flair_cadec_v2_xlm-roberta-large.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": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nw6RM8uotGih",
        "outputId": "11169211-eb95-4b65-b324-2ba2e47e7655"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\")\n",
        "\n",
        "os.chdir('/content/drive/MyDrive/SI 630 project')\n",
        "# os.listdir('.')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P9LwpBadtGih",
        "outputId": "546a62c7-715b-4804-829d-d2add7289ca2"
      },
      "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": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "os.listdir('datasets/CADEC/CADEC.v2/cadec')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "VNZtmxiPb5uV"
      },
      "outputs": [],
      "source": [
        "suf = '_v2'\n",
        "\n",
        "data_save_dir = 'data'+suf\n",
        "\n",
        "suf += '_xlm-roberta-large'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
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        "id": "BIUl2-W9tGii",
        "outputId": "a8c41db4-0527-4c3d-a3fd-f70fdd7d5f40"
      },
      "outputs": [
        {
          "data": {
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              "      <th>ner</th>\n",
              "      <th>meddra</th>\n",
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              "      <th>counts</th>\n",
              "      <th>counts_exploded_rank</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Drug</td>\n",
              "      <td>Lipitor</td>\n",
              "      <td>1081</td>\n",
              "      <td>1073</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ADR</td>\n",
              "      <td>CONCEPT_LESS</td>\n",
              "      <td>1016</td>\n",
              "      <td>266</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ADR</td>\n",
              "      <td>Severe_pain</td>\n",
              "      <td>722</td>\n",
              "      <td>247</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ADR</td>\n",
              "      <td>Myalgia</td>\n",
              "      <td>619</td>\n",
              "      <td>305</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ADR</td>\n",
              "      <td>Pain</td>\n",
              "      <td>292</td>\n",
              "      <td>260</td>\n",
              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1294</th>\n",
              "      <td>Finding</td>\n",
              "      <td>Senility</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1295</th>\n",
              "      <td>Drug</td>\n",
              "      <td>Seroquel</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1296</th>\n",
              "      <td>Drug</td>\n",
              "      <td>Solaraze</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1297</th>\n",
              "      <td>Drug</td>\n",
              "      <td>Tagamet</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1298</th>\n",
              "      <td>Drug</td>\n",
              "      <td>testosterone</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1233</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1299 rows × 5 columns</p>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
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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": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "meddra_counts = pd.read_csv(data_save_dir+'/meddra_counts.csv')\n",
        "meddra_counts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ADR</td>\n",
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              "      <td>247</td>\n",
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              "      <td>4</td>\n",
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              "    <tr>\n",
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              "      <td>ADR</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>ADR</td>\n",
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              "      <td>280</td>\n",
              "      <td>163</td>\n",
              "      <td>6</td>\n",
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              "    <tr>\n",
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              "    <tr>\n",
              "      <th>1268</th>\n",
              "      <td>ADR</td>\n",
              "      <td>Labyrinthitis</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1269</th>\n",
              "      <td>ADR</td>\n",
              "      <td>Dysphagia</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1275</th>\n",
              "      <td>ADR</td>\n",
              "      <td>Lupus_erythematosus</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1278</th>\n",
              "      <td>ADR</td>\n",
              "      <td>Cataract</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1283</th>\n",
              "      <td>ADR</td>\n",
              "      <td>Crying_associated_with_mood</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1233</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>800 rows × 5 columns</p>\n",
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              "\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": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "meddra_counts.loc[meddra_counts.ner == 'ADR',:]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hAGPini7tGii",
        "outputId": "f6bdf971-e110-4bb3-a691-ffe03e9de608"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[Sentence[117]: \"i feel a bit drowsy & have a little blurred vision , so far no gastric problems . ive been on arthrotec 50 for over 10 years on and off , only taking it when i needed it . due to my arthritis getting progressively worse , to the point where i am in tears with the agony , gp ' s started me on 75 twice a day and i have to take it . every day for the next month to see how i get on , here goes . so far its been very good , pains almost gone , but i feel a bit weird , didnt have that when on 50 .\" → [\"i\"/O/O, \"feel\"/O/O, \"a\"/O/O, \"bit\"/B-ADR/B-Drowsy, \"drowsy\"/E-ADR/E-Drowsy, \"&\"/O/O, \"have\"/O/O, \"a\"/O/O, \"little\"/B-ADR/B-Blurred_vision_-_hazy, \"blurred\"/I-ADR/I-Blurred_vision_-_hazy, \"vision\"/E-ADR/E-Blurred_vision_-_hazy, \",\"/O/O, \"so\"/O/O, \"far\"/O/O, \"no\"/O/O, \"gastric\"/B-ADR/B-Excessive_upper_gastrointestinal_gas, \"problems\"/E-ADR/E-Excessive_upper_gastrointestinal_gas, \".\"/O/O, \"ive\"/O/O, \"been\"/O/O, \"on\"/O/O, \"arthrotec\"/S-Drug/S-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\"/S-Disease/S-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\"/S-Symptom/S-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\"/S-Symptom/S-Pain, \"almost\"/O/O, \"gone\"/O/O, \",\"/O/O, \"but\"/O/O, \"i\"/O/O, \"feel\"/B-ADR/B-Malaise, \"a\"/I-ADR/I-Malaise, \"bit\"/I-ADR/I-Malaise, \"weird\"/E-ADR/E-Malaise, \",\"/O/O, \"didnt\"/O/O, \"have\"/O/O, \"that\"/O/O, \"when\"/O/O, \"on\"/O/O, \"50\"/O/O, \".\"/O/O],\n",
              " Sentence[39]: \"hunger pangs . brilliant , i have a new lease of life , i walk up & down steps properly , no longer sideways like a toddler , hip pain as gone other than if i jar it .\" → [\"hunger\"/B-ADR/B-Hunger_pain, \"pangs\"/E-ADR/E-Hunger_pain, \".\"/O/O, \"brilliant\"/O/O, \",\"/O/O, \"i\"/O/O, \"have\"/O/O, \"a\"/O/O, \"new\"/O/O, \"lease\"/O/O, \"of\"/O/O, \"life\"/O/O, \",\"/O/O, \"i\"/O/O, \"walk\"/B-Symptom/B-CONCEPT_LESS, \"up\"/I-Symptom/I-CONCEPT_LESS, \"&\"/I-Symptom/I-CONCEPT_LESS, \"down\"/I-Symptom/I-CONCEPT_LESS, \"steps\"/I-Symptom/I-CONCEPT_LESS, \"properly\"/O/O, \",\"/O/O, \"no\"/O/O, \"longer\"/O/O, \"sideways\"/E-Symptom/E-CONCEPT_LESS, \"like\"/O/O, \"a\"/O/O, \"toddler\"/O/O, \",\"/O/O, \"hip\"/B-Symptom/B-Hip_pain, \"pain\"/E-Symptom/E-Hip_pain, \"as\"/O/O, \"gone\"/O/O, \"other\"/O/O, \"than\"/O/O, \"if\"/O/O, \"i\"/O/O, \"jar\"/O/O, \"it\"/O/O, \".\"/O/O],\n",
              " Sentence[51]: \"no side effects for the first two months . then vaginal bleeding 2 wks after menstral cycle . stomach pain . canker sores in my mouth . headache . been off for 1 week still have bleeding . helped my pain alot . too scared to take this drug again .\" → [\"no\"/O/O, \"side\"/O/O, \"effects\"/O/O, \"for\"/O/O, \"the\"/O/O, \"first\"/O/O, \"two\"/O/O, \"months\"/O/O, \".\"/O/O, \"then\"/O/O, \"vaginal\"/B-ADR/B-Vaginal_bleeding, \"bleeding\"/E-ADR/E-Vaginal_bleeding, \"2\"/O/O, \"wks\"/O/O, \"after\"/O/O, \"menstral\"/O/O, \"cycle\"/O/O, \".\"/O/O, \"stomach\"/B-ADR/B-Stomach_ache, \"pain\"/E-ADR/E-Stomach_ache, \".\"/O/O, \"canker\"/B-ADR/B-Aphthous_ulcer, \"sores\"/E-ADR/E-Aphthous_ulcer, \"in\"/O/O, \"my\"/O/O, \"mouth\"/O/O, \".\"/O/O, \"headache\"/S-ADR/S-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\"/S-Symptom/S-Pain, \"alot\"/O/O, \".\"/O/O, \"too\"/O/O, \"scared\"/O/O, \"to\"/O/O, \"take\"/O/O, \"this\"/O/O, \"drug\"/O/O, \"again\"/O/O, \".\"/O/O],\n",
              " Sentence[68]: \"1st pill taken with food , a few hours after i experienced shortness of breath , a sense of depression , cramping , upset stomache will stop taking immediately . honestly can not recommend this drug , i am still experiencing side effects just after 1 pill and im supposed to take twice / day 75mg . i just cant keep going have to try something else .\" → [\"1st\"/O/O, \"pill\"/O/O, \"taken\"/O/O, \"with\"/O/O, \"food\"/O/O, \",\"/O/O, \"a\"/O/O, \"few\"/O/O, \"hours\"/O/O, \"after\"/O/O, \"i\"/O/O, \"experienced\"/O/O, \"shortness\"/B-ADR/B-Dyspnoea, \"of\"/I-ADR/I-Dyspnoea, \"breath\"/E-ADR/E-Dyspnoea, \",\"/O/O, \"a\"/O/O, \"sense\"/O/O, \"of\"/O/O, \"depression\"/S-ADR/S-Depression, \",\"/O/O, \"cramping\"/S-ADR/S-Muscle_cramp, \",\"/O/O, \"upset\"/B-ADR/B-Upset_stomach, \"stomache\"/E-ADR/E-Upset_stomach, \"will\"/O/O, \"stop\"/O/O, \"taking\"/O/O, \"immediately\"/O/O, \".\"/O/O, \"honestly\"/O/O, \"can\"/O/O, \"not\"/O/O, \"recommend\"/O/O, \"this\"/O/O, \"drug\"/O/O, \",\"/O/O, \"i\"/O/O, \"am\"/O/O, \"still\"/O/O, \"experiencing\"/O/O, \"side\"/O/O, \"effects\"/O/O, \"just\"/O/O, \"after\"/O/O, \"1\"/O/O, \"pill\"/O/O, \"and\"/O/O, \"im\"/O/O, \"supposed\"/O/O, \"to\"/O/O, \"take\"/O/O, \"twice\"/O/O, \"/\"/O/O, \"day\"/O/O, \"75mg\"/O/O, \".\"/O/O, \"i\"/O/O, \"just\"/O/O, \"cant\"/O/O, \"keep\"/O/O, \"going\"/O/O, \"have\"/O/O, \"to\"/O/O, \"try\"/O/O, \"something\"/O/O, \"else\"/O/O, \".\"/O/O],\n",
              " Sentence[50]: \"i have had no side effects been taking arthrotec a little over a year , have not noticed any side effects . it does help alot i noticed that when there are times when i forget to take it i cant stand or walk for any lengths of time .\" → [\"i\"/O/O, \"have\"/O/O, \"had\"/O/O, \"no\"/O/O, \"side\"/O/O, \"effects\"/O/O, \"been\"/O/O, \"taking\"/O/O, \"arthrotec\"/S-Drug/S-Arthrotec, \"a\"/O/O, \"little\"/O/O, \"over\"/O/O, \"a\"/O/O, \"year\"/O/O, \",\"/O/O, \"have\"/O/O, \"not\"/O/O, \"noticed\"/O/O, \"any\"/O/O, \"side\"/O/O, \"effects\"/O/O, \".\"/O/O, \"it\"/O/O, \"does\"/O/O, \"help\"/O/O, \"alot\"/O/O, \"i\"/O/O, \"noticed\"/O/O, \"that\"/O/O, \"when\"/O/O, \"there\"/O/O, \"are\"/O/O, \"times\"/O/O, \"when\"/O/O, \"i\"/O/O, \"forget\"/O/O, \"to\"/O/O, \"take\"/O/O, \"it\"/O/O, \"i\"/B-Symptom/B-Reduced_mobility, \"cant\"/I-Symptom/I-Reduced_mobility, \"stand\"/I-Symptom/I-Reduced_mobility, \"or\"/I-Symptom/I-Reduced_mobility, \"walk\"/I-Symptom/I-Reduced_mobility, \"for\"/I-Symptom/I-Reduced_mobility, \"any\"/I-Symptom/I-Reduced_mobility, \"lengths\"/I-Symptom/I-Reduced_mobility, \"of\"/I-Symptom/I-Reduced_mobility, \"time\"/E-Symptom/E-Reduced_mobility, \".\"/O/O],\n",
