flair_cadec_v3_spanbert_large_cased.ipynb

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    {
      "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": "77ac53a8-1c79-4b77-d679-81fd87e44a7a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\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": "bcbf0a98-02f9-44e2-c9a9-61fc4c467b87"
      },
      "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": "VNZtmxiPb5uV"
      },
      "outputs": [],
      "source": [
        "suf = '_v3'\n",
        "\n",
        "data_save_dir = 'data'+suf\n",
        "\n",
        "suf += '_spanbert-large-cased'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "BIUl2-W9tGii",
        "outputId": "0dfbb4fb-b19c-445d-dabe-135392fc74bf"
      },
      "outputs": [
        {
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              "      <th>meddra</th>\n",
              "      <th>counts_exploded</th>\n",
              "      <th>counts</th>\n",
              "      <th>counts_exploded_rank</th>\n",
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              "  <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",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <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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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
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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"
      ]
    },
    {
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      "execution_count": 7,
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              "      <th>2</th>\n",
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              "      <th>3</th>\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",
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              "    <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",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\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": "a1d061aa-6368-4e55-8b5e-aa2a62ea6675"
      },
      "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\"/I-ADR/I-Drowsy, \"&\"/O/O, \"have\"/O/O, \"a\"/O/O, \"little\"/B-ADR/B-Blurred_vision_-_hazy, \"blurred\"/I-ADR/I-Blurred_vision_-_hazy, \"vision\"/I-ADR/I-Blurred_vision_-_hazy, \",\"/O/O, \"so\"/O/O, \"far\"/O/O, \"no\"/O/O, \"gastric\"/B-ADR/B-Excessive_upper_gastrointestinal_gas, \"problems\"/I-ADR/I-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\"/I-ADR/I-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\"/I-ADR/I-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\"/I-Symptom/I-CONCEPT_LESS, \"like\"/O/O, \"a\"/O/O, \"toddler\"/O/O, \",\"/O/O, \"hip\"/B-Symptom/B-Hip_pain, \"pain\"/I-Symptom/I-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\"/I-ADR/I-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\"/I-ADR/I-Stomach_ache, \".\"/O/O, \"canker\"/B-ADR/B-Aphthous_ulcer, \"sores\"/I-ADR/I-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\"/I-ADR/I-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\"/I-ADR/I-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\"/I-Symptom/I-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\"/I-Symptom/I-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\"/I-ADR/I-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\"/I-ADR/I-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\"/I-Symptom/I-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\"/I-Disease/I-Osteoarthritis_of_knee, \"and\"/O/O, \"hands\"/I-Disease/I-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/I-ADR/B-Abdominal_swelling/B-Facial_swelling/I-Swelling_of_body_region/B-Swelling_of_lower_limb/B-Swelling_of_wrist_joint, \",\"/O/O, \"face\"/I-ADR/I-Facial_swelling, \",\"/O/O, \"wrists\"/I-ADR/I-Swelling_of_wrist_joint, \",\"/O/O, \"abdomen\"/I-ADR/I-Abdominal_swelling, \",\"/O/O, \"thighs\"/I-ADR/I-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\"/I-ADR/I-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\"/I-ADR/I-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\"/I-ADR/I-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, \")\"/I-Drug/I-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\"/I-ADR/I-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\"/I-ADR/I-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\"/I-ADR/I-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": "18b7f2b2-0e28-4566-c008-d8385b147ecc"
      },
      "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": "c190c940-9711-44d3-fcac-cf8d651f1844"
      },
      "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\"/I-ADR/I-Drowsy, \"&\"/O/O, \"have\"/O/O, \"a\"/O/O, \"little\"/B-ADR/B-Blurred_vision_-_hazy, \"blurred\"/I-ADR/I-Blurred_vision_-_hazy, \"vision\"/I-ADR/I-Blurred_vision_-_hazy, \",\"/O/O, \"so\"/O/O, \"far\"/O/O, \"no\"/O/O, \"gastric\"/B-ADR/B-Excessive_upper_gastrointestinal_gas, \"problems\"/I-ADR/I-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\"/I-ADR/I-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": "37e9a506-04ef-4e5e-b54f-ea9843eb73e4"
      },
      "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\"'/'I-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\"'/'I-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\"'/'I-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\"'/'I-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\"'/'I-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\"'/'I-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\"'/'I-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\"'/'I-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/",
          "height": 341,
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            "8a3c86b669cf454abe9112d2682d861c",
            "3530f97cce8047ff801dc5b7a7705d27",
            "7baea4a9e6e64b9a85da4af571873d89",
            "71b6042da1d942dd9c0c729aea5b1ed3",
            "db455fae2ee14dcfb50b6e631b2c9716",
            "73b81bfaac4a45de92b7052ca1fc6336",
            "dac1a6cb781a44bc869d931823341148",
            "4908cf605ec346609a2a4c588240a033",
            "1ae19e59bf2342d4b647fa06e3acf047",
            "c50c577a465c4033a43c256881303b5d",
            "e26ae6a491bd4f4ca6cc1ae54c4e3bea",
            "0c0f61469d634eaeb5197b52b6c736da",
            "613561b716cb4b76b6ecabd5cc3d1964"
          ]
        },
        "id": "QmNsy8AltGik",
        "outputId": "8d237b5f-c37f-4d82-da41-49470245c2fe"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 16:14:12,236 Filtering empty sentences\n",
            "2023-04-21 16:14:12,284 Corpus: 948 train + 119 dev + 119 test sentences\n",
            "2023-04-21 16:14:12,285 Computing label dictionary. Progress:\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "948it [00:00, 6967.93it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 16:14:12,426 Dictionary created for label 'ner' with 12 values: O (seen 79057 times), I-ADR (seen 7953 times), B-ADR (seen 4712 times), B-Drug (seen 1396 times), I-Finding (seen 349 times), B-Finding (seen 309 times), B-Disease (seen 231 times), B-Symptom (seen 222 times), I-Symptom (seen 218 times), I-Disease (seen 169 times), I-Drug (seen 161 times)\n",
            "False\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "data": {
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              "model_id": "2d849d81cb0e49dfa366379c94040c74",
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          "metadata": {},
          "output_type": "display_data"
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          "metadata": {},
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          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "True True False 1024\n",
            "{'is_token_embedding': True, 'is_document_embedding': False, 'allow_long_sentences': False, 'tokenizer': BertTokenizerFast(name_or_path='SpanBERT/spanbert-large-cased', vocab_size=28996, model_max_length=512, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'unk_token': '[UNK]', 'sep_token': '[SEP]', 'pad_token': '[PAD]', 'cls_token': '[CLS]', 'mask_token': '[MASK]', 'additional_special_tokens': ['[FLERT]']}, clean_up_tokenization_spaces=True), 'context_length': 64, 'context_dropout': 0.5, 'respect_document_boundaries': True, 'truncate': True, 'stride': 0, 'embedding_length': 1024, 'name': 'transformer-SpanBERT/spanbert-large-cased', 'fine_tune': True, 'use_lang_emb': False, 'force_max_length': True, 'feature_extractor': None, 'use_context_separator': True, 'needs_manual_ocr': False}\n",
            "BertTokenizerFast(name_or_path='SpanBERT/spanbert-large-cased', vocab_size=28996, model_max_length=512, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'unk_token': '[UNK]', 'sep_token': '[SEP]', 'pad_token': '[PAD]', 'cls_token': '[CLS]', 'mask_token': '[MASK]', 'additional_special_tokens': ['[FLERT]']}, clean_up_tokenization_spaces=True)\n",
            "2023-04-21 16:14:30,277 SequenceTagger predicts: Dictionary with 12 tags: <unk>, O, I-ADR, B-ADR, B-Drug, I-Finding, B-Finding, B-Disease, B-Symptom, I-Symptom, I-Disease, I-Drug\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",
        "\n",
        "embeddings = TransformerWordEmbeddings(model='SpanBERT/spanbert-large-cased',\n",
        "                                       layers=\"-1\",\n",
        "                                       subtoken_pooling=\"first\",\n",
        "                                       fine_tune=True,\n",
        "                                       use_context=True,\n",
        "                                       force_max_length=True,\n",
        "                                      #  truncate=True, # not accepted here\n",
        "                                      # #  is_document_embedding=True, # False is default\n",
        "                                      #  allow_long_sentences=True, # True is default; doesnt go through\n",
        "                                      #  embedding_length=512,\n",
        "                                       )\n",
        "# embeddings.allow_long_sentences = False\n",
        "embeddings.truncate = True\n",
        "# embeddings.embedding_length_internal = 512\n",
        "print(embeddings.force_max_length,embeddings.truncate,embeddings.allow_long_sentences,embeddings.embedding_length_internal)\n",
        "embeddings.tokenizer.model_max_length=512\n",
        "print(embeddings.to_args())\n",
        "print(embeddings.tokenizer)\n",
        "\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",
        "                        embeddings=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": "917028fc-8349-43d3-8371-ccaee41be2ef"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 16:14:30,705 tensorboard logging path is runs/tb_logs_v3_spanbert-large-cased\n",
            "2023-04-21 16:14:30,717 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:14:30,720 Model: \"SequenceTagger(\n",
            "  (embeddings): TransformerWordEmbeddings(\n",
            "    (model): BertModel(\n",
            "      (embeddings): BertEmbeddings(\n",
            "        (word_embeddings): Embedding(28997, 1024)\n",
            "        (position_embeddings): Embedding(512, 1024)\n",
            "        (token_type_embeddings): Embedding(2, 1024)\n",
            "        (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
            "        (dropout): Dropout(p=0.1, inplace=False)\n",
            "      )\n",
            "      (encoder): BertEncoder(\n",
            "        (layer): ModuleList(\n",
            "          (0-23): 24 x BertLayer(\n",
            "            (attention): BertAttention(\n",
            "              (self): BertSelfAttention(\n",
            "                (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
            "                (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
