.16 - samples/sec: 5139.30 - lr: 0.006250\n",
            "2023-04-21 01:54:25,727 epoch 22 - iter 21/79 - loss 0.46281450 - time (sec): 4.81 - samples/sec: 5032.07 - lr: 0.006250\n",
            "2023-04-21 01:54:27,148 epoch 22 - iter 28/79 - loss 0.46440001 - time (sec): 6.23 - samples/sec: 5139.70 - lr: 0.006250\n",
            "2023-04-21 01:54:29,051 epoch 22 - iter 35/79 - loss 0.45029245 - time (sec): 8.14 - samples/sec: 4931.98 - lr: 0.006250\n",
            "2023-04-21 01:54:30,560 epoch 22 - iter 42/79 - loss 0.44025362 - time (sec): 9.65 - samples/sec: 5026.35 - lr: 0.006250\n",
            "2023-04-21 01:54:32,730 epoch 22 - iter 49/79 - loss 0.42987162 - time (sec): 11.82 - samples/sec: 5021.91 - lr: 0.006250\n",
            "2023-04-21 01:54:34,071 epoch 22 - iter 56/79 - loss 0.43803604 - time (sec): 13.16 - samples/sec: 5041.62 - lr: 0.006250\n",
            "2023-04-21 01:54:36,222 epoch 22 - iter 63/79 - loss 0.43633383 - time (sec): 15.31 - samples/sec: 4890.61 - lr: 0.006250\n",
            "2023-04-21 01:54:38,059 epoch 22 - iter 70/79 - loss 0.43545431 - time (sec): 17.14 - samples/sec: 4893.14 - lr: 0.006250\n",
            "2023-04-21 01:54:39,714 epoch 22 - iter 77/79 - loss 0.42797353 - time (sec): 18.80 - samples/sec: 4898.01 - lr: 0.006250\n",
            "2023-04-21 01:54:40,204 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:54:40,206 EPOCH 22 done: loss 0.4278 - lr 0.006250\n"
          ]
        },
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          "text": [
            "2023-04-21 01:55:01,573 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
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        {
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          "text": [
            "2023-04-21 01:55:03,867 TRAIN : loss 0.31101834774017334 - f1-score (weighted avg)  0.3343\n"
          ]
        },
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          "text": [
            "2023-04-21 01:55:06,739 Evaluating as a multi-label problem: False\n",
            "2023-04-21 01:55:06,808 DEV : loss 0.32398301362991333 - f1-score (weighted avg)  0.3158\n",
            "2023-04-21 01:55:06,820 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 01:55:06,824 saving best model\n"
          ]
        },
        {
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          "output_type": "stream",
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          "text": [
            "2023-04-21 01:55:08,896 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:55:10,545 epoch 23 - iter 7/79 - loss 0.40767466 - time (sec): 1.65 - samples/sec: 4815.04 - lr: 0.006250\n",
            "2023-04-21 01:55:12,199 epoch 23 - iter 14/79 - loss 0.41630058 - time (sec): 3.30 - samples/sec: 5124.36 - lr: 0.006250\n",
            "2023-04-21 01:55:14,008 epoch 23 - iter 21/79 - loss 0.40522323 - time (sec): 5.11 - samples/sec: 4810.47 - lr: 0.006250\n",
            "2023-04-21 01:55:16,024 epoch 23 - iter 28/79 - loss 0.40691391 - time (sec): 7.13 - samples/sec: 4536.25 - lr: 0.006250\n",
            "2023-04-21 01:55:18,417 epoch 23 - iter 35/79 - loss 0.39510581 - time (sec): 9.52 - samples/sec: 4458.49 - lr: 0.006250\n",
            "2023-04-21 01:55:19,960 epoch 23 - iter 42/79 - loss 0.39058298 - time (sec): 11.06 - samples/sec: 4592.89 - lr: 0.006250\n",
            "2023-04-21 01:55:21,750 epoch 23 - iter 49/79 - loss 0.39471505 - time (sec): 12.85 - samples/sec: 4645.61 - lr: 0.006250\n",
            "2023-04-21 01:55:23,354 epoch 23 - iter 56/79 - loss 0.39512796 - time (sec): 14.46 - samples/sec: 4694.60 - lr: 0.006250\n",
            "2023-04-21 01:55:24,618 epoch 23 - iter 63/79 - loss 0.39501350 - time (sec): 15.72 - samples/sec: 4724.28 - lr: 0.006250\n",
            "2023-04-21 01:55:26,217 epoch 23 - iter 70/79 - loss 0.39605930 - time (sec): 17.32 - samples/sec: 4751.17 - lr: 0.006250\n",
            "2023-04-21 01:55:28,369 epoch 23 - iter 77/79 - loss 0.39382994 - time (sec): 19.47 - samples/sec: 4731.27 - lr: 0.006250\n",
            "2023-04-21 01:55:28,976 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:55:28,978 EPOCH 23 done: loss 0.3939 - lr 0.006250\n"
          ]
        },
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            "2023-04-21 01:55:51,521 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
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            "\n"
          ]
        },
        {
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          "text": [
            "2023-04-21 01:55:53,794 TRAIN : loss 0.3111548125743866 - f1-score (weighted avg)  0.3184\n"
          ]
        },
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          "text": [
            "2023-04-21 01:55:56,611 Evaluating as a multi-label problem: False\n",
            "2023-04-21 01:55:56,678 DEV : loss 0.3191471993923187 - f1-score (weighted avg)  0.3253\n",
            "2023-04-21 01:55:56,688 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 01:55:56,692 saving best model\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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            "2023-04-21 01:55:58,793 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:56:00,334 epoch 24 - iter 7/79 - loss 0.37479217 - time (sec): 1.54 - samples/sec: 4958.83 - lr: 0.006250\n",
            "2023-04-21 01:56:01,847 epoch 24 - iter 14/79 - loss 0.37781780 - time (sec): 3.05 - samples/sec: 5064.57 - lr: 0.006250\n",
            "2023-04-21 01:56:03,366 epoch 24 - iter 21/79 - loss 0.37838934 - time (sec): 4.57 - samples/sec: 5234.52 - lr: 0.006250\n",
            "2023-04-21 01:56:05,262 epoch 24 - iter 28/79 - loss 0.38408651 - time (sec): 6.47 - samples/sec: 5152.49 - lr: 0.006250\n",
            "2023-04-21 01:56:06,778 epoch 24 - iter 35/79 - loss 0.39168671 - time (sec): 7.98 - samples/sec: 5143.26 - lr: 0.006250\n",
            "2023-04-21 01:56:09,127 epoch 24 - iter 42/79 - loss 0.39011213 - time (sec): 10.33 - samples/sec: 4945.57 - lr: 0.006250\n",
            "2023-04-21 01:56:11,063 epoch 24 - iter 49/79 - loss 0.39464349 - time (sec): 12.27 - samples/sec: 4913.34 - lr: 0.006250\n",
            "2023-04-21 01:56:12,675 epoch 24 - iter 56/79 - loss 0.39548124 - time (sec): 13.88 - samples/sec: 4926.98 - lr: 0.006250\n",
            "2023-04-21 01:56:14,582 epoch 24 - iter 63/79 - loss 0.39087328 - time (sec): 15.79 - samples/sec: 4866.26 - lr: 0.006250\n",
            "2023-04-21 01:56:16,312 epoch 24 - iter 70/79 - loss 0.38317194 - time (sec): 17.52 - samples/sec: 4897.99 - lr: 0.006250\n",
            "2023-04-21 01:56:17,792 epoch 24 - iter 77/79 - loss 0.38936644 - time (sec): 19.00 - samples/sec: 4875.37 - lr: 0.006250\n",
            "2023-04-21 01:56:18,190 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:56:18,193 EPOCH 24 done: loss 0.3885 - lr 0.006250\n"
          ]
        },
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            "2023-04-21 01:56:39,497 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 01:56:43,067 TRAIN : loss 0.29382815957069397 - f1-score (weighted avg)  0.3261\n"
          ]
        },
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          "text": [
            "2023-04-21 01:56:45,905 Evaluating as a multi-label problem: False\n",
            "2023-04-21 01:56:45,973 DEV : loss 0.3001489043235779 - f1-score (weighted avg)  0.3316\n",
            "2023-04-21 01:56:45,982 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 01:56:45,987 saving best model\n"
          ]
        },
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          "output_type": "stream",
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            "2023-04-21 01:56:48,074 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:56:49,461 epoch 25 - iter 7/79 - loss 0.34111776 - time (sec): 1.39 - samples/sec: 4920.49 - lr: 0.006250\n",
            "2023-04-21 01:56:51,225 epoch 25 - iter 14/79 - loss 0.37429765 - time (sec): 3.15 - samples/sec: 4911.00 - lr: 0.006250\n",
            "2023-04-21 01:56:52,786 epoch 25 - iter 21/79 - loss 0.37994439 - time (sec): 4.71 - samples/sec: 5009.71 - lr: 0.006250\n",
            "2023-04-21 01:56:54,223 epoch 25 - iter 28/79 - loss 0.37543556 - time (sec): 6.15 - samples/sec: 5029.04 - lr: 0.006250\n",
            "2023-04-21 01:56:56,426 epoch 25 - iter 35/79 - loss 0.37683194 - time (sec): 8.35 - samples/sec: 4826.64 - lr: 0.006250\n",
            "2023-04-21 01:56:58,381 epoch 25 - iter 42/79 - loss 0.37972380 - time (sec): 10.31 - samples/sec: 4757.51 - lr: 0.006250\n",
            "2023-04-21 01:57:00,056 epoch 25 - iter 49/79 - loss 0.38286427 - time (sec): 11.98 - samples/sec: 4817.71 - lr: 0.006250\n",
            "2023-04-21 01:57:01,982 epoch 25 - iter 56/79 - loss 0.38211336 - time (sec): 13.91 - samples/sec: 4853.33 - lr: 0.006250\n",
            "2023-04-21 01:57:03,361 epoch 25 - iter 63/79 - loss 0.38897378 - time (sec): 15.29 - samples/sec: 4879.34 - lr: 0.006250\n",
            "2023-04-21 01:57:05,167 epoch 25 - iter 70/79 - loss 0.38552514 - time (sec): 17.09 - samples/sec: 4855.05 - lr: 0.006250\n",
            "2023-04-21 01:57:07,496 epoch 25 - iter 77/79 - loss 0.38154556 - time (sec): 19.42 - samples/sec: 4744.04 - lr: 0.006250\n",
            "2023-04-21 01:57:08,200 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:57:08,202 EPOCH 25 done: loss 0.3794 - lr 0.006250\n"
          ]
        },
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            "2023-04-21 01:57:29,532 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 01:57:31,823 TRAIN : loss 0.3006163537502289 - f1-score (weighted avg)  0.3238\n"
          ]
        },
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          "text": [
            "2023-04-21 01:57:34,628 Evaluating as a multi-label problem: False\n",
            "2023-04-21 01:57:34,696 DEV : loss 0.30892184376716614 - f1-score (weighted avg)  0.3197\n",
            "2023-04-21 01:57:34,706 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 01:57:34,710 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 01:57:36,316 epoch 26 - iter 7/79 - loss 0.34419619 - time (sec): 1.60 - samples/sec: 5430.12 - lr: 0.006250\n",
            "2023-04-21 01:57:37,791 epoch 26 - iter 14/79 - loss 0.38114838 - time (sec): 3.08 - samples/sec: 5289.42 - lr: 0.006250\n",
            "2023-04-21 01:57:40,066 epoch 26 - iter 21/79 - loss 0.37916820 - time (sec): 5.35 - samples/sec: 4886.47 - lr: 0.006250\n",
            "2023-04-21 01:57:42,068 epoch 26 - iter 28/79 - loss 0.38102810 - time (sec): 7.36 - samples/sec: 4785.18 - lr: 0.006250\n",
            "2023-04-21 01:57:43,851 epoch 26 - iter 35/79 - loss 0.37488598 - time (sec): 9.14 - samples/sec: 4804.01 - lr: 0.006250\n",
            "2023-04-21 01:57:45,841 epoch 26 - iter 42/79 - loss 0.38095423 - time (sec): 11.13 - samples/sec: 4727.02 - lr: 0.006250\n",
            "2023-04-21 01:57:47,357 epoch 26 - iter 49/79 - loss 0.38352358 - time (sec): 12.65 - samples/sec: 4733.98 - lr: 0.006250\n",
            "2023-04-21 01:57:48,881 epoch 26 - iter 56/79 - loss 0.38192774 - time (sec): 14.17 - samples/sec: 4792.79 - lr: 0.006250\n",
            "2023-04-21 01:57:50,533 epoch 26 - iter 63/79 - loss 0.38332296 - time (sec): 15.82 - samples/sec: 4791.78 - lr: 0.006250\n",
            "2023-04-21 01:57:52,141 epoch 26 - iter 70/79 - loss 0.38178756 - time (sec): 17.43 - samples/sec: 4800.87 - lr: 0.006250\n",
            "2023-04-21 01:57:53,860 epoch 26 - iter 77/79 - loss 0.38253156 - time (sec): 19.15 - samples/sec: 4799.31 - lr: 0.006250\n",
            "2023-04-21 01:57:54,417 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:57:54,419 EPOCH 26 done: loss 0.3813 - lr 0.006250\n"
          ]
        },
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          "text": [
            "2023-04-21 01:58:16,925 Evaluating as a multi-label problem: True\n",
            "2023-04-21 01:58:19,218 TRAIN : loss 0.31123775243759155 - f1-score (weighted avg)  0.2846\n"
          ]
        },
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          "output_type": "stream",
          "text": [
            "2023-04-21 01:58:22,054 Evaluating as a multi-label problem: False\n",
            "2023-04-21 01:58:22,124 DEV : loss 0.3202229142189026 - f1-score (weighted avg)  0.2774\n",
            "2023-04-21 01:58:22,134 Epoch    26: reducing learning rate of group 0 to 3.1250e-03.\n",
            "2023-04-21 01:58:22,136 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 01:58:22,140 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 01:58:23,801 epoch 27 - iter 7/79 - loss 0.39589420 - time (sec): 1.66 - samples/sec: 5107.35 - lr: 0.003125\n",
            "2023-04-21 01:58:25,342 epoch 27 - iter 14/79 - loss 0.37329475 - time (sec): 3.20 - samples/sec: 5023.43 - lr: 0.003125\n",
            "2023-04-21 01:58:27,262 epoch 27 - iter 21/79 - loss 0.36290826 - time (sec): 5.12 - samples/sec: 4802.68 - lr: 0.003125\n",
            "2023-04-21 01:58:28,953 epoch 27 - iter 28/79 - loss 0.36327456 - time (sec): 6.81 - samples/sec: 4887.48 - lr: 0.003125\n",
            "2023-04-21 01:58:30,488 epoch 27 - iter 35/79 - loss 0.36773621 - time (sec): 8.35 - samples/sec: 4920.42 - lr: 0.003125\n",
            "2023-04-21 01:58:32,363 epoch 27 - iter 42/79 - loss 0.37135928 - time (sec): 10.22 - samples/sec: 4879.36 - lr: 0.003125\n",
            "2023-04-21 01:58:34,279 epoch 27 - iter 49/79 - loss 0.36771604 - time (sec): 12.14 - samples/sec: 4924.53 - lr: 0.003125\n",
            "2023-04-21 01:58:35,816 epoch 27 - iter 56/79 - loss 0.36801515 - time (sec): 13.67 - samples/sec: 4957.96 - lr: 0.003125\n",
            "2023-04-21 01:58:37,371 epoch 27 - iter 63/79 - loss 0.36942914 - time (sec): 15.23 - samples/sec: 4943.04 - lr: 0.003125\n",
            "2023-04-21 01:58:39,209 epoch 27 - iter 70/79 - loss 0.37002948 - time (sec): 17.07 - samples/sec: 4935.28 - lr: 0.003125\n",
            "2023-04-21 01:58:41,173 epoch 27 - iter 77/79 - loss 0.36988788 - time (sec): 19.03 - samples/sec: 4866.15 - lr: 0.003125\n",
            "2023-04-21 01:58:41,954 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:58:41,956 EPOCH 27 done: loss 0.3690 - lr 0.003125\n"
          ]
        },
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          "text": [
            "2023-04-21 01:59:03,355 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
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        {
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          "text": [
            "2023-04-21 01:59:07,000 TRAIN : loss 0.28926950693130493 - f1-score (weighted avg)  0.3354\n"
          ]
        },
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          "text": [
            "2023-04-21 01:59:09,831 Evaluating as a multi-label problem: False\n",
            "2023-04-21 01:59:09,898 DEV : loss 0.29435789585113525 - f1-score (weighted avg)  0.334\n",
            "2023-04-21 01:59:09,907 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 01:59:09,912 saving best model\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 01:59:11,984 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:59:14,224 epoch 28 - iter 7/79 - loss 0.37715729 - time (sec): 2.24 - samples/sec: 3984.59 - lr: 0.003125\n",
            "2023-04-21 01:59:15,797 epoch 28 - iter 14/79 - loss 0.38518844 - time (sec): 3.81 - samples/sec: 4547.91 - lr: 0.003125\n",
            "2023-04-21 01:59:17,799 epoch 28 - iter 21/79 - loss 0.37645552 - time (sec): 5.81 - samples/sec: 4561.32 - lr: 0.003125\n",
            "2023-04-21 01:59:19,355 epoch 28 - iter 28/79 - loss 0.37297700 - time (sec): 7.37 - samples/sec: 4695.75 - lr: 0.003125\n",
            "2023-04-21 01:59:21,286 epoch 28 - iter 35/79 - loss 0.36561258 - time (sec): 9.30 - samples/sec: 4667.14 - lr: 0.003125\n",
            "2023-04-21 01:59:22,892 epoch 28 - iter 42/79 - loss 0.37182146 - time (sec): 10.91 - samples/sec: 4670.71 - lr: 0.003125\n",
            "2023-04-21 01:59:24,649 epoch 28 - iter 49/79 - loss 0.36839314 - time (sec): 12.66 - samples/sec: 4656.39 - lr: 0.003125\n",
            "2023-04-21 01:59:26,619 epoch 28 - iter 56/79 - loss 0.36770431 - time (sec): 14.63 - samples/sec: 4686.63 - lr: 0.003125\n",
            "2023-04-21 01:59:28,150 epoch 28 - iter 63/79 - loss 0.36787213 - time (sec): 16.16 - samples/sec: 4718.14 - lr: 0.003125\n",
            "2023-04-21 01:59:29,987 epoch 28 - iter 70/79 - loss 0.36477247 - time (sec): 18.00 - samples/sec: 4723.73 - lr: 0.003125\n",
            "2023-04-21 01:59:31,766 epoch 28 - iter 77/79 - loss 0.36360824 - time (sec): 19.78 - samples/sec: 4690.89 - lr: 0.003125\n",
            "2023-04-21 01:59:32,239 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 01:59:32,241 EPOCH 28 done: loss 0.3632 - lr 0.003125\n"
          ]
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          "text": [
            "2023-04-21 01:59:53,633 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
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          "text": [
            "2023-04-21 01:59:55,928 TRAIN : loss 0.28587400913238525 - f1-score (weighted avg)  0.3332\n"