              " Sentence[89]: \"it seems as if bleeding forever is a side effect but doctors tell you that its not the drug . i have been on arthotec for three months i still have pain and stiffness . at first week into this drug i was in so much pain i sat on the floor at work and thought i was gaving a baby . i always feel weak and almost fainted a bunch of times . consider somthing that works i am switching today as this isnt in my head .\" → [\"it\"/O/O, \"seems\"/O/O, \"as\"/O/O, \"if\"/O/O, \"bleeding\"/S-ADR/S-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\"/S-Drug/S-Arthrotec, \"for\"/O/O, \"three\"/O/O, \"months\"/O/O, \"i\"/O/O, \"still\"/O/O, \"have\"/O/O, \"pain\"/B-Symptom/B-Pain, \"and\"/I-Symptom/I-Pain, \"stiffness\"/E-Symptom/E-Pain, \".\"/O/O, \"at\"/O/O, \"first\"/O/O, \"week\"/O/O, \"into\"/O/O, \"this\"/O/O, \"drug\"/O/O, \"i\"/O/O, \"was\"/O/O, \"in\"/O/O, \"so\"/B-ADR/B-Pain, \"much\"/I-ADR/I-Pain, \"pain\"/E-ADR/E-Pain, \"i\"/O/O, \"sat\"/O/O, \"on\"/O/O, \"the\"/O/O, \"floor\"/O/O, \"at\"/O/O, \"work\"/O/O, \"and\"/O/O, \"thought\"/O/O, \"i\"/O/O, \"was\"/O/O, \"gaving\"/O/O, \"a\"/O/O, \"baby\"/O/O, \".\"/O/O, \"i\"/O/O, \"always\"/O/O, \"feel\"/B-ADR/B-Malaise_and_fatigue, \"weak\"/I-ADR/I-Malaise_and_fatigue, \"and\"/I-ADR/I-Malaise_and_fatigue, \"almost\"/I-ADR/I-Malaise_and_fatigue, \"fainted\"/E-ADR/E-Malaise_and_fatigue, \"a\"/O/O, \"bunch\"/O/O, \"of\"/O/O, \"times\"/O/O, \".\"/O/O, \"consider\"/O/O, \"somthing\"/O/O, \"that\"/O/O, \"works\"/O/O, \"i\"/O/O, \"am\"/O/O, \"switching\"/O/O, \"today\"/O/O, \"as\"/O/O, \"this\"/O/O, \"isnt\"/O/O, \"in\"/O/O, \"my\"/O/O, \"head\"/O/O, \".\"/O/O],\n",
              " Sentence[56]: \"some diarrhea , some constipation , fatigue . it works very well for the heel ; i couldnt walk on it very well before taking it . i take 75mg . twice a day . also have severe osteoarthritis in the knees and hands , but it doesnt seem to relieve that pain at all .\" → [\"some\"/O/O, \"diarrhea\"/S-ADR/S-Diarrhoea, \",\"/O/O, \"some\"/O/O, \"constipation\"/S-ADR/S-Constipation, \",\"/O/O, \"fatigue\"/S-ADR/S-Fatigue, \".\"/O/O, \"it\"/O/O, \"works\"/O/O, \"very\"/O/O, \"well\"/O/O, \"for\"/O/O, \"the\"/B-Symptom/B-Walking_disability, \"heel\"/I-Symptom/I-Walking_disability, \";\"/O/O, \"i\"/I-Symptom/I-Walking_disability, \"couldnt\"/I-Symptom/I-Walking_disability, \"walk\"/I-Symptom/I-Walking_disability, \"on\"/I-Symptom/I-Walking_disability, \"it\"/O/O, \"very\"/I-Symptom/I-Walking_disability, \"well\"/E-Symptom/E-Walking_disability, \"before\"/O/O, \"taking\"/O/O, \"it\"/O/O, \".\"/O/O, \"i\"/O/O, \"take\"/O/O, \"75mg\"/O/O, \".\"/O/O, \"twice\"/O/O, \"a\"/O/O, \"day\"/O/O, \".\"/O/O, \"also\"/O/O, \"have\"/O/O, \"severe\"/B-Disease/B-Osteoarthritis_of_knee/B-Osteoarthrosis_of_hand, \"osteoarthritis\"/I-Disease/I-Osteoarthritis_of_knee/I-Osteoarthrosis_of_hand, \"in\"/I-Disease/I-Osteoarthritis_of_knee/I-Osteoarthrosis_of_hand, \"the\"/I-Disease/I-Osteoarthritis_of_knee/I-Osteoarthrosis_of_hand, \"knees\"/E-Disease/E-Osteoarthritis_of_knee, \"and\"/O/O, \"hands\"/E-Disease/E-Osteoarthrosis_of_hand, \",\"/O/O, \"but\"/O/O, \"it\"/O/O, \"doesnt\"/O/O, \"seem\"/O/O, \"to\"/O/O, \"relieve\"/O/O, \"that\"/O/O, \"pain\"/S-Symptom/S-Pain, \"at\"/O/O, \"all\"/O/O, \".\"/O/O],\n",
              " Sentence[84]: \"ive been on 75mg twice daily for 13 days , with recent body swelling , face , wrists , abdomen , thighs - clothes fit tight . insomina is much worse . i have forgetfulnes and confussion - really bad - almost dangerous . have felt \" empty stomach \" feeling , but not bothersome . have had more loose stools , but tolerable . im looking for a more natural therapy , checking into ha ( hyaluronic acid ) to replace arthrotec .\" → [\"ive\"/O/O, \"been\"/O/O, \"on\"/O/O, \"75mg\"/O/O, \"twice\"/O/O, \"daily\"/O/O, \"for\"/O/O, \"13\"/O/O, \"days\"/O/O, \",\"/O/O, \"with\"/O/O, \"recent\"/O/O, \"body\"/B-ADR/B-Swelling_of_body_region, \"swelling\"/B-ADR/E-ADR/B-Abdominal_swelling/B-Facial_swelling/E-Swelling_of_body_region/B-Swelling_of_lower_limb/B-Swelling_of_wrist_joint, \",\"/O/O, \"face\"/E-ADR/E-Facial_swelling, \",\"/O/O, \"wrists\"/E-ADR/E-Swelling_of_wrist_joint, \",\"/O/O, \"abdomen\"/E-ADR/E-Abdominal_swelling, \",\"/O/O, \"thighs\"/E-ADR/E-Swelling_of_lower_limb, \"-\"/O/O, \"clothes\"/O/O, \"fit\"/O/O, \"tight\"/O/O, \".\"/O/O, \"insomina\"/S-ADR/S-Insomnia, \"is\"/O/O, \"much\"/O/O, \"worse\"/O/O, \".\"/O/O, \"i\"/O/O, \"have\"/O/O, \"forgetfulnes\"/B-ADR/B-Forgetful, \"and\"/I-ADR/I-Forgetful, \"confussion\"/E-ADR/E-Forgetful, \"-\"/O/O, \"really\"/O/O, \"bad\"/O/O, \"-\"/O/O, \"almost\"/O/O, \"dangerous\"/O/O, \".\"/O/O, \"have\"/O/O, \"felt\"/O/O, \"\"\"/B-ADR/B-Feeling_empty, \"empty\"/I-ADR/I-Feeling_empty, \"stomach\"/I-ADR/I-Feeling_empty, \"\"\"/I-ADR/I-Feeling_empty, \"feeling\"/E-ADR/E-Feeling_empty, \",\"/O/O, \"but\"/O/O, \"not\"/O/O, \"bothersome\"/O/O, \".\"/O/O, \"have\"/O/O, \"had\"/O/O, \"more\"/O/O, \"loose\"/B-ADR/B-Loose_stool, \"stools\"/E-ADR/E-Loose_stool, \",\"/O/O, \"but\"/O/O, \"tolerable\"/O/O, \".\"/O/O, \"im\"/O/O, \"looking\"/O/O, \"for\"/O/O, \"a\"/O/O, \"more\"/O/O, \"natural\"/O/O, \"therapy\"/O/O, \",\"/O/O, \"checking\"/O/O, \"into\"/O/O, \"ha\"/B-Drug/B-CONCEPT_LESS, \"(\"/I-Drug/I-CONCEPT_LESS, \"hyaluronic\"/I-Drug/I-CONCEPT_LESS, \"acid\"/I-Drug/I-CONCEPT_LESS, \")\"/E-Drug/E-CONCEPT_LESS, \"to\"/O/O, \"replace\"/O/O, \"arthrotec\"/S-Drug/S-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\"/S-Drug/S-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\"/S-Drug/S-Arthrotec, \"and\"/O/O, \"was\"/O/O, \"almost\"/O/O, \"pain\"/S-Symptom/S-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\"/S-Finding/S-Arthritis, \".\"/O/O],\n",
              " Sentence[28]: \"hospitalization due extreme rectal bleed that required blood transfusion . i was extremely sick and initially felt i might not survive . should be removed from market .\" → [\"hospitalization\"/O/O, \"due\"/O/O, \"extreme\"/O/O, \"rectal\"/B-ADR/B-Rectal_haemorrhage, \"bleed\"/E-ADR/E-Rectal_haemorrhage, \"that\"/O/O, \"required\"/O/O, \"blood\"/O/O, \"transfusion\"/O/O, \".\"/O/O, \"i\"/O/O, \"was\"/O/O, \"extremely\"/B-ADR/B-Generally_unwell, \"sick\"/E-ADR/E-Generally_unwell, \"and\"/O/O, \"initially\"/O/O, \"felt\"/B-ADR/B-CONCEPT_LESS, \"i\"/I-ADR/I-CONCEPT_LESS, \"might\"/I-ADR/I-CONCEPT_LESS, \"not\"/I-ADR/I-CONCEPT_LESS, \"survive\"/E-ADR/E-CONCEPT_LESS, \".\"/O/O, \"should\"/O/O, \"be\"/O/O, \"removed\"/O/O, \"from\"/O/O, \"market\"/O/O, \".\"/O/O]]"
            ]
          },
          "execution_count": 9,
          "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": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sW10pPAPtGij",
        "outputId": "e7a6d344-4f82-4bd3-f286-f200f8082498"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1186"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(docs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EMT5TsNStGij",
        "outputId": "e1c603ee-1569-4efc-a638-2c45e3461164"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Sentence[117]: \"i feel a bit drowsy & have a little blurred vision , so far no gastric problems . ive been on arthrotec 50 for over 10 years on and off , only taking it when i needed it . due to my arthritis getting progressively worse , to the point where i am in tears with the agony , gp ' s started me on 75 twice a day and i have to take it . every day for the next month to see how i get on , here goes . so far its been very good , pains almost gone , but i feel a bit weird , didnt have that when on 50 .\" → [\"i\"/O/O, \"feel\"/O/O, \"a\"/O/O, \"bit\"/B-ADR/B-Drowsy, \"drowsy\"/E-ADR/E-Drowsy, \"&\"/O/O, \"have\"/O/O, \"a\"/O/O, \"little\"/B-ADR/B-Blurred_vision_-_hazy, \"blurred\"/I-ADR/I-Blurred_vision_-_hazy, \"vision\"/E-ADR/E-Blurred_vision_-_hazy, \",\"/O/O, \"so\"/O/O, \"far\"/O/O, \"no\"/O/O, \"gastric\"/B-ADR/B-Excessive_upper_gastrointestinal_gas, \"problems\"/E-ADR/E-Excessive_upper_gastrointestinal_gas, \".\"/O/O, \"ive\"/O/O, \"been\"/O/O, \"on\"/O/O, \"arthrotec\"/S-Drug/S-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\"/S-Disease/S-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\"/S-Symptom/S-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\"/S-Symptom/S-Pain, \"almost\"/O/O, \"gone\"/O/O, \",\"/O/O, \"but\"/O/O, \"i\"/O/O, \"feel\"/B-ADR/B-Malaise, \"a\"/I-ADR/I-Malaise, \"bit\"/I-ADR/I-Malaise, \"weird\"/E-ADR/E-Malaise, \",\"/O/O, \"didnt\"/O/O, \"have\"/O/O, \"that\"/O/O, \"when\"/O/O, \"on\"/O/O, \"50\"/O/O, \".\"/O/O]"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "temp = docs[0]\n",
        "temp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2EeTdfHstGij",
        "outputId": "996935e7-2e45-4526-85e0-3db44f71f779"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'ner': ['Token[0]: \"i\"'/'O' (1.0),\n",
              "  'Token[1]: \"feel\"'/'O' (1.0),\n",
              "  'Token[2]: \"a\"'/'O' (1.0),\n",
              "  'Token[3]: \"bit\"'/'B-ADR' (1.0),\n",
              "  'Token[4]: \"drowsy\"'/'E-ADR' (1.0),\n",
              "  'Token[5]: \"&\"'/'O' (1.0),\n",
              "  'Token[6]: \"have\"'/'O' (1.0),\n",
              "  'Token[7]: \"a\"'/'O' (1.0),\n",
              "  'Token[8]: \"little\"'/'B-ADR' (1.0),\n",
              "  'Token[9]: \"blurred\"'/'I-ADR' (1.0),\n",
              "  'Token[10]: \"vision\"'/'E-ADR' (1.0),\n",
              "  'Token[11]: \",\"'/'O' (1.0),\n",
              "  'Token[12]: \"so\"'/'O' (1.0),\n",
              "  'Token[13]: \"far\"'/'O' (1.0),\n",
              "  'Token[14]: \"no\"'/'O' (1.0),\n",
              "  'Token[15]: \"gastric\"'/'B-ADR' (1.0),\n",
              "  'Token[16]: \"problems\"'/'E-ADR' (1.0),\n",
              "  'Token[17]: \".\"'/'O' (1.0),\n",
              "  'Token[18]: \"ive\"'/'O' (1.0),\n",
              "  'Token[19]: \"been\"'/'O' (1.0),\n",
              "  'Token[20]: \"on\"'/'O' (1.0),\n",
              "  'Token[21]: \"arthrotec\"'/'S-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\"'/'S-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\"'/'S-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\"'/'S-Symptom' (1.0),\n",
              "  'Token[100]: \"almost\"'/'O' (1.0),\n",
              "  'Token[101]: \"gone\"'/'O' (1.0),\n",
              "  'Token[102]: \",\"'/'O' (1.0),\n",
              "  'Token[103]: \"but\"'/'O' (1.0),\n",
              "  'Token[104]: \"i\"'/'O' (1.0),\n",
              "  'Token[105]: \"feel\"'/'B-ADR' (1.0),\n",
              "  'Token[106]: \"a\"'/'I-ADR' (1.0),\n",
              "  'Token[107]: \"bit\"'/'I-ADR' (1.0),\n",
              "  'Token[108]: \"weird\"'/'E-ADR' (1.0),\n",
              "  'Token[109]: \",\"'/'O' (1.0),\n",
              "  'Token[110]: \"didnt\"'/'O' (1.0),\n",
              "  'Token[111]: \"have\"'/'O' (1.0),\n",
              "  'Token[112]: \"that\"'/'O' (1.0),\n",
              "  'Token[113]: \"when\"'/'O' (1.0),\n",
              "  'Token[114]: \"on\"'/'O' (1.0),\n",
              "  'Token[115]: \"50\"'/'O' (1.0),\n",
              "  'Token[116]: \".\"'/'O' (1.0)],\n",
              " 'meddra': ['Token[0]: \"i\"'/'O' (1.0),\n",
              "  'Token[1]: \"feel\"'/'O' (1.0),\n",
              "  'Token[2]: \"a\"'/'O' (1.0),\n",
              "  'Token[3]: \"bit\"'/'B-Drowsy' (1.0),\n",
              "  'Token[4]: \"drowsy\"'/'E-Drowsy' (1.0),\n",
              "  'Token[5]: \"&\"'/'O' (1.0),\n",
              "  'Token[6]: \"have\"'/'O' (1.0),\n",
              "  'Token[7]: \"a\"'/'O' (1.0),\n",
              "  'Token[8]: \"little\"'/'B-Blurred_vision_-_hazy' (1.0),\n",
              "  'Token[9]: \"blurred\"'/'I-Blurred_vision_-_hazy' (1.0),\n",
              "  'Token[10]: \"vision\"'/'E-Blurred_vision_-_hazy' (1.0),\n",
              "  'Token[11]: \",\"'/'O' (1.0),\n",
              "  'Token[12]: \"so\"'/'O' (1.0),\n",
              "  'Token[13]: \"far\"'/'O' (1.0),\n",
              "  'Token[14]: \"no\"'/'O' (1.0),\n",
              "  'Token[15]: \"gastric\"'/'B-Excessive_upper_gastrointestinal_gas' (1.0),\n",
              "  'Token[16]: \"problems\"'/'E-Excessive_upper_gastrointestinal_gas' (1.0),\n",
              "  'Token[17]: \".\"'/'O' (1.0),\n",
              "  'Token[18]: \"ive\"'/'O' (1.0),\n",
              "  'Token[19]: \"been\"'/'O' (1.0),\n",
              "  'Token[20]: \"on\"'/'O' (1.0),\n",