            "                (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
            "                (dropout): Dropout(p=0.1, inplace=False)\n",
            "              )\n",
            "              (output): BertSelfOutput(\n",
            "                (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
            "                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
            "                (dropout): Dropout(p=0.1, inplace=False)\n",
            "              )\n",
            "            )\n",
            "            (intermediate): BertIntermediate(\n",
            "              (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
            "              (intermediate_act_fn): GELUActivation()\n",
            "            )\n",
            "            (output): BertOutput(\n",
            "              (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
            "              (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (pooler): BertPooler(\n",
            "        (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
            "        (activation): Tanh()\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (locked_dropout): LockedDropout(p=0.5)\n",
            "  (embedding2nn): Linear(in_features=1024, out_features=1024, bias=True)\n",
            "  (linear): Linear(in_features=1024, out_features=12, bias=True)\n",
            "  (loss_function): CrossEntropyLoss()\n",
            ")\"\n",
            "2023-04-21 16:14:30,722 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:14:30,723 Corpus: \"Corpus: 948 train + 119 dev + 119 test sentences\"\n",
            "2023-04-21 16:14:30,725 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:14:30,726 Parameters:\n",
            "2023-04-21 16:14:30,728  - learning_rate: \"0.000005\"\n",
            "2023-04-21 16:14:30,729  - mini_batch_size: \"15\"\n",
            "2023-04-21 16:14:30,731  - patience: \"1\"\n",
            "2023-04-21 16:14:30,733  - anneal_factor: \"0.5\"\n",
            "2023-04-21 16:14:30,735  - max_epochs: \"100\"\n",
            "2023-04-21 16:14:30,736  - shuffle: \"True\"\n",
            "2023-04-21 16:14:30,737  - train_with_dev: \"False\"\n",
            "2023-04-21 16:14:30,739  - batch_growth_annealing: \"False\"\n",
            "2023-04-21 16:14:30,740 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:14:30,743 Model training base path: \"flair_ner_model_v3_spanbert-large-cased\"\n",
            "2023-04-21 16:14:30,744 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:14:30,747 Device: cuda:0\n",
            "2023-04-21 16:14:30,749 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:14:30,751 Embeddings storage mode: gpu\n",
            "2023-04-21 16:14:30,752 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:14:39,986 epoch 1 - iter 6/64 - loss 2.33951131 - time (sec): 9.23 - samples/sec: 1039.91 - lr: 0.000005\n",
            "2023-04-21 16:14:46,615 epoch 1 - iter 12/64 - loss 2.19488367 - time (sec): 15.86 - samples/sec: 1120.51 - lr: 0.000005\n",
            "2023-04-21 16:14:53,287 epoch 1 - iter 18/64 - loss 2.04024363 - time (sec): 22.53 - samples/sec: 1169.03 - lr: 0.000005\n",
            "2023-04-21 16:14:59,972 epoch 1 - iter 24/64 - loss 1.90437049 - time (sec): 29.22 - samples/sec: 1205.44 - lr: 0.000005\n",
            "2023-04-21 16:15:06,594 epoch 1 - iter 30/64 - loss 1.79934373 - time (sec): 35.84 - samples/sec: 1219.62 - lr: 0.000005\n",
            "2023-04-21 16:15:13,304 epoch 1 - iter 36/64 - loss 1.71718616 - time (sec): 42.55 - samples/sec: 1241.93 - lr: 0.000005\n",
            "2023-04-21 16:15:20,125 epoch 1 - iter 42/64 - loss 1.64780198 - time (sec): 49.37 - samples/sec: 1267.14 - lr: 0.000005\n",
            "2023-04-21 16:15:26,800 epoch 1 - iter 48/64 - loss 1.58107483 - time (sec): 56.05 - samples/sec: 1274.31 - lr: 0.000005\n",
            "2023-04-21 16:15:33,456 epoch 1 - iter 54/64 - loss 1.52864695 - time (sec): 62.70 - samples/sec: 1282.55 - lr: 0.000005\n",
            "2023-04-21 16:15:40,142 epoch 1 - iter 60/64 - loss 1.47874166 - time (sec): 69.39 - samples/sec: 1288.67 - lr: 0.000005\n",
            "2023-04-21 16:15:43,844 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:15:43,845 EPOCH 1 done: loss 1.4540 - lr 0.000005\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 64/64 [00:25<00:00,  2.52it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 16:16:09,324 Evaluating as a multi-label problem: True\n",
            "2023-04-21 16:16:12,140 TRAIN : loss 0.986102819442749 - f1-score (weighted avg)  0.0\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:03<00:00,  2.50it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 16:16:15,488 Evaluating as a multi-label problem: False\n",
            "2023-04-21 16:16:15,550 DEV : loss 0.9820303320884705 - f1-score (weighted avg)  0.0\n",
            "2023-04-21 16:16:15,570 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 16:16:15,575 saving best model\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 16:16:25,499 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:16:32,225 epoch 2 - iter 6/64 - loss 1.02124873 - time (sec): 6.72 - samples/sec: 1404.25 - lr: 0.000005\n",
            "2023-04-21 16:16:38,876 epoch 2 - iter 12/64 - loss 0.99118074 - time (sec): 13.37 - samples/sec: 1359.43 - lr: 0.000005\n",
            "2023-04-21 16:16:45,622 epoch 2 - iter 18/64 - loss 0.97102090 - time (sec): 20.12 - samples/sec: 1357.41 - lr: 0.000005\n",
            "2023-04-21 16:16:52,339 epoch 2 - iter 24/64 - loss 0.95199718 - time (sec): 26.84 - samples/sec: 1419.82 - lr: 0.000005\n",
            "2023-04-21 16:16:59,042 epoch 2 - iter 30/64 - loss 0.93875993 - time (sec): 33.54 - samples/sec: 1403.78 - lr: 0.000005\n",
            "2023-04-21 16:17:05,690 epoch 2 - iter 36/64 - loss 0.93100102 - time (sec): 40.19 - samples/sec: 1384.59 - lr: 0.000005\n",
            "2023-04-21 16:17:12,535 epoch 2 - iter 42/64 - loss 0.92977286 - time (sec): 47.03 - samples/sec: 1379.76 - lr: 0.000005\n",
            "2023-04-21 16:17:19,270 epoch 2 - iter 48/64 - loss 0.91981849 - time (sec): 53.77 - samples/sec: 1376.11 - lr: 0.000005\n",
            "2023-04-21 16:17:25,905 epoch 2 - iter 54/64 - loss 0.91462775 - time (sec): 60.40 - samples/sec: 1356.76 - lr: 0.000005\n",
            "2023-04-21 16:17:32,566 epoch 2 - iter 60/64 - loss 0.90620223 - time (sec): 67.06 - samples/sec: 1345.49 - lr: 0.000005\n",
            "2023-04-21 16:17:36,154 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:17:36,156 EPOCH 2 done: loss 0.9032 - lr 0.000005\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 64/64 [00:24<00:00,  2.61it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 16:18:00,768 Evaluating as a multi-label problem: True\n",
            "2023-04-21 16:18:02,534 TRAIN : loss 0.7920640707015991 - f1-score (weighted avg)  0.0\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:03<00:00,  2.54it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 16:18:05,853 Evaluating as a multi-label problem: False\n",
            "2023-04-21 16:18:05,917 DEV : loss 0.789519190788269 - f1-score (weighted avg)  0.0\n",
            "2023-04-21 16:18:05,936 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 16:18:05,940 saving best model\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 16:18:16,370 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:18:23,069 epoch 3 - iter 6/64 - loss 0.82206419 - time (sec): 6.70 - samples/sec: 1268.68 - lr: 0.000005\n",
            "2023-04-21 16:18:29,735 epoch 3 - iter 12/64 - loss 0.78950553 - time (sec): 13.36 - samples/sec: 1316.00 - lr: 0.000005\n",
            "2023-04-21 16:18:36,478 epoch 3 - iter 18/64 - loss 0.78792549 - time (sec): 20.11 - samples/sec: 1376.63 - lr: 0.000005\n",
            "2023-04-21 16:18:43,163 epoch 3 - iter 24/64 - loss 0.78932312 - time (sec): 26.79 - samples/sec: 1394.60 - lr: 0.000005\n",
            "2023-04-21 16:18:49,872 epoch 3 - iter 30/64 - loss 0.78388194 - time (sec): 33.50 - samples/sec: 1374.26 - lr: 0.000005\n",
            "2023-04-21 16:18:56,548 epoch 3 - iter 36/64 - loss 0.77568084 - time (sec): 40.18 - samples/sec: 1358.39 - lr: 0.000005\n",
            "2023-04-21 16:19:03,460 epoch 3 - iter 42/64 - loss 0.78260238 - time (sec): 47.09 - samples/sec: 1372.15 - lr: 0.000005\n",
            "2023-04-21 16:19:10,190 epoch 3 - iter 48/64 - loss 0.78096502 - time (sec): 53.82 - samples/sec: 1343.69 - lr: 0.000005\n",
            "2023-04-21 16:19:16,815 epoch 3 - iter 54/64 - loss 0.77591834 - time (sec): 60.44 - samples/sec: 1331.81 - lr: 0.000005\n",
            "2023-04-21 16:19:23,527 epoch 3 - iter 60/64 - loss 0.76302602 - time (sec): 67.15 - samples/sec: 1329.76 - lr: 0.000005\n",
            "2023-04-21 16:19:27,135 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:19:27,136 EPOCH 3 done: loss 0.7572 - lr 0.000005\n"
          ]
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            "2023-04-21 16:19:52,853 Evaluating as a multi-label problem: True\n",
            "2023-04-21 16:19:54,631 TRAIN : loss 0.6033528447151184 - f1-score (weighted avg)  0.0\n"
          ]
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            "2023-04-21 16:19:57,995 Evaluating as a multi-label problem: False\n",
            "2023-04-21 16:19:58,064 DEV : loss 0.6117203235626221 - f1-score (weighted avg)  0.0\n",
            "2023-04-21 16:19:58,084 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 16:19:58,088 saving best model\n"
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            "2023-04-21 16:20:10,941 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:20:17,630 epoch 4 - iter 6/64 - loss 0.59085915 - time (sec): 6.69 - samples/sec: 1397.01 - lr: 0.000005\n",
            "2023-04-21 16:20:24,304 epoch 4 - iter 12/64 - loss 0.59013338 - time (sec): 13.36 - samples/sec: 1318.86 - lr: 0.000005\n",
            "2023-04-21 16:20:31,039 epoch 4 - iter 18/64 - loss 0.58832346 - time (sec): 20.09 - samples/sec: 1334.62 - lr: 0.000005\n",
            "2023-04-21 16:20:37,711 epoch 4 - iter 24/64 - loss 0.58472533 - time (sec): 26.77 - samples/sec: 1297.86 - lr: 0.000005\n",
            "2023-04-21 16:20:44,587 epoch 4 - iter 30/64 - loss 0.58554434 - time (sec): 33.64 - samples/sec: 1321.47 - lr: 0.000005\n",
            "2023-04-21 16:20:51,363 epoch 4 - iter 36/64 - loss 0.57470752 - time (sec): 40.42 - samples/sec: 1334.51 - lr: 0.000005\n",
            "2023-04-21 16:20:59,080 epoch 4 - iter 42/64 - loss 0.56151367 - time (sec): 48.14 - samples/sec: 1306.06 - lr: 0.000005\n",
            "2023-04-21 16:21:05,775 epoch 4 - iter 48/64 - loss 0.55153203 - time (sec): 54.83 - samples/sec: 1320.56 - lr: 0.000005\n",
            "2023-04-21 16:21:12,468 epoch 4 - iter 54/64 - loss 0.54169734 - time (sec): 61.52 - samples/sec: 1320.95 - lr: 0.000005\n",
            "2023-04-21 16:21:19,147 epoch 4 - iter 60/64 - loss 0.54199359 - time (sec): 68.20 - samples/sec: 1315.14 - lr: 0.000005\n",
            "2023-04-21 16:21:22,786 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:21:22,787 EPOCH 4 done: loss 0.5391 - lr 0.000005\n"
          ]
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            "2023-04-21 16:21:47,617 Evaluating as a multi-label problem: True\n",
            "2023-04-21 16:21:50,536 TRAIN : loss 0.4731231927871704 - f1-score (weighted avg)  0.2689\n"
          ]
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            "2023-04-21 16:21:53,888 Evaluating as a multi-label problem: False\n",
            "2023-04-21 16:21:53,955 DEV : loss 0.48401886224746704 - f1-score (weighted avg)  0.2305\n",
            "2023-04-21 16:21:53,975 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 16:21:53,979 saving best model\n"