          ]
        },
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          "text": [
            "2023-04-21 01:59:58,743 Evaluating as a multi-label problem: False\n",
            "2023-04-21 01:59:58,811 DEV : loss 0.2950611710548401 - f1-score (weighted avg)  0.3238\n",
            "2023-04-21 01:59:58,821 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 01:59:58,825 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 02:00:00,692 epoch 29 - iter 7/79 - loss 0.32162528 - time (sec): 1.87 - samples/sec: 4239.71 - lr: 0.003125\n",
            "2023-04-21 02:00:02,383 epoch 29 - iter 14/79 - loss 0.36214639 - time (sec): 3.56 - samples/sec: 4668.13 - lr: 0.003125\n",
            "2023-04-21 02:00:04,077 epoch 29 - iter 21/79 - loss 0.36692807 - time (sec): 5.25 - samples/sec: 4631.82 - lr: 0.003125\n",
            "2023-04-21 02:00:05,611 epoch 29 - iter 28/79 - loss 0.36747046 - time (sec): 6.78 - samples/sec: 4940.89 - lr: 0.003125\n",
            "2023-04-21 02:00:07,302 epoch 29 - iter 35/79 - loss 0.36563254 - time (sec): 8.47 - samples/sec: 5046.71 - lr: 0.003125\n",
            "2023-04-21 02:00:09,408 epoch 29 - iter 42/79 - loss 0.36238841 - time (sec): 10.58 - samples/sec: 4832.56 - lr: 0.003125\n",
            "2023-04-21 02:00:11,269 epoch 29 - iter 49/79 - loss 0.36377981 - time (sec): 12.44 - samples/sec: 4843.42 - lr: 0.003125\n",
            "2023-04-21 02:00:13,181 epoch 29 - iter 56/79 - loss 0.35538567 - time (sec): 14.35 - samples/sec: 4789.19 - lr: 0.003125\n",
            "2023-04-21 02:00:14,793 epoch 29 - iter 63/79 - loss 0.35732458 - time (sec): 15.97 - samples/sec: 4801.63 - lr: 0.003125\n",
            "2023-04-21 02:00:16,608 epoch 29 - iter 70/79 - loss 0.35587420 - time (sec): 17.78 - samples/sec: 4759.55 - lr: 0.003125\n",
            "2023-04-21 02:00:18,104 epoch 29 - iter 77/79 - loss 0.35662347 - time (sec): 19.28 - samples/sec: 4785.24 - lr: 0.003125\n",
            "2023-04-21 02:00:18,519 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:00:18,520 EPOCH 29 done: loss 0.3563 - lr 0.003125\n"
          ]
        },
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            "2023-04-21 02:00:41,187 Evaluating as a multi-label problem: True\n",
            "2023-04-21 02:00:43,483 TRAIN : loss 0.29707828164100647 - f1-score (weighted avg)  0.3378\n"
          ]
        },
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          "text": [
            "2023-04-21 02:00:46,272 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:00:46,339 DEV : loss 0.30746668577194214 - f1-score (weighted avg)  0.3328\n",
            "2023-04-21 02:00:46,349 Epoch    29: reducing learning rate of group 0 to 1.5625e-03.\n",
            "2023-04-21 02:00:46,351 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 02:00:46,355 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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            "2023-04-21 02:00:48,163 epoch 30 - iter 7/79 - loss 0.35559110 - time (sec): 1.81 - samples/sec: 4823.13 - lr: 0.001563\n",
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            "2023-04-21 02:00:53,063 epoch 30 - iter 28/79 - loss 0.34702324 - time (sec): 6.71 - samples/sec: 4937.12 - lr: 0.001563\n",
            "2023-04-21 02:00:54,845 epoch 30 - iter 35/79 - loss 0.34704986 - time (sec): 8.49 - samples/sec: 4868.88 - lr: 0.001563\n",
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            "2023-04-21 02:01:00,698 epoch 30 - iter 56/79 - loss 0.34947639 - time (sec): 14.34 - samples/sec: 4754.46 - lr: 0.001563\n",
            "2023-04-21 02:01:02,244 epoch 30 - iter 63/79 - loss 0.34762538 - time (sec): 15.89 - samples/sec: 4790.59 - lr: 0.001563\n",
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            "2023-04-21 02:01:05,558 epoch 30 - iter 77/79 - loss 0.34875912 - time (sec): 19.20 - samples/sec: 4816.52 - lr: 0.001563\n",
            "2023-04-21 02:01:05,976 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:01:05,978 EPOCH 30 done: loss 0.3485 - lr 0.001563\n"
          ]
        },
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            "2023-04-21 02:01:27,542 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 02:01:29,837 TRAIN : loss 0.28387215733528137 - f1-score (weighted avg)  0.3372\n"
          ]
        },
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          "text": [
            "2023-04-21 02:01:32,685 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:01:32,753 DEV : loss 0.29396435618400574 - f1-score (weighted avg)  0.3253\n",
            "2023-04-21 02:01:32,764 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 02:01:32,768 saving best model\n"
          ]
        },
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          "text": [
            "2023-04-21 02:01:34,866 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:01:36,414 epoch 31 - iter 7/79 - loss 0.35829673 - time (sec): 1.55 - samples/sec: 5386.53 - lr: 0.001563\n",
            "2023-04-21 02:01:38,015 epoch 31 - iter 14/79 - loss 0.33469096 - time (sec): 3.15 - samples/sec: 5203.76 - lr: 0.001563\n",
            "2023-04-21 02:01:39,911 epoch 31 - iter 21/79 - loss 0.34186734 - time (sec): 5.04 - samples/sec: 4957.90 - lr: 0.001563\n",
            "2023-04-21 02:01:41,638 epoch 31 - iter 28/79 - loss 0.35200924 - time (sec): 6.77 - samples/sec: 4934.55 - lr: 0.001563\n",
            "2023-04-21 02:01:43,937 epoch 31 - iter 35/79 - loss 0.35545686 - time (sec): 9.07 - samples/sec: 4654.85 - lr: 0.001563\n",
            "2023-04-21 02:01:45,705 epoch 31 - iter 42/79 - loss 0.35751819 - time (sec): 10.84 - samples/sec: 4633.29 - lr: 0.001563\n",
            "2023-04-21 02:01:47,715 epoch 31 - iter 49/79 - loss 0.35573020 - time (sec): 12.85 - samples/sec: 4566.31 - lr: 0.001563\n",
            "2023-04-21 02:01:49,251 epoch 31 - iter 56/79 - loss 0.35308793 - time (sec): 14.38 - samples/sec: 4557.02 - lr: 0.001563\n",
            "2023-04-21 02:01:50,841 epoch 31 - iter 63/79 - loss 0.35371219 - time (sec): 15.97 - samples/sec: 4651.13 - lr: 0.001563\n",
            "2023-04-21 02:01:52,596 epoch 31 - iter 70/79 - loss 0.35249129 - time (sec): 17.73 - samples/sec: 4663.37 - lr: 0.001563\n",
            "2023-04-21 02:01:54,560 epoch 31 - iter 77/79 - loss 0.35042699 - time (sec): 19.69 - samples/sec: 4703.74 - lr: 0.001563\n",
            "2023-04-21 02:01:55,009 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:01:55,011 EPOCH 31 done: loss 0.3494 - lr 0.001563\n"
          ]
        },
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            "2023-04-21 02:02:17,515 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 02:02:19,818 TRAIN : loss 0.28765761852264404 - f1-score (weighted avg)  0.3067\n"
          ]
        },
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          "text": [
            "2023-04-21 02:02:22,603 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:02:22,670 DEV : loss 0.2948721945285797 - f1-score (weighted avg)  0.3083\n",
            "2023-04-21 02:02:22,680 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 02:02:22,684 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 02:02:24,353 epoch 32 - iter 7/79 - loss 0.38533648 - time (sec): 1.67 - samples/sec: 5025.42 - lr: 0.001563\n",
            "2023-04-21 02:02:25,883 epoch 32 - iter 14/79 - loss 0.35707374 - time (sec): 3.20 - samples/sec: 5136.54 - lr: 0.001563\n",
            "2023-04-21 02:02:28,132 epoch 32 - iter 21/79 - loss 0.35548083 - time (sec): 5.45 - samples/sec: 4630.58 - lr: 0.001563\n",
            "2023-04-21 02:02:29,917 epoch 32 - iter 28/79 - loss 0.35937109 - time (sec): 7.23 - samples/sec: 4696.79 - lr: 0.001563\n",
            "2023-04-21 02:02:31,609 epoch 32 - iter 35/79 - loss 0.35089743 - time (sec): 8.92 - samples/sec: 4732.00 - lr: 0.001563\n",
            "2023-04-21 02:02:33,208 epoch 32 - iter 42/79 - loss 0.34976912 - time (sec): 10.52 - samples/sec: 4774.34 - lr: 0.001563\n",
            "2023-04-21 02:02:35,299 epoch 32 - iter 49/79 - loss 0.34500137 - time (sec): 12.61 - samples/sec: 4707.61 - lr: 0.001563\n",
            "2023-04-21 02:02:36,671 epoch 32 - iter 56/79 - loss 0.34369602 - time (sec): 13.99 - samples/sec: 4835.74 - lr: 0.001563\n",
            "2023-04-21 02:02:38,320 epoch 32 - iter 63/79 - loss 0.34474398 - time (sec): 15.63 - samples/sec: 4859.66 - lr: 0.001563\n",
            "2023-04-21 02:02:39,866 epoch 32 - iter 70/79 - loss 0.34328412 - time (sec): 17.18 - samples/sec: 4882.52 - lr: 0.001563\n",
            "2023-04-21 02:02:41,502 epoch 32 - iter 77/79 - loss 0.34420209 - time (sec): 18.82 - samples/sec: 4911.45 - lr: 0.001563\n",
            "2023-04-21 02:02:41,971 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:02:41,973 EPOCH 32 done: loss 0.3464 - lr 0.001563\n"
          ]
        },
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            "2023-04-21 02:03:03,488 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
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            "\n"
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        {
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          "text": [
            "2023-04-21 02:03:07,013 TRAIN : loss 0.27820318937301636 - f1-score (weighted avg)  0.3257\n"
          ]
        },
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          "text": [
            "2023-04-21 02:03:09,820 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:03:09,888 DEV : loss 0.2860194444656372 - f1-score (weighted avg)  0.3281\n",
            "2023-04-21 02:03:09,899 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 02:03:09,903 saving best model\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 02:03:11,992 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:03:13,936 epoch 33 - iter 7/79 - loss 0.27634413 - time (sec): 1.92 - samples/sec: 4657.59 - lr: 0.001563\n",
            "2023-04-21 02:03:15,610 epoch 33 - iter 14/79 - loss 0.32335964 - time (sec): 3.59 - samples/sec: 4929.71 - lr: 0.001563\n",
            "2023-04-21 02:03:17,474 epoch 33 - iter 21/79 - loss 0.33187738 - time (sec): 5.46 - samples/sec: 4805.63 - lr: 0.001563\n",
            "2023-04-21 02:03:19,141 epoch 33 - iter 28/79 - loss 0.33999516 - time (sec): 7.12 - samples/sec: 4823.95 - lr: 0.001563\n",
            "2023-04-21 02:03:20,468 epoch 33 - iter 35/79 - loss 0.34515393 - time (sec): 8.45 - samples/sec: 4939.71 - lr: 0.001563\n",
            "2023-04-21 02:03:22,141 epoch 33 - iter 42/79 - loss 0.34464805 - time (sec): 10.12 - samples/sec: 4980.75 - lr: 0.001563\n",
            "2023-04-21 02:03:23,883 epoch 33 - iter 49/79 - loss 0.34547401 - time (sec): 11.87 - samples/sec: 4977.98 - lr: 0.001563\n",
            "2023-04-21 02:03:25,546 epoch 33 - iter 56/79 - loss 0.34213938 - time (sec): 13.53 - samples/sec: 4986.01 - lr: 0.001563\n",
            "2023-04-21 02:03:27,299 epoch 33 - iter 63/79 - loss 0.34554472 - time (sec): 15.28 - samples/sec: 4970.08 - lr: 0.001563\n",
            "2023-04-21 02:03:29,565 epoch 33 - iter 70/79 - loss 0.34523959 - time (sec): 17.55 - samples/sec: 4843.60 - lr: 0.001563\n",
            "2023-04-21 02:03:31,147 epoch 33 - iter 77/79 - loss 0.34935839 - time (sec): 19.13 - samples/sec: 4822.48 - lr: 0.001563\n",
            "2023-04-21 02:03:31,830 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:03:31,832 EPOCH 33 done: loss 0.3490 - lr 0.001563\n"
          ]
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            "2023-04-21 02:03:53,194 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 02:03:55,489 TRAIN : loss 0.2817997634410858 - f1-score (weighted avg)  0.3224\n"
          ]
        },
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          "text": [
            "2023-04-21 02:03:58,322 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:03:58,391 DEV : loss 0.2896963655948639 - f1-score (weighted avg)  0.3222\n",
            "2023-04-21 02:03:58,401 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 02:03:58,406 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 02:03:59,968 epoch 34 - iter 7/79 - loss 0.34756484 - time (sec): 1.56 - samples/sec: 5693.25 - lr: 0.001563\n",
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            "2023-04-21 02:04:03,672 epoch 34 - iter 21/79 - loss 0.34813849 - time (sec): 5.27 - samples/sec: 4868.97 - lr: 0.001563\n",
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            "2023-04-21 02:04:07,300 epoch 34 - iter 35/79 - loss 0.33847826 - time (sec): 8.89 - samples/sec: 4793.93 - lr: 0.001563\n",
            "2023-04-21 02:04:08,824 epoch 34 - iter 42/79 - loss 0.34372066 - time (sec): 10.42 - samples/sec: 4882.10 - lr: 0.001563\n",
            "2023-04-21 02:04:10,484 epoch 34 - iter 49/79 - loss 0.34785527 - time (sec): 12.08 - samples/sec: 4885.31 - lr: 0.001563\n",
            "2023-04-21 02:04:12,234 epoch 34 - iter 56/79 - loss 0.34645800 - time (sec): 13.83 - samples/sec: 4901.11 - lr: 0.001563\n",
            "2023-04-21 02:04:13,870 epoch 34 - iter 63/79 - loss 0.34310265 - time (sec): 15.46 - samples/sec: 4929.83 - lr: 0.001563\n",
            "2023-04-21 02:04:15,653 epoch 34 - iter 70/79 - loss 0.34157463 - time (sec): 17.25 - samples/sec: 4848.75 - lr: 0.001563\n",
            "2023-04-21 02:04:17,304 epoch 34 - iter 77/79 - loss 0.33990505 - time (sec): 18.90 - samples/sec: 4862.34 - lr: 0.001563\n",
            "2023-04-21 02:04:18,218 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:04:18,220 EPOCH 34 done: loss 0.3382 - lr 0.001563\n"
          ]
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          "text": [
            "2023-04-21 02:04:40,962 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
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          "text": [
            "2023-04-21 02:04:43,234 TRAIN : loss 0.2837710678577423 - f1-score (weighted avg)  0.3287\n"
          ]
        },
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          "text": [
            "2023-04-21 02:04:46,056 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:04:46,125 DEV : loss 0.291992723941803 - f1-score (weighted avg)  0.3358\n",
            "2023-04-21 02:04:46,134 Epoch    34: reducing learning rate of group 0 to 7.8125e-04.\n",
            "2023-04-21 02:04:46,136 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 02:04:46,140 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 02:04:47,870 epoch 35 - iter 7/79 - loss 0.31187466 - time (sec): 1.73 - samples/sec: 4808.26 - lr: 0.000781\n",
            "2023-04-21 02:04:49,314 epoch 35 - iter 14/79 - loss 0.32570011 - time (sec): 3.17 - samples/sec: 5087.51 - lr: 0.000781\n",
            "2023-04-21 02:04:50,745 epoch 35 - iter 21/79 - loss 0.33421713 - time (sec): 4.60 - samples/sec: 5227.94 - lr: 0.000781\n",
            "2023-04-21 02:04:52,686 epoch 35 - iter 28/79 - loss 0.33381470 - time (sec): 6.54 - samples/sec: 5266.08 - lr: 0.000781\n",
            "2023-04-21 02:04:54,591 epoch 35 - iter 35/79 - loss 0.32833692 - time (sec): 8.45 - samples/sec: 5113.56 - lr: 0.000781\n",
            "2023-04-21 02:04:56,111 epoch 35 - iter 42/79 - loss 0.33477092 - time (sec): 9.97 - samples/sec: 5033.64 - lr: 0.000781\n",
            "2023-04-21 02:04:57,946 epoch 35 - iter 49/79 - loss 0.33236683 - time (sec): 11.80 - samples/sec: 4978.73 - lr: 0.000781\n",
            "2023-04-21 02:04:59,824 epoch 35 - iter 56/79 - loss 0.32958243 - time (sec): 13.68 - samples/sec: 4988.32 - lr: 0.000781\n",
            "2023-04-21 02:05:01,974 epoch 35 - iter 63/79 - loss 0.33270371 - time (sec): 15.83 - samples/sec: 4831.10 - lr: 0.000781\n",
            "2023-04-21 02:05:03,377 epoch 35 - iter 70/79 - loss 0.33761002 - time (sec): 17.24 - samples/sec: 4888.57 - lr: 0.000781\n",
            "2023-04-21 02:05:04,951 epoch 35 - iter 77/79 - loss 0.33613545 - time (sec): 18.81 - samples/sec: 4924.58 - lr: 0.000781\n",
            "2023-04-21 02:05:05,331 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:05:05,333 EPOCH 35 done: loss 0.3373 - lr 0.000781\n"
          ]
        },
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          "output_type": "stream",
          "text": [
            "2023-04-21 02:05:26,683 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
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        {
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          "text": [
            "2023-04-21 02:05:30,303 TRAIN : loss 0.27676016092300415 - f1-score (weighted avg)  0.319\n"
          ]
        },
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            "2023-04-21 02:05:33,184 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:05:33,251 DEV : loss 0.2835405468940735 - f1-score (weighted avg)  0.3188\n",
            "2023-04-21 02:05:33,261 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 02:05:33,265 saving best model\n"
          ]
        },
        {
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            "\n"
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            "2023-04-21 02:05:35,388 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:05:37,313 epoch 36 - iter 7/79 - loss 0.32092319 - time (sec): 1.92 - samples/sec: 4337.24 - lr: 0.000781\n",