              "  'Token[21]: \"arthrotec\"'/'S-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\"'/'S-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\"'/'S-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\"'/'S-Pain' (1.0),\n",
              "  'Token[100]: \"almost\"'/'O' (1.0),\n",
              "  'Token[101]: \"gone\"'/'O' (1.0),\n",
              "  'Token[102]: \",\"'/'O' (1.0),\n",
              "  'Token[103]: \"but\"'/'O' (1.0),\n",
              "  'Token[104]: \"i\"'/'O' (1.0),\n",
              "  'Token[105]: \"feel\"'/'B-Malaise' (1.0),\n",
              "  'Token[106]: \"a\"'/'I-Malaise' (1.0),\n",
              "  'Token[107]: \"bit\"'/'I-Malaise' (1.0),\n",
              "  'Token[108]: \"weird\"'/'E-Malaise' (1.0),\n",
              "  'Token[109]: \",\"'/'O' (1.0),\n",
              "  'Token[110]: \"didnt\"'/'O' (1.0),\n",
              "  'Token[111]: \"have\"'/'O' (1.0),\n",
              "  'Token[112]: \"that\"'/'O' (1.0),\n",
              "  'Token[113]: \"when\"'/'O' (1.0),\n",
              "  'Token[114]: \"on\"'/'O' (1.0),\n",
              "  'Token[115]: \"50\"'/'O' (1.0),\n",
              "  'Token[116]: \".\"'/'O' (1.0)]}"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "temp.annotation_layers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "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": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329,
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            "2bf1df949ce049e9a2aca0b3fbb2959f",
            "49c2d07fafd84ee7a3ca433d85fef759",
            "075eaf94c6c746cdb3cb49a8838bcf14",
            "f60434ac6a5e48b88ba51bcd81f294a8",
            "ee2696e6cc2f4f058984b83da17bf728",
            "7cce0ec173464b638d19494a02d09b1a",
            "c0374e3a316c48a0ba20665c13553beb",
            "dbed94fed06e40eea450316cbb5d4aaf",
            "0a0687a8be6f4cf2aece04f3627de133",
            "fc8ff30eeccc4b3aa145a779ad830c68",
            "4e1f065fbac344cb9ff0c9690ad690d9",
            "ee9e83b4ea4e487197cbb67eb0a2cdfa",
            "a0c68b159585488b958a006607be5f37",
            "36dde37455bf404ab6d02ad441d669c2",
            "37126fd4a21b4c2ca925fc4d8498847a",
            "0d6221b848d84e32992a96244ba9f098",
            "e1a5c0a353cf41328e967429bd4fbeb3",
            "3ae24f03189641b58e13a5e730b42d13",
            "718e29f3284e4e808ea713c25173c2c2",
            "923cc57fd8e2415fb50e15f42a90b42e",
            "b3104b099cf44dd4af382b9196869f53",
            "816078e721f84f3b9a1429cf0f034a98",
            "dd80f7dc56064fc7a177c29e9c9a9f90"
          ]
        },
        "id": "QmNsy8AltGik",
        "outputId": "1a9150e3-6036-445b-f181-0266bb59bb34"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 06:56:19,895 Filtering empty sentences\n",
            "2023-04-21 06:56:19,947 Corpus: 948 train + 119 dev + 119 test sentences\n",
            "2023-04-21 06:56:19,949 Computing label dictionary. Progress:\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "948it [00:00, 6794.01it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 06:56:20,093 Dictionary created for label 'ner' with 22 values: O (seen 79057 times), I-ADR (seen 4449 times), E-ADR (seen 3506 times), B-ADR (seen 3349 times), S-ADR (seen 1363 times), S-Drug (seen 1286 times), B-Finding (seen 200 times), E-Finding (seen 200 times), I-Finding (seen 149 times), B-Disease (seen 119 times), E-Disease (seen 119 times), E-Symptom (seen 114 times), S-Disease (seen 112 times), S-Symptom (seen 112 times), E-Drug (seen 111 times), B-Symptom (seen 110 times), B-Drug (seen 110 times), S-Finding (seen 109 times), I-Symptom (seen 104 times), I-Drug (seen 50 times)\n",
            "False\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
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            "\n"
          ]
        },
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        },
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 06:57:11,688 SequenceTagger predicts: Dictionary with 22 tags: <unk>, O, I-ADR, E-ADR, B-ADR, S-ADR, S-Drug, B-Finding, E-Finding, I-Finding, B-Disease, E-Disease, E-Symptom, S-Disease, S-Symptom, E-Drug, B-Symptom, B-Drug, S-Finding, I-Symptom, I-Drug, I-Disease\n",
            "True\n",
            "False\n"
          ]
        }
      ],
      "source": [
        "from flair.data import Corpus\n",
        "\n",
        "from flair.embeddings import WordEmbeddings, FlairEmbeddings, StackedEmbeddings, TransformerWordEmbeddings\n",
        "from flair.models import SequenceTagger\n",
        "from flair.trainers import ModelTrainer\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from torch.optim import AdamW, SGD\n",
        "\n",
        "from flair.visual.training_curves import Plotter\n",
        "plotter = Plotter()\n",
        "\n",
        "\n",
        "\n",
        "train_docs, test_docs = train_test_split(docs, test_size=0.2, random_state=321)\n",
        "dev_docs, test_docs = train_test_split(test_docs, test_size=0.5, random_state=123)\n",
        "\n",
        "corpus = Corpus(train=train_docs, dev=dev_docs, test=test_docs) # Corpus: 948 train + 119 dev + 119 test sentences\n",
        "corpus.filter_empty_sentences()\n",
        "# # create a tag dictionary\n",
        "labels = ['ADR', 'Drug', 'Finding', 'Symptom', 'Disease']\n",
        "# tag_dictionary = Dictionary(add_unk=True)\n",
        "# for label in labels:\n",
        "#     tag_dictionary.add_item(label)\n",
        "tag_dictionary = corpus.make_label_dictionary(label_type='ner')\n",
        "print(tag_dictionary.span_labels)\n",
        "\n",
        "# create a stacked embedding using GloVe and Flair embeddings\n",
        "# stacked_embeddings = StackedEmbeddings([\n",
        "#     WordEmbeddings('glove',\n",
        "#                    force_cpu=False,\n",
        "#                 #    embedding_length=1024,\n",
        "#                   #  fine_tune=True,\n",
        "#                    ),\n",
        "#     # TransformerWordEmbeddings('emilyalsentzer/Bio_ClinicalBERT', layers=\"-1\",\n",
        "#     #                                    subtoken_pooling=\"first\",\n",
        "#     #                                    is_document_embedding=True,\n",
        "#     #                                    fine_tune=True,\n",
        "#     #                                    use_context=True,\n",
        "#     #                                    max_seq_length=1024,\n",
        "#     #                                    ),\n",
        "#     FlairEmbeddings('news-forward',\n",
        "#                     # fine_tune=True\n",
        "#                     ),\n",
        "#     FlairEmbeddings('news-backward',\n",
        "#                     # fine_tune=True\n",
        "#                     ),\n",
        "# ])\n",
        "# https://github.com/flairNLP/flair/blob/master/resources/docs/embeddings/FLAIR_EMBEDDINGS.md\n",
        "\n",
        "\n",
        "embeddings = TransformerWordEmbeddings(model='xlm-roberta-large',\n",
        "                                       layers=\"-1\",\n",
        "                                       subtoken_pooling=\"first\",\n",
        "                                       fine_tune=True,\n",
        "                                       use_context=True,\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": 15,
      "metadata": {
        "id": "fh0zU2jHAA4J"
      },
      "outputs": [],
      "source": [
        "# !tensorboard --logdir=runs\n",
        "# http://localhost:6006/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UKTMzxV8zF9o",
        "outputId": "31749068-55b4-44f1-f72b-fb70b664e6f2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 06:57:12,142 tensorboard logging path is runs/tb_logs_v2_transformer\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/flair/trainers/trainer.py:69: UserWarning: There should be no best model saved at epoch 1 except there is a model from previous trainings in your training folder. All previous best models will be deleted.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 06:57:12,768 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 06:57:12,772 Model: \"SequenceTagger(\n",
            "  (embeddings): TransformerWordEmbeddings(\n",
            "    (model): XLMRobertaModel(\n",
            "      (embeddings): XLMRobertaEmbeddings(\n",
            "        (word_embeddings): Embedding(250003, 1024)\n",
            "        (position_embeddings): Embedding(514, 1024, padding_idx=1)\n",
            "        (token_type_embeddings): Embedding(1, 1024)\n",
            "        (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
            "        (dropout): Dropout(p=0.1, inplace=False)\n",
            "      )\n",
            "      (encoder): XLMRobertaEncoder(\n",
            "        (layer): ModuleList(\n",
            "          (0-23): 24 x XLMRobertaLayer(\n",
            "            (attention): XLMRobertaAttention(\n",
            "              (self): XLMRobertaSelfAttention(\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): XLMRobertaSelfOutput(\n",
            "                (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
            "                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
            "                (dropout): Dropout(p=0.1, inplace=False)\n",
            "              )\n",
            "            )\n",
            "            (intermediate): XLMRobertaIntermediate(\n",
            "              (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
            "              (intermediate_act_fn): GELUActivation()\n",
            "            )\n",
            "            (output): XLMRobertaOutput(\n",
            "              (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
            "              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (pooler): XLMRobertaPooler(\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",
            "  (linear): Linear(in_features=1024, out_features=22, bias=True)\n",
            "  (loss_function): CrossEntropyLoss()\n",
            ")\"\n",
            "2023-04-21 06:57:12,773 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 06:57:12,775 Corpus: \"Corpus: 948 train + 119 dev + 119 test sentences\"\n",
            "2023-04-21 06:57:12,776 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 06:57:12,777 Parameters:\n",
            "2023-04-21 06:57:12,779  - learning_rate: \"0.000005\"\n",
            "2023-04-21 06:57:12,780  - mini_batch_size: \"10\"\n",
            "2023-04-21 06:57:12,781  - patience: \"1\"\n",
            "2023-04-21 06:57:12,783  - anneal_factor: \"0.5\"\n",
            "2023-04-21 06:57:12,784  - max_epochs: \"100\"\n",
            "2023-04-21 06:57:12,785  - shuffle: \"True\"\n",
            "2023-04-21 06:57:12,786  - train_with_dev: \"False\"\n",
            "2023-04-21 06:57:12,788  - batch_growth_annealing: \"False\"\n",
            "2023-04-21 06:57:12,789 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 06:57:12,790 Model training base path: \"flair_ner_model_v2_transformer\"\n",
            "2023-04-21 06:57:12,792 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 06:57:12,793 Device: cuda:0\n",
            "2023-04-21 06:57:12,795 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 06:57:12,796 Embeddings storage mode: gpu\n",
            "2023-04-21 06:57:12,797 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 06:57:20,956 epoch 1 - iter 9/95 - loss 3.31537493 - time (sec): 8.16 - samples/sec: 1176.92 - lr: 0.000005\n",
            "2023-04-21 06:57:25,943 epoch 1 - iter 18/95 - loss 2.84080816 - time (sec): 13.14 - samples/sec: 1352.11 - lr: 0.000005\n",
            "2023-04-21 06:57:31,290 epoch 1 - iter 27/95 - loss 2.30361592 - time (sec): 18.49 - samples/sec: 1424.52 - lr: 0.000005\n",
            "2023-04-21 06:57:36,597 epoch 1 - iter 36/95 - loss 1.96003260 - time (sec): 23.80 - samples/sec: 1479.95 - lr: 0.000005\n",