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            "2023-04-21 16:22:04,014 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:22:10,827 epoch 5 - iter 6/64 - loss 0.48387460 - time (sec): 6.81 - samples/sec: 1416.52 - lr: 0.000005\n",
            "2023-04-21 16:22:17,486 epoch 5 - iter 12/64 - loss 0.48703173 - time (sec): 13.46 - samples/sec: 1347.44 - lr: 0.000005\n",
            "2023-04-21 16:22:24,345 epoch 5 - iter 18/64 - loss 0.51018098 - time (sec): 20.32 - samples/sec: 1329.52 - lr: 0.000005\n",
            "2023-04-21 16:22:31,019 epoch 5 - iter 24/64 - loss 0.49540422 - time (sec): 27.00 - samples/sec: 1345.27 - lr: 0.000005\n",
            "2023-04-21 16:22:37,708 epoch 5 - iter 30/64 - loss 0.48186320 - time (sec): 33.69 - samples/sec: 1334.34 - lr: 0.000005\n",
            "2023-04-21 16:22:44,357 epoch 5 - iter 36/64 - loss 0.47875044 - time (sec): 40.34 - samples/sec: 1298.95 - lr: 0.000005\n",
            "2023-04-21 16:22:51,052 epoch 5 - iter 42/64 - loss 0.46854939 - time (sec): 47.03 - samples/sec: 1336.27 - lr: 0.000005\n",
            "2023-04-21 16:22:57,700 epoch 5 - iter 48/64 - loss 0.46440800 - time (sec): 53.68 - samples/sec: 1337.13 - lr: 0.000005\n",
            "2023-04-21 16:23:04,339 epoch 5 - iter 54/64 - loss 0.46073526 - time (sec): 60.32 - samples/sec: 1324.67 - lr: 0.000005\n",
            "2023-04-21 16:23:11,077 epoch 5 - iter 60/64 - loss 0.45647446 - time (sec): 67.06 - samples/sec: 1327.95 - lr: 0.000005\n",
            "2023-04-21 16:23:14,698 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:23:14,701 EPOCH 5 done: loss 0.4507 - lr 0.000005\n"
          ]
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            "2023-04-21 16:23:39,731 Evaluating as a multi-label problem: True\n",
            "2023-04-21 16:23:42,726 TRAIN : loss 0.3915712833404541 - f1-score (weighted avg)  0.2623\n"
          ]
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          "text": [
            "2023-04-21 16:23:46,095 Evaluating as a multi-label problem: False\n",
            "2023-04-21 16:23:46,161 DEV : loss 0.4178542196750641 - f1-score (weighted avg)  0.2197\n",
            "2023-04-21 16:23:46,180 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 16:23:46,191 saving best model\n"
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            "2023-04-21 16:23:55,733 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:24:02,562 epoch 6 - iter 6/64 - loss 0.42062308 - time (sec): 6.83 - samples/sec: 1520.50 - lr: 0.000005\n",
            "2023-04-21 16:24:09,398 epoch 6 - iter 12/64 - loss 0.45656116 - time (sec): 13.66 - samples/sec: 1401.16 - lr: 0.000005\n",
            "2023-04-21 16:24:16,143 epoch 6 - iter 18/64 - loss 0.43833915 - time (sec): 20.41 - samples/sec: 1355.41 - lr: 0.000005\n",
            "2023-04-21 16:24:22,820 epoch 6 - iter 24/64 - loss 0.43237600 - time (sec): 27.08 - samples/sec: 1368.14 - lr: 0.000005\n",
            "2023-04-21 16:24:29,485 epoch 6 - iter 30/64 - loss 0.41893171 - time (sec): 33.75 - samples/sec: 1348.56 - lr: 0.000005\n",
            "2023-04-21 16:24:36,100 epoch 6 - iter 36/64 - loss 0.41627039 - time (sec): 40.36 - samples/sec: 1336.20 - lr: 0.000005\n",
            "2023-04-21 16:24:42,856 epoch 6 - iter 42/64 - loss 0.40311162 - time (sec): 47.12 - samples/sec: 1365.23 - lr: 0.000005\n",
            "2023-04-21 16:24:49,483 epoch 6 - iter 48/64 - loss 0.40060281 - time (sec): 53.75 - samples/sec: 1341.07 - lr: 0.000005\n",
            "2023-04-21 16:24:56,146 epoch 6 - iter 54/64 - loss 0.39709642 - time (sec): 60.41 - samples/sec: 1328.75 - lr: 0.000005\n",
            "2023-04-21 16:25:02,829 epoch 6 - iter 60/64 - loss 0.39074809 - time (sec): 67.09 - samples/sec: 1340.17 - lr: 0.000005\n",
            "2023-04-21 16:25:06,505 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:25:06,507 EPOCH 6 done: loss 0.3914 - lr 0.000005\n"
          ]
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            "2023-04-21 16:25:31,294 Evaluating as a multi-label problem: True\n",
            "2023-04-21 16:25:33,148 TRAIN : loss 0.3359929025173187 - f1-score (weighted avg)  0.4931\n"
          ]
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          "text": [
            "2023-04-21 16:25:37,500 Evaluating as a multi-label problem: False\n",
            "2023-04-21 16:25:37,568 DEV : loss 0.36398202180862427 - f1-score (weighted avg)  0.4463\n",
            "2023-04-21 16:25:37,588 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 16:25:37,592 saving best model\n"
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            "2023-04-21 16:25:47,571 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:25:54,350 epoch 7 - iter 6/64 - loss 0.33915135 - time (sec): 6.78 - samples/sec: 1304.40 - lr: 0.000005\n",
            "2023-04-21 16:26:01,060 epoch 7 - iter 12/64 - loss 0.34563643 - time (sec): 13.49 - samples/sec: 1312.71 - lr: 0.000005\n",
            "2023-04-21 16:26:07,727 epoch 7 - iter 18/64 - loss 0.32574829 - time (sec): 20.15 - samples/sec: 1365.42 - lr: 0.000005\n",
            "2023-04-21 16:26:14,429 epoch 7 - iter 24/64 - loss 0.32989132 - time (sec): 26.86 - samples/sec: 1348.00 - lr: 0.000005\n",
            "2023-04-21 16:26:21,309 epoch 7 - iter 30/64 - loss 0.34172968 - time (sec): 33.74 - samples/sec: 1378.30 - lr: 0.000005\n",
            "2023-04-21 16:26:27,968 epoch 7 - iter 36/64 - loss 0.34393222 - time (sec): 40.39 - samples/sec: 1367.01 - lr: 0.000005\n",
            "2023-04-21 16:26:34,743 epoch 7 - iter 42/64 - loss 0.34448502 - time (sec): 47.17 - samples/sec: 1365.53 - lr: 0.000005\n",
            "2023-04-21 16:26:41,412 epoch 7 - iter 48/64 - loss 0.34326624 - time (sec): 53.84 - samples/sec: 1356.72 - lr: 0.000005\n",
            "2023-04-21 16:26:48,064 epoch 7 - iter 54/64 - loss 0.34067990 - time (sec): 60.49 - samples/sec: 1349.84 - lr: 0.000005\n",
            "2023-04-21 16:26:54,782 epoch 7 - iter 60/64 - loss 0.33397058 - time (sec): 67.21 - samples/sec: 1346.07 - lr: 0.000005\n",
            "2023-04-21 16:26:58,399 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:26:58,402 EPOCH 7 done: loss 0.3315 - lr 0.000005\n"
          ]
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            "2023-04-21 16:27:23,209 Evaluating as a multi-label problem: True\n",
            "2023-04-21 16:27:26,213 TRAIN : loss 0.2824366092681885 - f1-score (weighted avg)  0.6824\n"
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          "text": [
            "2023-04-21 16:27:29,563 Evaluating as a multi-label problem: False\n",
            "2023-04-21 16:27:29,633 DEV : loss 0.34281811118125916 - f1-score (weighted avg)  0.6118\n",
            "2023-04-21 16:27:29,653 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 16:27:29,658 saving best model\n"
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            "2023-04-21 16:27:39,322 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:27:46,011 epoch 8 - iter 6/64 - loss 0.29162501 - time (sec): 6.69 - samples/sec: 1193.48 - lr: 0.000005\n",
            "2023-04-21 16:27:52,880 epoch 8 - iter 12/64 - loss 0.32055089 - time (sec): 13.56 - samples/sec: 1373.42 - lr: 0.000005\n",
            "2023-04-21 16:27:59,526 epoch 8 - iter 18/64 - loss 0.31171585 - time (sec): 20.20 - samples/sec: 1336.11 - lr: 0.000005\n",
            "2023-04-21 16:28:06,162 epoch 8 - iter 24/64 - loss 0.30308458 - time (sec): 26.84 - samples/sec: 1307.22 - lr: 0.000005\n",
            "2023-04-21 16:28:12,869 epoch 8 - iter 30/64 - loss 0.29787322 - time (sec): 33.55 - samples/sec: 1290.56 - lr: 0.000005\n",
            "2023-04-21 16:28:19,559 epoch 8 - iter 36/64 - loss 0.29217527 - time (sec): 40.24 - samples/sec: 1305.65 - lr: 0.000005\n",
            "2023-04-21 16:28:26,313 epoch 8 - iter 42/64 - loss 0.29152016 - time (sec): 46.99 - samples/sec: 1320.68 - lr: 0.000005\n",
            "2023-04-21 16:28:32,989 epoch 8 - iter 48/64 - loss 0.28845231 - time (sec): 53.67 - samples/sec: 1313.53 - lr: 0.000005\n",
            "2023-04-21 16:28:39,713 epoch 8 - iter 54/64 - loss 0.28445554 - time (sec): 60.39 - samples/sec: 1317.33 - lr: 0.000005\n",
            "2023-04-21 16:28:46,413 epoch 8 - iter 60/64 - loss 0.27860871 - time (sec): 67.09 - samples/sec: 1342.44 - lr: 0.000005\n",
            "2023-04-21 16:28:50,068 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:28:50,069 EPOCH 8 done: loss 0.2767 - lr 0.000005\n"
          ]
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            "2023-04-21 16:29:14,871 Evaluating as a multi-label problem: True\n",
            "2023-04-21 16:29:17,928 TRAIN : loss 0.22873802483081818 - f1-score (weighted avg)  0.756\n"
          ]
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          "text": [
            "2023-04-21 16:29:21,302 Evaluating as a multi-label problem: False\n",
            "2023-04-21 16:29:21,369 DEV : loss 0.2969987094402313 - f1-score (weighted avg)  0.6678\n",
            "2023-04-21 16:29:21,390 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 16:29:21,394 saving best model\n"
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            "2023-04-21 16:29:31,045 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:29:37,781 epoch 9 - iter 6/64 - loss 0.24322404 - time (sec): 6.73 - samples/sec: 1257.05 - lr: 0.000005\n",
            "2023-04-21 16:29:44,493 epoch 9 - iter 12/64 - loss 0.23312013 - time (sec): 13.45 - samples/sec: 1362.13 - lr: 0.000005\n",
            "2023-04-21 16:29:51,272 epoch 9 - iter 18/64 - loss 0.25477403 - time (sec): 20.22 - samples/sec: 1398.14 - lr: 0.000005\n",
            "2023-04-21 16:29:57,917 epoch 9 - iter 24/64 - loss 0.24904185 - time (sec): 26.87 - samples/sec: 1361.93 - lr: 0.000005\n",
            "2023-04-21 16:30:04,591 epoch 9 - iter 30/64 - loss 0.24308796 - time (sec): 33.54 - samples/sec: 1340.05 - lr: 0.000005\n",
            "2023-04-21 16:30:11,241 epoch 9 - iter 36/64 - loss 0.24021142 - time (sec): 40.19 - samples/sec: 1297.92 - lr: 0.000005\n",
            "2023-04-21 16:30:17,907 epoch 9 - iter 42/64 - loss 0.24018979 - time (sec): 46.86 - samples/sec: 1298.02 - lr: 0.000005\n",
            "2023-04-21 16:30:24,603 epoch 9 - iter 48/64 - loss 0.23485975 - time (sec): 53.55 - samples/sec: 1313.13 - lr: 0.000005\n",
            "2023-04-21 16:30:31,463 epoch 9 - iter 54/64 - loss 0.24209100 - time (sec): 60.42 - samples/sec: 1321.17 - lr: 0.000005\n",
            "2023-04-21 16:30:38,180 epoch 9 - iter 60/64 - loss 0.23713346 - time (sec): 67.13 - samples/sec: 1344.33 - lr: 0.000005\n",
            "2023-04-21 16:30:41,793 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:30:41,795 EPOCH 9 done: loss 0.2384 - lr 0.000005\n"
          ]
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            "2023-04-21 16:31:06,607 Evaluating as a multi-label problem: True\n",
            "2023-04-21 16:31:09,719 TRAIN : loss 0.2051694542169571 - f1-score (weighted avg)  0.765\n"
          ]
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          "text": [
            "2023-04-21 16:31:13,060 Evaluating as a multi-label problem: False\n",
            "2023-04-21 16:31:13,130 DEV : loss 0.28591614961624146 - f1-score (weighted avg)  0.6742\n",
            "2023-04-21 16:31:13,150 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 16:31:13,154 saving best model\n"
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            "2023-04-21 16:31:23,449 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:31:30,334 epoch 10 - iter 6/64 - loss 0.31132309 - time (sec): 6.88 - samples/sec: 1376.19 - lr: 0.000005\n",
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            "2023-04-21 16:31:43,777 epoch 10 - iter 18/64 - loss 0.23944902 - time (sec): 20.32 - samples/sec: 1330.40 - lr: 0.000005\n",
            "2023-04-21 16:31:50,450 epoch 10 - iter 24/64 - loss 0.23209270 - time (sec): 27.00 - samples/sec: 1343.02 - lr: 0.000005\n",