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            "2023-04-21 02:05:55,117 epoch 36 - iter 77/79 - loss 0.33363903 - time (sec): 19.73 - samples/sec: 4687.68 - lr: 0.000781\n",
            "2023-04-21 02:05:55,706 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:05:55,708 EPOCH 36 done: loss 0.3328 - lr 0.000781\n"
          ]
        },
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            "2023-04-21 02:06:17,095 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
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            "\n"
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          "text": [
            "2023-04-21 02:06:19,374 TRAIN : loss 0.27634552121162415 - f1-score (weighted avg)  0.3291\n"
          ]
        },
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            "2023-04-21 02:06:22,192 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:06:22,259 DEV : loss 0.2839028835296631 - f1-score (weighted avg)  0.3371\n",
            "2023-04-21 02:06:22,268 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 02:06:22,272 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
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            "2023-04-21 02:06:23,778 epoch 37 - iter 7/79 - loss 0.34032456 - time (sec): 1.50 - samples/sec: 4692.54 - lr: 0.000781\n",
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            "2023-04-21 02:06:38,151 epoch 37 - iter 63/79 - loss 0.33917735 - time (sec): 15.88 - samples/sec: 4762.08 - lr: 0.000781\n",
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            "2023-04-21 02:06:41,336 epoch 37 - iter 77/79 - loss 0.33520903 - time (sec): 19.06 - samples/sec: 4861.63 - lr: 0.000781\n",
            "2023-04-21 02:06:41,863 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:06:41,865 EPOCH 37 done: loss 0.3334 - lr 0.000781\n"
          ]
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            "2023-04-21 02:07:04,551 Evaluating as a multi-label problem: True\n",
            "2023-04-21 02:07:06,818 TRAIN : loss 0.27950868010520935 - f1-score (weighted avg)  0.3246\n"
          ]
        },
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          "text": [
            "2023-04-21 02:07:09,646 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:07:09,716 DEV : loss 0.2869514524936676 - f1-score (weighted avg)  0.3336\n",
            "2023-04-21 02:07:09,727 Epoch    37: reducing learning rate of group 0 to 3.9063e-04.\n",
            "2023-04-21 02:07:09,729 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 02:07:09,733 ----------------------------------------------------------------------------------------------------\n"
          ]
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            "2023-04-21 02:07:12,091 epoch 38 - iter 7/79 - loss 0.32058187 - time (sec): 2.36 - samples/sec: 3816.01 - lr: 0.000391\n",
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            "2023-04-21 02:07:28,977 epoch 38 - iter 77/79 - loss 0.33309671 - time (sec): 19.24 - samples/sec: 4823.02 - lr: 0.000391\n",
            "2023-04-21 02:07:29,329 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:07:29,330 EPOCH 38 done: loss 0.3323 - lr 0.000391\n"
          ]
        },
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            "2023-04-21 02:07:50,805 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 02:07:54,413 TRAIN : loss 0.27465951442718506 - f1-score (weighted avg)  0.3319\n"
          ]
        },
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          "text": [
            "2023-04-21 02:07:57,266 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:07:57,332 DEV : loss 0.28304457664489746 - f1-score (weighted avg)  0.3355\n",
            "2023-04-21 02:07:57,342 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 02:07:57,346 saving best model\n"
          ]
        },
        {
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          "output_type": "stream",
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            "2023-04-21 02:07:59,450 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:08:00,867 epoch 39 - iter 7/79 - loss 0.30007036 - time (sec): 1.41 - samples/sec: 5336.63 - lr: 0.000391\n",
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            "2023-04-21 02:08:03,965 epoch 39 - iter 21/79 - loss 0.33179134 - time (sec): 4.51 - samples/sec: 5332.25 - lr: 0.000391\n",
            "2023-04-21 02:08:06,057 epoch 39 - iter 28/79 - loss 0.32903975 - time (sec): 6.61 - samples/sec: 4927.70 - lr: 0.000391\n",
            "2023-04-21 02:08:08,105 epoch 39 - iter 35/79 - loss 0.32638195 - time (sec): 8.65 - samples/sec: 4717.19 - lr: 0.000391\n",
            "2023-04-21 02:08:09,768 epoch 39 - iter 42/79 - loss 0.32537553 - time (sec): 10.32 - samples/sec: 4840.29 - lr: 0.000391\n",
            "2023-04-21 02:08:11,383 epoch 39 - iter 49/79 - loss 0.32743153 - time (sec): 11.93 - samples/sec: 4833.96 - lr: 0.000391\n",
            "2023-04-21 02:08:12,809 epoch 39 - iter 56/79 - loss 0.32815222 - time (sec): 13.36 - samples/sec: 4942.41 - lr: 0.000391\n",
            "2023-04-21 02:08:14,577 epoch 39 - iter 63/79 - loss 0.33217800 - time (sec): 15.12 - samples/sec: 4934.93 - lr: 0.000391\n",
            "2023-04-21 02:08:16,117 epoch 39 - iter 70/79 - loss 0.33250011 - time (sec): 16.67 - samples/sec: 4936.83 - lr: 0.000391\n",
            "2023-04-21 02:08:18,360 epoch 39 - iter 77/79 - loss 0.33178453 - time (sec): 18.91 - samples/sec: 4857.31 - lr: 0.000391\n",
            "2023-04-21 02:08:18,952 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:08:18,954 EPOCH 39 done: loss 0.3324 - lr 0.000391\n"
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          "text": [
            "2023-04-21 02:08:40,412 Evaluating as a multi-label problem: True\n"
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        },
        {
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 02:08:42,696 TRAIN : loss 0.273924320936203 - f1-score (weighted avg)  0.3297\n"
          ]
        },
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          "text": [
            "2023-04-21 02:08:45,474 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:08:45,539 DEV : loss 0.2815655767917633 - f1-score (weighted avg)  0.3288\n",
            "2023-04-21 02:08:45,550 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 02:08:45,554 saving best model\n"
          ]
        },
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            "2023-04-21 02:08:47,646 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:08:48,968 epoch 40 - iter 7/79 - loss 0.33165338 - time (sec): 1.32 - samples/sec: 5087.37 - lr: 0.000391\n",
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            "2023-04-21 02:08:58,361 epoch 40 - iter 42/79 - loss 0.32894122 - time (sec): 10.71 - samples/sec: 4867.61 - lr: 0.000391\n",
            "2023-04-21 02:08:59,919 epoch 40 - iter 49/79 - loss 0.32759736 - time (sec): 12.27 - samples/sec: 4876.93 - lr: 0.000391\n",
            "2023-04-21 02:09:01,764 epoch 40 - iter 56/79 - loss 0.32951103 - time (sec): 14.12 - samples/sec: 4805.34 - lr: 0.000391\n",
            "2023-04-21 02:09:03,561 epoch 40 - iter 63/79 - loss 0.32867185 - time (sec): 15.91 - samples/sec: 4769.27 - lr: 0.000391\n",
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            "2023-04-21 02:09:07,306 epoch 40 - iter 77/79 - loss 0.32991313 - time (sec): 19.66 - samples/sec: 4697.27 - lr: 0.000391\n",
            "2023-04-21 02:09:07,884 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:09:07,886 EPOCH 40 done: loss 0.3304 - lr 0.000391\n"
          ]
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            "2023-04-21 02:09:29,285 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 02:09:32,818 TRAIN : loss 0.2751326262950897 - f1-score (weighted avg)  0.3371\n"
          ]
        },
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          "text": [
            "2023-04-21 02:09:35,635 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:09:35,702 DEV : loss 0.2838353216648102 - f1-score (weighted avg)  0.3339\n",
            "2023-04-21 02:09:35,713 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 02:09:35,717 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 02:09:37,886 epoch 41 - iter 7/79 - loss 0.32361254 - time (sec): 2.17 - samples/sec: 3885.78 - lr: 0.000391\n",
            "2023-04-21 02:09:39,476 epoch 41 - iter 14/79 - loss 0.33426796 - time (sec): 3.76 - samples/sec: 4450.45 - lr: 0.000391\n",
            "2023-04-21 02:09:41,228 epoch 41 - iter 21/79 - loss 0.32939203 - time (sec): 5.51 - samples/sec: 4577.82 - lr: 0.000391\n",
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            "2023-04-21 02:09:44,706 epoch 41 - iter 35/79 - loss 0.32958590 - time (sec): 8.99 - samples/sec: 4570.29 - lr: 0.000391\n",
            "2023-04-21 02:09:46,327 epoch 41 - iter 42/79 - loss 0.32874787 - time (sec): 10.61 - samples/sec: 4615.49 - lr: 0.000391\n",
            "2023-04-21 02:09:47,824 epoch 41 - iter 49/79 - loss 0.32787637 - time (sec): 12.10 - samples/sec: 4612.74 - lr: 0.000391\n",
            "2023-04-21 02:09:49,486 epoch 41 - iter 56/79 - loss 0.32884383 - time (sec): 13.77 - samples/sec: 4688.61 - lr: 0.000391\n",
            "2023-04-21 02:09:51,059 epoch 41 - iter 63/79 - loss 0.32981237 - time (sec): 15.34 - samples/sec: 4753.90 - lr: 0.000391\n",
            "2023-04-21 02:09:52,885 epoch 41 - iter 70/79 - loss 0.33147191 - time (sec): 17.17 - samples/sec: 4770.83 - lr: 0.000391\n",
            "2023-04-21 02:09:54,862 epoch 41 - iter 77/79 - loss 0.33081877 - time (sec): 19.14 - samples/sec: 4806.58 - lr: 0.000391\n",
            "2023-04-21 02:09:55,383 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:09:55,384 EPOCH 41 done: loss 0.3314 - lr 0.000391\n"
          ]
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          "text": [
            "2023-04-21 02:10:16,849 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
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          "text": [
            "2023-04-21 02:10:19,160 TRAIN : loss 0.27294236421585083 - f1-score (weighted avg)  0.3353\n"
          ]
        },
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          "text": [
            "2023-04-21 02:10:21,984 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:10:22,053 DEV : loss 0.2816306948661804 - f1-score (weighted avg)  0.3329\n",
            "2023-04-21 02:10:22,063 Epoch    41: reducing learning rate of group 0 to 1.9531e-04.\n",
            "2023-04-21 02:10:22,065 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 02:10:22,069 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
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            "2023-04-21 02:10:23,721 epoch 42 - iter 7/79 - loss 0.30691339 - time (sec): 1.65 - samples/sec: 4627.50 - lr: 0.000195\n",
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            "2023-04-21 02:10:28,635 epoch 42 - iter 28/79 - loss 0.32681682 - time (sec): 6.56 - samples/sec: 4946.96 - lr: 0.000195\n",
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            "2023-04-21 02:10:31,687 epoch 42 - iter 42/79 - loss 0.33218425 - time (sec): 9.62 - samples/sec: 5126.90 - lr: 0.000195\n",
            "2023-04-21 02:10:33,490 epoch 42 - iter 49/79 - loss 0.33405031 - time (sec): 11.42 - samples/sec: 5061.11 - lr: 0.000195\n",
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            "2023-04-21 02:10:37,188 epoch 42 - iter 63/79 - loss 0.33335224 - time (sec): 15.12 - samples/sec: 4967.71 - lr: 0.000195\n",
            "2023-04-21 02:10:39,122 epoch 42 - iter 70/79 - loss 0.33080648 - time (sec): 17.05 - samples/sec: 4892.67 - lr: 0.000195\n",
            "2023-04-21 02:10:41,545 epoch 42 - iter 77/79 - loss 0.33044215 - time (sec): 19.47 - samples/sec: 4790.15 - lr: 0.000195\n",
            "2023-04-21 02:10:41,890 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:10:41,892 EPOCH 42 done: loss 0.3299 - lr 0.000195\n"
          ]
        },
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            "2023-04-21 02:11:04,469 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 02:11:06,747 TRAIN : loss 0.2743704915046692 - f1-score (weighted avg)  0.3281\n"
          ]
        },
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            "2023-04-21 02:11:09,656 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:11:09,725 DEV : loss 0.2819850742816925 - f1-score (weighted avg)  0.3312\n",
            "2023-04-21 02:11:09,734 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 02:11:09,739 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
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          "output_type": "stream",
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          "text": [
            "2023-04-21 02:11:11,641 epoch 43 - iter 7/79 - loss 0.34398019 - time (sec): 1.90 - samples/sec: 4704.95 - lr: 0.000195\n",
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            "2023-04-21 02:11:15,087 epoch 43 - iter 21/79 - loss 0.33100252 - time (sec): 5.35 - samples/sec: 4778.55 - lr: 0.000195\n",
            "2023-04-21 02:11:17,031 epoch 43 - iter 28/79 - loss 0.32690706 - time (sec): 7.29 - samples/sec: 4728.94 - lr: 0.000195\n",
            "2023-04-21 02:11:18,719 epoch 43 - iter 35/79 - loss 0.32797235 - time (sec): 8.98 - samples/sec: 4724.30 - lr: 0.000195\n",
            "2023-04-21 02:11:20,331 epoch 43 - iter 42/79 - loss 0.32680565 - time (sec): 10.59 - samples/sec: 4765.83 - lr: 0.000195\n",
            "2023-04-21 02:11:22,211 epoch 43 - iter 49/79 - loss 0.32257003 - time (sec): 12.47 - samples/sec: 4795.11 - lr: 0.000195\n",
            "2023-04-21 02:11:23,763 epoch 43 - iter 56/79 - loss 0.32279525 - time (sec): 14.02 - samples/sec: 4850.37 - lr: 0.000195\n",
            "2023-04-21 02:11:25,331 epoch 43 - iter 63/79 - loss 0.32536528 - time (sec): 15.59 - samples/sec: 4900.50 - lr: 0.000195\n",
            "2023-04-21 02:11:26,718 epoch 43 - iter 70/79 - loss 0.32671005 - time (sec): 16.98 - samples/sec: 4944.50 - lr: 0.000195\n",
            "2023-04-21 02:11:28,742 epoch 43 - iter 77/79 - loss 0.32712349 - time (sec): 19.00 - samples/sec: 4872.51 - lr: 0.000195\n",
            "2023-04-21 02:11:29,177 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:11:29,179 EPOCH 43 done: loss 0.3276 - lr 0.000195\n"
          ]
        },
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            "2023-04-21 02:11:50,516 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 02:11:54,115 TRAIN : loss 0.2736373543739319 - f1-score (weighted avg)  0.328\n"
          ]
        },
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          "text": [
            "2023-04-21 02:11:56,907 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:11:56,974 DEV : loss 0.28201931715011597 - f1-score (weighted avg)  0.323\n",
            "2023-04-21 02:11:56,984 Epoch    43: reducing learning rate of group 0 to 9.7656e-05.\n",
            "2023-04-21 02:11:56,986 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 02:11:56,989 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 02:11:58,742 epoch 44 - iter 7/79 - loss 0.29543882 - time (sec): 1.75 - samples/sec: 4395.24 - lr: 0.000098\n",
            "2023-04-21 02:12:00,532 epoch 44 - iter 14/79 - loss 0.32155484 - time (sec): 3.54 - samples/sec: 4495.50 - lr: 0.000098\n",
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            "2023-04-21 02:12:04,150 epoch 44 - iter 28/79 - loss 0.32513985 - time (sec): 7.16 - samples/sec: 4547.19 - lr: 0.000098\n",
            "2023-04-21 02:12:05,848 epoch 44 - iter 35/79 - loss 0.32076548 - time (sec): 8.86 - samples/sec: 4616.87 - lr: 0.000098\n",
            "2023-04-21 02:12:07,491 epoch 44 - iter 42/79 - loss 0.31655809 - time (sec): 10.50 - samples/sec: 4665.96 - lr: 0.000098\n",
            "2023-04-21 02:12:09,094 epoch 44 - iter 49/79 - loss 0.31929105 - time (sec): 12.10 - samples/sec: 4682.45 - lr: 0.000098\n",
            "2023-04-21 02:12:10,629 epoch 44 - iter 56/79 - loss 0.32170197 - time (sec): 13.64 - samples/sec: 4788.18 - lr: 0.000098\n",
            "2023-04-21 02:12:12,631 epoch 44 - iter 63/79 - loss 0.32500053 - time (sec): 15.64 - samples/sec: 4747.81 - lr: 0.000098\n",
            "2023-04-21 02:12:14,159 epoch 44 - iter 70/79 - loss 0.32874233 - time (sec): 17.17 - samples/sec: 4796.55 - lr: 0.000098\n",
            "2023-04-21 02:12:16,337 epoch 44 - iter 77/79 - loss 0.33030724 - time (sec): 19.35 - samples/sec: 4759.85 - lr: 0.000098\n",
            "2023-04-21 02:12:16,792 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:12:16,794 EPOCH 44 done: loss 0.3290 - lr 0.000098\n"
          ]
        },
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          "text": [
            "2023-04-21 02:12:38,205 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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        {
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          "text": [