            "2023-04-21 06:57:41,380 epoch 1 - iter 45/95 - loss 1.72422295 - time (sec): 28.58 - samples/sec: 1529.40 - lr: 0.000005\n",
            "2023-04-21 06:57:46,874 epoch 1 - iter 54/95 - loss 1.54731431 - time (sec): 34.07 - samples/sec: 1550.84 - lr: 0.000005\n",
            "2023-04-21 06:57:52,886 epoch 1 - iter 63/95 - loss 1.40167082 - time (sec): 40.09 - samples/sec: 1560.62 - lr: 0.000005\n",
            "2023-04-21 06:57:58,043 epoch 1 - iter 72/95 - loss 1.29255650 - time (sec): 45.24 - samples/sec: 1578.55 - lr: 0.000005\n",
            "2023-04-21 06:58:03,400 epoch 1 - iter 81/95 - loss 1.20743608 - time (sec): 50.60 - samples/sec: 1589.28 - lr: 0.000005\n",
            "2023-04-21 06:58:09,103 epoch 1 - iter 90/95 - loss 1.13710552 - time (sec): 56.30 - samples/sec: 1588.12 - lr: 0.000005\n",
            "2023-04-21 06:58:12,416 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 06:58:12,418 EPOCH 1 done: loss 1.0976 - lr 0.000005\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 95/95 [00:22<00:00,  4.19it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 06:58:35,238 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 06:58:38,983 TRAIN : loss 0.4131237268447876 - f1-score (weighted avg)  0.3225\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 12/12 [00:03<00:00,  3.98it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 06:58:42,765 Evaluating as a multi-label problem: False\n",
            "2023-04-21 06:58:42,832 DEV : loss 0.43227553367614746 - f1-score (weighted avg)  0.2909\n",
            "2023-04-21 06:58:42,853 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 06:58:42,857 saving best model\n",
            "2023-04-21 06:59:01,485 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 06:59:07,194 epoch 2 - iter 9/95 - loss 0.50615531 - time (sec): 5.71 - samples/sec: 1659.90 - lr: 0.000005\n",
            "2023-04-21 06:59:12,737 epoch 2 - iter 18/95 - loss 0.48922422 - time (sec): 11.25 - samples/sec: 1625.64 - lr: 0.000005\n",
            "2023-04-21 06:59:18,134 epoch 2 - iter 27/95 - loss 0.48562147 - time (sec): 16.65 - samples/sec: 1675.00 - lr: 0.000005\n",
            "2023-04-21 06:59:23,888 epoch 2 - iter 36/95 - loss 0.48285089 - time (sec): 22.40 - samples/sec: 1624.51 - lr: 0.000005\n",
            "2023-04-21 06:59:28,758 epoch 2 - iter 45/95 - loss 0.48159081 - time (sec): 27.27 - samples/sec: 1628.23 - lr: 0.000005\n",
            "2023-04-21 06:59:34,219 epoch 2 - iter 54/95 - loss 0.47758704 - time (sec): 32.73 - samples/sec: 1629.29 - lr: 0.000005\n",
            "2023-04-21 06:59:39,952 epoch 2 - iter 63/95 - loss 0.46362584 - time (sec): 38.46 - samples/sec: 1642.67 - lr: 0.000005\n",
            "2023-04-21 06:59:45,208 epoch 2 - iter 72/95 - loss 0.45650324 - time (sec): 43.72 - samples/sec: 1661.29 - lr: 0.000005\n",
            "2023-04-21 06:59:50,491 epoch 2 - iter 81/95 - loss 0.44500024 - time (sec): 49.00 - samples/sec: 1647.03 - lr: 0.000005\n",
            "2023-04-21 06:59:56,517 epoch 2 - iter 90/95 - loss 0.44036990 - time (sec): 55.03 - samples/sec: 1635.42 - lr: 0.000005\n",
            "2023-04-21 06:59:59,101 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 06:59:59,102 EPOCH 2 done: loss 0.4394 - lr 0.000005\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 95/95 [00:21<00:00,  4.33it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 07:00:21,145 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 07:00:24,976 TRAIN : loss 0.29493236541748047 - f1-score (weighted avg)  0.5941\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 12/12 [00:03<00:00,  3.89it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 07:00:28,205 Evaluating as a multi-label problem: False\n",
            "2023-04-21 07:00:28,273 DEV : loss 0.32167720794677734 - f1-score (weighted avg)  0.5615\n",
            "2023-04-21 07:00:28,293 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 07:00:28,297 saving best model\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 07:00:48,508 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:00:54,235 epoch 3 - iter 9/95 - loss 0.33972984 - time (sec): 5.72 - samples/sec: 1615.63 - lr: 0.000005\n",
            "2023-04-21 07:00:59,579 epoch 3 - iter 18/95 - loss 0.34712288 - time (sec): 11.07 - samples/sec: 1645.90 - lr: 0.000005\n",
            "2023-04-21 07:01:04,934 epoch 3 - iter 27/95 - loss 0.35016271 - time (sec): 16.42 - samples/sec: 1647.92 - lr: 0.000005\n",
            "2023-04-21 07:01:11,164 epoch 3 - iter 36/95 - loss 0.35166682 - time (sec): 22.65 - samples/sec: 1613.07 - lr: 0.000005\n",
            "2023-04-21 07:01:16,085 epoch 3 - iter 45/95 - loss 0.35134837 - time (sec): 27.57 - samples/sec: 1635.80 - lr: 0.000005\n",
            "2023-04-21 07:01:21,670 epoch 3 - iter 54/95 - loss 0.34784148 - time (sec): 33.16 - samples/sec: 1626.79 - lr: 0.000005\n",
            "2023-04-21 07:01:27,427 epoch 3 - iter 63/95 - loss 0.34599351 - time (sec): 38.92 - samples/sec: 1619.52 - lr: 0.000005\n",
            "2023-04-21 07:01:32,764 epoch 3 - iter 72/95 - loss 0.34129118 - time (sec): 44.25 - samples/sec: 1630.11 - lr: 0.000005\n",
            "2023-04-21 07:01:38,309 epoch 3 - iter 81/95 - loss 0.34086702 - time (sec): 49.80 - samples/sec: 1630.73 - lr: 0.000005\n",
            "2023-04-21 07:01:43,711 epoch 3 - iter 90/95 - loss 0.34183717 - time (sec): 55.20 - samples/sec: 1622.24 - lr: 0.000005\n",
            "2023-04-21 07:01:46,642 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:01:46,644 EPOCH 3 done: loss 0.3394 - lr 0.000005\n"
          ]
        },
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            "2023-04-21 07:02:08,737 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
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            "\n"
          ]
        },
        {
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          "text": [
            "2023-04-21 07:02:11,462 TRAIN : loss 0.2359333336353302 - f1-score (weighted avg)  0.6721\n"
          ]
        },
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          "text": [
            "2023-04-21 07:02:14,644 Evaluating as a multi-label problem: False\n",
            "2023-04-21 07:02:14,716 DEV : loss 0.2743408977985382 - f1-score (weighted avg)  0.6257\n",
            "2023-04-21 07:02:14,737 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 07:02:14,741 saving best model\n"
          ]
        },
        {
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          "text": [
            "2023-04-21 07:02:34,735 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:02:39,949 epoch 4 - iter 9/95 - loss 0.30563761 - time (sec): 5.21 - samples/sec: 1656.87 - lr: 0.000005\n",
            "2023-04-21 07:02:45,174 epoch 4 - iter 18/95 - loss 0.31402040 - time (sec): 10.44 - samples/sec: 1627.54 - lr: 0.000005\n",
            "2023-04-21 07:02:50,769 epoch 4 - iter 27/95 - loss 0.29259127 - time (sec): 16.03 - samples/sec: 1624.89 - lr: 0.000005\n",
            "2023-04-21 07:02:56,672 epoch 4 - iter 36/95 - loss 0.28179464 - time (sec): 21.93 - samples/sec: 1591.86 - lr: 0.000005\n",
            "2023-04-21 07:03:03,287 epoch 4 - iter 45/95 - loss 0.29106429 - time (sec): 28.55 - samples/sec: 1552.85 - lr: 0.000005\n",
            "2023-04-21 07:03:08,628 epoch 4 - iter 54/95 - loss 0.29433911 - time (sec): 33.89 - samples/sec: 1567.76 - lr: 0.000005\n",
            "2023-04-21 07:03:13,846 epoch 4 - iter 63/95 - loss 0.29618739 - time (sec): 39.11 - samples/sec: 1584.66 - lr: 0.000005\n",
            "2023-04-21 07:03:19,475 epoch 4 - iter 72/95 - loss 0.29634839 - time (sec): 44.74 - samples/sec: 1585.57 - lr: 0.000005\n",
            "2023-04-21 07:03:25,396 epoch 4 - iter 81/95 - loss 0.29357409 - time (sec): 50.66 - samples/sec: 1595.13 - lr: 0.000005\n",
            "2023-04-21 07:03:31,080 epoch 4 - iter 90/95 - loss 0.29225961 - time (sec): 56.34 - samples/sec: 1596.55 - lr: 0.000005\n",
            "2023-04-21 07:03:34,056 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:03:34,058 EPOCH 4 done: loss 0.2953 - lr 0.000005\n"
          ]
        },
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          "text": [
            "2023-04-21 07:03:56,218 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 07:04:00,071 TRAIN : loss 0.21932698786258698 - f1-score (weighted avg)  0.6963\n"
          ]
        },
        {
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          "text": [
            "2023-04-21 07:04:03,275 Evaluating as a multi-label problem: False\n",
            "2023-04-21 07:04:03,343 DEV : loss 0.26840412616729736 - f1-score (weighted avg)  0.6372\n",
            "2023-04-21 07:04:03,364 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 07:04:03,368 saving best model\n"
          ]
        },
        {
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          "output_type": "stream",
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            "2023-04-21 07:04:23,358 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:04:28,684 epoch 5 - iter 9/95 - loss 0.23953490 - time (sec): 5.32 - samples/sec: 1542.12 - lr: 0.000005\n",
            "2023-04-21 07:04:34,550 epoch 5 - iter 18/95 - loss 0.25133598 - time (sec): 11.19 - samples/sec: 1694.35 - lr: 0.000005\n",
            "2023-04-21 07:04:39,811 epoch 5 - iter 27/95 - loss 0.25622697 - time (sec): 16.45 - samples/sec: 1706.66 - lr: 0.000005\n",
            "2023-04-21 07:04:45,863 epoch 5 - iter 36/95 - loss 0.25547987 - time (sec): 22.50 - samples/sec: 1686.46 - lr: 0.000005\n",
            "2023-04-21 07:04:50,739 epoch 5 - iter 45/95 - loss 0.25675980 - time (sec): 27.38 - samples/sec: 1727.17 - lr: 0.000005\n",
            "2023-04-21 07:04:55,501 epoch 5 - iter 54/95 - loss 0.26197625 - time (sec): 32.14 - samples/sec: 1703.45 - lr: 0.000005\n",
            "2023-04-21 07:05:01,814 epoch 5 - iter 63/95 - loss 0.25923735 - time (sec): 38.45 - samples/sec: 1679.04 - lr: 0.000005\n",
            "2023-04-21 07:05:07,897 epoch 5 - iter 72/95 - loss 0.25473618 - time (sec): 44.54 - samples/sec: 1649.51 - lr: 0.000005\n",
            "2023-04-21 07:05:12,427 epoch 5 - iter 81/95 - loss 0.25835809 - time (sec): 49.07 - samples/sec: 1662.96 - lr: 0.000005\n",
            "2023-04-21 07:05:18,108 epoch 5 - iter 90/95 - loss 0.25818394 - time (sec): 54.75 - samples/sec: 1646.55 - lr: 0.000005\n",
            "2023-04-21 07:05:20,945 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:05:20,947 EPOCH 5 done: loss 0.2604 - lr 0.000005\n"
          ]
        },
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          "text": [
            "2023-04-21 07:05:43,019 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
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          "output_type": "stream",
          "text": [
            "2023-04-21 07:05:47,039 TRAIN : loss 0.19356925785541534 - f1-score (weighted avg)  0.7254\n"
          ]
        },
        {
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          "output_type": "stream",
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          "text": [
            "2023-04-21 07:05:50,215 Evaluating as a multi-label problem: False\n",
            "2023-04-21 07:05:50,285 DEV : loss 0.25782081484794617 - f1-score (weighted avg)  0.6496\n",
            "2023-04-21 07:05:50,305 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 07:05:50,309 saving best model\n"
          ]
        },
        {
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          "output_type": "stream",
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          "text": [
            "2023-04-21 07:06:09,764 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:06:15,192 epoch 6 - iter 9/95 - loss 0.22508385 - time (sec): 5.42 - samples/sec: 1682.00 - lr: 0.000005\n",
            "2023-04-21 07:06:20,290 epoch 6 - iter 18/95 - loss 0.25146872 - time (sec): 10.52 - samples/sec: 1653.55 - lr: 0.000005\n",
            "2023-04-21 07:06:25,898 epoch 6 - iter 27/95 - loss 0.25125703 - time (sec): 16.13 - samples/sec: 1669.91 - lr: 0.000005\n",