            "2023-04-21 16:31:57,141 epoch 10 - iter 30/64 - loss 0.22605316 - time (sec): 33.69 - samples/sec: 1324.54 - lr: 0.000005\n",
            "2023-04-21 16:32:03,815 epoch 10 - iter 36/64 - loss 0.22325121 - time (sec): 40.36 - samples/sec: 1327.38 - lr: 0.000005\n",
            "2023-04-21 16:32:10,527 epoch 10 - iter 42/64 - loss 0.22040914 - time (sec): 47.08 - samples/sec: 1353.81 - lr: 0.000005\n",
            "2023-04-21 16:32:17,275 epoch 10 - iter 48/64 - loss 0.22062728 - time (sec): 53.82 - samples/sec: 1334.38 - lr: 0.000005\n",
            "2023-04-21 16:32:24,036 epoch 10 - iter 54/64 - loss 0.22126517 - time (sec): 60.58 - samples/sec: 1326.16 - lr: 0.000005\n",
            "2023-04-21 16:32:30,691 epoch 10 - iter 60/64 - loss 0.21645940 - time (sec): 67.24 - samples/sec: 1341.91 - lr: 0.000005\n",
            "2023-04-21 16:32:34,284 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:32:34,286 EPOCH 10 done: loss 0.2152 - lr 0.000005\n"
          ]
        },
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            "2023-04-21 16:32:59,071 Evaluating as a multi-label problem: True\n",
            "2023-04-21 16:33:00,948 TRAIN : loss 0.18524512648582458 - f1-score (weighted avg)  0.7765\n"
          ]
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            "2023-04-21 16:33:05,278 Evaluating as a multi-label problem: False\n",
            "2023-04-21 16:33:05,345 DEV : loss 0.27624422311782837 - f1-score (weighted avg)  0.6709\n",
            "2023-04-21 16:33:05,365 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 16:33:05,369 saving best model\n"
          ]
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            "2023-04-21 16:33:15,044 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:33:21,738 epoch 11 - iter 6/64 - loss 0.17849619 - time (sec): 6.69 - samples/sec: 1322.65 - lr: 0.000005\n",
            "2023-04-21 16:33:28,564 epoch 11 - iter 12/64 - loss 0.23104621 - time (sec): 13.51 - samples/sec: 1313.35 - lr: 0.000005\n",
            "2023-04-21 16:33:35,202 epoch 11 - iter 18/64 - loss 0.22965714 - time (sec): 20.15 - samples/sec: 1301.48 - lr: 0.000005\n",
            "2023-04-21 16:33:41,895 epoch 11 - iter 24/64 - loss 0.21642853 - time (sec): 26.84 - samples/sec: 1299.65 - lr: 0.000005\n",
            "2023-04-21 16:33:48,614 epoch 11 - iter 30/64 - loss 0.20578544 - time (sec): 33.56 - samples/sec: 1328.39 - lr: 0.000005\n",
            "2023-04-21 16:33:55,283 epoch 11 - iter 36/64 - loss 0.20666714 - time (sec): 40.23 - samples/sec: 1326.70 - lr: 0.000005\n",
            "2023-04-21 16:34:01,976 epoch 11 - iter 42/64 - loss 0.20537109 - time (sec): 46.92 - samples/sec: 1321.76 - lr: 0.000005\n",
            "2023-04-21 16:34:08,679 epoch 11 - iter 48/64 - loss 0.20318032 - time (sec): 53.63 - samples/sec: 1315.79 - lr: 0.000005\n",
            "2023-04-21 16:34:15,381 epoch 11 - iter 54/64 - loss 0.19690015 - time (sec): 60.33 - samples/sec: 1329.71 - lr: 0.000005\n",
            "2023-04-21 16:34:22,033 epoch 11 - iter 60/64 - loss 0.19662867 - time (sec): 66.98 - samples/sec: 1328.13 - lr: 0.000005\n",
            "2023-04-21 16:34:25,756 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:34:25,758 EPOCH 11 done: loss 0.1984 - lr 0.000005\n"
          ]
        },
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            "2023-04-21 16:34:50,584 Evaluating as a multi-label problem: True\n",
            "2023-04-21 16:34:53,655 TRAIN : loss 0.17494212090969086 - f1-score (weighted avg)  0.7798\n"
          ]
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            "2023-04-21 16:34:57,014 Evaluating as a multi-label problem: False\n",
            "2023-04-21 16:34:57,081 DEV : loss 0.28348758816719055 - f1-score (weighted avg)  0.6675\n",
            "2023-04-21 16:34:57,101 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 16:34:57,105 ----------------------------------------------------------------------------------------------------\n"
          ]
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            "2023-04-21 16:35:03,739 epoch 12 - iter 6/64 - loss 0.16655283 - time (sec): 6.63 - samples/sec: 1290.91 - lr: 0.000005\n",
            "2023-04-21 16:35:10,385 epoch 12 - iter 12/64 - loss 0.15889231 - time (sec): 13.28 - samples/sec: 1299.35 - lr: 0.000005\n",
            "2023-04-21 16:35:17,028 epoch 12 - iter 18/64 - loss 0.16102441 - time (sec): 19.92 - samples/sec: 1268.32 - lr: 0.000005\n",
            "2023-04-21 16:35:23,683 epoch 12 - iter 24/64 - loss 0.16609287 - time (sec): 26.58 - samples/sec: 1298.72 - lr: 0.000005\n",
            "2023-04-21 16:35:30,464 epoch 12 - iter 30/64 - loss 0.18800130 - time (sec): 33.36 - samples/sec: 1291.87 - lr: 0.000005\n",
            "2023-04-21 16:35:37,130 epoch 12 - iter 36/64 - loss 0.18744623 - time (sec): 40.02 - samples/sec: 1315.19 - lr: 0.000005\n",
            "2023-04-21 16:35:43,838 epoch 12 - iter 42/64 - loss 0.18313862 - time (sec): 46.73 - samples/sec: 1340.79 - lr: 0.000005\n",
            "2023-04-21 16:35:50,553 epoch 12 - iter 48/64 - loss 0.18016474 - time (sec): 53.45 - samples/sec: 1349.10 - lr: 0.000005\n",
            "2023-04-21 16:35:57,325 epoch 12 - iter 54/64 - loss 0.18433405 - time (sec): 60.22 - samples/sec: 1349.78 - lr: 0.000005\n",
            "2023-04-21 16:36:04,021 epoch 12 - iter 60/64 - loss 0.18514715 - time (sec): 66.91 - samples/sec: 1346.31 - lr: 0.000005\n",
            "2023-04-21 16:36:07,615 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:36:07,617 EPOCH 12 done: loss 0.1829 - lr 0.000005\n"
          ]
        },
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            "2023-04-21 16:36:32,295 Evaluating as a multi-label problem: True\n",
            "2023-04-21 16:36:35,274 TRAIN : loss 0.1551417112350464 - f1-score (weighted avg)  0.7958\n"
          ]
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          "text": [
            "2023-04-21 16:36:38,607 Evaluating as a multi-label problem: False\n",
            "2023-04-21 16:36:38,677 DEV : loss 0.28231310844421387 - f1-score (weighted avg)  0.6707\n",
            "2023-04-21 16:36:38,697 Epoch    12: reducing learning rate of group 0 to 2.5000e-06.\n",
            "2023-04-21 16:36:38,700 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 16:36:38,703 ----------------------------------------------------------------------------------------------------\n"
          ]
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            "2023-04-21 16:36:45,327 epoch 13 - iter 6/64 - loss 0.15371794 - time (sec): 6.62 - samples/sec: 1266.23 - lr: 0.000003\n",
            "2023-04-21 16:36:52,001 epoch 13 - iter 12/64 - loss 0.14452136 - time (sec): 13.30 - samples/sec: 1257.68 - lr: 0.000003\n",
            "2023-04-21 16:36:58,884 epoch 13 - iter 18/64 - loss 0.18813026 - time (sec): 20.18 - samples/sec: 1335.21 - lr: 0.000003\n",
            "2023-04-21 16:37:05,625 epoch 13 - iter 24/64 - loss 0.18360342 - time (sec): 26.92 - samples/sec: 1334.92 - lr: 0.000003\n",
            "2023-04-21 16:37:12,309 epoch 13 - iter 30/64 - loss 0.17950281 - time (sec): 33.60 - samples/sec: 1349.18 - lr: 0.000003\n",
            "2023-04-21 16:37:18,986 epoch 13 - iter 36/64 - loss 0.17699626 - time (sec): 40.28 - samples/sec: 1343.40 - lr: 0.000003\n",
            "2023-04-21 16:37:25,626 epoch 13 - iter 42/64 - loss 0.17226640 - time (sec): 46.92 - samples/sec: 1312.36 - lr: 0.000003\n",
            "2023-04-21 16:37:32,331 epoch 13 - iter 48/64 - loss 0.16847660 - time (sec): 53.63 - samples/sec: 1338.68 - lr: 0.000003\n",
            "2023-04-21 16:37:38,984 epoch 13 - iter 54/64 - loss 0.16729904 - time (sec): 60.28 - samples/sec: 1339.01 - lr: 0.000003\n",
            "2023-04-21 16:37:45,632 epoch 13 - iter 60/64 - loss 0.16722436 - time (sec): 66.93 - samples/sec: 1345.98 - lr: 0.000003\n",
            "2023-04-21 16:37:49,219 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:37:49,220 EPOCH 13 done: loss 0.1683 - lr 0.000003\n"
          ]
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            "2023-04-21 16:38:13,913 Evaluating as a multi-label problem: True\n",
            "2023-04-21 16:38:15,771 TRAIN : loss 0.14666293561458588 - f1-score (weighted avg)  0.8002\n"
          ]
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            "2023-04-21 16:38:20,098 Evaluating as a multi-label problem: False\n",
            "2023-04-21 16:38:20,169 DEV : loss 0.2791156768798828 - f1-score (weighted avg)  0.6701\n",
            "2023-04-21 16:38:20,189 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 16:38:20,194 ----------------------------------------------------------------------------------------------------\n"
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            "2023-04-21 16:38:26,857 epoch 14 - iter 6/64 - loss 0.16854351 - time (sec): 6.66 - samples/sec: 1295.74 - lr: 0.000003\n",
            "2023-04-21 16:38:33,544 epoch 14 - iter 12/64 - loss 0.16038858 - time (sec): 13.35 - samples/sec: 1395.50 - lr: 0.000003\n",
            "2023-04-21 16:38:40,235 epoch 14 - iter 18/64 - loss 0.15516017 - time (sec): 20.04 - samples/sec: 1375.03 - lr: 0.000003\n",
            "2023-04-21 16:38:46,884 epoch 14 - iter 24/64 - loss 0.14675110 - time (sec): 26.69 - samples/sec: 1363.91 - lr: 0.000003\n",
            "2023-04-21 16:38:53,564 epoch 14 - iter 30/64 - loss 0.15281822 - time (sec): 33.37 - samples/sec: 1340.09 - lr: 0.000003\n",
            "2023-04-21 16:39:00,203 epoch 14 - iter 36/64 - loss 0.15268251 - time (sec): 40.01 - samples/sec: 1319.87 - lr: 0.000003\n",
            "2023-04-21 16:39:06,892 epoch 14 - iter 42/64 - loss 0.14860270 - time (sec): 46.70 - samples/sec: 1342.55 - lr: 0.000003\n",
            "2023-04-21 16:39:13,537 epoch 14 - iter 48/64 - loss 0.14802584 - time (sec): 53.34 - samples/sec: 1327.67 - lr: 0.000003\n",
            "2023-04-21 16:39:20,327 epoch 14 - iter 54/64 - loss 0.15143965 - time (sec): 60.13 - samples/sec: 1345.57 - lr: 0.000003\n",
            "2023-04-21 16:39:27,153 epoch 14 - iter 60/64 - loss 0.16233669 - time (sec): 66.96 - samples/sec: 1341.43 - lr: 0.000003\n",
            "2023-04-21 16:39:30,777 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:39:30,779 EPOCH 14 done: loss 0.1610 - lr 0.000003\n"
          ]
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            "2023-04-21 16:39:55,492 Evaluating as a multi-label problem: True\n",
            "2023-04-21 16:39:58,487 TRAIN : loss 0.13808199763298035 - f1-score (weighted avg)  0.8144\n"
          ]
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          "text": [
            "2023-04-21 16:40:01,838 Evaluating as a multi-label problem: False\n",
            "2023-04-21 16:40:01,906 DEV : loss 0.27454912662506104 - f1-score (weighted avg)  0.6813\n",
            "2023-04-21 16:40:01,925 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 16:40:01,929 saving best model\n"
          ]
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            "2023-04-21 16:40:11,708 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:40:18,600 epoch 15 - iter 6/64 - loss 0.25809613 - time (sec): 6.89 - samples/sec: 1373.33 - lr: 0.000003\n",
            "2023-04-21 16:40:25,359 epoch 15 - iter 12/64 - loss 0.21511999 - time (sec): 13.64 - samples/sec: 1386.18 - lr: 0.000003\n",
            "2023-04-21 16:40:32,029 epoch 15 - iter 18/64 - loss 0.18196942 - time (sec): 20.31 - samples/sec: 1404.38 - lr: 0.000003\n",
            "2023-04-21 16:40:38,714 epoch 15 - iter 24/64 - loss 0.17168870 - time (sec): 27.00 - samples/sec: 1397.56 - lr: 0.000003\n",
            "2023-04-21 16:40:45,410 epoch 15 - iter 30/64 - loss 0.16378225 - time (sec): 33.70 - samples/sec: 1385.48 - lr: 0.000003\n",