            "2023-04-21 02:12:40,483 TRAIN : loss 0.27396631240844727 - f1-score (weighted avg)  0.3306\n"
          ]
        },
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          "text": [
            "2023-04-21 02:12:43,280 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:12:43,349 DEV : loss 0.28222206234931946 - f1-score (weighted avg)  0.3286\n",
            "2023-04-21 02:12:43,360 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 02:12:43,364 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
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          "output_type": "stream",
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          "text": [
            "2023-04-21 02:12:44,857 epoch 45 - iter 7/79 - loss 0.35966743 - time (sec): 1.49 - samples/sec: 5132.62 - lr: 0.000098\n",
            "2023-04-21 02:12:46,695 epoch 45 - iter 14/79 - loss 0.32853225 - time (sec): 3.33 - samples/sec: 4918.78 - lr: 0.000098\n",
            "2023-04-21 02:12:48,375 epoch 45 - iter 21/79 - loss 0.32794608 - time (sec): 5.01 - samples/sec: 5109.96 - lr: 0.000098\n",
            "2023-04-21 02:12:49,969 epoch 45 - iter 28/79 - loss 0.33380880 - time (sec): 6.60 - samples/sec: 5051.96 - lr: 0.000098\n",
            "2023-04-21 02:12:51,855 epoch 45 - iter 35/79 - loss 0.34315218 - time (sec): 8.49 - samples/sec: 4858.85 - lr: 0.000098\n",
            "2023-04-21 02:12:53,276 epoch 45 - iter 42/79 - loss 0.34015325 - time (sec): 9.91 - samples/sec: 4994.60 - lr: 0.000098\n",
            "2023-04-21 02:12:54,975 epoch 45 - iter 49/79 - loss 0.33633599 - time (sec): 11.61 - samples/sec: 4943.54 - lr: 0.000098\n",
            "2023-04-21 02:12:56,821 epoch 45 - iter 56/79 - loss 0.33468831 - time (sec): 13.46 - samples/sec: 4884.67 - lr: 0.000098\n",
            "2023-04-21 02:12:58,601 epoch 45 - iter 63/79 - loss 0.33423775 - time (sec): 15.24 - samples/sec: 4871.21 - lr: 0.000098\n",
            "2023-04-21 02:13:00,496 epoch 45 - iter 70/79 - loss 0.33404144 - time (sec): 17.13 - samples/sec: 4855.07 - lr: 0.000098\n",
            "2023-04-21 02:13:02,730 epoch 45 - iter 77/79 - loss 0.33033313 - time (sec): 19.36 - samples/sec: 4781.85 - lr: 0.000098\n",
            "2023-04-21 02:13:03,120 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:13:03,121 EPOCH 45 done: loss 0.3296 - lr 0.000098\n"
          ]
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          "text": [
            "2023-04-21 02:13:25,693 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 02:13:27,975 TRAIN : loss 0.2734178304672241 - f1-score (weighted avg)  0.333\n"
          ]
        },
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          "text": [
            "2023-04-21 02:13:30,801 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:13:30,868 DEV : loss 0.2812582552433014 - f1-score (weighted avg)  0.3399\n",
            "2023-04-21 02:13:30,877 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 02:13:30,882 saving best model\n"
          ]
        },
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          "output_type": "stream",
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          "text": [
            "2023-04-21 02:13:32,988 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:13:34,663 epoch 46 - iter 7/79 - loss 0.33852682 - time (sec): 1.67 - samples/sec: 5034.00 - lr: 0.000098\n",
            "2023-04-21 02:13:36,258 epoch 46 - iter 14/79 - loss 0.32864235 - time (sec): 3.27 - samples/sec: 5062.92 - lr: 0.000098\n",
            "2023-04-21 02:13:37,976 epoch 46 - iter 21/79 - loss 0.33429045 - time (sec): 4.99 - samples/sec: 5059.25 - lr: 0.000098\n",
            "2023-04-21 02:13:40,559 epoch 46 - iter 28/79 - loss 0.33390256 - time (sec): 7.57 - samples/sec: 4468.76 - lr: 0.000098\n",
            "2023-04-21 02:13:41,972 epoch 46 - iter 35/79 - loss 0.33073805 - time (sec): 8.98 - samples/sec: 4595.38 - lr: 0.000098\n",
            "2023-04-21 02:13:43,928 epoch 46 - iter 42/79 - loss 0.33852513 - time (sec): 10.94 - samples/sec: 4496.64 - lr: 0.000098\n",
            "2023-04-21 02:13:45,942 epoch 46 - iter 49/79 - loss 0.33210394 - time (sec): 12.95 - samples/sec: 4470.82 - lr: 0.000098\n",
            "2023-04-21 02:13:47,643 epoch 46 - iter 56/79 - loss 0.33442653 - time (sec): 14.65 - samples/sec: 4501.67 - lr: 0.000098\n",
            "2023-04-21 02:13:49,576 epoch 46 - iter 63/79 - loss 0.33205187 - time (sec): 16.59 - samples/sec: 4608.56 - lr: 0.000098\n",
            "2023-04-21 02:13:51,319 epoch 46 - iter 70/79 - loss 0.32900089 - time (sec): 18.33 - samples/sec: 4657.63 - lr: 0.000098\n",
            "2023-04-21 02:13:52,656 epoch 46 - iter 77/79 - loss 0.32957527 - time (sec): 19.67 - samples/sec: 4714.55 - lr: 0.000098\n",
            "2023-04-21 02:13:53,014 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:13:53,016 EPOCH 46 done: loss 0.3294 - lr 0.000098\n"
          ]
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          "text": [
            "2023-04-21 02:14:14,321 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 02:14:17,907 TRAIN : loss 0.2739681601524353 - f1-score (weighted avg)  0.3316\n"
          ]
        },
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          "text": [
            "2023-04-21 02:14:20,713 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:14:20,780 DEV : loss 0.28225356340408325 - f1-score (weighted avg)  0.335\n",
            "2023-04-21 02:14:20,790 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 02:14:20,794 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
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          "text": [
            "2023-04-21 02:14:22,446 epoch 47 - iter 7/79 - loss 0.34121998 - time (sec): 1.65 - samples/sec: 4321.95 - lr: 0.000098\n",
            "2023-04-21 02:14:24,205 epoch 47 - iter 14/79 - loss 0.35122423 - time (sec): 3.41 - samples/sec: 4464.37 - lr: 0.000098\n",
            "2023-04-21 02:14:26,162 epoch 47 - iter 21/79 - loss 0.34102764 - time (sec): 5.37 - samples/sec: 4482.11 - lr: 0.000098\n",
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            "2023-04-21 02:14:29,158 epoch 47 - iter 35/79 - loss 0.33408197 - time (sec): 8.36 - samples/sec: 4829.60 - lr: 0.000098\n",
            "2023-04-21 02:14:30,517 epoch 47 - iter 42/79 - loss 0.33779526 - time (sec): 9.72 - samples/sec: 4889.80 - lr: 0.000098\n",
            "2023-04-21 02:14:32,107 epoch 47 - iter 49/79 - loss 0.33537443 - time (sec): 11.31 - samples/sec: 4883.53 - lr: 0.000098\n",
            "2023-04-21 02:14:33,890 epoch 47 - iter 56/79 - loss 0.33230243 - time (sec): 13.09 - samples/sec: 4952.13 - lr: 0.000098\n",
            "2023-04-21 02:14:35,998 epoch 47 - iter 63/79 - loss 0.32805171 - time (sec): 15.20 - samples/sec: 4864.40 - lr: 0.000098\n",
            "2023-04-21 02:14:37,650 epoch 47 - iter 70/79 - loss 0.32771790 - time (sec): 16.85 - samples/sec: 4930.05 - lr: 0.000098\n",
            "2023-04-21 02:14:40,068 epoch 47 - iter 77/79 - loss 0.32462668 - time (sec): 19.27 - samples/sec: 4780.58 - lr: 0.000098\n",
            "2023-04-21 02:14:40,594 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:14:40,596 EPOCH 47 done: loss 0.3262 - lr 0.000098\n"
          ]
        },
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          "text": [
            "2023-04-21 02:15:01,965 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
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          "text": [
            "2023-04-21 02:15:04,256 TRAIN : loss 0.2729083299636841 - f1-score (weighted avg)  0.3297\n"
          ]
        },
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          "output_type": "stream",
          "text": [
            "2023-04-21 02:15:07,150 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:15:07,216 DEV : loss 0.28077390789985657 - f1-score (weighted avg)  0.3304\n",
            "2023-04-21 02:15:07,226 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 02:15:07,230 saving best model\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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            "2023-04-21 02:15:09,327 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:15:11,243 epoch 48 - iter 7/79 - loss 0.34534516 - time (sec): 1.90 - samples/sec: 4483.18 - lr: 0.000098\n",
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            "2023-04-21 02:15:14,307 epoch 48 - iter 21/79 - loss 0.33987210 - time (sec): 4.96 - samples/sec: 4663.19 - lr: 0.000098\n",
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            "2023-04-21 02:15:22,252 epoch 48 - iter 49/79 - loss 0.33547655 - time (sec): 12.91 - samples/sec: 4562.46 - lr: 0.000098\n",
            "2023-04-21 02:15:23,919 epoch 48 - iter 56/79 - loss 0.33283836 - time (sec): 14.58 - samples/sec: 4620.06 - lr: 0.000098\n",
            "2023-04-21 02:15:25,927 epoch 48 - iter 63/79 - loss 0.33504055 - time (sec): 16.58 - samples/sec: 4609.17 - lr: 0.000098\n",
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            "2023-04-21 02:15:29,153 epoch 48 - iter 77/79 - loss 0.33080370 - time (sec): 19.81 - samples/sec: 4675.68 - lr: 0.000098\n",
            "2023-04-21 02:15:29,493 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:15:29,494 EPOCH 48 done: loss 0.3311 - lr 0.000098\n"
          ]
        },
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            "2023-04-21 02:15:52,065 Evaluating as a multi-label problem: True\n",
            "2023-04-21 02:15:54,351 TRAIN : loss 0.2740212082862854 - f1-score (weighted avg)  0.3286\n"
          ]
        },
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          "text": [
            "2023-04-21 02:15:57,161 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:15:57,227 DEV : loss 0.2816661596298218 - f1-score (weighted avg)  0.3321\n",
            "2023-04-21 02:15:57,237 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 02:15:57,241 ----------------------------------------------------------------------------------------------------\n"
          ]
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          "text": [
            "2023-04-21 02:15:58,777 epoch 49 - iter 7/79 - loss 0.32428832 - time (sec): 1.54 - samples/sec: 5485.59 - lr: 0.000098\n",
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            "2023-04-21 02:16:09,980 epoch 49 - iter 49/79 - loss 0.32691547 - time (sec): 12.74 - samples/sec: 4573.50 - lr: 0.000098\n",
            "2023-04-21 02:16:11,796 epoch 49 - iter 56/79 - loss 0.32607776 - time (sec): 14.55 - samples/sec: 4547.32 - lr: 0.000098\n",
            "2023-04-21 02:16:13,519 epoch 49 - iter 63/79 - loss 0.33007353 - time (sec): 16.28 - samples/sec: 4592.02 - lr: 0.000098\n",
            "2023-04-21 02:16:15,046 epoch 49 - iter 70/79 - loss 0.33119135 - time (sec): 17.80 - samples/sec: 4724.38 - lr: 0.000098\n",
            "2023-04-21 02:16:16,754 epoch 49 - iter 77/79 - loss 0.32997825 - time (sec): 19.51 - samples/sec: 4730.78 - lr: 0.000098\n",
            "2023-04-21 02:16:17,263 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:16:17,265 EPOCH 49 done: loss 0.3288 - lr 0.000098\n"
          ]
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            "2023-04-21 02:16:38,600 Evaluating as a multi-label problem: True\n"
          ]
        },
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            "\n"
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            "2023-04-21 02:16:40,874 TRAIN : loss 0.2733156979084015 - f1-score (weighted avg)  0.3317\n"
          ]
        },
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            "2023-04-21 02:16:44,922 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:16:44,995 DEV : loss 0.28127720952033997 - f1-score (weighted avg)  0.338\n",
            "2023-04-21 02:16:45,004 Epoch    49: reducing learning rate of group 0 to 4.8828e-05.\n",
            "2023-04-21 02:16:45,006 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 02:16:45,010 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
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            "2023-04-21 02:16:47,171 epoch 50 - iter 7/79 - loss 0.33480976 - time (sec): 2.16 - samples/sec: 4099.02 - lr: 0.000049\n",
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            "2023-04-21 02:16:51,989 epoch 50 - iter 28/79 - loss 0.32512395 - time (sec): 6.98 - samples/sec: 4783.72 - lr: 0.000049\n",
            "2023-04-21 02:16:53,790 epoch 50 - iter 35/79 - loss 0.32255443 - time (sec): 8.78 - samples/sec: 4700.70 - lr: 0.000049\n",
            "2023-04-21 02:16:55,977 epoch 50 - iter 42/79 - loss 0.32145244 - time (sec): 10.97 - samples/sec: 4545.18 - lr: 0.000049\n",
            "2023-04-21 02:16:57,805 epoch 50 - iter 49/79 - loss 0.31908164 - time (sec): 12.79 - samples/sec: 4600.47 - lr: 0.000049\n",
            "2023-04-21 02:16:59,478 epoch 50 - iter 56/79 - loss 0.32363069 - time (sec): 14.47 - samples/sec: 4650.30 - lr: 0.000049\n",
            "2023-04-21 02:17:00,971 epoch 50 - iter 63/79 - loss 0.32744793 - time (sec): 15.96 - samples/sec: 4718.22 - lr: 0.000049\n",
            "2023-04-21 02:17:02,828 epoch 50 - iter 70/79 - loss 0.32741771 - time (sec): 17.82 - samples/sec: 4748.96 - lr: 0.000049\n",
            "2023-04-21 02:17:04,355 epoch 50 - iter 77/79 - loss 0.32825413 - time (sec): 19.34 - samples/sec: 4799.50 - lr: 0.000049\n",
            "2023-04-21 02:17:04,775 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:17:04,777 EPOCH 50 done: loss 0.3293 - lr 0.000049\n"
          ]
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            "2023-04-21 02:17:26,155 Evaluating as a multi-label problem: True\n"
          ]
        },
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 02:17:28,451 TRAIN : loss 0.2731970548629761 - f1-score (weighted avg)  0.3312\n"
          ]
        },
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          "text": [
            "2023-04-21 02:17:31,291 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:17:31,359 DEV : loss 0.28102555871009827 - f1-score (weighted avg)  0.3378\n",
            "2023-04-21 02:17:31,368 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 02:17:31,371 ----------------------------------------------------------------------------------------------------\n"
          ]
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          "output_type": "stream",
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          "text": [
            "2023-04-21 02:17:33,590 epoch 51 - iter 7/79 - loss 0.31828980 - time (sec): 2.22 - samples/sec: 3824.14 - lr: 0.000049\n",
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            "2023-04-21 02:17:42,442 epoch 51 - iter 42/79 - loss 0.32737561 - time (sec): 11.07 - samples/sec: 4548.18 - lr: 0.000049\n",
            "2023-04-21 02:17:44,020 epoch 51 - iter 49/79 - loss 0.32806608 - time (sec): 12.65 - samples/sec: 4697.44 - lr: 0.000049\n",
            "2023-04-21 02:17:45,517 epoch 51 - iter 56/79 - loss 0.33007519 - time (sec): 14.14 - samples/sec: 4753.85 - lr: 0.000049\n",
            "2023-04-21 02:17:47,141 epoch 51 - iter 63/79 - loss 0.32847565 - time (sec): 15.77 - samples/sec: 4785.35 - lr: 0.000049\n",
            "2023-04-21 02:17:48,723 epoch 51 - iter 70/79 - loss 0.32711220 - time (sec): 17.35 - samples/sec: 4840.10 - lr: 0.000049\n",
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            "2023-04-21 02:17:50,723 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:17:50,725 EPOCH 51 done: loss 0.3296 - lr 0.000049\n"
          ]
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            "2023-04-21 02:18:13,461 Evaluating as a multi-label problem: True\n",
            "2023-04-21 02:18:15,748 TRAIN : loss 0.272991418838501 - f1-score (weighted avg)  0.3312\n"
          ]
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          "text": [
            "2023-04-21 02:18:18,577 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:18:18,645 DEV : loss 0.28084027767181396 - f1-score (weighted avg)  0.3332\n",
            "2023-04-21 02:18:18,655 Epoch    51: reducing learning rate of group 0 to 2.4414e-05.\n",
            "2023-04-21 02:18:18,657 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 02:18:18,660 ----------------------------------------------------------------------------------------------------\n"
          ]
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          "output_type": "stream",
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          "text": [
            "2023-04-21 02:18:20,370 epoch 52 - iter 7/79 - loss 0.34951341 - time (sec): 1.71 - samples/sec: 5146.94 - lr: 0.000024\n",
            "2023-04-21 02:18:22,391 epoch 52 - iter 14/79 - loss 0.33139764 - time (sec): 3.73 - samples/sec: 4396.77 - lr: 0.000024\n",
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            "2023-04-21 02:18:27,262 epoch 52 - iter 35/79 - loss 0.32588082 - time (sec): 8.60 - samples/sec: 4685.57 - lr: 0.000024\n",
            "2023-04-21 02:18:28,913 epoch 52 - iter 42/79 - loss 0.32548270 - time (sec): 10.25 - samples/sec: 4700.37 - lr: 0.000024\n",