            "2023-04-21 07:06:31,670 epoch 6 - iter 36/95 - loss 0.23766320 - time (sec): 21.90 - samples/sec: 1653.41 - lr: 0.000005\n",
            "2023-04-21 07:06:36,663 epoch 6 - iter 45/95 - loss 0.23621596 - time (sec): 26.90 - samples/sec: 1655.55 - lr: 0.000005\n",
            "2023-04-21 07:06:42,202 epoch 6 - iter 54/95 - loss 0.23067577 - time (sec): 32.43 - samples/sec: 1645.49 - lr: 0.000005\n",
            "2023-04-21 07:06:48,030 epoch 6 - iter 63/95 - loss 0.22652085 - time (sec): 38.26 - samples/sec: 1651.36 - lr: 0.000005\n",
            "2023-04-21 07:06:53,374 epoch 6 - iter 72/95 - loss 0.22838614 - time (sec): 43.61 - samples/sec: 1642.39 - lr: 0.000005\n",
            "2023-04-21 07:06:59,041 epoch 6 - iter 81/95 - loss 0.22708755 - time (sec): 49.27 - samples/sec: 1636.67 - lr: 0.000005\n",
            "2023-04-21 07:07:05,066 epoch 6 - iter 90/95 - loss 0.23034720 - time (sec): 55.30 - samples/sec: 1625.89 - lr: 0.000005\n",
            "2023-04-21 07:07:08,582 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:07:08,583 EPOCH 6 done: loss 0.2322 - lr 0.000005\n"
          ]
        },
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          "text": [
            "2023-04-21 07:07:30,704 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 07:07:33,432 TRAIN : loss 0.17041897773742676 - f1-score (weighted avg)  0.761\n"
          ]
        },
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          "output_type": "stream",
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          "output_type": "stream",
          "text": [
            "2023-04-21 07:07:37,613 Evaluating as a multi-label problem: False\n",
            "2023-04-21 07:07:37,689 DEV : loss 0.2543083131313324 - f1-score (weighted avg)  0.6727\n",
            "2023-04-21 07:07:37,710 BAD EPOCHS (no improvement): 0\n",
            "2023-04-21 07:07:37,714 saving best model\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 07:07:57,678 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:08:02,834 epoch 7 - iter 9/95 - loss 0.20445010 - time (sec): 5.15 - samples/sec: 1635.21 - lr: 0.000005\n",
            "2023-04-21 07:08:09,065 epoch 7 - iter 18/95 - loss 0.21643859 - time (sec): 11.39 - samples/sec: 1575.30 - lr: 0.000005\n",
            "2023-04-21 07:08:14,342 epoch 7 - iter 27/95 - loss 0.22921659 - time (sec): 16.66 - samples/sec: 1605.86 - lr: 0.000005\n",
            "2023-04-21 07:08:20,025 epoch 7 - iter 36/95 - loss 0.21999663 - time (sec): 22.34 - samples/sec: 1617.25 - lr: 0.000005\n",
            "2023-04-21 07:08:25,478 epoch 7 - iter 45/95 - loss 0.21824463 - time (sec): 27.80 - samples/sec: 1637.07 - lr: 0.000005\n",
            "2023-04-21 07:08:31,874 epoch 7 - iter 54/95 - loss 0.21045341 - time (sec): 34.19 - samples/sec: 1629.21 - lr: 0.000005\n",
            "2023-04-21 07:08:37,166 epoch 7 - iter 63/95 - loss 0.21040409 - time (sec): 39.49 - samples/sec: 1603.54 - lr: 0.000005\n",
            "2023-04-21 07:08:42,709 epoch 7 - iter 72/95 - loss 0.20880433 - time (sec): 45.03 - samples/sec: 1618.61 - lr: 0.000005\n",
            "2023-04-21 07:08:48,893 epoch 7 - iter 81/95 - loss 0.20901187 - time (sec): 51.21 - samples/sec: 1599.18 - lr: 0.000005\n",
            "2023-04-21 07:08:53,683 epoch 7 - iter 90/95 - loss 0.20895587 - time (sec): 56.00 - samples/sec: 1601.73 - lr: 0.000005\n",
            "2023-04-21 07:08:56,734 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:08:56,736 EPOCH 7 done: loss 0.2095 - lr 0.000005\n"
          ]
        },
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          "text": [
            "2023-04-21 07:09:18,922 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 07:09:22,778 TRAIN : loss 0.15869013965129852 - f1-score (weighted avg)  0.7754\n"
          ]
        },
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          "output_type": "stream",
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          "output_type": "stream",
          "text": [
            "2023-04-21 07:09:25,970 Evaluating as a multi-label problem: False\n",
            "2023-04-21 07:09:26,038 DEV : loss 0.25603732466697693 - f1-score (weighted avg)  0.6784\n",
            "2023-04-21 07:09:26,059 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 07:09:26,063 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 07:09:31,781 epoch 8 - iter 9/95 - loss 0.19727700 - time (sec): 5.72 - samples/sec: 1560.28 - lr: 0.000005\n",
            "2023-04-21 07:09:37,116 epoch 8 - iter 18/95 - loss 0.19322846 - time (sec): 11.05 - samples/sec: 1592.49 - lr: 0.000005\n",
            "2023-04-21 07:09:42,515 epoch 8 - iter 27/95 - loss 0.18919846 - time (sec): 16.45 - samples/sec: 1591.29 - lr: 0.000005\n",
            "2023-04-21 07:09:48,379 epoch 8 - iter 36/95 - loss 0.19688976 - time (sec): 22.31 - samples/sec: 1590.67 - lr: 0.000005\n",
            "2023-04-21 07:09:53,373 epoch 8 - iter 45/95 - loss 0.19888562 - time (sec): 27.31 - samples/sec: 1602.81 - lr: 0.000005\n",
            "2023-04-21 07:09:59,001 epoch 8 - iter 54/95 - loss 0.19436060 - time (sec): 32.94 - samples/sec: 1589.68 - lr: 0.000005\n",
            "2023-04-21 07:10:04,369 epoch 8 - iter 63/95 - loss 0.20080812 - time (sec): 38.30 - samples/sec: 1583.23 - lr: 0.000005\n",
            "2023-04-21 07:10:09,977 epoch 8 - iter 72/95 - loss 0.20349020 - time (sec): 43.91 - samples/sec: 1596.97 - lr: 0.000005\n",
            "2023-04-21 07:10:15,725 epoch 8 - iter 81/95 - loss 0.19966816 - time (sec): 49.66 - samples/sec: 1595.04 - lr: 0.000005\n",
            "2023-04-21 07:10:21,673 epoch 8 - iter 90/95 - loss 0.19473526 - time (sec): 55.61 - samples/sec: 1608.32 - lr: 0.000005\n",
            "2023-04-21 07:10:24,334 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:10:24,335 EPOCH 8 done: loss 0.1949 - lr 0.000005\n"
          ]
        },
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          "text": [
            "2023-04-21 07:10:46,482 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
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          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
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          "text": [
            "2023-04-21 07:10:50,357 TRAIN : loss 0.14811904728412628 - f1-score (weighted avg)  0.7898\n"
          ]
        },
        {
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          "output_type": "stream",
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          "output_type": "stream",
          "text": [
            "2023-04-21 07:10:53,533 Evaluating as a multi-label problem: False\n",
            "2023-04-21 07:10:53,602 DEV : loss 0.26694801449775696 - f1-score (weighted avg)  0.6872\n",
            "2023-04-21 07:10:53,621 Epoch     8: reducing learning rate of group 0 to 2.5000e-06.\n",
            "2023-04-21 07:10:53,623 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 07:10:53,627 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 07:10:59,403 epoch 9 - iter 9/95 - loss 0.16636702 - time (sec): 5.78 - samples/sec: 1737.47 - lr: 0.000003\n",
            "2023-04-21 07:11:04,555 epoch 9 - iter 18/95 - loss 0.15714943 - time (sec): 10.93 - samples/sec: 1794.24 - lr: 0.000003\n",
            "2023-04-21 07:11:10,552 epoch 9 - iter 27/95 - loss 0.15642907 - time (sec): 16.92 - samples/sec: 1752.35 - lr: 0.000003\n",
            "2023-04-21 07:11:16,046 epoch 9 - iter 36/95 - loss 0.16099000 - time (sec): 22.42 - samples/sec: 1727.14 - lr: 0.000003\n",
            "2023-04-21 07:11:21,747 epoch 9 - iter 45/95 - loss 0.16809787 - time (sec): 28.12 - samples/sec: 1704.56 - lr: 0.000003\n",
            "2023-04-21 07:11:26,822 epoch 9 - iter 54/95 - loss 0.17582250 - time (sec): 33.19 - samples/sec: 1677.05 - lr: 0.000003\n",
            "2023-04-21 07:11:30,988 epoch 9 - iter 63/95 - loss 0.17715260 - time (sec): 37.36 - samples/sec: 1666.88 - lr: 0.000003\n",
            "2023-04-21 07:11:36,680 epoch 9 - iter 72/95 - loss 0.17740371 - time (sec): 43.05 - samples/sec: 1651.68 - lr: 0.000003\n",
            "2023-04-21 07:11:41,764 epoch 9 - iter 81/95 - loss 0.17635352 - time (sec): 48.14 - samples/sec: 1637.55 - lr: 0.000003\n",
            "2023-04-21 07:11:47,763 epoch 9 - iter 90/95 - loss 0.17211395 - time (sec): 54.14 - samples/sec: 1648.17 - lr: 0.000003\n",
            "2023-04-21 07:11:50,779 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:11:50,781 EPOCH 9 done: loss 0.1724 - lr 0.000003\n"
          ]
        },
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          "text": [
            "2023-04-21 07:12:12,832 Evaluating as a multi-label problem: True\n"
          ]
        },
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 07:12:16,752 TRAIN : loss 0.13000380992889404 - f1-score (weighted avg)  0.8153\n"
          ]
        },
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          "text": [
            "2023-04-21 07:12:19,923 Evaluating as a multi-label problem: False\n",
            "2023-04-21 07:12:19,991 DEV : loss 0.26449358463287354 - f1-score (weighted avg)  0.6901\n",
            "2023-04-21 07:12:20,011 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 07:12:20,015 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 07:12:25,451 epoch 10 - iter 9/95 - loss 0.17729608 - time (sec): 5.43 - samples/sec: 1771.41 - lr: 0.000003\n",
            "2023-04-21 07:12:30,695 epoch 10 - iter 18/95 - loss 0.18015182 - time (sec): 10.68 - samples/sec: 1654.19 - lr: 0.000003\n",
            "2023-04-21 07:12:36,645 epoch 10 - iter 27/95 - loss 0.17144173 - time (sec): 16.63 - samples/sec: 1645.75 - lr: 0.000003\n",
            "2023-04-21 07:12:42,388 epoch 10 - iter 36/95 - loss 0.16962831 - time (sec): 22.37 - samples/sec: 1596.00 - lr: 0.000003\n",
            "2023-04-21 07:12:48,775 epoch 10 - iter 45/95 - loss 0.16118857 - time (sec): 28.76 - samples/sec: 1575.58 - lr: 0.000003\n",
            "2023-04-21 07:12:54,211 epoch 10 - iter 54/95 - loss 0.16259931 - time (sec): 34.20 - samples/sec: 1604.27 - lr: 0.000003\n",
            "2023-04-21 07:12:59,132 epoch 10 - iter 63/95 - loss 0.16226631 - time (sec): 39.12 - samples/sec: 1610.64 - lr: 0.000003\n",
            "2023-04-21 07:13:04,357 epoch 10 - iter 72/95 - loss 0.16552963 - time (sec): 44.34 - samples/sec: 1612.79 - lr: 0.000003\n",
            "2023-04-21 07:13:09,273 epoch 10 - iter 81/95 - loss 0.16474581 - time (sec): 49.26 - samples/sec: 1634.87 - lr: 0.000003\n",
            "2023-04-21 07:13:14,469 epoch 10 - iter 90/95 - loss 0.16451544 - time (sec): 54.45 - samples/sec: 1648.08 - lr: 0.000003\n",
            "2023-04-21 07:13:17,302 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:13:17,304 EPOCH 10 done: loss 0.1626 - lr 0.000003\n"
          ]
        },
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            "2023-04-21 07:13:39,323 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 07:13:42,054 TRAIN : loss 0.11832153052091599 - f1-score (weighted avg)  0.8286\n"
          ]
        },
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          "text": [
            "2023-04-21 07:13:46,237 Evaluating as a multi-label problem: False\n",
            "2023-04-21 07:13:46,306 DEV : loss 0.2546929717063904 - f1-score (weighted avg)  0.6902\n",
            "2023-04-21 07:13:46,326 Epoch    10: reducing learning rate of group 0 to 1.2500e-06.\n",
            "2023-04-21 07:13:46,328 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 07:13:46,331 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:13:51,510 epoch 11 - iter 9/95 - loss 0.14451168 - time (sec): 5.18 - samples/sec: 1643.15 - lr: 0.000001\n",
            "2023-04-21 07:13:57,033 epoch 11 - iter 18/95 - loss 0.14821641 - time (sec): 10.70 - samples/sec: 1651.57 - lr: 0.000001\n",
            "2023-04-21 07:14:01,892 epoch 11 - iter 27/95 - loss 0.15197228 - time (sec): 15.56 - samples/sec: 1686.68 - lr: 0.000001\n",