            "2023-04-21 16:40:52,051 epoch 15 - iter 36/64 - loss 0.16311372 - time (sec): 40.34 - samples/sec: 1349.40 - lr: 0.000003\n",
            "2023-04-21 16:40:58,773 epoch 15 - iter 42/64 - loss 0.16049274 - time (sec): 47.06 - samples/sec: 1379.47 - lr: 0.000003\n",
            "2023-04-21 16:41:05,431 epoch 15 - iter 48/64 - loss 0.15717043 - time (sec): 53.72 - samples/sec: 1341.17 - lr: 0.000003\n",
            "2023-04-21 16:41:12,103 epoch 15 - iter 54/64 - loss 0.15345820 - time (sec): 60.39 - samples/sec: 1357.19 - lr: 0.000003\n",
            "2023-04-21 16:41:18,788 epoch 15 - iter 60/64 - loss 0.15438378 - time (sec): 67.07 - samples/sec: 1351.15 - lr: 0.000003\n",
            "2023-04-21 16:41:22,394 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:41:22,396 EPOCH 15 done: loss 0.1520 - lr 0.000003\n"
          ]
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            "2023-04-21 16:41:47,189 Evaluating as a multi-label problem: True\n",
            "2023-04-21 16:41:50,245 TRAIN : loss 0.13684611022472382 - f1-score (weighted avg)  0.821\n"
          ]
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          "text": [
            "2023-04-21 16:41:53,618 Evaluating as a multi-label problem: False\n",
            "2023-04-21 16:41:53,690 DEV : loss 0.2791268229484558 - f1-score (weighted avg)  0.6844\n",
            "2023-04-21 16:41:53,710 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 16:41:53,715 ----------------------------------------------------------------------------------------------------\n"
          ]
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            "2023-04-21 16:42:00,424 epoch 16 - iter 6/64 - loss 0.13485861 - time (sec): 6.71 - samples/sec: 1357.05 - lr: 0.000003\n",
            "2023-04-21 16:42:07,079 epoch 16 - iter 12/64 - loss 0.13241487 - time (sec): 13.36 - samples/sec: 1317.05 - lr: 0.000003\n",
            "2023-04-21 16:42:13,930 epoch 16 - iter 18/64 - loss 0.16133217 - time (sec): 20.21 - samples/sec: 1367.37 - lr: 0.000003\n",
            "2023-04-21 16:42:20,608 epoch 16 - iter 24/64 - loss 0.15338556 - time (sec): 26.89 - samples/sec: 1390.25 - lr: 0.000003\n",
            "2023-04-21 16:42:27,269 epoch 16 - iter 30/64 - loss 0.14789256 - time (sec): 33.55 - samples/sec: 1405.89 - lr: 0.000003\n",
            "2023-04-21 16:42:33,995 epoch 16 - iter 36/64 - loss 0.14357286 - time (sec): 40.28 - samples/sec: 1396.24 - lr: 0.000003\n",
            "2023-04-21 16:42:40,662 epoch 16 - iter 42/64 - loss 0.14275077 - time (sec): 46.95 - samples/sec: 1380.99 - lr: 0.000003\n",
            "2023-04-21 16:42:47,322 epoch 16 - iter 48/64 - loss 0.14472441 - time (sec): 53.61 - samples/sec: 1370.06 - lr: 0.000003\n",
            "2023-04-21 16:42:54,039 epoch 16 - iter 54/64 - loss 0.14578106 - time (sec): 60.32 - samples/sec: 1354.57 - lr: 0.000003\n",
            "2023-04-21 16:43:00,715 epoch 16 - iter 60/64 - loss 0.14804688 - time (sec): 67.00 - samples/sec: 1342.83 - lr: 0.000003\n",
            "2023-04-21 16:43:04,308 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:43:04,310 EPOCH 16 done: loss 0.1484 - lr 0.000003\n"
          ]
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          "text": [
            "2023-04-21 16:43:29,067 Evaluating as a multi-label problem: True\n",
            "2023-04-21 16:43:32,119 TRAIN : loss 0.1278633177280426 - f1-score (weighted avg)  0.8347\n"
          ]
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          "text": [
            "2023-04-21 16:43:35,464 Evaluating as a multi-label problem: False\n",
            "2023-04-21 16:43:35,532 DEV : loss 0.27127304673194885 - f1-score (weighted avg)  0.6884\n",
            "2023-04-21 16:43:35,552 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 16:43:35,557 saving best model\n"
          ]
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            "2023-04-21 16:43:45,037 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:43:51,974 epoch 17 - iter 6/64 - loss 0.23419236 - time (sec): 6.93 - samples/sec: 1338.50 - lr: 0.000003\n",
            "2023-04-21 16:43:58,624 epoch 17 - iter 12/64 - loss 0.18721365 - time (sec): 13.59 - samples/sec: 1368.05 - lr: 0.000003\n",
            "2023-04-21 16:44:05,307 epoch 17 - iter 18/64 - loss 0.16879814 - time (sec): 20.27 - samples/sec: 1339.28 - lr: 0.000003\n",
            "2023-04-21 16:44:11,942 epoch 17 - iter 24/64 - loss 0.15770108 - time (sec): 26.90 - samples/sec: 1333.04 - lr: 0.000003\n",
            "2023-04-21 16:44:18,601 epoch 17 - iter 30/64 - loss 0.15333381 - time (sec): 33.56 - samples/sec: 1337.03 - lr: 0.000003\n",
            "2023-04-21 16:44:25,218 epoch 17 - iter 36/64 - loss 0.14548694 - time (sec): 40.18 - samples/sec: 1304.58 - lr: 0.000003\n",
            "2023-04-21 16:44:32,010 epoch 17 - iter 42/64 - loss 0.14757517 - time (sec): 46.97 - samples/sec: 1348.04 - lr: 0.000003\n",
            "2023-04-21 16:44:38,678 epoch 17 - iter 48/64 - loss 0.14456013 - time (sec): 53.64 - samples/sec: 1372.37 - lr: 0.000003\n",
            "2023-04-21 16:44:45,321 epoch 17 - iter 54/64 - loss 0.14268645 - time (sec): 60.28 - samples/sec: 1357.88 - lr: 0.000003\n",
            "2023-04-21 16:44:52,038 epoch 17 - iter 60/64 - loss 0.14249945 - time (sec): 67.00 - samples/sec: 1349.23 - lr: 0.000003\n",
            "2023-04-21 16:44:55,646 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:44:55,647 EPOCH 17 done: loss 0.1425 - lr 0.000003\n"
          ]
        },
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            "2023-04-21 16:45:20,531 Evaluating as a multi-label problem: True\n",
            "2023-04-21 16:45:23,661 TRAIN : loss 0.12029071152210236 - f1-score (weighted avg)  0.8477\n"
          ]
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            "2023-04-21 16:45:27,039 Evaluating as a multi-label problem: False\n",
            "2023-04-21 16:45:27,108 DEV : loss 0.2804987132549286 - f1-score (weighted avg)  0.7005\n",
            "2023-04-21 16:45:27,128 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 16:45:27,132 ----------------------------------------------------------------------------------------------------\n"
          ]
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            "2023-04-21 16:45:33,787 epoch 18 - iter 6/64 - loss 0.13566914 - time (sec): 6.65 - samples/sec: 1293.10 - lr: 0.000003\n",
            "2023-04-21 16:45:40,455 epoch 18 - iter 12/64 - loss 0.12636799 - time (sec): 13.32 - samples/sec: 1381.24 - lr: 0.000003\n",
            "2023-04-21 16:45:47,091 epoch 18 - iter 18/64 - loss 0.12493610 - time (sec): 19.96 - samples/sec: 1324.73 - lr: 0.000003\n",
            "2023-04-21 16:45:53,779 epoch 18 - iter 24/64 - loss 0.13271208 - time (sec): 26.65 - samples/sec: 1303.65 - lr: 0.000003\n",
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            "2023-04-21 16:46:07,165 epoch 18 - iter 36/64 - loss 0.12746612 - time (sec): 40.03 - samples/sec: 1340.50 - lr: 0.000003\n",
            "2023-04-21 16:46:13,886 epoch 18 - iter 42/64 - loss 0.12673775 - time (sec): 46.75 - samples/sec: 1364.02 - lr: 0.000003\n",
            "2023-04-21 16:46:20,635 epoch 18 - iter 48/64 - loss 0.12896263 - time (sec): 53.50 - samples/sec: 1376.48 - lr: 0.000003\n",
            "2023-04-21 16:46:27,329 epoch 18 - iter 54/64 - loss 0.12811535 - time (sec): 60.20 - samples/sec: 1368.92 - lr: 0.000003\n",
            "2023-04-21 16:46:33,951 epoch 18 - iter 60/64 - loss 0.12671403 - time (sec): 66.82 - samples/sec: 1341.89 - lr: 0.000003\n",
            "2023-04-21 16:46:37,698 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:46:37,700 EPOCH 18 done: loss 0.1364 - lr 0.000003\n"
          ]
        },
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            "2023-04-21 16:47:02,418 Evaluating as a multi-label problem: True\n",
            "2023-04-21 16:47:04,276 TRAIN : loss 0.11911486089229584 - f1-score (weighted avg)  0.8467\n"
          ]
        },
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          "text": [
            "2023-04-21 16:47:07,628 Evaluating as a multi-label problem: False\n",
            "2023-04-21 16:47:07,697 DEV : loss 0.27640819549560547 - f1-score (weighted avg)  0.6961\n",
            "2023-04-21 16:47:07,718 Epoch    18: reducing learning rate of group 0 to 1.2500e-06.\n",
            "2023-04-21 16:47:07,720 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 16:47:07,724 ----------------------------------------------------------------------------------------------------\n"
          ]
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            "2023-04-21 16:47:14,361 epoch 19 - iter 6/64 - loss 0.11779804 - time (sec): 6.64 - samples/sec: 1335.24 - lr: 0.000001\n",
            "2023-04-21 16:47:21,015 epoch 19 - iter 12/64 - loss 0.10503245 - time (sec): 13.29 - samples/sec: 1326.52 - lr: 0.000001\n",
            "2023-04-21 16:47:27,674 epoch 19 - iter 18/64 - loss 0.10003574 - time (sec): 19.95 - samples/sec: 1296.19 - lr: 0.000001\n",
            "2023-04-21 16:47:34,525 epoch 19 - iter 24/64 - loss 0.12889518 - time (sec): 26.80 - samples/sec: 1317.06 - lr: 0.000001\n",
            "2023-04-21 16:47:41,177 epoch 19 - iter 30/64 - loss 0.12781542 - time (sec): 33.45 - samples/sec: 1321.31 - lr: 0.000001\n",
            "2023-04-21 16:47:47,894 epoch 19 - iter 36/64 - loss 0.13358979 - time (sec): 40.17 - samples/sec: 1309.57 - lr: 0.000001\n",
            "2023-04-21 16:47:54,640 epoch 19 - iter 42/64 - loss 0.13323280 - time (sec): 46.91 - samples/sec: 1333.76 - lr: 0.000001\n",
            "2023-04-21 16:48:01,295 epoch 19 - iter 48/64 - loss 0.13421649 - time (sec): 53.57 - samples/sec: 1327.39 - lr: 0.000001\n",
            "2023-04-21 16:48:08,017 epoch 19 - iter 54/64 - loss 0.13024325 - time (sec): 60.29 - samples/sec: 1353.38 - lr: 0.000001\n",
            "2023-04-21 16:48:14,663 epoch 19 - iter 60/64 - loss 0.13256158 - time (sec): 66.94 - samples/sec: 1331.94 - lr: 0.000001\n",
            "2023-04-21 16:48:18,273 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:48:18,275 EPOCH 19 done: loss 0.1311 - lr 0.000001\n"
          ]
        },
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            "2023-04-21 16:48:43,967 Evaluating as a multi-label problem: True\n",
            "2023-04-21 16:48:46,989 TRAIN : loss 0.11433394998311996 - f1-score (weighted avg)  0.8531\n"
          ]
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          "text": [
            "2023-04-21 16:48:50,346 Evaluating as a multi-label problem: False\n",
            "2023-04-21 16:48:50,415 DEV : loss 0.2814003527164459 - f1-score (weighted avg)  0.7016\n",
            "2023-04-21 16:48:50,435 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 16:48:50,439 ----------------------------------------------------------------------------------------------------\n"
          ]
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          "text": [
            "2023-04-21 16:48:57,241 epoch 20 - iter 6/64 - loss 0.19304028 - time (sec): 6.80 - samples/sec: 1315.78 - lr: 0.000001\n",
            "2023-04-21 16:49:03,966 epoch 20 - iter 12/64 - loss 0.14790577 - time (sec): 13.53 - samples/sec: 1417.74 - lr: 0.000001\n",
            "2023-04-21 16:49:10,730 epoch 20 - iter 18/64 - loss 0.14425667 - time (sec): 20.29 - samples/sec: 1381.56 - lr: 0.000001\n",
            "2023-04-21 16:49:17,393 epoch 20 - iter 24/64 - loss 0.14413156 - time (sec): 26.95 - samples/sec: 1383.06 - lr: 0.000001\n",
            "2023-04-21 16:49:24,071 epoch 20 - iter 30/64 - loss 0.13421705 - time (sec): 33.63 - samples/sec: 1364.97 - lr: 0.000001\n",
            "2023-04-21 16:49:30,759 epoch 20 - iter 36/64 - loss 0.13013312 - time (sec): 40.32 - samples/sec: 1390.72 - lr: 0.000001\n",
            "2023-04-21 16:49:37,423 epoch 20 - iter 42/64 - loss 0.12826924 - time (sec): 46.98 - samples/sec: 1397.29 - lr: 0.000001\n",