            "2023-04-21 02:18:30,544 epoch 52 - iter 49/79 - loss 0.32574394 - time (sec): 11.88 - samples/sec: 4848.28 - lr: 0.000024\n",
            "2023-04-21 02:18:32,282 epoch 52 - iter 56/79 - loss 0.32509634 - time (sec): 13.62 - samples/sec: 4843.85 - lr: 0.000024\n",
            "2023-04-21 02:18:33,536 epoch 52 - iter 63/79 - loss 0.32796133 - time (sec): 14.87 - samples/sec: 4922.26 - lr: 0.000024\n",
            "2023-04-21 02:18:35,314 epoch 52 - iter 70/79 - loss 0.33016407 - time (sec): 16.65 - samples/sec: 4943.09 - lr: 0.000024\n",
            "2023-04-21 02:18:37,582 epoch 52 - iter 77/79 - loss 0.33230759 - time (sec): 18.92 - samples/sec: 4885.89 - lr: 0.000024\n",
            "2023-04-21 02:18:38,019 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:18:38,020 EPOCH 52 done: loss 0.3318 - lr 0.000024\n"
          ]
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          "text": [
            "2023-04-21 02:18:59,373 Evaluating as a multi-label problem: True\n"
          ]
        },
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 02:19:01,685 TRAIN : loss 0.2734385132789612 - f1-score (weighted avg)  0.33\n"
          ]
        },
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          "text": [
            "2023-04-21 02:19:05,646 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:19:05,717 DEV : loss 0.28133654594421387 - f1-score (weighted avg)  0.3308\n",
            "2023-04-21 02:19:05,728 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 02:19:05,732 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:19:07,608 epoch 53 - iter 7/79 - loss 0.30966509 - time (sec): 1.87 - samples/sec: 5177.00 - lr: 0.000024\n",
            "2023-04-21 02:19:09,191 epoch 53 - iter 14/79 - loss 0.31978815 - time (sec): 3.46 - samples/sec: 5101.08 - lr: 0.000024\n",
            "2023-04-21 02:19:11,244 epoch 53 - iter 21/79 - loss 0.31451299 - time (sec): 5.51 - samples/sec: 4972.67 - lr: 0.000024\n",
            "2023-04-21 02:19:13,080 epoch 53 - iter 28/79 - loss 0.31867573 - time (sec): 7.35 - samples/sec: 4921.09 - lr: 0.000024\n",
            "2023-04-21 02:19:14,509 epoch 53 - iter 35/79 - loss 0.32572935 - time (sec): 8.78 - samples/sec: 5010.43 - lr: 0.000024\n",
            "2023-04-21 02:19:16,175 epoch 53 - iter 42/79 - loss 0.32625746 - time (sec): 10.44 - samples/sec: 5054.91 - lr: 0.000024\n",
            "2023-04-21 02:19:18,056 epoch 53 - iter 49/79 - loss 0.32638510 - time (sec): 12.32 - samples/sec: 4957.95 - lr: 0.000024\n",
            "2023-04-21 02:19:19,389 epoch 53 - iter 56/79 - loss 0.33193023 - time (sec): 13.66 - samples/sec: 4992.03 - lr: 0.000024\n",
            "2023-04-21 02:19:21,141 epoch 53 - iter 63/79 - loss 0.33133872 - time (sec): 15.41 - samples/sec: 4940.54 - lr: 0.000024\n",
            "2023-04-21 02:19:22,581 epoch 53 - iter 70/79 - loss 0.33069996 - time (sec): 16.85 - samples/sec: 4982.23 - lr: 0.000024\n",
            "2023-04-21 02:19:24,278 epoch 53 - iter 77/79 - loss 0.32816937 - time (sec): 18.55 - samples/sec: 4975.00 - lr: 0.000024\n",
            "2023-04-21 02:19:24,733 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:19:24,734 EPOCH 53 done: loss 0.3288 - lr 0.000024\n"
          ]
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          "text": [
            "2023-04-21 02:19:46,164 Evaluating as a multi-label problem: True\n"
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        },
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 02:19:48,472 TRAIN : loss 0.27345770597457886 - f1-score (weighted avg)  0.3302\n"
          ]
        },
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          "text": [
            "2023-04-21 02:19:51,292 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:19:51,359 DEV : loss 0.2813499867916107 - f1-score (weighted avg)  0.3315\n",
            "2023-04-21 02:19:51,369 Epoch    53: reducing learning rate of group 0 to 1.2207e-05.\n",
            "2023-04-21 02:19:51,370 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 02:19:51,374 ----------------------------------------------------------------------------------------------------\n"
          ]
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          "output_type": "stream",
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            "2023-04-21 02:19:53,315 epoch 54 - iter 7/79 - loss 0.30744159 - time (sec): 1.94 - samples/sec: 4574.93 - lr: 0.000012\n",
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            "2023-04-21 02:20:00,376 epoch 54 - iter 35/79 - loss 0.31899894 - time (sec): 9.00 - samples/sec: 4726.14 - lr: 0.000012\n",
            "2023-04-21 02:20:02,101 epoch 54 - iter 42/79 - loss 0.32466039 - time (sec): 10.73 - samples/sec: 4806.73 - lr: 0.000012\n",
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            "2023-04-21 02:20:06,906 epoch 54 - iter 63/79 - loss 0.32535489 - time (sec): 15.53 - samples/sec: 4799.28 - lr: 0.000012\n",
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            "2023-04-21 02:20:10,887 epoch 54 - iter 77/79 - loss 0.32470911 - time (sec): 19.51 - samples/sec: 4749.42 - lr: 0.000012\n",
            "2023-04-21 02:20:11,305 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:20:11,307 EPOCH 54 done: loss 0.3268 - lr 0.000012\n"
          ]
        },
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            "2023-04-21 02:20:33,877 Evaluating as a multi-label problem: True\n",
            "2023-04-21 02:20:36,162 TRAIN : loss 0.27316907048225403 - f1-score (weighted avg)  0.3309\n"
          ]
        },
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            "2023-04-21 02:20:38,985 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:20:39,052 DEV : loss 0.2810463607311249 - f1-score (weighted avg)  0.3329\n",
            "2023-04-21 02:20:39,063 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 02:20:39,070 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
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          "output_type": "stream",
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            "\n"
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            "2023-04-21 02:20:40,841 epoch 55 - iter 7/79 - loss 0.34266665 - time (sec): 1.77 - samples/sec: 5573.20 - lr: 0.000012\n",
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            "2023-04-21 02:20:51,178 epoch 55 - iter 49/79 - loss 0.33321934 - time (sec): 12.11 - samples/sec: 4786.79 - lr: 0.000012\n",
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            "2023-04-21 02:20:54,591 epoch 55 - iter 63/79 - loss 0.32990547 - time (sec): 15.52 - samples/sec: 4886.08 - lr: 0.000012\n",
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            "2023-04-21 02:20:58,042 epoch 55 - iter 77/79 - loss 0.32856508 - time (sec): 18.97 - samples/sec: 4848.14 - lr: 0.000012\n",
            "2023-04-21 02:20:58,849 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:20:58,851 EPOCH 55 done: loss 0.3280 - lr 0.000012\n"
          ]
        },
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            "2023-04-21 02:21:20,158 Evaluating as a multi-label problem: True\n"
          ]
        },
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            "\n"
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          "text": [
            "2023-04-21 02:21:22,437 TRAIN : loss 0.27297043800354004 - f1-score (weighted avg)  0.3316\n"
          ]
        },
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          "text": [
            "2023-04-21 02:21:25,288 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:21:25,354 DEV : loss 0.28088369965553284 - f1-score (weighted avg)  0.3381\n",
            "2023-04-21 02:21:25,365 Epoch    55: reducing learning rate of group 0 to 6.1035e-06.\n",
            "2023-04-21 02:21:25,367 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 02:21:25,370 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
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            "2023-04-21 02:21:26,824 epoch 56 - iter 7/79 - loss 0.32572479 - time (sec): 1.45 - samples/sec: 5379.22 - lr: 0.000006\n",
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            "2023-04-21 02:21:31,790 epoch 56 - iter 28/79 - loss 0.33493290 - time (sec): 6.42 - samples/sec: 4965.72 - lr: 0.000006\n",
            "2023-04-21 02:21:33,661 epoch 56 - iter 35/79 - loss 0.34317427 - time (sec): 8.29 - samples/sec: 4843.00 - lr: 0.000006\n",
            "2023-04-21 02:21:35,164 epoch 56 - iter 42/79 - loss 0.33898198 - time (sec): 9.79 - samples/sec: 4893.87 - lr: 0.000006\n",
            "2023-04-21 02:21:37,012 epoch 56 - iter 49/79 - loss 0.33541633 - time (sec): 11.64 - samples/sec: 4904.76 - lr: 0.000006\n",
            "2023-04-21 02:21:38,425 epoch 56 - iter 56/79 - loss 0.33314483 - time (sec): 13.05 - samples/sec: 5013.57 - lr: 0.000006\n",
            "2023-04-21 02:21:40,655 epoch 56 - iter 63/79 - loss 0.33259123 - time (sec): 15.28 - samples/sec: 4952.36 - lr: 0.000006\n",
            "2023-04-21 02:21:42,412 epoch 56 - iter 70/79 - loss 0.33133654 - time (sec): 17.04 - samples/sec: 4900.04 - lr: 0.000006\n",
            "2023-04-21 02:21:44,508 epoch 56 - iter 77/79 - loss 0.32921277 - time (sec): 19.14 - samples/sec: 4830.63 - lr: 0.000006\n",
            "2023-04-21 02:21:45,070 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:21:45,072 EPOCH 56 done: loss 0.3285 - lr 0.000006\n"
          ]
        },
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          "text": [
            "2023-04-21 02:22:07,707 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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        {
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          "text": [
            "2023-04-21 02:22:09,993 TRAIN : loss 0.27287936210632324 - f1-score (weighted avg)  0.3315\n"
          ]
        },
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          "text": [
            "2023-04-21 02:22:12,812 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:22:12,878 DEV : loss 0.28080326318740845 - f1-score (weighted avg)  0.3381\n",
            "2023-04-21 02:22:12,888 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 02:22:12,892 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 02:22:15,290 epoch 57 - iter 7/79 - loss 0.32753405 - time (sec): 2.40 - samples/sec: 3834.45 - lr: 0.000006\n",
            "2023-04-21 02:22:16,865 epoch 57 - iter 14/79 - loss 0.31841745 - time (sec): 3.97 - samples/sec: 4420.78 - lr: 0.000006\n",
            "2023-04-21 02:22:18,429 epoch 57 - iter 21/79 - loss 0.32011120 - time (sec): 5.54 - samples/sec: 4641.41 - lr: 0.000006\n",
            "2023-04-21 02:22:20,054 epoch 57 - iter 28/79 - loss 0.33109483 - time (sec): 7.16 - samples/sec: 4808.09 - lr: 0.000006\n",
            "2023-04-21 02:22:21,649 epoch 57 - iter 35/79 - loss 0.33109550 - time (sec): 8.75 - samples/sec: 4799.16 - lr: 0.000006\n",
            "2023-04-21 02:22:23,442 epoch 57 - iter 42/79 - loss 0.32486666 - time (sec): 10.55 - samples/sec: 4868.03 - lr: 0.000006\n",
            "2023-04-21 02:22:25,187 epoch 57 - iter 49/79 - loss 0.32555370 - time (sec): 12.29 - samples/sec: 4873.45 - lr: 0.000006\n",
            "2023-04-21 02:22:26,960 epoch 57 - iter 56/79 - loss 0.32600616 - time (sec): 14.07 - samples/sec: 4817.96 - lr: 0.000006\n",
            "2023-04-21 02:22:28,940 epoch 57 - iter 63/79 - loss 0.32387688 - time (sec): 16.05 - samples/sec: 4783.40 - lr: 0.000006\n",
            "2023-04-21 02:22:30,531 epoch 57 - iter 70/79 - loss 0.32617592 - time (sec): 17.64 - samples/sec: 4793.91 - lr: 0.000006\n",
            "2023-04-21 02:22:32,295 epoch 57 - iter 77/79 - loss 0.33070831 - time (sec): 19.40 - samples/sec: 4784.39 - lr: 0.000006\n",
            "2023-04-21 02:22:32,719 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:22:32,721 EPOCH 57 done: loss 0.3300 - lr 0.000006\n"
          ]
        },
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            "2023-04-21 02:22:55,365 Evaluating as a multi-label problem: True\n",
            "2023-04-21 02:22:57,630 TRAIN : loss 0.2727651298046112 - f1-score (weighted avg)  0.3323\n"
          ]
        },
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          "text": [
            "2023-04-21 02:23:00,475 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:23:00,542 DEV : loss 0.28066810965538025 - f1-score (weighted avg)  0.3381\n",
            "2023-04-21 02:23:00,554 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 02:23:00,558 saving best model\n"
          ]
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          "output_type": "stream",
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          "text": [
            "2023-04-21 02:23:02,650 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:23:04,162 epoch 58 - iter 7/79 - loss 0.33716551 - time (sec): 1.51 - samples/sec: 5355.26 - lr: 0.000006\n",
            "2023-04-21 02:23:05,612 epoch 58 - iter 14/79 - loss 0.34360963 - time (sec): 2.96 - samples/sec: 5492.15 - lr: 0.000006\n",
            "2023-04-21 02:23:07,399 epoch 58 - iter 21/79 - loss 0.35067227 - time (sec): 4.75 - samples/sec: 5095.62 - lr: 0.000006\n",
            "2023-04-21 02:23:08,910 epoch 58 - iter 28/79 - loss 0.34957327 - time (sec): 6.26 - samples/sec: 5222.08 - lr: 0.000006\n",
            "2023-04-21 02:23:10,624 epoch 58 - iter 35/79 - loss 0.34747397 - time (sec): 7.97 - samples/sec: 5227.27 - lr: 0.000006\n",
            "2023-04-21 02:23:12,460 epoch 58 - iter 42/79 - loss 0.34379487 - time (sec): 9.81 - samples/sec: 5116.90 - lr: 0.000006\n",
            "2023-04-21 02:23:14,870 epoch 58 - iter 49/79 - loss 0.34108055 - time (sec): 12.22 - samples/sec: 4870.74 - lr: 0.000006\n",
            "2023-04-21 02:23:16,774 epoch 58 - iter 56/79 - loss 0.33722765 - time (sec): 14.12 - samples/sec: 4848.10 - lr: 0.000006\n",
            "2023-04-21 02:23:18,400 epoch 58 - iter 63/79 - loss 0.33791945 - time (sec): 15.75 - samples/sec: 4815.10 - lr: 0.000006\n",
            "2023-04-21 02:23:20,140 epoch 58 - iter 70/79 - loss 0.33538540 - time (sec): 17.49 - samples/sec: 4812.65 - lr: 0.000006\n",
            "2023-04-21 02:23:21,950 epoch 58 - iter 77/79 - loss 0.33143737 - time (sec): 19.30 - samples/sec: 4794.81 - lr: 0.000006\n",
            "2023-04-21 02:23:22,490 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:23:22,491 EPOCH 58 done: loss 0.3310 - lr 0.000006\n"
          ]
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            "2023-04-21 02:23:43,819 Evaluating as a multi-label problem: True\n"
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        },
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            "\n"
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          "text": [
            "2023-04-21 02:23:46,098 TRAIN : loss 0.2728068232536316 - f1-score (weighted avg)  0.3327\n"
          ]
        },
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          "text": [
            "2023-04-21 02:23:48,913 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:23:48,979 DEV : loss 0.2806825041770935 - f1-score (weighted avg)  0.3381\n",
            "2023-04-21 02:23:48,989 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 02:23:48,993 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
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          "output_type": "stream",
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          "text": [
            "2023-04-21 02:23:50,550 epoch 59 - iter 7/79 - loss 0.33535028 - time (sec): 1.56 - samples/sec: 5174.25 - lr: 0.000006\n",
            "2023-04-21 02:23:52,338 epoch 59 - iter 14/79 - loss 0.32728431 - time (sec): 3.34 - samples/sec: 4615.11 - lr: 0.000006\n",
            "2023-04-21 02:23:53,936 epoch 59 - iter 21/79 - loss 0.31732510 - time (sec): 4.94 - samples/sec: 4844.76 - lr: 0.000006\n",
            "2023-04-21 02:23:55,554 epoch 59 - iter 28/79 - loss 0.31539039 - time (sec): 6.56 - samples/sec: 4932.25 - lr: 0.000006\n",
            "2023-04-21 02:23:57,101 epoch 59 - iter 35/79 - loss 0.32591475 - time (sec): 8.11 - samples/sec: 5014.75 - lr: 0.000006\n",
            "2023-04-21 02:23:58,715 epoch 59 - iter 42/79 - loss 0.32093016 - time (sec): 9.72 - samples/sec: 5058.91 - lr: 0.000006\n",
            "2023-04-21 02:24:00,735 epoch 59 - iter 49/79 - loss 0.32562310 - time (sec): 11.74 - samples/sec: 4890.56 - lr: 0.000006\n",
            "2023-04-21 02:24:02,808 epoch 59 - iter 56/79 - loss 0.32946261 - time (sec): 13.81 - samples/sec: 4771.16 - lr: 0.000006\n",
            "2023-04-21 02:24:04,468 epoch 59 - iter 63/79 - loss 0.33176071 - time (sec): 15.47 - samples/sec: 4794.73 - lr: 0.000006\n",
            "2023-04-21 02:24:06,193 epoch 59 - iter 70/79 - loss 0.33606893 - time (sec): 17.20 - samples/sec: 4875.72 - lr: 0.000006\n",
            "2023-04-21 02:24:08,208 epoch 59 - iter 77/79 - loss 0.33433216 - time (sec): 19.21 - samples/sec: 4811.91 - lr: 0.000006\n",
            "2023-04-21 02:24:08,743 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:24:08,745 EPOCH 59 done: loss 0.3327 - lr 0.000006\n"