            "2023-04-21 07:14:07,638 epoch 11 - iter 36/95 - loss 0.14637132 - time (sec): 21.30 - samples/sec: 1719.47 - lr: 0.000001\n",
            "2023-04-21 07:14:13,190 epoch 11 - iter 45/95 - loss 0.15072547 - time (sec): 26.86 - samples/sec: 1702.77 - lr: 0.000001\n",
            "2023-04-21 07:14:18,562 epoch 11 - iter 54/95 - loss 0.15167095 - time (sec): 32.23 - samples/sec: 1679.58 - lr: 0.000001\n",
            "2023-04-21 07:14:24,779 epoch 11 - iter 63/95 - loss 0.15150093 - time (sec): 38.45 - samples/sec: 1644.98 - lr: 0.000001\n",
            "2023-04-21 07:14:29,863 epoch 11 - iter 72/95 - loss 0.15360482 - time (sec): 43.53 - samples/sec: 1655.80 - lr: 0.000001\n",
            "2023-04-21 07:14:35,105 epoch 11 - iter 81/95 - loss 0.15535150 - time (sec): 48.77 - samples/sec: 1654.96 - lr: 0.000001\n",
            "2023-04-21 07:14:41,643 epoch 11 - iter 90/95 - loss 0.15461574 - time (sec): 55.31 - samples/sec: 1639.25 - lr: 0.000001\n",
            "2023-04-21 07:14:44,148 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:14:44,149 EPOCH 11 done: loss 0.1548 - lr 0.000001\n"
          ]
        },
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          "text": [
            "2023-04-21 07:15:06,206 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
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            "\n"
          ]
        },
        {
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          "text": [
            "2023-04-21 07:15:10,065 TRAIN : loss 0.11708444356918335 - f1-score (weighted avg)  0.8338\n"
          ]
        },
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          "text": [
            "2023-04-21 07:15:13,272 Evaluating as a multi-label problem: False\n",
            "2023-04-21 07:15:13,351 DEV : loss 0.2686716914176941 - f1-score (weighted avg)  0.6932\n",
            "2023-04-21 07:15:13,373 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 07:15:13,378 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 07:15:18,531 epoch 12 - iter 9/95 - loss 0.17131934 - time (sec): 5.15 - samples/sec: 1716.82 - lr: 0.000001\n",
            "2023-04-21 07:15:24,103 epoch 12 - iter 18/95 - loss 0.15632184 - time (sec): 10.72 - samples/sec: 1719.50 - lr: 0.000001\n",
            "2023-04-21 07:15:29,152 epoch 12 - iter 27/95 - loss 0.14816569 - time (sec): 15.77 - samples/sec: 1689.65 - lr: 0.000001\n",
            "2023-04-21 07:15:34,923 epoch 12 - iter 36/95 - loss 0.14584519 - time (sec): 21.54 - samples/sec: 1675.10 - lr: 0.000001\n",
            "2023-04-21 07:15:40,277 epoch 12 - iter 45/95 - loss 0.14471520 - time (sec): 26.90 - samples/sec: 1648.87 - lr: 0.000001\n",
            "2023-04-21 07:15:45,977 epoch 12 - iter 54/95 - loss 0.14931201 - time (sec): 32.60 - samples/sec: 1631.87 - lr: 0.000001\n",
            "2023-04-21 07:15:51,353 epoch 12 - iter 63/95 - loss 0.15007939 - time (sec): 37.97 - samples/sec: 1662.98 - lr: 0.000001\n",
            "2023-04-21 07:15:56,880 epoch 12 - iter 72/95 - loss 0.14930329 - time (sec): 43.50 - samples/sec: 1661.43 - lr: 0.000001\n",
            "2023-04-21 07:16:01,886 epoch 12 - iter 81/95 - loss 0.14935018 - time (sec): 48.51 - samples/sec: 1659.35 - lr: 0.000001\n",
            "2023-04-21 07:16:07,187 epoch 12 - iter 90/95 - loss 0.14861322 - time (sec): 53.81 - samples/sec: 1657.50 - lr: 0.000001\n",
            "2023-04-21 07:16:10,623 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:16:10,625 EPOCH 12 done: loss 0.1488 - lr 0.000001\n"
          ]
        },
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          "text": [
            "2023-04-21 07:16:32,677 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
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        {
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          "text": [
            "2023-04-21 07:16:36,621 TRAIN : loss 0.10720328986644745 - f1-score (weighted avg)  0.8459\n"
          ]
        },
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          "text": [
            "2023-04-21 07:16:39,820 Evaluating as a multi-label problem: False\n",
            "2023-04-21 07:16:39,889 DEV : loss 0.2666051685810089 - f1-score (weighted avg)  0.6893\n",
            "2023-04-21 07:16:39,909 Epoch    12: reducing learning rate of group 0 to 6.2500e-07.\n",
            "2023-04-21 07:16:39,911 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 07:16:39,914 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 07:16:45,459 epoch 13 - iter 9/95 - loss 0.16478931 - time (sec): 5.54 - samples/sec: 1630.11 - lr: 0.000001\n",
            "2023-04-21 07:16:51,531 epoch 13 - iter 18/95 - loss 0.15212684 - time (sec): 11.62 - samples/sec: 1588.46 - lr: 0.000001\n",
            "2023-04-21 07:16:56,649 epoch 13 - iter 27/95 - loss 0.15242407 - time (sec): 16.73 - samples/sec: 1533.57 - lr: 0.000001\n",
            "2023-04-21 07:17:02,377 epoch 13 - iter 36/95 - loss 0.14323837 - time (sec): 22.46 - samples/sec: 1616.37 - lr: 0.000001\n",
            "2023-04-21 07:17:08,085 epoch 13 - iter 45/95 - loss 0.14264204 - time (sec): 28.17 - samples/sec: 1633.61 - lr: 0.000001\n",
            "2023-04-21 07:17:13,850 epoch 13 - iter 54/95 - loss 0.14114411 - time (sec): 33.93 - samples/sec: 1630.39 - lr: 0.000001\n",
            "2023-04-21 07:17:19,345 epoch 13 - iter 63/95 - loss 0.13907258 - time (sec): 39.43 - samples/sec: 1633.33 - lr: 0.000001\n",
            "2023-04-21 07:17:24,701 epoch 13 - iter 72/95 - loss 0.13953599 - time (sec): 44.79 - samples/sec: 1628.59 - lr: 0.000001\n",
            "2023-04-21 07:17:30,012 epoch 13 - iter 81/95 - loss 0.13984790 - time (sec): 50.10 - samples/sec: 1618.80 - lr: 0.000001\n",
            "2023-04-21 07:17:35,753 epoch 13 - iter 90/95 - loss 0.14248101 - time (sec): 55.84 - samples/sec: 1620.65 - lr: 0.000001\n",
            "2023-04-21 07:17:38,352 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:17:38,353 EPOCH 13 done: loss 0.1425 - lr 0.000001\n"
          ]
        },
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          "text": [
            "2023-04-21 07:18:00,409 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
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          "output_type": "stream",
          "text": [
            "\n"
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        {
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          "text": [
            "2023-04-21 07:18:03,115 TRAIN : loss 0.10769189894199371 - f1-score (weighted avg)  0.8439\n"
          ]
        },
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          "text": [
            "2023-04-21 07:18:07,305 Evaluating as a multi-label problem: False\n",
            "2023-04-21 07:18:07,374 DEV : loss 0.27092432975769043 - f1-score (weighted avg)  0.6956\n",
            "2023-04-21 07:18:07,394 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 07:18:07,405 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:18:13,072 epoch 14 - iter 9/95 - loss 0.12172698 - time (sec): 5.67 - samples/sec: 1546.24 - lr: 0.000001\n",
            "2023-04-21 07:18:18,226 epoch 14 - iter 18/95 - loss 0.13343244 - time (sec): 10.82 - samples/sec: 1595.00 - lr: 0.000001\n",
            "2023-04-21 07:18:24,248 epoch 14 - iter 27/95 - loss 0.12324797 - time (sec): 16.84 - samples/sec: 1630.58 - lr: 0.000001\n",
            "2023-04-21 07:18:29,926 epoch 14 - iter 36/95 - loss 0.13198821 - time (sec): 22.52 - samples/sec: 1627.37 - lr: 0.000001\n",
            "2023-04-21 07:18:34,870 epoch 14 - iter 45/95 - loss 0.13466269 - time (sec): 27.46 - samples/sec: 1655.84 - lr: 0.000001\n",
            "2023-04-21 07:18:39,945 epoch 14 - iter 54/95 - loss 0.13920673 - time (sec): 32.54 - samples/sec: 1619.47 - lr: 0.000001\n",
            "2023-04-21 07:18:45,488 epoch 14 - iter 63/95 - loss 0.13726270 - time (sec): 38.08 - samples/sec: 1618.75 - lr: 0.000001\n",
            "2023-04-21 07:18:51,854 epoch 14 - iter 72/95 - loss 0.13782741 - time (sec): 44.45 - samples/sec: 1620.25 - lr: 0.000001\n",
            "2023-04-21 07:18:57,231 epoch 14 - iter 81/95 - loss 0.13935206 - time (sec): 49.82 - samples/sec: 1621.27 - lr: 0.000001\n",
            "2023-04-21 07:19:03,206 epoch 14 - iter 90/95 - loss 0.14037920 - time (sec): 55.80 - samples/sec: 1629.71 - lr: 0.000001\n",
            "2023-04-21 07:19:05,912 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:19:05,914 EPOCH 14 done: loss 0.1413 - lr 0.000001\n"
          ]
        },
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          "text": [
            "2023-04-21 07:19:27,929 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
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          "text": [
            "2023-04-21 07:19:31,804 TRAIN : loss 0.10326381027698517 - f1-score (weighted avg)  0.85\n"
          ]
        },
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          "output_type": "stream",
          "text": [
            "2023-04-21 07:19:35,018 Evaluating as a multi-label problem: False\n",
            "2023-04-21 07:19:35,088 DEV : loss 0.2704702615737915 - f1-score (weighted avg)  0.6922\n",
            "2023-04-21 07:19:35,108 Epoch    14: reducing learning rate of group 0 to 3.1250e-07.\n",
            "2023-04-21 07:19:35,110 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 07:19:35,114 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 07:19:40,318 epoch 15 - iter 9/95 - loss 0.14075208 - time (sec): 5.20 - samples/sec: 1605.47 - lr: 0.000000\n",
            "2023-04-21 07:19:45,834 epoch 15 - iter 18/95 - loss 0.13328283 - time (sec): 10.72 - samples/sec: 1715.64 - lr: 0.000000\n",
            "2023-04-21 07:19:51,070 epoch 15 - iter 27/95 - loss 0.13837990 - time (sec): 15.95 - samples/sec: 1701.86 - lr: 0.000000\n",
            "2023-04-21 07:19:56,729 epoch 15 - iter 36/95 - loss 0.13618612 - time (sec): 21.61 - samples/sec: 1681.97 - lr: 0.000000\n",
            "2023-04-21 07:20:01,991 epoch 15 - iter 45/95 - loss 0.13356997 - time (sec): 26.88 - samples/sec: 1670.84 - lr: 0.000000\n",
            "2023-04-21 07:20:07,478 epoch 15 - iter 54/95 - loss 0.13229546 - time (sec): 32.36 - samples/sec: 1651.82 - lr: 0.000000\n",
            "2023-04-21 07:20:13,131 epoch 15 - iter 63/95 - loss 0.13493891 - time (sec): 38.02 - samples/sec: 1647.44 - lr: 0.000000\n",
            "2023-04-21 07:20:18,464 epoch 15 - iter 72/95 - loss 0.13752786 - time (sec): 43.35 - samples/sec: 1638.03 - lr: 0.000000\n",
            "2023-04-21 07:20:23,669 epoch 15 - iter 81/95 - loss 0.13668968 - time (sec): 48.55 - samples/sec: 1656.21 - lr: 0.000000\n",
            "2023-04-21 07:20:28,738 epoch 15 - iter 90/95 - loss 0.13693089 - time (sec): 53.62 - samples/sec: 1665.07 - lr: 0.000000\n",
            "2023-04-21 07:20:32,033 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:20:32,034 EPOCH 15 done: loss 0.1354 - lr 0.000000\n"
          ]
        },
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          "text": [
            "2023-04-21 07:20:54,089 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
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          "output_type": "stream",
          "text": [
            "\n"
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          "text": [
            "2023-04-21 07:20:58,170 TRAIN : loss 0.10196240246295929 - f1-score (weighted avg)  0.853\n"
          ]
        },
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          "output_type": "stream",
          "text": [
            "2023-04-21 07:21:01,363 Evaluating as a multi-label problem: False\n",
            "2023-04-21 07:21:01,433 DEV : loss 0.2712968587875366 - f1-score (weighted avg)  0.693\n",
            "2023-04-21 07:21:01,453 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 07:21:01,457 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
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          "output_type": "stream",