            "2023-04-21 16:49:44,065 epoch 20 - iter 48/64 - loss 0.12955727 - time (sec): 53.62 - samples/sec: 1365.50 - lr: 0.000001\n",
            "2023-04-21 16:49:50,687 epoch 20 - iter 54/64 - loss 0.12959541 - time (sec): 60.25 - samples/sec: 1345.40 - lr: 0.000001\n",
            "2023-04-21 16:49:57,399 epoch 20 - iter 60/64 - loss 0.12882086 - time (sec): 66.96 - samples/sec: 1349.45 - lr: 0.000001\n",
            "2023-04-21 16:50:00,988 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:50:00,990 EPOCH 20 done: loss 0.1284 - lr 0.000001\n"
          ]
        },
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            "2023-04-21 16:50:25,708 Evaluating as a multi-label problem: True\n",
            "2023-04-21 16:50:27,555 TRAIN : loss 0.11076810210943222 - f1-score (weighted avg)  0.8601\n"
          ]
        },
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          "text": [
            "2023-04-21 16:50:31,888 Evaluating as a multi-label problem: False\n",
            "2023-04-21 16:50:31,956 DEV : loss 0.28186070919036865 - f1-score (weighted avg)  0.704\n",
            "2023-04-21 16:50:31,976 Epoch    20: reducing learning rate of group 0 to 6.2500e-07.\n",
            "2023-04-21 16:50:31,978 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 16:50:31,981 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
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          "text": [
            "2023-04-21 16:50:38,634 epoch 21 - iter 6/64 - loss 0.11020954 - time (sec): 6.65 - samples/sec: 1266.38 - lr: 0.000001\n",
            "2023-04-21 16:50:45,320 epoch 21 - iter 12/64 - loss 0.10298137 - time (sec): 13.34 - samples/sec: 1392.14 - lr: 0.000001\n",
            "2023-04-21 16:50:52,028 epoch 21 - iter 18/64 - loss 0.10399261 - time (sec): 20.04 - samples/sec: 1425.51 - lr: 0.000001\n",
            "2023-04-21 16:50:58,794 epoch 21 - iter 24/64 - loss 0.11209736 - time (sec): 26.81 - samples/sec: 1448.53 - lr: 0.000001\n",
            "2023-04-21 16:51:05,456 epoch 21 - iter 30/64 - loss 0.11231287 - time (sec): 33.47 - samples/sec: 1414.38 - lr: 0.000001\n",
            "2023-04-21 16:51:12,091 epoch 21 - iter 36/64 - loss 0.11309457 - time (sec): 40.11 - samples/sec: 1374.07 - lr: 0.000001\n",
            "2023-04-21 16:51:18,727 epoch 21 - iter 42/64 - loss 0.11435523 - time (sec): 46.74 - samples/sec: 1365.71 - lr: 0.000001\n",
            "2023-04-21 16:51:25,449 epoch 21 - iter 48/64 - loss 0.11506922 - time (sec): 53.47 - samples/sec: 1357.25 - lr: 0.000001\n",
            "2023-04-21 16:51:32,261 epoch 21 - iter 54/64 - loss 0.12419136 - time (sec): 60.28 - samples/sec: 1367.27 - lr: 0.000001\n",
            "2023-04-21 16:51:38,886 epoch 21 - iter 60/64 - loss 0.12715951 - time (sec): 66.90 - samples/sec: 1355.43 - lr: 0.000001\n",
            "2023-04-21 16:51:42,492 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:51:42,493 EPOCH 21 done: loss 0.1265 - lr 0.000001\n"
          ]
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          "text": [
            "2023-04-21 16:52:07,170 Evaluating as a multi-label problem: True\n",
            "2023-04-21 16:52:10,181 TRAIN : loss 0.11049961298704147 - f1-score (weighted avg)  0.8597\n"
          ]
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          "text": [
            "2023-04-21 16:52:13,534 Evaluating as a multi-label problem: False\n",
            "2023-04-21 16:52:13,603 DEV : loss 0.2817040979862213 - f1-score (weighted avg)  0.7029\n",
            "2023-04-21 16:52:13,623 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 16:52:13,627 ----------------------------------------------------------------------------------------------------\n"
          ]
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          "text": [
            "2023-04-21 16:52:20,370 epoch 22 - iter 6/64 - loss 0.15216067 - time (sec): 6.74 - samples/sec: 1374.14 - lr: 0.000001\n",
            "2023-04-21 16:52:27,032 epoch 22 - iter 12/64 - loss 0.12989971 - time (sec): 13.40 - samples/sec: 1330.50 - lr: 0.000001\n",
            "2023-04-21 16:52:33,720 epoch 22 - iter 18/64 - loss 0.12107836 - time (sec): 20.09 - samples/sec: 1305.40 - lr: 0.000001\n",
            "2023-04-21 16:52:40,365 epoch 22 - iter 24/64 - loss 0.11740731 - time (sec): 26.74 - samples/sec: 1313.50 - lr: 0.000001\n",
            "2023-04-21 16:52:47,058 epoch 22 - iter 30/64 - loss 0.11468640 - time (sec): 33.43 - samples/sec: 1346.55 - lr: 0.000001\n",
            "2023-04-21 16:52:53,741 epoch 22 - iter 36/64 - loss 0.11457861 - time (sec): 40.11 - samples/sec: 1372.07 - lr: 0.000001\n",
            "2023-04-21 16:53:00,425 epoch 22 - iter 42/64 - loss 0.11245723 - time (sec): 46.80 - samples/sec: 1371.16 - lr: 0.000001\n",
            "2023-04-21 16:53:07,136 epoch 22 - iter 48/64 - loss 0.11300231 - time (sec): 53.51 - samples/sec: 1356.51 - lr: 0.000001\n",
            "2023-04-21 16:53:13,767 epoch 22 - iter 54/64 - loss 0.11438770 - time (sec): 60.14 - samples/sec: 1341.83 - lr: 0.000001\n",
            "2023-04-21 16:53:20,570 epoch 22 - iter 60/64 - loss 0.12569707 - time (sec): 66.94 - samples/sec: 1347.52 - lr: 0.000001\n",
            "2023-04-21 16:53:24,173 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:53:24,175 EPOCH 22 done: loss 0.1252 - lr 0.000001\n"
          ]
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          "text": [
            "2023-04-21 16:53:48,879 Evaluating as a multi-label problem: True\n",
            "2023-04-21 16:53:51,903 TRAIN : loss 0.109440378844738 - f1-score (weighted avg)  0.8593\n"
          ]
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          "text": [
            "2023-04-21 16:53:55,248 Evaluating as a multi-label problem: False\n",
            "2023-04-21 16:53:55,315 DEV : loss 0.2849375903606415 - f1-score (weighted avg)  0.7011\n",
            "2023-04-21 16:53:55,334 Epoch    22: reducing learning rate of group 0 to 3.1250e-07.\n",
            "2023-04-21 16:53:55,336 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 16:53:55,340 ----------------------------------------------------------------------------------------------------\n"
          ]
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          "text": [
            "2023-04-21 16:54:01,985 epoch 23 - iter 6/64 - loss 0.08704830 - time (sec): 6.64 - samples/sec: 1214.51 - lr: 0.000000\n",
            "2023-04-21 16:54:08,894 epoch 23 - iter 12/64 - loss 0.18030803 - time (sec): 13.55 - samples/sec: 1341.98 - lr: 0.000000\n",
            "2023-04-21 16:54:15,528 epoch 23 - iter 18/64 - loss 0.16566359 - time (sec): 20.19 - samples/sec: 1342.43 - lr: 0.000000\n",
            "2023-04-21 16:54:22,199 epoch 23 - iter 24/64 - loss 0.15122513 - time (sec): 26.86 - samples/sec: 1353.14 - lr: 0.000000\n",
            "2023-04-21 16:54:28,908 epoch 23 - iter 30/64 - loss 0.14515781 - time (sec): 33.57 - samples/sec: 1352.75 - lr: 0.000000\n",
            "2023-04-21 16:54:35,573 epoch 23 - iter 36/64 - loss 0.13967027 - time (sec): 40.23 - samples/sec: 1354.23 - lr: 0.000000\n",
            "2023-04-21 16:54:42,217 epoch 23 - iter 42/64 - loss 0.13566152 - time (sec): 46.88 - samples/sec: 1344.09 - lr: 0.000000\n",
            "2023-04-21 16:54:48,920 epoch 23 - iter 48/64 - loss 0.13359079 - time (sec): 53.58 - samples/sec: 1348.18 - lr: 0.000000\n",
            "2023-04-21 16:54:55,552 epoch 23 - iter 54/64 - loss 0.12906776 - time (sec): 60.21 - samples/sec: 1332.77 - lr: 0.000000\n",
            "2023-04-21 16:55:02,229 epoch 23 - iter 60/64 - loss 0.12619544 - time (sec): 66.89 - samples/sec: 1351.29 - lr: 0.000000\n",
            "2023-04-21 16:55:05,811 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:55:05,813 EPOCH 23 done: loss 0.1242 - lr 0.000000\n"
          ]
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          "text": [
            "2023-04-21 16:55:30,516 Evaluating as a multi-label problem: True\n",
            "2023-04-21 16:55:33,525 TRAIN : loss 0.10914214700460434 - f1-score (weighted avg)  0.8594\n"
          ]
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          "text": [
            "2023-04-21 16:55:36,877 Evaluating as a multi-label problem: False\n",
            "2023-04-21 16:55:36,946 DEV : loss 0.2851102352142334 - f1-score (weighted avg)  0.7027\n",
            "2023-04-21 16:55:36,965 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 16:55:36,969 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
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            "2023-04-21 16:55:43,645 epoch 24 - iter 6/64 - loss 0.11515855 - time (sec): 6.68 - samples/sec: 1300.67 - lr: 0.000000\n",
            "2023-04-21 16:55:50,328 epoch 24 - iter 12/64 - loss 0.10362051 - time (sec): 13.36 - samples/sec: 1307.07 - lr: 0.000000\n",
            "2023-04-21 16:55:56,970 epoch 24 - iter 18/64 - loss 0.10372403 - time (sec): 20.00 - samples/sec: 1297.60 - lr: 0.000000\n",
            "2023-04-21 16:56:03,708 epoch 24 - iter 24/64 - loss 0.10494026 - time (sec): 26.74 - samples/sec: 1338.16 - lr: 0.000000\n",
            "2023-04-21 16:56:10,360 epoch 24 - iter 30/64 - loss 0.11074958 - time (sec): 33.39 - samples/sec: 1330.26 - lr: 0.000000\n",
            "2023-04-21 16:56:17,020 epoch 24 - iter 36/64 - loss 0.11292903 - time (sec): 40.05 - samples/sec: 1354.49 - lr: 0.000000\n",
            "2023-04-21 16:56:23,682 epoch 24 - iter 42/64 - loss 0.11312846 - time (sec): 46.71 - samples/sec: 1356.06 - lr: 0.000000\n",
            "2023-04-21 16:56:30,337 epoch 24 - iter 48/64 - loss 0.11307861 - time (sec): 53.37 - samples/sec: 1342.79 - lr: 0.000000\n",
            "2023-04-21 16:56:36,974 epoch 24 - iter 54/64 - loss 0.11335383 - time (sec): 60.00 - samples/sec: 1334.75 - lr: 0.000000\n",
            "2023-04-21 16:56:43,887 epoch 24 - iter 60/64 - loss 0.12488969 - time (sec): 66.92 - samples/sec: 1345.28 - lr: 0.000000\n",
            "2023-04-21 16:56:47,475 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:56:47,477 EPOCH 24 done: loss 0.1236 - lr 0.000000\n"
          ]
        },
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            "2023-04-21 16:57:12,233 Evaluating as a multi-label problem: True\n",
            "2023-04-21 16:57:14,090 TRAIN : loss 0.10843140631914139 - f1-score (weighted avg)  0.8613\n"
          ]
        },
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            "2023-04-21 16:57:18,471 Evaluating as a multi-label problem: False\n",
            "2023-04-21 16:57:18,541 DEV : loss 0.2850811779499054 - f1-score (weighted avg)  0.7\n",
            "2023-04-21 16:57:18,561 Epoch    24: reducing learning rate of group 0 to 1.5625e-07.\n",
            "2023-04-21 16:57:18,563 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 16:57:18,567 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:57:25,195 epoch 25 - iter 6/64 - loss 0.11613097 - time (sec): 6.63 - samples/sec: 1300.64 - lr: 0.000000\n",
            "2023-04-21 16:57:31,882 epoch 25 - iter 12/64 - loss 0.11772462 - time (sec): 13.31 - samples/sec: 1365.71 - lr: 0.000000\n",
            "2023-04-21 16:57:38,513 epoch 25 - iter 18/64 - loss 0.11733645 - time (sec): 19.94 - samples/sec: 1291.82 - lr: 0.000000\n",
            "2023-04-21 16:57:45,141 epoch 25 - iter 24/64 - loss 0.11512328 - time (sec): 26.57 - samples/sec: 1262.44 - lr: 0.000000\n",
            "2023-04-21 16:57:51,836 epoch 25 - iter 30/64 - loss 0.11237022 - time (sec): 33.27 - samples/sec: 1296.60 - lr: 0.000000\n",
            "2023-04-21 16:57:58,555 epoch 25 - iter 36/64 - loss 0.11846766 - time (sec): 39.99 - samples/sec: 1280.92 - lr: 0.000000\n",
            "2023-04-21 16:58:05,237 epoch 25 - iter 42/64 - loss 0.11765056 - time (sec): 46.67 - samples/sec: 1277.08 - lr: 0.000000\n",
            "2023-04-21 16:58:11,907 epoch 25 - iter 48/64 - loss 0.11596170 - time (sec): 53.34 - samples/sec: 1286.96 - lr: 0.000000\n",