          ]
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          "text": [
            "2023-04-21 02:24:31,280 Evaluating as a multi-label problem: True\n"
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        },
        {
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 02:24:33,586 TRAIN : loss 0.27292051911354065 - f1-score (weighted avg)  0.3315\n"
          ]
        },
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          "text": [
            "2023-04-21 02:24:36,403 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:24:36,472 DEV : loss 0.280784010887146 - f1-score (weighted avg)  0.3381\n",
            "2023-04-21 02:24:36,482 Epoch    59: reducing learning rate of group 0 to 3.0518e-06.\n",
            "2023-04-21 02:24:36,484 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 02:24:36,488 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
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          "output_type": "stream",
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          "text": [
            "2023-04-21 02:24:38,165 epoch 60 - iter 7/79 - loss 0.33189626 - time (sec): 1.67 - samples/sec: 5460.09 - lr: 0.000003\n",
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            "2023-04-21 02:24:52,131 epoch 60 - iter 63/79 - loss 0.33197686 - time (sec): 15.64 - samples/sec: 4853.37 - lr: 0.000003\n",
            "2023-04-21 02:24:54,024 epoch 60 - iter 70/79 - loss 0.33436562 - time (sec): 17.53 - samples/sec: 4834.48 - lr: 0.000003\n",
            "2023-04-21 02:24:55,731 epoch 60 - iter 77/79 - loss 0.33224830 - time (sec): 19.24 - samples/sec: 4824.49 - lr: 0.000003\n",
            "2023-04-21 02:24:56,090 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:24:56,091 EPOCH 60 done: loss 0.3329 - lr 0.000003\n"
          ]
        },
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            "2023-04-21 02:25:17,569 Evaluating as a multi-label problem: True\n"
          ]
        },
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            "\n"
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            "2023-04-21 02:25:21,101 TRAIN : loss 0.2729510962963104 - f1-score (weighted avg)  0.3312\n"
          ]
        },
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            "2023-04-21 02:25:23,914 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:25:23,982 DEV : loss 0.28081274032592773 - f1-score (weighted avg)  0.3376\n",
            "2023-04-21 02:25:23,992 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 02:25:23,996 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
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          "output_type": "stream",
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            "2023-04-21 02:25:25,620 epoch 61 - iter 7/79 - loss 0.36003658 - time (sec): 1.62 - samples/sec: 4835.60 - lr: 0.000003\n",
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            "2023-04-21 02:25:30,675 epoch 61 - iter 28/79 - loss 0.33832664 - time (sec): 6.68 - samples/sec: 5002.09 - lr: 0.000003\n",
            "2023-04-21 02:25:33,019 epoch 61 - iter 35/79 - loss 0.33545518 - time (sec): 9.02 - samples/sec: 4695.59 - lr: 0.000003\n",
            "2023-04-21 02:25:35,134 epoch 61 - iter 42/79 - loss 0.33060278 - time (sec): 11.14 - samples/sec: 4584.05 - lr: 0.000003\n",
            "2023-04-21 02:25:36,648 epoch 61 - iter 49/79 - loss 0.33141019 - time (sec): 12.65 - samples/sec: 4576.42 - lr: 0.000003\n",
            "2023-04-21 02:25:38,187 epoch 61 - iter 56/79 - loss 0.33386008 - time (sec): 14.19 - samples/sec: 4657.92 - lr: 0.000003\n",
            "2023-04-21 02:25:39,975 epoch 61 - iter 63/79 - loss 0.32828644 - time (sec): 15.98 - samples/sec: 4726.59 - lr: 0.000003\n",
            "2023-04-21 02:25:41,597 epoch 61 - iter 70/79 - loss 0.32851069 - time (sec): 17.60 - samples/sec: 4829.49 - lr: 0.000003\n",
            "2023-04-21 02:25:42,968 epoch 61 - iter 77/79 - loss 0.32797828 - time (sec): 18.97 - samples/sec: 4857.37 - lr: 0.000003\n",
            "2023-04-21 02:25:43,570 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:25:43,571 EPOCH 61 done: loss 0.3302 - lr 0.000003\n"
          ]
        },
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            "2023-04-21 02:26:05,010 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 02:26:07,292 TRAIN : loss 0.272946834564209 - f1-score (weighted avg)  0.3316\n"
          ]
        },
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          "text": [
            "2023-04-21 02:26:10,140 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:26:10,209 DEV : loss 0.28081151843070984 - f1-score (weighted avg)  0.3381\n",
            "2023-04-21 02:26:10,219 Epoch    61: reducing learning rate of group 0 to 1.5259e-06.\n",
            "2023-04-21 02:26:10,220 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 02:26:10,224 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 02:26:12,057 epoch 62 - iter 7/79 - loss 0.32189203 - time (sec): 1.83 - samples/sec: 4406.58 - lr: 0.000002\n",
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            "2023-04-21 02:26:19,646 epoch 62 - iter 35/79 - loss 0.32707148 - time (sec): 9.42 - samples/sec: 4763.14 - lr: 0.000002\n",
            "2023-04-21 02:26:21,564 epoch 62 - iter 42/79 - loss 0.32167537 - time (sec): 11.34 - samples/sec: 4791.43 - lr: 0.000002\n",
            "2023-04-21 02:26:23,179 epoch 62 - iter 49/79 - loss 0.32086928 - time (sec): 12.95 - samples/sec: 4797.50 - lr: 0.000002\n",
            "2023-04-21 02:26:24,768 epoch 62 - iter 56/79 - loss 0.32117897 - time (sec): 14.54 - samples/sec: 4792.07 - lr: 0.000002\n",
            "2023-04-21 02:26:26,064 epoch 62 - iter 63/79 - loss 0.32031296 - time (sec): 15.84 - samples/sec: 4827.97 - lr: 0.000002\n",
            "2023-04-21 02:26:27,727 epoch 62 - iter 70/79 - loss 0.32276837 - time (sec): 17.50 - samples/sec: 4836.54 - lr: 0.000002\n",
            "2023-04-21 02:26:29,188 epoch 62 - iter 77/79 - loss 0.32542721 - time (sec): 18.96 - samples/sec: 4882.71 - lr: 0.000002\n",
            "2023-04-21 02:26:29,758 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:26:29,760 EPOCH 62 done: loss 0.3255 - lr 0.000002\n"
          ]
        },
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            "2023-04-21 02:26:52,302 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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        {
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          "text": [
            "2023-04-21 02:26:54,588 TRAIN : loss 0.2729355990886688 - f1-score (weighted avg)  0.3315\n"
          ]
        },
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          "text": [
            "2023-04-21 02:26:57,396 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:26:57,466 DEV : loss 0.28080815076828003 - f1-score (weighted avg)  0.3381\n",
            "2023-04-21 02:26:57,476 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 02:26:57,480 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 02:26:59,345 epoch 63 - iter 7/79 - loss 0.33787756 - time (sec): 1.86 - samples/sec: 4412.41 - lr: 0.000002\n",
            "2023-04-21 02:27:00,995 epoch 63 - iter 14/79 - loss 0.34405516 - time (sec): 3.51 - samples/sec: 4863.10 - lr: 0.000002\n",
            "2023-04-21 02:27:02,736 epoch 63 - iter 21/79 - loss 0.33250718 - time (sec): 5.25 - samples/sec: 4777.92 - lr: 0.000002\n",
            "2023-04-21 02:27:04,791 epoch 63 - iter 28/79 - loss 0.32420010 - time (sec): 7.31 - samples/sec: 4900.32 - lr: 0.000002\n",
            "2023-04-21 02:27:06,757 epoch 63 - iter 35/79 - loss 0.32201419 - time (sec): 9.28 - samples/sec: 4805.20 - lr: 0.000002\n",
            "2023-04-21 02:27:08,228 epoch 63 - iter 42/79 - loss 0.32655779 - time (sec): 10.75 - samples/sec: 4868.32 - lr: 0.000002\n",
            "2023-04-21 02:27:10,259 epoch 63 - iter 49/79 - loss 0.32226129 - time (sec): 12.78 - samples/sec: 4861.92 - lr: 0.000002\n",
            "2023-04-21 02:27:11,762 epoch 63 - iter 56/79 - loss 0.32422201 - time (sec): 14.28 - samples/sec: 4870.12 - lr: 0.000002\n",
            "2023-04-21 02:27:13,217 epoch 63 - iter 63/79 - loss 0.32540983 - time (sec): 15.74 - samples/sec: 4893.14 - lr: 0.000002\n",
            "2023-04-21 02:27:14,703 epoch 63 - iter 70/79 - loss 0.32574437 - time (sec): 17.22 - samples/sec: 4909.08 - lr: 0.000002\n",
            "2023-04-21 02:27:16,483 epoch 63 - iter 77/79 - loss 0.32788791 - time (sec): 19.00 - samples/sec: 4877.41 - lr: 0.000002\n",
            "2023-04-21 02:27:16,848 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:27:16,850 EPOCH 63 done: loss 0.3274 - lr 0.000002\n"
          ]
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            "2023-04-21 02:27:38,294 Evaluating as a multi-label problem: True\n"
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        },
        {
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          "output_type": "stream",
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            "\n"
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        {
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          "text": [
            "2023-04-21 02:27:41,957 TRAIN : loss 0.27291539311408997 - f1-score (weighted avg)  0.3315\n"
          ]
        },
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          "text": [
            "2023-04-21 02:27:44,748 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:27:44,815 DEV : loss 0.2807895243167877 - f1-score (weighted avg)  0.3381\n",
            "2023-04-21 02:27:44,825 Epoch    63: reducing learning rate of group 0 to 7.6294e-07.\n",
            "2023-04-21 02:27:44,827 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 02:27:44,831 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
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          "output_type": "stream",
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          "text": [
            "2023-04-21 02:27:46,650 epoch 64 - iter 7/79 - loss 0.35963655 - time (sec): 1.82 - samples/sec: 4805.26 - lr: 0.000001\n",
            "2023-04-21 02:27:48,183 epoch 64 - iter 14/79 - loss 0.35368352 - time (sec): 3.35 - samples/sec: 5131.15 - lr: 0.000001\n",
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            "2023-04-21 02:27:52,080 epoch 64 - iter 28/79 - loss 0.33626770 - time (sec): 7.25 - samples/sec: 4891.65 - lr: 0.000001\n",
            "2023-04-21 02:27:53,788 epoch 64 - iter 35/79 - loss 0.33577996 - time (sec): 8.96 - samples/sec: 4765.96 - lr: 0.000001\n",
            "2023-04-21 02:27:55,177 epoch 64 - iter 42/79 - loss 0.33349627 - time (sec): 10.34 - samples/sec: 4810.70 - lr: 0.000001\n",
            "2023-04-21 02:27:56,864 epoch 64 - iter 49/79 - loss 0.33379933 - time (sec): 12.03 - samples/sec: 4869.54 - lr: 0.000001\n",
            "2023-04-21 02:27:58,531 epoch 64 - iter 56/79 - loss 0.32928461 - time (sec): 13.70 - samples/sec: 4870.36 - lr: 0.000001\n",
            "2023-04-21 02:28:00,436 epoch 64 - iter 63/79 - loss 0.33497189 - time (sec): 15.60 - samples/sec: 4864.73 - lr: 0.000001\n",
            "2023-04-21 02:28:02,440 epoch 64 - iter 70/79 - loss 0.33381630 - time (sec): 17.61 - samples/sec: 4797.88 - lr: 0.000001\n",
            "2023-04-21 02:28:04,246 epoch 64 - iter 77/79 - loss 0.33140744 - time (sec): 19.41 - samples/sec: 4761.68 - lr: 0.000001\n",
            "2023-04-21 02:28:04,773 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:28:04,775 EPOCH 64 done: loss 0.3306 - lr 0.000001\n"
          ]
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          "text": [
            "2023-04-21 02:28:26,148 Evaluating as a multi-label problem: True\n"
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        },
        {
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          "output_type": "stream",
          "text": [
            "\n"
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          "text": [
            "2023-04-21 02:28:28,418 TRAIN : loss 0.27292314171791077 - f1-score (weighted avg)  0.3315\n"
          ]
        },
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          "text": [
            "2023-04-21 02:28:31,230 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:28:31,296 DEV : loss 0.28079745173454285 - f1-score (weighted avg)  0.3381\n",
            "2023-04-21 02:28:31,307 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 02:28:31,311 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
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          "output_type": "stream",
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          "text": [
            "2023-04-21 02:28:32,924 epoch 65 - iter 7/79 - loss 0.32623896 - time (sec): 1.61 - samples/sec: 5134.02 - lr: 0.000001\n",
            "2023-04-21 02:28:34,400 epoch 65 - iter 14/79 - loss 0.33807981 - time (sec): 3.09 - samples/sec: 5067.56 - lr: 0.000001\n",
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            "2023-04-21 02:28:37,935 epoch 65 - iter 28/79 - loss 0.32927047 - time (sec): 6.62 - samples/sec: 5039.07 - lr: 0.000001\n",
            "2023-04-21 02:28:39,472 epoch 65 - iter 35/79 - loss 0.33322486 - time (sec): 8.16 - samples/sec: 5109.35 - lr: 0.000001\n",
            "2023-04-21 02:28:41,954 epoch 65 - iter 42/79 - loss 0.32213088 - time (sec): 10.64 - samples/sec: 4834.44 - lr: 0.000001\n",
            "2023-04-21 02:28:43,668 epoch 65 - iter 49/79 - loss 0.32714429 - time (sec): 12.35 - samples/sec: 4765.12 - lr: 0.000001\n",
            "2023-04-21 02:28:45,300 epoch 65 - iter 56/79 - loss 0.32567704 - time (sec): 13.99 - samples/sec: 4797.81 - lr: 0.000001\n",
            "2023-04-21 02:28:47,250 epoch 65 - iter 63/79 - loss 0.32698190 - time (sec): 15.94 - samples/sec: 4856.97 - lr: 0.000001\n",
            "2023-04-21 02:28:48,985 epoch 65 - iter 70/79 - loss 0.32657330 - time (sec): 17.67 - samples/sec: 4852.90 - lr: 0.000001\n",
            "2023-04-21 02:28:50,171 epoch 65 - iter 77/79 - loss 0.32856650 - time (sec): 18.86 - samples/sec: 4894.06 - lr: 0.000001\n",
            "2023-04-21 02:28:50,718 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:28:50,720 EPOCH 65 done: loss 0.3290 - lr 0.000001\n"
          ]
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          "text": [
            "2023-04-21 02:29:13,371 Evaluating as a multi-label problem: True\n"
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        },
        {
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          "output_type": "stream",
          "text": [
            "\n"
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          "text": [
            "2023-04-21 02:29:15,649 TRAIN : loss 0.272925466299057 - f1-score (weighted avg)  0.3315\n"
          ]
        },
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          "text": [
            "2023-04-21 02:29:18,474 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:29:18,543 DEV : loss 0.28080233931541443 - f1-score (weighted avg)  0.3376\n",
            "2023-04-21 02:29:18,554 Epoch    65: reducing learning rate of group 0 to 3.8147e-07.\n",
            "2023-04-21 02:29:18,556 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 02:29:18,559 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
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          "output_type": "stream",
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          "text": [
            "2023-04-21 02:29:20,278 epoch 66 - iter 7/79 - loss 0.32778363 - time (sec): 1.72 - samples/sec: 4664.43 - lr: 0.000000\n",
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            "2023-04-21 02:29:37,910 epoch 66 - iter 77/79 - loss 0.33030815 - time (sec): 19.35 - samples/sec: 4790.74 - lr: 0.000000\n",
            "2023-04-21 02:29:38,328 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:29:38,330 EPOCH 66 done: loss 0.3301 - lr 0.000000\n"
          ]
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            "2023-04-21 02:29:59,745 Evaluating as a multi-label problem: True\n"
          ]
        },
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            "\n"
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            "2023-04-21 02:30:03,309 TRAIN : loss 0.2729237973690033 - f1-score (weighted avg)  0.3314\n"
          ]
        },
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            "2023-04-21 02:30:06,107 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:30:06,179 DEV : loss 0.2807956337928772 - f1-score (weighted avg)  0.3376\n",
            "2023-04-21 02:30:06,189 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 02:30:06,193 ----------------------------------------------------------------------------------------------------\n"
          ]
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            "2023-04-21 02:30:07,951 epoch 67 - iter 7/79 - loss 0.32330111 - time (sec): 1.76 - samples/sec: 4700.66 - lr: 0.000000\n",