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          "text": [
            "2023-04-21 07:21:06,363 epoch 16 - iter 9/95 - loss 0.15086616 - time (sec): 4.90 - samples/sec: 1681.62 - lr: 0.000000\n",
            "2023-04-21 07:21:12,526 epoch 16 - iter 18/95 - loss 0.14424629 - time (sec): 11.07 - samples/sec: 1645.48 - lr: 0.000000\n",
            "2023-04-21 07:21:18,644 epoch 16 - iter 27/95 - loss 0.13878342 - time (sec): 17.19 - samples/sec: 1656.55 - lr: 0.000000\n",
            "2023-04-21 07:21:24,110 epoch 16 - iter 36/95 - loss 0.13789251 - time (sec): 22.65 - samples/sec: 1650.07 - lr: 0.000000\n",
            "2023-04-21 07:21:29,274 epoch 16 - iter 45/95 - loss 0.13746768 - time (sec): 27.82 - samples/sec: 1653.31 - lr: 0.000000\n",
            "2023-04-21 07:21:35,086 epoch 16 - iter 54/95 - loss 0.13265111 - time (sec): 33.63 - samples/sec: 1661.40 - lr: 0.000000\n",
            "2023-04-21 07:21:40,555 epoch 16 - iter 63/95 - loss 0.13800288 - time (sec): 39.10 - samples/sec: 1614.37 - lr: 0.000000\n",
            "2023-04-21 07:21:46,491 epoch 16 - iter 72/95 - loss 0.13930921 - time (sec): 45.03 - samples/sec: 1611.06 - lr: 0.000000\n",
            "2023-04-21 07:21:51,870 epoch 16 - iter 81/95 - loss 0.13862054 - time (sec): 50.41 - samples/sec: 1611.26 - lr: 0.000000\n",
            "2023-04-21 07:21:57,308 epoch 16 - iter 90/95 - loss 0.13797501 - time (sec): 55.85 - samples/sec: 1626.21 - lr: 0.000000\n",
            "2023-04-21 07:21:59,915 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:21:59,916 EPOCH 16 done: loss 0.1378 - lr 0.000000\n"
          ]
        },
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            "2023-04-21 07:22:21,904 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
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            "\n"
          ]
        },
        {
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          "text": [
            "2023-04-21 07:22:24,613 TRAIN : loss 0.10105723142623901 - f1-score (weighted avg)  0.8545\n"
          ]
        },
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          "text": [
            "2023-04-21 07:22:28,801 Evaluating as a multi-label problem: False\n",
            "2023-04-21 07:22:28,870 DEV : loss 0.27236244082450867 - f1-score (weighted avg)  0.6956\n",
            "2023-04-21 07:22:28,890 Epoch    16: reducing learning rate of group 0 to 1.5625e-07.\n",
            "2023-04-21 07:22:28,892 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 07:22:28,895 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:22:34,154 epoch 17 - iter 9/95 - loss 0.13477091 - time (sec): 5.26 - samples/sec: 1487.51 - lr: 0.000000\n",
            "2023-04-21 07:22:39,740 epoch 17 - iter 18/95 - loss 0.13444610 - time (sec): 10.84 - samples/sec: 1547.13 - lr: 0.000000\n",
            "2023-04-21 07:22:44,957 epoch 17 - iter 27/95 - loss 0.13496790 - time (sec): 16.06 - samples/sec: 1580.64 - lr: 0.000000\n",
            "2023-04-21 07:22:50,004 epoch 17 - iter 36/95 - loss 0.13416131 - time (sec): 21.11 - samples/sec: 1616.44 - lr: 0.000000\n",
            "2023-04-21 07:22:55,590 epoch 17 - iter 45/95 - loss 0.13241109 - time (sec): 26.69 - samples/sec: 1610.08 - lr: 0.000000\n",
            "2023-04-21 07:23:00,829 epoch 17 - iter 54/95 - loss 0.13022981 - time (sec): 31.93 - samples/sec: 1626.75 - lr: 0.000000\n",
            "2023-04-21 07:23:06,500 epoch 17 - iter 63/95 - loss 0.13200746 - time (sec): 37.60 - samples/sec: 1629.05 - lr: 0.000000\n",
            "2023-04-21 07:23:12,231 epoch 17 - iter 72/95 - loss 0.13079107 - time (sec): 43.33 - samples/sec: 1646.28 - lr: 0.000000\n",
            "2023-04-21 07:23:17,936 epoch 17 - iter 81/95 - loss 0.13505746 - time (sec): 49.04 - samples/sec: 1636.37 - lr: 0.000000\n",
            "2023-04-21 07:23:23,598 epoch 17 - iter 90/95 - loss 0.13517013 - time (sec): 54.70 - samples/sec: 1637.70 - lr: 0.000000\n",
            "2023-04-21 07:23:26,615 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:23:26,616 EPOCH 17 done: loss 0.1354 - lr 0.000000\n"
          ]
        },
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            "2023-04-21 07:23:48,616 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 07:23:52,491 TRAIN : loss 0.10119921714067459 - f1-score (weighted avg)  0.8546\n"
          ]
        },
        {
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          "output_type": "stream",
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          "output_type": "stream",
          "text": [
            "2023-04-21 07:23:55,716 Evaluating as a multi-label problem: False\n",
            "2023-04-21 07:23:55,786 DEV : loss 0.27388402819633484 - f1-score (weighted avg)  0.6977\n",
            "2023-04-21 07:23:55,806 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 07:23:55,810 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
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          "text": [
            "2023-04-21 07:24:01,330 epoch 18 - iter 9/95 - loss 0.13270085 - time (sec): 5.52 - samples/sec: 1672.94 - lr: 0.000000\n",
            "2023-04-21 07:24:07,417 epoch 18 - iter 18/95 - loss 0.13715185 - time (sec): 11.61 - samples/sec: 1667.68 - lr: 0.000000\n",
            "2023-04-21 07:24:12,942 epoch 18 - iter 27/95 - loss 0.14095364 - time (sec): 17.13 - samples/sec: 1638.23 - lr: 0.000000\n",
            "2023-04-21 07:24:18,351 epoch 18 - iter 36/95 - loss 0.13845659 - time (sec): 22.54 - samples/sec: 1626.72 - lr: 0.000000\n",
            "2023-04-21 07:24:24,103 epoch 18 - iter 45/95 - loss 0.13806565 - time (sec): 28.29 - samples/sec: 1598.55 - lr: 0.000000\n",
            "2023-04-21 07:24:29,444 epoch 18 - iter 54/95 - loss 0.13547245 - time (sec): 33.63 - samples/sec: 1617.38 - lr: 0.000000\n",
            "2023-04-21 07:24:34,983 epoch 18 - iter 63/95 - loss 0.13461914 - time (sec): 39.17 - samples/sec: 1604.71 - lr: 0.000000\n",
            "2023-04-21 07:24:40,963 epoch 18 - iter 72/95 - loss 0.13362146 - time (sec): 45.15 - samples/sec: 1615.28 - lr: 0.000000\n",
            "2023-04-21 07:24:46,350 epoch 18 - iter 81/95 - loss 0.13469129 - time (sec): 50.54 - samples/sec: 1605.30 - lr: 0.000000\n",
            "2023-04-21 07:24:51,785 epoch 18 - iter 90/95 - loss 0.13518633 - time (sec): 55.97 - samples/sec: 1605.80 - lr: 0.000000\n",
            "2023-04-21 07:24:54,944 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:24:54,945 EPOCH 18 done: loss 0.1361 - lr 0.000000\n"
          ]
        },
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          "text": [
            "2023-04-21 07:25:16,995 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
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          "output_type": "stream",
          "text": [
            "2023-04-21 07:25:20,935 TRAIN : loss 0.09984735399484634 - f1-score (weighted avg)  0.856\n"
          ]
        },
        {
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          "output_type": "stream",
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          "output_type": "stream",
          "text": [
            "2023-04-21 07:25:24,148 Evaluating as a multi-label problem: False\n",
            "2023-04-21 07:25:24,217 DEV : loss 0.27410396933555603 - f1-score (weighted avg)  0.6988\n",
            "2023-04-21 07:25:24,238 Epoch    18: reducing learning rate of group 0 to 7.8125e-08.\n",
            "2023-04-21 07:25:24,240 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 07:25:24,243 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
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            "\n"
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          "text": [
            "2023-04-21 07:25:29,721 epoch 19 - iter 9/95 - loss 0.12533697 - time (sec): 5.48 - samples/sec: 1903.79 - lr: 0.000000\n",
            "2023-04-21 07:25:35,690 epoch 19 - iter 18/95 - loss 0.14321046 - time (sec): 11.44 - samples/sec: 1710.99 - lr: 0.000000\n",
            "2023-04-21 07:25:41,015 epoch 19 - iter 27/95 - loss 0.14519383 - time (sec): 16.77 - samples/sec: 1644.76 - lr: 0.000000\n",
            "2023-04-21 07:25:46,893 epoch 19 - iter 36/95 - loss 0.14391936 - time (sec): 22.65 - samples/sec: 1616.72 - lr: 0.000000\n",
            "2023-04-21 07:25:52,060 epoch 19 - iter 45/95 - loss 0.14417416 - time (sec): 27.82 - samples/sec: 1623.79 - lr: 0.000000\n",
            "2023-04-21 07:25:57,360 epoch 19 - iter 54/95 - loss 0.14170297 - time (sec): 33.11 - samples/sec: 1651.01 - lr: 0.000000\n",
            "2023-04-21 07:26:02,470 epoch 19 - iter 63/95 - loss 0.14040375 - time (sec): 38.23 - samples/sec: 1647.95 - lr: 0.000000\n",
            "2023-04-21 07:26:07,814 epoch 19 - iter 72/95 - loss 0.13921835 - time (sec): 43.57 - samples/sec: 1645.61 - lr: 0.000000\n",
            "2023-04-21 07:26:13,743 epoch 19 - iter 81/95 - loss 0.13592682 - time (sec): 49.50 - samples/sec: 1636.36 - lr: 0.000000\n",
            "2023-04-21 07:26:19,195 epoch 19 - iter 90/95 - loss 0.13449187 - time (sec): 54.95 - samples/sec: 1634.26 - lr: 0.000000\n",
            "2023-04-21 07:26:21,916 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:26:21,918 EPOCH 19 done: loss 0.1333 - lr 0.000000\n"
          ]
        },
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          "text": [
            "2023-04-21 07:26:43,980 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
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        {
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          "output_type": "stream",
          "text": [
            "2023-04-21 07:26:46,700 TRAIN : loss 0.09979742765426636 - f1-score (weighted avg)  0.8559\n"
          ]
        },
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          "output_type": "stream",
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          "output_type": "stream",
          "text": [
            "2023-04-21 07:26:49,878 Evaluating as a multi-label problem: False\n",
            "2023-04-21 07:26:49,946 DEV : loss 0.27468565106391907 - f1-score (weighted avg)  0.6983\n",
            "2023-04-21 07:26:49,966 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 07:26:49,970 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
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        {
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          "text": [
            "2023-04-21 07:26:55,250 epoch 20 - iter 9/95 - loss 0.13894820 - time (sec): 5.28 - samples/sec: 1653.68 - lr: 0.000000\n",
            "2023-04-21 07:27:00,614 epoch 20 - iter 18/95 - loss 0.14063536 - time (sec): 10.64 - samples/sec: 1630.44 - lr: 0.000000\n",
            "2023-04-21 07:27:05,801 epoch 20 - iter 27/95 - loss 0.14013693 - time (sec): 15.83 - samples/sec: 1687.08 - lr: 0.000000\n",
            "2023-04-21 07:27:10,635 epoch 20 - iter 36/95 - loss 0.13514892 - time (sec): 20.66 - samples/sec: 1671.34 - lr: 0.000000\n",
            "2023-04-21 07:27:17,249 epoch 20 - iter 45/95 - loss 0.13339199 - time (sec): 27.28 - samples/sec: 1616.63 - lr: 0.000000\n",
            "2023-04-21 07:27:23,502 epoch 20 - iter 54/95 - loss 0.12912216 - time (sec): 33.53 - samples/sec: 1636.92 - lr: 0.000000\n",
            "2023-04-21 07:27:28,538 epoch 20 - iter 63/95 - loss 0.13125534 - time (sec): 38.57 - samples/sec: 1631.58 - lr: 0.000000\n",
            "2023-04-21 07:27:33,870 epoch 20 - iter 72/95 - loss 0.13194652 - time (sec): 43.90 - samples/sec: 1630.97 - lr: 0.000000\n",
            "2023-04-21 07:27:40,335 epoch 20 - iter 81/95 - loss 0.13192211 - time (sec): 50.36 - samples/sec: 1625.42 - lr: 0.000000\n",
            "2023-04-21 07:27:45,875 epoch 20 - iter 90/95 - loss 0.13210220 - time (sec): 55.90 - samples/sec: 1620.80 - lr: 0.000000\n",
            "2023-04-21 07:27:48,715 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:27:48,717 EPOCH 20 done: loss 0.1332 - lr 0.000000\n"
          ]
        },
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          "output_type": "stream",