            "2023-04-21 16:58:18,602 epoch 25 - iter 54/64 - loss 0.11349586 - time (sec): 60.03 - samples/sec: 1308.14 - lr: 0.000000\n",
            "2023-04-21 16:58:25,296 epoch 25 - iter 60/64 - loss 0.11212079 - time (sec): 66.73 - samples/sec: 1325.65 - lr: 0.000000\n",
            "2023-04-21 16:58:29,097 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 16:58:29,099 EPOCH 25 done: loss 0.1231 - lr 0.000000\n"
          ]
        },
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            "2023-04-21 16:58:53,770 Evaluating as a multi-label problem: True\n",
            "2023-04-21 16:58:56,770 TRAIN : loss 0.10815412551164627 - f1-score (weighted avg)  0.8606\n"
          ]
        },
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          "text": [
            "2023-04-21 16:59:00,145 Evaluating as a multi-label problem: False\n",
            "2023-04-21 16:59:00,212 DEV : loss 0.28329312801361084 - f1-score (weighted avg)  0.7032\n",
            "2023-04-21 16:59:00,232 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 16:59:00,236 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 16:59:06,886 epoch 26 - iter 6/64 - loss 0.11654737 - time (sec): 6.65 - samples/sec: 1304.63 - lr: 0.000000\n",
            "2023-04-21 16:59:13,553 epoch 26 - iter 12/64 - loss 0.10983926 - time (sec): 13.32 - samples/sec: 1379.64 - lr: 0.000000\n",
            "2023-04-21 16:59:20,167 epoch 26 - iter 18/64 - loss 0.10718921 - time (sec): 19.93 - samples/sec: 1304.84 - lr: 0.000000\n",
            "2023-04-21 16:59:26,963 epoch 26 - iter 24/64 - loss 0.12936098 - time (sec): 26.73 - samples/sec: 1301.97 - lr: 0.000000\n",
            "2023-04-21 16:59:33,601 epoch 26 - iter 30/64 - loss 0.12312775 - time (sec): 33.36 - samples/sec: 1314.38 - lr: 0.000000\n",
            "2023-04-21 16:59:40,381 epoch 26 - iter 36/64 - loss 0.12476262 - time (sec): 40.14 - samples/sec: 1315.86 - lr: 0.000000\n",
            "2023-04-21 16:59:47,087 epoch 26 - iter 42/64 - loss 0.12314300 - time (sec): 46.85 - samples/sec: 1332.88 - lr: 0.000000\n",
            "2023-04-21 16:59:53,760 epoch 26 - iter 48/64 - loss 0.12080410 - time (sec): 53.52 - samples/sec: 1337.34 - lr: 0.000000\n",
            "2023-04-21 17:00:00,408 epoch 26 - iter 54/64 - loss 0.12053928 - time (sec): 60.17 - samples/sec: 1334.25 - lr: 0.000000\n",
            "2023-04-21 17:00:07,084 epoch 26 - iter 60/64 - loss 0.12390654 - time (sec): 66.85 - samples/sec: 1346.43 - lr: 0.000000\n",
            "2023-04-21 17:00:10,695 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 17:00:10,697 EPOCH 26 done: loss 0.1219 - lr 0.000000\n"
          ]
        },
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            "2023-04-21 17:00:35,420 Evaluating as a multi-label problem: True\n",
            "2023-04-21 17:00:38,439 TRAIN : loss 0.10728350281715393 - f1-score (weighted avg)  0.8623\n"
          ]
        },
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          "text": [
            "2023-04-21 17:00:41,791 Evaluating as a multi-label problem: False\n",
            "2023-04-21 17:00:41,861 DEV : loss 0.2841992974281311 - f1-score (weighted avg)  0.7033\n",
            "2023-04-21 17:00:41,881 Epoch    26: reducing learning rate of group 0 to 7.8125e-08.\n",
            "2023-04-21 17:00:41,883 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 17:00:41,886 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 17:00:48,500 epoch 27 - iter 6/64 - loss 0.09957168 - time (sec): 6.61 - samples/sec: 1279.50 - lr: 0.000000\n",
            "2023-04-21 17:00:55,209 epoch 27 - iter 12/64 - loss 0.11173736 - time (sec): 13.32 - samples/sec: 1286.32 - lr: 0.000000\n",
            "2023-04-21 17:01:01,890 epoch 27 - iter 18/64 - loss 0.10946969 - time (sec): 20.00 - samples/sec: 1336.28 - lr: 0.000000\n",
            "2023-04-21 17:01:08,514 epoch 27 - iter 24/64 - loss 0.11017526 - time (sec): 26.63 - samples/sec: 1312.52 - lr: 0.000000\n",
            "2023-04-21 17:01:15,455 epoch 27 - iter 30/64 - loss 0.13685721 - time (sec): 33.57 - samples/sec: 1379.31 - lr: 0.000000\n",
            "2023-04-21 17:01:22,129 epoch 27 - iter 36/64 - loss 0.13031152 - time (sec): 40.24 - samples/sec: 1365.29 - lr: 0.000000\n",
            "2023-04-21 17:01:28,792 epoch 27 - iter 42/64 - loss 0.12672414 - time (sec): 46.90 - samples/sec: 1363.89 - lr: 0.000000\n",
            "2023-04-21 17:01:35,471 epoch 27 - iter 48/64 - loss 0.12373681 - time (sec): 53.58 - samples/sec: 1367.43 - lr: 0.000000\n",
            "2023-04-21 17:01:42,110 epoch 27 - iter 54/64 - loss 0.12301600 - time (sec): 60.22 - samples/sec: 1349.53 - lr: 0.000000\n",
            "2023-04-21 17:01:48,824 epoch 27 - iter 60/64 - loss 0.12147964 - time (sec): 66.94 - samples/sec: 1357.93 - lr: 0.000000\n",
            "2023-04-21 17:01:52,414 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 17:01:52,415 EPOCH 27 done: loss 0.1214 - lr 0.000000\n"
          ]
        },
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            "2023-04-21 17:02:17,135 Evaluating as a multi-label problem: True\n",
            "2023-04-21 17:02:18,996 TRAIN : loss 0.10756486654281616 - f1-score (weighted avg)  0.8614\n"
          ]
        },
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          "text": [
            "2023-04-21 17:02:23,312 Evaluating as a multi-label problem: False\n",
            "2023-04-21 17:02:23,381 DEV : loss 0.28473731875419617 - f1-score (weighted avg)  0.7023\n",
            "2023-04-21 17:02:23,401 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 17:02:23,405 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 17:02:30,070 epoch 28 - iter 6/64 - loss 0.10898361 - time (sec): 6.66 - samples/sec: 1411.74 - lr: 0.000000\n",
            "2023-04-21 17:02:36,740 epoch 28 - iter 12/64 - loss 0.10990296 - time (sec): 13.33 - samples/sec: 1352.96 - lr: 0.000000\n",
            "2023-04-21 17:02:43,444 epoch 28 - iter 18/64 - loss 0.10796487 - time (sec): 20.04 - samples/sec: 1368.61 - lr: 0.000000\n",
            "2023-04-21 17:02:50,073 epoch 28 - iter 24/64 - loss 0.10505135 - time (sec): 26.67 - samples/sec: 1329.33 - lr: 0.000000\n",
            "2023-04-21 17:02:56,821 epoch 28 - iter 30/64 - loss 0.10708834 - time (sec): 33.41 - samples/sec: 1375.83 - lr: 0.000000\n",
            "2023-04-21 17:03:03,509 epoch 28 - iter 36/64 - loss 0.10417016 - time (sec): 40.10 - samples/sec: 1344.63 - lr: 0.000000\n",
            "2023-04-21 17:03:10,342 epoch 28 - iter 42/64 - loss 0.11992787 - time (sec): 46.93 - samples/sec: 1361.64 - lr: 0.000000\n",
            "2023-04-21 17:03:16,989 epoch 28 - iter 48/64 - loss 0.12016862 - time (sec): 53.58 - samples/sec: 1345.85 - lr: 0.000000\n",
            "2023-04-21 17:03:23,650 epoch 28 - iter 54/64 - loss 0.12022577 - time (sec): 60.24 - samples/sec: 1339.15 - lr: 0.000000\n",
            "2023-04-21 17:03:30,404 epoch 28 - iter 60/64 - loss 0.12169527 - time (sec): 67.00 - samples/sec: 1343.61 - lr: 0.000000\n",
            "2023-04-21 17:03:33,995 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 17:03:33,996 EPOCH 28 done: loss 0.1208 - lr 0.000000\n"
          ]
        },
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          "text": [
            "2023-04-21 17:03:58,772 Evaluating as a multi-label problem: True\n",
            "2023-04-21 17:04:01,777 TRAIN : loss 0.10727710276842117 - f1-score (weighted avg)  0.862\n"
          ]
        },
        {
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          "text": [
            "2023-04-21 17:04:05,137 Evaluating as a multi-label problem: False\n",
            "2023-04-21 17:04:05,206 DEV : loss 0.28490185737609863 - f1-score (weighted avg)  0.7026\n",
            "2023-04-21 17:04:05,226 Epoch    28: reducing learning rate of group 0 to 3.9063e-08.\n",
            "2023-04-21 17:04:05,229 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 17:04:05,232 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 17:04:11,851 epoch 29 - iter 6/64 - loss 0.09463798 - time (sec): 6.62 - samples/sec: 1308.47 - lr: 0.000000\n",
            "2023-04-21 17:04:18,562 epoch 29 - iter 12/64 - loss 0.10391159 - time (sec): 13.33 - samples/sec: 1350.48 - lr: 0.000000\n",
            "2023-04-21 17:04:25,333 epoch 29 - iter 18/64 - loss 0.11357999 - time (sec): 20.10 - samples/sec: 1359.96 - lr: 0.000000\n",
            "2023-04-21 17:04:32,045 epoch 29 - iter 24/64 - loss 0.10755519 - time (sec): 26.81 - samples/sec: 1372.57 - lr: 0.000000\n",
            "2023-04-21 17:04:38,682 epoch 29 - iter 30/64 - loss 0.10872039 - time (sec): 33.45 - samples/sec: 1365.59 - lr: 0.000000\n",
            "2023-04-21 17:04:45,299 epoch 29 - iter 36/64 - loss 0.10844061 - time (sec): 40.06 - samples/sec: 1347.99 - lr: 0.000000\n",
            "2023-04-21 17:04:51,947 epoch 29 - iter 42/64 - loss 0.10726846 - time (sec): 46.71 - samples/sec: 1345.82 - lr: 0.000000\n",
            "2023-04-21 17:04:58,616 epoch 29 - iter 48/64 - loss 0.11040013 - time (sec): 53.38 - samples/sec: 1345.60 - lr: 0.000000\n",
            "2023-04-21 17:05:05,444 epoch 29 - iter 54/64 - loss 0.12209816 - time (sec): 60.21 - samples/sec: 1355.26 - lr: 0.000000\n",
            "2023-04-21 17:05:12,091 epoch 29 - iter 60/64 - loss 0.12019298 - time (sec): 66.86 - samples/sec: 1348.76 - lr: 0.000000\n",
            "2023-04-21 17:05:15,685 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 17:05:15,687 EPOCH 29 done: loss 0.1199 - lr 0.000000\n"
          ]
        },
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          "text": [
            "2023-04-21 17:05:40,366 Evaluating as a multi-label problem: True\n",
            "2023-04-21 17:05:43,420 TRAIN : loss 0.10708489269018173 - f1-score (weighted avg)  0.8623\n"
          ]
        },
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          "text": [
            "2023-04-21 17:05:46,777 Evaluating as a multi-label problem: False\n",
            "2023-04-21 17:05:46,844 DEV : loss 0.28473150730133057 - f1-score (weighted avg)  0.7026\n",
            "2023-04-21 17:05:46,864 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 17:05:46,868 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 17:05:53,579 epoch 30 - iter 6/64 - loss 0.11129157 - time (sec): 6.71 - samples/sec: 1249.67 - lr: 0.000000\n",
            "2023-04-21 17:06:00,209 epoch 30 - iter 12/64 - loss 0.11159993 - time (sec): 13.34 - samples/sec: 1277.78 - lr: 0.000000\n",
            "2023-04-21 17:06:06,893 epoch 30 - iter 18/64 - loss 0.10939833 - time (sec): 20.02 - samples/sec: 1313.27 - lr: 0.000000\n",
            "2023-04-21 17:06:13,573 epoch 30 - iter 24/64 - loss 0.11252582 - time (sec): 26.70 - samples/sec: 1324.18 - lr: 0.000000\n",
            "2023-04-21 17:06:20,219 epoch 30 - iter 30/64 - loss 0.11364042 - time (sec): 33.35 - samples/sec: 1337.14 - lr: 0.000000\n",
            "2023-04-21 17:06:26,964 epoch 30 - iter 36/64 - loss 0.11729575 - time (sec): 40.09 - samples/sec: 1343.46 - lr: 0.000000\n",
            "2023-04-21 17:06:33,648 epoch 30 - iter 42/64 - loss 0.11705272 - time (sec): 46.78 - samples/sec: 1363.02 - lr: 0.000000\n",
            "2023-04-21 17:06:40,320 epoch 30 - iter 48/64 - loss 0.11632784 - time (sec): 53.45 - samples/sec: 1353.44 - lr: 0.000000\n",
            "2023-04-21 17:06:46,974 epoch 30 - iter 54/64 - loss 0.11489481 - time (sec): 60.10 - samples/sec: 1344.12 - lr: 0.000000\n",
            "2023-04-21 17:06:53,845 epoch 30 - iter 60/64 - loss 0.12205575 - time (sec): 66.97 - samples/sec: 1350.76 - lr: 0.000000\n",
            "2023-04-21 17:06:57,433 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 17:06:57,435 EPOCH 30 done: loss 0.1212 - lr 0.000000\n"
          ]
        },
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          "text": [