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            "2023-04-21 02:30:21,965 epoch 67 - iter 63/79 - loss 0.32604730 - time (sec): 15.77 - samples/sec: 4873.29 - lr: 0.000000\n",
            "2023-04-21 02:30:23,530 epoch 67 - iter 70/79 - loss 0.32501150 - time (sec): 17.33 - samples/sec: 4901.12 - lr: 0.000000\n",
            "2023-04-21 02:30:25,204 epoch 67 - iter 77/79 - loss 0.32725591 - time (sec): 19.01 - samples/sec: 4880.50 - lr: 0.000000\n",
            "2023-04-21 02:30:25,753 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:30:25,755 EPOCH 67 done: loss 0.3271 - lr 0.000000\n"
          ]
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            "2023-04-21 02:30:47,214 Evaluating as a multi-label problem: True\n"
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        },
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            "\n"
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            "2023-04-21 02:30:49,493 TRAIN : loss 0.27292001247406006 - f1-score (weighted avg)  0.3315\n"
          ]
        },
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            "2023-04-21 02:30:52,321 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:30:52,389 DEV : loss 0.28079622983932495 - f1-score (weighted avg)  0.3379\n",
            "2023-04-21 02:30:52,398 Epoch    67: reducing learning rate of group 0 to 1.9073e-07.\n",
            "2023-04-21 02:30:52,401 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 02:30:52,405 ----------------------------------------------------------------------------------------------------\n"
          ]
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            "2023-04-21 02:30:54,127 epoch 68 - iter 7/79 - loss 0.31449012 - time (sec): 1.72 - samples/sec: 4787.05 - lr: 0.000000\n",
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            "2023-04-21 02:31:11,961 epoch 68 - iter 77/79 - loss 0.32390297 - time (sec): 19.55 - samples/sec: 4750.57 - lr: 0.000000\n",
            "2023-04-21 02:31:12,327 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:31:12,329 EPOCH 68 done: loss 0.3242 - lr 0.000000\n"
          ]
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            "2023-04-21 02:31:34,909 Evaluating as a multi-label problem: True\n"
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        },
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            "\n"
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          "text": [
            "2023-04-21 02:31:37,203 TRAIN : loss 0.2729175388813019 - f1-score (weighted avg)  0.3316\n"
          ]
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            "2023-04-21 02:31:40,093 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:31:40,160 DEV : loss 0.2807907462120056 - f1-score (weighted avg)  0.3379\n",
            "2023-04-21 02:31:40,171 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 02:31:40,176 ----------------------------------------------------------------------------------------------------\n"
          ]
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          "text": [
            "2023-04-21 02:31:41,797 epoch 69 - iter 7/79 - loss 0.34565006 - time (sec): 1.62 - samples/sec: 4749.04 - lr: 0.000000\n",
            "2023-04-21 02:31:43,629 epoch 69 - iter 14/79 - loss 0.32763678 - time (sec): 3.45 - samples/sec: 4810.37 - lr: 0.000000\n",
            "2023-04-21 02:31:45,222 epoch 69 - iter 21/79 - loss 0.32735353 - time (sec): 5.05 - samples/sec: 4858.55 - lr: 0.000000\n",
            "2023-04-21 02:31:47,478 epoch 69 - iter 28/79 - loss 0.32931272 - time (sec): 7.30 - samples/sec: 4578.60 - lr: 0.000000\n",
            "2023-04-21 02:31:49,391 epoch 69 - iter 35/79 - loss 0.33063280 - time (sec): 9.21 - samples/sec: 4680.70 - lr: 0.000000\n",
            "2023-04-21 02:31:51,161 epoch 69 - iter 42/79 - loss 0.32726237 - time (sec): 10.98 - samples/sec: 4722.03 - lr: 0.000000\n",
            "2023-04-21 02:31:52,826 epoch 69 - iter 49/79 - loss 0.32524106 - time (sec): 12.65 - samples/sec: 4788.38 - lr: 0.000000\n",
            "2023-04-21 02:31:54,383 epoch 69 - iter 56/79 - loss 0.32792115 - time (sec): 14.21 - samples/sec: 4849.39 - lr: 0.000000\n",
            "2023-04-21 02:31:55,954 epoch 69 - iter 63/79 - loss 0.32779951 - time (sec): 15.78 - samples/sec: 4834.89 - lr: 0.000000\n",
            "2023-04-21 02:31:57,617 epoch 69 - iter 70/79 - loss 0.32888642 - time (sec): 17.44 - samples/sec: 4832.56 - lr: 0.000000\n",
            "2023-04-21 02:31:59,362 epoch 69 - iter 77/79 - loss 0.32972024 - time (sec): 19.18 - samples/sec: 4828.51 - lr: 0.000000\n",
            "2023-04-21 02:31:59,773 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:31:59,775 EPOCH 69 done: loss 0.3298 - lr 0.000000\n"
          ]
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            "2023-04-21 02:32:21,207 Evaluating as a multi-label problem: True\n"
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            "2023-04-21 02:32:24,855 TRAIN : loss 0.2729159891605377 - f1-score (weighted avg)  0.3317\n"
          ]
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            "2023-04-21 02:32:27,647 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:32:27,716 DEV : loss 0.28079134225845337 - f1-score (weighted avg)  0.3379\n",
            "2023-04-21 02:32:27,725 Epoch    69: reducing learning rate of group 0 to 9.5367e-08.\n",
            "2023-04-21 02:32:27,727 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 02:32:27,730 ----------------------------------------------------------------------------------------------------\n"
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            "2023-04-21 02:32:29,634 epoch 70 - iter 7/79 - loss 0.32261603 - time (sec): 1.90 - samples/sec: 5198.75 - lr: 0.000000\n",
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            "2023-04-21 02:32:43,748 epoch 70 - iter 63/79 - loss 0.33139217 - time (sec): 16.02 - samples/sec: 4732.40 - lr: 0.000000\n",
            "2023-04-21 02:32:45,453 epoch 70 - iter 70/79 - loss 0.33041977 - time (sec): 17.72 - samples/sec: 4784.71 - lr: 0.000000\n",
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            "2023-04-21 02:32:47,334 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:32:47,336 EPOCH 70 done: loss 0.3300 - lr 0.000000\n"
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            "2023-04-21 02:33:08,653 Evaluating as a multi-label problem: True\n"
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        },
        {
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          "output_type": "stream",
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            "\n"
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            "2023-04-21 02:33:10,939 TRAIN : loss 0.27291426062583923 - f1-score (weighted avg)  0.3315\n"
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            "2023-04-21 02:33:13,743 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:33:13,813 DEV : loss 0.2807898223400116 - f1-score (weighted avg)  0.3379\n",
            "2023-04-21 02:33:13,824 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 02:33:13,828 ----------------------------------------------------------------------------------------------------\n"
          ]
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            "2023-04-21 02:33:15,724 epoch 71 - iter 7/79 - loss 0.30661308 - time (sec): 1.89 - samples/sec: 4415.63 - lr: 0.000000\n",
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            "2023-04-21 02:33:31,010 epoch 71 - iter 70/79 - loss 0.32430081 - time (sec): 17.18 - samples/sec: 4887.52 - lr: 0.000000\n",
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            "2023-04-21 02:33:33,745 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:33:33,747 EPOCH 71 done: loss 0.3238 - lr 0.000000\n"
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            "2023-04-21 02:33:56,294 Evaluating as a multi-label problem: True\n",
            "2023-04-21 02:33:58,592 TRAIN : loss 0.272912859916687 - f1-score (weighted avg)  0.3314\n"
          ]
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            "2023-04-21 02:34:01,396 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:34:01,465 DEV : loss 0.28078553080558777 - f1-score (weighted avg)  0.3379\n",
            "2023-04-21 02:34:01,474 Epoch    71: reducing learning rate of group 0 to 4.7684e-08.\n",
            "2023-04-21 02:34:01,476 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 02:34:01,480 ----------------------------------------------------------------------------------------------------\n"
          ]
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            "2023-04-21 02:34:03,099 epoch 72 - iter 7/79 - loss 0.32869165 - time (sec): 1.62 - samples/sec: 5409.07 - lr: 0.000000\n",
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            "2023-04-21 02:34:19,039 epoch 72 - iter 70/79 - loss 0.33603832 - time (sec): 17.56 - samples/sec: 4796.21 - lr: 0.000000\n",
            "2023-04-21 02:34:20,547 epoch 72 - iter 77/79 - loss 0.33299318 - time (sec): 19.06 - samples/sec: 4848.45 - lr: 0.000000\n",
            "2023-04-21 02:34:21,013 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:34:21,015 EPOCH 72 done: loss 0.3319 - lr 0.000000\n"
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          "text": [
            "2023-04-21 02:34:42,341 Evaluating as a multi-label problem: True\n"
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        },
        {
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          "output_type": "stream",
          "text": [
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          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 02:34:44,623 TRAIN : loss 0.27291348576545715 - f1-score (weighted avg)  0.3314\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 10/10 [00:02<00:00,  3.64it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 02:34:47,454 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:34:47,520 DEV : loss 0.28078705072402954 - f1-score (weighted avg)  0.3379\n",
            "2023-04-21 02:34:47,531 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 02:34:47,535 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 02:34:49,082 epoch 73 - iter 7/79 - loss 0.36651644 - time (sec): 1.54 - samples/sec: 5030.78 - lr: 0.000000\n",
            "2023-04-21 02:34:50,538 epoch 73 - iter 14/79 - loss 0.33050752 - time (sec): 3.00 - samples/sec: 5070.38 - lr: 0.000000\n",
            "2023-04-21 02:34:52,110 epoch 73 - iter 21/79 - loss 0.32204657 - time (sec): 4.57 - samples/sec: 5243.77 - lr: 0.000000\n",
            "2023-04-21 02:34:53,702 epoch 73 - iter 28/79 - loss 0.32413388 - time (sec): 6.16 - samples/sec: 4972.74 - lr: 0.000000\n",
            "2023-04-21 02:34:55,945 epoch 73 - iter 35/79 - loss 0.32326655 - time (sec): 8.41 - samples/sec: 4849.01 - lr: 0.000000\n",
            "2023-04-21 02:34:57,711 epoch 73 - iter 42/79 - loss 0.32521846 - time (sec): 10.17 - samples/sec: 4830.98 - lr: 0.000000\n",
            "2023-04-21 02:34:59,534 epoch 73 - iter 49/79 - loss 0.32593629 - time (sec): 12.00 - samples/sec: 4791.04 - lr: 0.000000\n",
            "2023-04-21 02:35:01,304 epoch 73 - iter 56/79 - loss 0.32460837 - time (sec): 13.77 - samples/sec: 4751.10 - lr: 0.000000\n",
            "2023-04-21 02:35:02,850 epoch 73 - iter 63/79 - loss 0.32205356 - time (sec): 15.31 - samples/sec: 4870.69 - lr: 0.000000\n",
            "2023-04-21 02:35:04,645 epoch 73 - iter 70/79 - loss 0.32544986 - time (sec): 17.11 - samples/sec: 4890.17 - lr: 0.000000\n",
            "2023-04-21 02:35:06,435 epoch 73 - iter 77/79 - loss 0.32743004 - time (sec): 18.90 - samples/sec: 4874.43 - lr: 0.000000\n",
            "2023-04-21 02:35:06,912 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:35:06,913 EPOCH 73 done: loss 0.3280 - lr 0.000000\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 79/79 [00:22<00:00,  3.51it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 02:35:29,545 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 02:35:31,845 TRAIN : loss 0.27291399240493774 - f1-score (weighted avg)  0.3314\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 10/10 [00:02<00:00,  3.62it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 02:35:34,691 Evaluating as a multi-label problem: False\n",
            "2023-04-21 02:35:34,763 DEV : loss 0.2807901203632355 - f1-score (weighted avg)  0.3379\n",
            "2023-04-21 02:35:34,774 Epoch    73: reducing learning rate of group 0 to 2.3842e-08.\n",
            "2023-04-21 02:35:34,777 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 02:35:34,780 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:35:34,782 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:35:34,784 learning rate too small - quitting training!\n",
            "2023-04-21 02:35:34,785 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 02:35:37,061 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:35:38,939 SequenceTagger predicts: Dictionary with 19 tags: <unk>, O, B-ADR, I-ADR, E-ADR, B-Drug, B-Finding, B-Disease, B-Symptom, E-Finding, I-Finding, E-Disease, E-Symptom, E-Drug, I-Symptom, I-Drug, I-Disease, <START>, <STOP>\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 10/10 [00:03<00:00,  2.66it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 02:35:43,085 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 02:35:43,367 0.5043\t0.2951\t0.3723\t0.86\n",
            "2023-04-21 02:35:43,368 \n",
            "Results:\n",
            "- F-score (micro) 0.3723\n",
            "- F-score (macro) 0.0858\n",
            "- Accuracy 0.86\n",
            "\n",
            "By class:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       B-ADR     0.6549    0.4844    0.5569       576\n",
            "       I-ADR     0.2586    0.1714    0.2062       525\n",
            "       E-ADR     0.5590    0.4932    0.5240       442\n",
            "      B-Drug     0.0000    0.0000    0.0000       219\n",
            "   B-Finding     0.0000    0.0000    0.0000        52\n",
            "   E-Finding     0.0000    0.0000    0.0000        27\n",
            "      E-Drug     0.0000    0.0000    0.0000        27\n",
            "      I-Drug     0.0000    0.0000    0.0000        25\n",
            "   B-Symptom     0.0000    0.0000    0.0000        22\n",
            "   B-Disease     0.0000    0.0000    0.0000        20\n",
            "   I-Symptom     0.0000    0.0000    0.0000        20\n",
            "   E-Symptom     0.0000    0.0000    0.0000        13\n",
            "   E-Disease     0.0000    0.0000    0.0000         9\n",
            "   I-Finding     0.0000    0.0000    0.0000         9\n",
            "   I-Disease     0.0000    0.0000    0.0000         3\n",
            "\n",
            "   micro avg     0.5043    0.2951    0.3723      1989\n",
            "   macro avg     0.0982    0.0766    0.0858      1989\n",
            "weighted avg     0.3821    0.2951    0.3321      1989\n",
            " samples avg     0.0496    0.0496    0.0496      1989\n",
            "\n",
            "2023-04-21 02:35:43,369 ----------------------------------------------------------------------------------------------------\n"
          ]
        }
      ],
      "source": [
        "hist = trainer.train('flair_ner_model'+suf,\n",
        "              embeddings_storage_mode='gpu',\n",
        "              gold_label_dictionary_for_eval=tag_dictionary,\n",
        "              mini_batch_size = 12,\n",
        "              eval_batch_size = 12,\n",
        "              patience=1,\n",
        "              anneal_factor = 0.5,\n",
        "              anneal_against_dev_loss=True,\n",
        "              main_evaluation_metric = (\"weighted avg\", \"f1-score\"),\n",
        "              metrics_for_tensorboard=[(\"weighted avg\", 'precision'), (\"weighted avg\", 'recall'), (\"macro avg\", 'f1-score'), (\"macro avg\", 'precision'), (\"macro avg\", 'recall')],\n",
        "              use_tensorboard=True,\n",
        "              tensorboard_comment='Flair NER',\n",
        "              tensorboard_log_dir='runs/tb_logs'+suf,\n",
        "              min_learning_rate=3e-8,\n",
        "              optimizer=AdamW,\n",
        "              learning_rate=1e-1,\n",
        "              weight_decay=0.001,\n",
        "              max_epochs=100,\n",
        "              monitor_train=True,\n",
        "              save_optimizer_state=True,)\n",
        "# hist"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "ExiLRreqXsDJ"
      },
      "outputs": [],
      "source": [
        "# print(hist.keys())\n",
        "# hist"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "P5Uu5qkN0pia",
        "outputId": "028336b2-16f7-4fcb-9327-e80a70810e2a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "_v1\n",
            "2023-04-21 02:38:38,690 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:38:38,691 WARNING: No LOSS found for test split in this data.\n",
            "2023-04-21 02:38:38,692 Are you sure you want to plot LOSS and not another value?\n",