            "2023-04-21 17:07:22,168 Evaluating as a multi-label problem: True\n",
            "2023-04-21 17:07:24,044 TRAIN : loss 0.10700857639312744 - f1-score (weighted avg)  0.8624\n"
          ]
        },
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          "text": [
            "2023-04-21 17:07:28,390 Evaluating as a multi-label problem: False\n",
            "2023-04-21 17:07:28,458 DEV : loss 0.2846706807613373 - f1-score (weighted avg)  0.7026\n",
            "2023-04-21 17:07:28,477 Epoch    30: reducing learning rate of group 0 to 1.9531e-08.\n",
            "2023-04-21 17:07:28,480 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 17:07:28,484 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 17:07:28,485 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 17:07:28,487 learning rate too small - quitting training!\n",
            "2023-04-21 17:07:28,488 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 17:07:37,826 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 17:07:50,476 SequenceTagger predicts: Dictionary with 12 tags: <unk>, O, I-ADR, B-ADR, B-Drug, I-Finding, B-Finding, B-Disease, B-Symptom, I-Symptom, I-Disease, I-Drug\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:03<00:00,  2.40it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 17:07:54,224 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 17:07:54,469 0.7618\t0.7557\t0.7587\t0.9385\n",
            "2023-04-21 17:07:54,471 \n",
            "Results:\n",
            "- F-score (micro) 0.7587\n",
            "- F-score (macro) 0.2897\n",
            "- Accuracy 0.9385\n",
            "\n",
            "By class:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       I-ADR     0.7589    0.8139    0.7854       967\n",
            "       B-ADR     0.7815    0.8507    0.8146       576\n",
            "      B-Drug     0.7794    0.9680    0.8635       219\n",
            "   B-Finding     0.4167    0.1923    0.2632        52\n",
            "      I-Drug     0.0000    0.0000    0.0000        52\n",
            "   I-Finding     0.3636    0.1111    0.1702        36\n",
            "   I-Symptom     0.0000    0.0000    0.0000        33\n",
            "   B-Disease     0.0000    0.0000    0.0000        20\n",
            "   B-Symptom     0.0000    0.0000    0.0000        22\n",
            "   I-Disease     0.0000    0.0000    0.0000        12\n",
            "\n",
            "   micro avg     0.7618    0.7557    0.7587      1989\n",
            "   macro avg     0.3100    0.2936    0.2897      1989\n",
            "weighted avg     0.6986    0.7557    0.7228      1989\n",
            " samples avg     0.1270    0.1269    0.1270      1989\n",
            "\n",
            "2023-04-21 17:07:54,473 ----------------------------------------------------------------------------------------------------\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 = 15,\n",
        "              eval_batch_size = 15,\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=5.0e-6,\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": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "P5Uu5qkN0pia",
        "outputId": "9405559f-a323-4c26-dab2-794f3a8e38f7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "_v3_spanbert-large-cased\n",
            "2023-04-21 17:07:54,498 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 17:07:54,498 WARNING: No LOSS found for test split in this data.\n",
            "2023-04-21 17:07:54,499 Are you sure you want to plot LOSS and not another value?\n",
            "2023-04-21 17:07:54,500 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 17:07:54,525 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 17:07:54,526 WARNING: No F1 found for test split in this data.\n",
            "2023-04-21 17:07:54,527 Are you sure you want to plot F1 and not another value?\n",
            "2023-04-21 17:07:54,528 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 17:07:55,634 Loss and F1 plots are saved in flair_ner_model_v3_spanbert-large-cased/training.png\n"
          ]
        },
        {
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            "text/plain": [
              "<Figure size 1500x1000 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 64/64 [00:24<00:00,  2.58it/s]\n"
          ]
        },
        {
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            "2023-04-21 17:08:20,894 Evaluating as a multi-label problem: True\n"
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        {
          "name": "stderr",
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          "text": [
            "100%|██████████| 8/8 [00:03<00:00,  2.51it/s]"
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            "2023-04-21 17:08:27,059 Evaluating as a multi-label problem: False\n"
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            "\n",
            "100%|██████████| 8/8 [00:03<00:00,  2.54it/s]"
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            "2023-04-21 17:08:30,304 Evaluating as a multi-label problem: True\n"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>split</th>\n",
              "      <th>index</th>\n",
              "      <th>I-ADR</th>\n",
              "      <th>B-ADR</th>\n",
              "      <th>B-Drug</th>\n",
              "      <th>B-Finding</th>\n",
              "      <th>I-Finding</th>\n",
              "      <th>B-Disease</th>\n",
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              "      <th>I-Disease</th>\n",
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              "      <th>samples avg</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>train</td>\n",
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              "      <th>1</th>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>train</td>\n",
              "      <td>f1-score</td>\n",
              "      <td>0.929671</td>\n",
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              "      <td>0.834651</td>\n",
              "      <td>0.145180</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>train</td>\n",
              "      <td>support</td>\n",
              "      <td>7953.000000</td>\n",
              "      <td>4712.000000</td>\n",
              "      <td>1396.000000</td>\n",
              "      <td>309.000000</td>\n",
              "      <td>349.000000</td>\n",
              "      <td>231.000000</td>\n",
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              "      <td>218.0</td>\n",
              "      <td>169.000000</td>\n",
              "      <td>161.0</td>\n",
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              "      <td>15720.000000</td>\n",
              "      <td>15720.000000</td>\n",
              "      <td>15720.000000</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>dev</td>\n",
              "      <td>precision</td>\n",
              "      <td>0.699352</td>\n",
              "      <td>0.775910</td>\n",
              "      <td>0.790179</td>\n",
              "      <td>0.243243</td>\n",
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              "      <th>5</th>\n",
              "      <td>dev</td>\n",
              "      <td>recall</td>\n",
              "      <td>0.782609</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>test</td>\n",
              "      <td>recall</td>\n",
              "      <td>0.813857</td>\n",
              "      <td>0.850694</td>\n",
              "      <td>0.968037</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>test</td>\n",
              "      <td>f1-score</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>test</td>\n",
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            "text/plain": [
              "    split      index        I-ADR        B-ADR       B-Drug   B-Finding  \\\n",
              "0   train  precision     0.890591     0.866654     0.805507    0.358974   \n",
              "1   train     recall     0.972337     0.950340     0.984957    0.317152   \n",
              "2   train   f1-score     0.929671     0.906569     0.886239    0.336770   \n",
              "3   train    support  7953.000000  4712.000000  1396.000000  309.000000   \n",
              "4     dev  precision     0.699352     0.775910     0.790179    0.243243   \n",
              "5     dev     recall     0.782609     0.850998     0.961957    0.140625   \n",
              "6     dev   f1-score     0.738642     0.811722     0.867647    0.178218   \n",
              "7     dev    support   966.000000   651.000000   184.000000   64.000000   \n",
              "8    test  precision     0.758920     0.781499     0.779412    0.416667   \n",
              "9    test     recall     0.813857     0.850694     0.968037    0.192308   \n",
              "10   test   f1-score     0.785429     0.814630     0.863544    0.263158   \n",
              "11   test    support   967.000000   576.000000   219.000000   52.000000   \n",
              "\n",
              "     I-Finding   B-Disease  B-Symptom  I-Symptom   I-Disease  I-Drug  \\\n",
              "0     0.389262    0.276596        0.0        0.0    0.461538     0.0   \n",
              "1     0.166189    0.056277        0.0        0.0    0.035503     0.0   \n",
              "2     0.232932    0.093525        0.0        0.0    0.065934     0.0   \n",
              "3   349.000000  231.000000      222.0      218.0  169.000000   161.0   \n",
              "4     0.190476    0.000000        0.0        0.0    0.500000     0.0   \n",
              "5     0.074074    0.000000        0.0        0.0    0.071429     0.0   \n",
              "6     0.106667    0.000000        0.0        0.0    0.125000     0.0   \n",
              "7    54.000000   31.000000       25.0       36.0   28.000000    27.0   \n",
              "8     0.363636    0.000000        0.0        0.0    0.000000     0.0   \n",
              "9     0.111111    0.000000        0.0        0.0    0.000000     0.0   \n",
              "10    0.170213    0.000000        0.0        0.0    0.000000     0.0   \n",
              "11   36.000000   20.000000       22.0       33.0   12.000000    52.0   \n",
              "\n",
              "       micro avg     macro avg  weighted avg   samples avg  \n",
              "0       0.857971      0.404912      0.806597      0.145204  \n",
              "1       0.875382      0.348276      0.875382      0.145167  \n",
              "2       0.866589      0.345164      0.834651      0.145180  \n",
              "3   15720.000000  15720.000000  15720.000000  15720.000000  \n",
              "4       0.720038      0.319916      0.661151           NaN  \n",
              "5       0.727009      0.288169      0.727009           NaN  \n",
              "6       0.723507      0.282790      0.688418           NaN  \n",
              "7    2066.000000   2066.000000   2066.000000           NaN  \n",
              "8       0.761784      0.310013      0.698576      0.127007  \n",
              "9       0.755656      0.293601      0.755656      0.126922  \n",
              "10      0.758708      0.289697      0.722808      0.126951  \n",
              "11   1989.000000   1989.000000   1989.000000   1989.000000  "
            ]
          },
          "execution_count": 17,
          "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=15,\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=15,\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=15,\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": 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."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6zpEn1bgtGin"
      },
      "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": {
        "id": "yrmL98VatGin"
      },
      "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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