            "2023-04-21 02:38:38,692 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:38:38,717 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:38:38,718 WARNING: No F1 found for test split in this data.\n",
            "2023-04-21 02:38:38,719 Are you sure you want to plot F1 and not another value?\n",
            "2023-04-21 02:38:38,719 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 02:38:39,870 Loss and F1 plots are saved in flair_ner_model_v1/training.png\n"
          ]
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 1500x1000 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 79/79 [00:34<00:00,  2.31it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 02:39:14,510 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n",
            "100%|██████████| 10/10 [00:04<00:00,  2.38it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 02:39:20,997 Evaluating as a multi-label problem: False\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n",
            "100%|██████████| 10/10 [00:04<00:00,  2.26it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 02:39:25,523 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "data": {
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              "    <div class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>split</th>\n",
              "      <th>index</th>\n",
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              "      <th>E-Symptom</th>\n",
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              "      <th>I-Drug</th>\n",
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              "      <th>micro avg</th>\n",
              "      <th>macro avg</th>\n",
              "      <th>weighted avg</th>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.095961</td>\n",
              "      <td>0.385948</td>\n",
              "      <td>0.048855</td>\n",
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              "      <th>2</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.369675</td>\n",
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              "      <td>0.048841</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>train</td>\n",
              "      <td>support</td>\n",
              "      <td>4712.000000</td>\n",
              "      <td>4449.000000</td>\n",
              "      <td>3506.000000</td>\n",
              "      <td>1396.0</td>\n",
              "      <td>309.0</td>\n",
              "      <td>231.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>...</td>\n",
              "      <td>119.0</td>\n",
              "      <td>114.0</td>\n",
              "      <td>111.0</td>\n",
              "      <td>104.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>15722.000000</td>\n",
              "      <td>15722.000000</td>\n",
              "      <td>15722.000000</td>\n",
              "      <td>15722.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>dev</td>\n",
              "      <td>precision</td>\n",
              "      <td>0.657205</td>\n",
              "      <td>0.257971</td>\n",
              "      <td>0.553140</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.508628</td>\n",
              "      <td>0.097888</td>\n",
              "      <td>0.391855</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>dev</td>\n",
              "      <td>recall</td>\n",
              "      <td>0.462366</td>\n",
              "      <td>0.172147</td>\n",
              "      <td>0.510022</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.299613</td>\n",
              "      <td>0.076302</td>\n",
              "      <td>0.299613</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>dev</td>\n",
              "      <td>f1-score</td>\n",
              "      <td>0.542831</td>\n",
              "      <td>0.206497</td>\n",
              "      <td>0.530707</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.377094</td>\n",
              "      <td>0.085336</td>\n",
              "      <td>0.338059</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>dev</td>\n",
              "      <td>support</td>\n",
              "      <td>651.000000</td>\n",
              "      <td>517.000000</td>\n",
              "      <td>449.000000</td>\n",
              "      <td>184.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>...</td>\n",
              "      <td>15.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>2066.000000</td>\n",
              "      <td>2066.000000</td>\n",
              "      <td>2066.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>test</td>\n",
              "      <td>precision</td>\n",
              "      <td>0.654930</td>\n",
              "      <td>0.258621</td>\n",
              "      <td>0.558974</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.504296</td>\n",
              "      <td>0.098168</td>\n",
              "      <td>0.382143</td>\n",
              "      <td>0.049603</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>test</td>\n",
              "      <td>recall</td>\n",
              "      <td>0.484375</td>\n",
              "      <td>0.171429</td>\n",
              "      <td>0.493213</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.295123</td>\n",
              "      <td>0.076601</td>\n",
              "      <td>0.295123</td>\n",
              "      <td>0.049561</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>test</td>\n",
              "      <td>f1-score</td>\n",
              "      <td>0.556886</td>\n",
              "      <td>0.206186</td>\n",
              "      <td>0.524038</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.372344</td>\n",
              "      <td>0.085807</td>\n",
              "      <td>0.332146</td>\n",
              "      <td>0.049575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>test</td>\n",
              "      <td>support</td>\n",
              "      <td>576.000000</td>\n",
              "      <td>525.000000</td>\n",
              "      <td>442.000000</td>\n",
              "      <td>219.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>...</td>\n",
              "      <td>9.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1989.000000</td>\n",
              "      <td>1989.000000</td>\n",
              "      <td>1989.000000</td>\n",
              "      <td>1989.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>12 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cd4bd802-082d-4ef8-9d75-3b9f7288d294')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  "
            ],
            "text/plain": [
              "    split      index        B-ADR        I-ADR        E-ADR  B-Drug  \\\n",
              "0   train  precision     0.623118     0.286095     0.530208     0.0   \n",
              "1   train     recall     0.465620     0.168802     0.480605     0.0   \n",
              "2   train   f1-score     0.532977     0.212327     0.504189     0.0   \n",
              "3   train    support  4712.000000  4449.000000  3506.000000  1396.0   \n",
              "4     dev  precision     0.657205     0.257971     0.553140     0.0   \n",
              "5     dev     recall     0.462366     0.172147     0.510022     0.0   \n",
              "6     dev   f1-score     0.542831     0.206497     0.530707     0.0   \n",
              "7     dev    support   651.000000   517.000000   449.000000   184.0   \n",
              "8    test  precision     0.654930     0.258621     0.558974     0.0   \n",
              "9    test     recall     0.484375     0.171429     0.493213     0.0   \n",
              "10   test   f1-score     0.556886     0.206186     0.524038     0.0   \n",
              "11   test    support   576.000000   525.000000   442.000000   219.0   \n",
              "\n",
              "    B-Finding  B-Disease  B-Symptom  E-Finding  ...  E-Disease  E-Symptom  \\\n",
              "0         0.0        0.0        0.0        0.0  ...        0.0        0.0   \n",
              "1         0.0        0.0        0.0        0.0  ...        0.0        0.0   \n",
              "2         0.0        0.0        0.0        0.0  ...        0.0        0.0   \n",
              "3       309.0      231.0      222.0      200.0  ...      119.0      114.0   \n",
              "4         0.0        0.0        0.0        0.0  ...        0.0        0.0   \n",
              "5         0.0        0.0        0.0        0.0  ...        0.0        0.0   \n",
              "6         0.0        0.0        0.0        0.0  ...        0.0        0.0   \n",
              "7        64.0       31.0       25.0       34.0  ...       15.0       13.0   \n",
              "8         0.0        0.0        0.0        0.0  ...        0.0        0.0   \n",
              "9         0.0        0.0        0.0        0.0  ...        0.0        0.0   \n",
              "10        0.0        0.0        0.0        0.0  ...        0.0        0.0   \n",
              "11       52.0       20.0       22.0       27.0  ...        9.0       13.0   \n",
              "\n",
              "    E-Drug  I-Symptom  I-Drug  I-Disease     micro avg     macro avg  \\\n",
              "0      0.0        0.0     0.0        0.0      0.496408      0.095961   \n",
              "1      0.0        0.0     0.0        0.0      0.294492      0.074335   \n",
              "2      0.0        0.0     0.0        0.0      0.369675      0.083300   \n",
              "3    111.0      104.0    50.0       50.0  15722.000000  15722.000000   \n",
              "4      0.0        0.0     0.0        0.0      0.508628      0.097888   \n",
              "5      0.0        0.0     0.0        0.0      0.299613      0.076302   \n",
              "6      0.0        0.0     0.0        0.0      0.377094      0.085336   \n",
              "7     20.0       23.0     7.0       13.0   2066.000000   2066.000000   \n",
              "8      0.0        0.0     0.0        0.0      0.504296      0.098168   \n",
              "9      0.0        0.0     0.0        0.0      0.295123      0.076601   \n",
              "10     0.0        0.0     0.0        0.0      0.372344      0.085807   \n",
              "11    27.0       20.0    25.0        3.0   1989.000000   1989.000000   \n",
              "\n",
              "    weighted avg   samples avg  \n",
              "0       0.385948      0.048855  \n",
              "1       0.294492      0.048834  \n",
              "2       0.332255      0.048841  \n",
              "3   15722.000000  15722.000000  \n",
              "4       0.391855           NaN  \n",
              "5       0.299613           NaN  \n",
              "6       0.338059           NaN  \n",
              "7    2066.000000           NaN  \n",
              "8       0.382143      0.049603  \n",
              "9       0.295123      0.049561  \n",
              "10      0.332146      0.049575  \n",
              "11   1989.000000   1989.000000  \n",
              "\n",
              "[12 rows x 21 columns]"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(suf)\n",
        "\n",
        "os.mkdir('flair_ner_model'+suf+'/classification_report')\n",
        "plotter.plot_training_curves('flair_ner_model'+suf+'/loss.tsv')\n",
        "\n",
        "# evaluate the tagger on the test set\n",
        "result = tagger.evaluate(corpus.train, \n",
        "                         embeddings_storage_mode='gpu',\n",
        "                         mini_batch_size=12,\n",
        "                         main_evaluation_metric = (\"weighted avg\", \"f1-score\"),\n",
        "                         gold_label_type='ner',\n",
        "                         gold_label_dictionary=tag_dictionary)\n",
        "open('flair_ner_model'+suf+'/classification_report/train.txt', 'w').write(str(result))\n",
        "res1 = pd.DataFrame.from_dict(result.classification_report).reset_index()\n",
        "res1.insert(loc=0, column='split', value='train')\n",
        "\n",
        "result = tagger.evaluate(corpus.dev, \n",
        "                         embeddings_storage_mode='gpu',\n",
        "                         mini_batch_size=12,\n",
        "                         main_evaluation_metric = (\"weighted avg\", \"f1-score\"),\n",
        "                         gold_label_type='ner',\n",
        "                         gold_label_dictionary=tag_dictionary)\n",
        "open('flair_ner_model'+suf+'/classification_report/dev.txt', 'w').write(str(result))\n",
        "res2 = pd.DataFrame.from_dict(result.classification_report).reset_index()\n",
        "res2.insert(loc=0, column='split', value='dev')\n",
        "\n",
        "result = tagger.evaluate(corpus.test, \n",
        "                         embeddings_storage_mode='gpu',\n",
        "                         mini_batch_size=12,\n",
        "                         main_evaluation_metric = (\"weighted avg\", \"f1-score\"),\n",
        "                         gold_label_type='ner',\n",
        "                         gold_label_dictionary=tag_dictionary)\n",
        "open('flair_ner_model'+suf+'/classification_report/test.txt', 'w').write(str(result))\n",
        "res3 = pd.DataFrame.from_dict(result.classification_report).reset_index()\n",
        "res3.insert(loc=0, column='split', value='test')\n",
        "\n",
        "res = pd.concat([res1,res2,res3],ignore_index=True)\n",
        "res.to_csv('flair_ner_model'+suf+'/classification_report.csv',index=False)\n",
        "res"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "capiQJ2bb6NS"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "VvIDCtYJkY-G"
      },
      "outputs": [],
      "source": [
        "# import time\n",
        "# start_time = time.time()\n",
        "# while time.time() - start_time < 1:\n",
        "#     time.sleep(1)\n",
        "# print('Session has timed out (2 minutes). Disconnecting...')\n",
        "# !kill -9 -1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tGILL4RFtGil"
      },
      "source": [
        "Layering can be a good idea if the NER tags in the first layer have their own set of dependent values in the second layer. This can help capture more information about the entities being recognized and provide more context for downstream tasks. However, it also increases the complexity of the annotation and the training process, so it should be used judiciously. In general, it's a good idea to start with a simple tag scheme and gradually increase complexity as needed."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JoZ5D6aEtGin"
      },
      "source": [
        "    does it really matter if u use the E header\n",
        "\n",
        "It depends on the specific task and the requirements of the downstream model. In some cases, using the E tag can help disambiguate between overlapping entities, while in other cases it may not be necessary or even desired. Ultimately, it's up to the developer to determine the appropriate tagging scheme for their specific use case."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BGt2RhM6tGin"
      },
      "source": [
        "    would adding POS as an extra NER layer help model performance or not\n",
        "\n",
        "Adding POS as an extra NER layer may or may not help model performance, depending on the specific use case and data being analyzed.\n",
        "\n",
        "On one hand, incorporating POS tags could provide additional context to the model and help it better distinguish between named entities and other parts of speech. For example, knowing that a word is a noun or verb could help the model determine if it is likely to be a named entity or not.\n",
        "\n",
        "On the other hand, adding an additional layer could also increase model complexity and training time without necessarily improving performance. It may also require additional manual annotation efforts to create a labeled dataset with both NER and POS tags.\n",
        "\n",
        "Ultimately, whether to include POS as an additional layer will depend on the specific requirements and goals of the project, as well as the available data and resources."
      ]
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      "source": [
        "    besides bioes ordering (which is up-to-down), it doesn't matter what order the tags in the same domain appear in left-to-right?\n",
        "\n",
        "Yes, that is correct. As long as the BIOES order is maintained vertically, the order of tags in the same domain from left-to-right should not impact training in flai"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "    different typenames must be treated as separate tasks?\n",
        "\n",
        "Yes, that's correct. Different typenames should be treated as separate tasks because they represent different types of annotations that you want to predict. For example, in a biomedical NER task, you might want to predict the entity types of both drugs and diseases, which would be represented by two different typenames in your annotation data. Predicting both types of entities would require different models and possibly different evaluation metrics."
      ]
    }
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