          "text": [
            "2023-04-21 07:28:10,759 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
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        {
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          "output_type": "stream",
          "text": [
            "2023-04-21 07:28:14,640 TRAIN : loss 0.10002043843269348 - f1-score (weighted avg)  0.8556\n"
          ]
        },
        {
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          "output_type": "stream",
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          "output_type": "stream",
          "text": [
            "2023-04-21 07:28:17,849 Evaluating as a multi-label problem: False\n",
            "2023-04-21 07:28:17,920 DEV : loss 0.275593101978302 - f1-score (weighted avg)  0.6974\n",
            "2023-04-21 07:28:17,940 Epoch    20: reducing learning rate of group 0 to 3.9063e-08.\n",
            "2023-04-21 07:28:17,942 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 07:28:17,945 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
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          "output_type": "stream",
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            "\n"
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          "output_type": "stream",
          "text": [
            "2023-04-21 07:28:22,979 epoch 21 - iter 9/95 - loss 0.13453530 - time (sec): 5.03 - samples/sec: 1773.61 - lr: 0.000000\n",
            "2023-04-21 07:28:28,322 epoch 21 - iter 18/95 - loss 0.12935095 - time (sec): 10.37 - samples/sec: 1703.62 - lr: 0.000000\n",
            "2023-04-21 07:28:33,959 epoch 21 - iter 27/95 - loss 0.12478924 - time (sec): 16.01 - samples/sec: 1712.58 - lr: 0.000000\n",
            "2023-04-21 07:28:39,968 epoch 21 - iter 36/95 - loss 0.12839764 - time (sec): 22.02 - samples/sec: 1677.48 - lr: 0.000000\n",
            "2023-04-21 07:28:45,053 epoch 21 - iter 45/95 - loss 0.12754474 - time (sec): 27.11 - samples/sec: 1684.12 - lr: 0.000000\n",
            "2023-04-21 07:28:50,152 epoch 21 - iter 54/95 - loss 0.12644125 - time (sec): 32.20 - samples/sec: 1674.12 - lr: 0.000000\n",
            "2023-04-21 07:28:55,559 epoch 21 - iter 63/95 - loss 0.12803107 - time (sec): 37.61 - samples/sec: 1673.87 - lr: 0.000000\n",
            "2023-04-21 07:29:01,333 epoch 21 - iter 72/95 - loss 0.12682842 - time (sec): 43.39 - samples/sec: 1663.61 - lr: 0.000000\n",
            "2023-04-21 07:29:06,460 epoch 21 - iter 81/95 - loss 0.12823658 - time (sec): 48.51 - samples/sec: 1658.21 - lr: 0.000000\n",
            "2023-04-21 07:29:11,996 epoch 21 - iter 90/95 - loss 0.13196846 - time (sec): 54.05 - samples/sec: 1669.27 - lr: 0.000000\n",
            "2023-04-21 07:29:14,646 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:29:14,648 EPOCH 21 done: loss 0.1331 - lr 0.000000\n"
          ]
        },
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          "output_type": "stream",
          "text": [
            "2023-04-21 07:29:36,667 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
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          "output_type": "stream",
          "text": [
            "2023-04-21 07:29:40,578 TRAIN : loss 0.10026019811630249 - f1-score (weighted avg)  0.8559\n"
          ]
        },
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          "output_type": "stream",
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          "output_type": "stream",
          "text": [
            "2023-04-21 07:29:43,757 Evaluating as a multi-label problem: False\n",
            "2023-04-21 07:29:43,825 DEV : loss 0.2759714424610138 - f1-score (weighted avg)  0.6954\n",
            "2023-04-21 07:29:43,845 BAD EPOCHS (no improvement): 1\n",
            "2023-04-21 07:29:43,850 ----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
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            "\n"
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          "output_type": "stream",
          "text": [
            "2023-04-21 07:29:49,797 epoch 22 - iter 9/95 - loss 0.13184655 - time (sec): 5.95 - samples/sec: 1630.57 - lr: 0.000000\n",
            "2023-04-21 07:29:55,180 epoch 22 - iter 18/95 - loss 0.14376210 - time (sec): 11.33 - samples/sec: 1522.40 - lr: 0.000000\n",
            "2023-04-21 07:30:00,548 epoch 22 - iter 27/95 - loss 0.14505086 - time (sec): 16.70 - samples/sec: 1498.77 - lr: 0.000000\n",
            "2023-04-21 07:30:06,324 epoch 22 - iter 36/95 - loss 0.13672661 - time (sec): 22.47 - samples/sec: 1537.82 - lr: 0.000000\n",
            "2023-04-21 07:30:11,321 epoch 22 - iter 45/95 - loss 0.13635902 - time (sec): 27.47 - samples/sec: 1556.04 - lr: 0.000000\n",
            "2023-04-21 07:30:17,121 epoch 22 - iter 54/95 - loss 0.13400335 - time (sec): 33.27 - samples/sec: 1579.03 - lr: 0.000000\n",
            "2023-04-21 07:30:22,484 epoch 22 - iter 63/95 - loss 0.13498355 - time (sec): 38.63 - samples/sec: 1597.80 - lr: 0.000000\n",
            "2023-04-21 07:30:28,548 epoch 22 - iter 72/95 - loss 0.13374533 - time (sec): 44.70 - samples/sec: 1599.79 - lr: 0.000000\n",
            "2023-04-21 07:30:33,468 epoch 22 - iter 81/95 - loss 0.13430482 - time (sec): 49.62 - samples/sec: 1628.50 - lr: 0.000000\n",
            "2023-04-21 07:30:39,730 epoch 22 - iter 90/95 - loss 0.13501218 - time (sec): 55.88 - samples/sec: 1616.60 - lr: 0.000000\n",
            "2023-04-21 07:30:42,294 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:30:42,296 EPOCH 22 done: loss 0.1336 - lr 0.000000\n"
          ]
        },
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          "output_type": "stream",
          "text": [
            "2023-04-21 07:31:04,381 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 07:31:07,107 TRAIN : loss 0.09985252469778061 - f1-score (weighted avg)  0.8565\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 12/12 [00:03<00:00,  3.95it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 07:31:11,310 Evaluating as a multi-label problem: False\n",
            "2023-04-21 07:31:11,379 DEV : loss 0.27577492594718933 - f1-score (weighted avg)  0.6964\n",
            "2023-04-21 07:31:11,399 Epoch    22: reducing learning rate of group 0 to 1.9531e-08.\n",
            "2023-04-21 07:31:11,401 BAD EPOCHS (no improvement): 2\n",
            "2023-04-21 07:31:11,404 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:31:11,406 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:31:11,408 learning rate too small - quitting training!\n",
            "2023-04-21 07:31:11,409 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:31:29,926 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:31:52,311 SequenceTagger predicts: Dictionary with 22 tags: <unk>, O, I-ADR, E-ADR, B-ADR, S-ADR, S-Drug, B-Finding, E-Finding, I-Finding, B-Disease, E-Disease, E-Symptom, S-Disease, S-Symptom, E-Drug, B-Symptom, B-Drug, S-Finding, I-Symptom, I-Drug, I-Disease\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 12/12 [00:03<00:00,  3.68it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 07:31:56,485 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 07:31:56,821 0.732\t0.732\t0.732\t0.9328\n",
            "2023-04-21 07:31:56,823 \n",
            "Results:\n",
            "- F-score (micro) 0.732\n",
            "- F-score (macro) 0.296\n",
            "- Accuracy 0.9328\n",
            "\n",
            "By class:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       I-ADR     0.6755    0.7295    0.7015       525\n",
            "       E-ADR     0.7840    0.8620    0.8211       442\n",
            "       B-ADR     0.7484    0.8469    0.7946       418\n",
            "      S-Drug     0.8571    0.9688    0.9095       192\n",
            "       S-ADR     0.7041    0.7532    0.7278       158\n",
            "      B-Drug     0.6000    0.3333    0.4286        27\n",
            "   E-Finding     0.4286    0.2222    0.2927        27\n",
            "      E-Drug     0.8000    0.2963    0.4324        27\n",
            "   B-Disease     0.2000    0.6250    0.3030         8\n",
            "   B-Finding     0.1429    0.0417    0.0645        24\n",
            "   S-Finding     0.0000    0.0000    0.0000        28\n",
            "      I-Drug     0.0000    0.0000    0.0000        25\n",
            "   I-Symptom     0.0000    0.0000    0.0000        20\n",
            "   S-Disease     0.6667    0.3333    0.4444        12\n",
            "   B-Symptom     0.0000    0.0000    0.0000        13\n",
            "   E-Symptom     0.0000    0.0000    0.0000        13\n",
            "   E-Disease     0.0000    0.0000    0.0000         9\n",
            "   I-Finding     0.0000    0.0000    0.0000         9\n",
            "   S-Symptom     0.0000    0.0000    0.0000         9\n",
            "   I-Disease     0.0000    0.0000    0.0000         3\n",
            "\n",
            "   micro avg     0.7320    0.7320    0.7320      1989\n",
            "   macro avg     0.3304    0.3006    0.2960      1989\n",
            "weighted avg     0.6798    0.7320    0.7006      1989\n",
            " samples avg     0.1230    0.1230    0.1230      1989\n",
            "\n",
            "2023-04-21 07:31:56,824 ----------------------------------------------------------------------------------------------------\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 = 10,\n",
        "              eval_batch_size = 10,\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": 17,
      "metadata": {
        "id": "ExiLRreqXsDJ"
      },
      "outputs": [],
      "source": [
        "# print(hist.keys())\n",
        "# hist"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "P5Uu5qkN0pia",
        "outputId": "97b8c0fe-1139-4931-fa44-dfa051de9e7f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "_v2_transformer\n",
            "2023-04-21 07:31:56,848 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:31:56,850 WARNING: No LOSS found for test split in this data.\n",
            "2023-04-21 07:31:56,851 Are you sure you want to plot LOSS and not another value?\n",
            "2023-04-21 07:31:56,852 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:31:56,876 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:31:56,877 WARNING: No F1 found for test split in this data.\n",
            "2023-04-21 07:31:56,878 Are you sure you want to plot F1 and not another value?\n",
            "2023-04-21 07:31:56,880 ----------------------------------------------------------------------------------------------------\n",
            "2023-04-21 07:31:57,931 Loss and F1 plots are saved in flair_ner_model_v2_transformer/training.png\n"
          ]
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 1500x1000 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 79/79 [00:22<00:00,  3.51it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 07:32:20,869 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n",
            "100%|██████████| 10/10 [00:03<00:00,  3.30it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 07:32:27,770 Evaluating as a multi-label problem: False\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n",
            "100%|██████████| 10/10 [00:03<00:00,  3.12it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-04-21 07:32:31,066 Evaluating as a multi-label problem: True\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-9e2dd8f4-c8e3-4a5a-aa2c-96bdfb11ba9f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <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>E-ADR</th>\n",
              "      <th>B-ADR</th>\n",
              "      <th>S-ADR</th>\n",
              "      <th>S-Drug</th>\n",
              "      <th>E-Finding</th>\n",
              "      <th>B-Disease</th>\n",
              "      <th>B-Finding</th>\n",
              "      <th>...</th>\n",
              "      <th>E-Symptom</th>\n",
              "      <th>S-Symptom</th>\n",
              "      <th>B-Symptom</th>\n",
              "      <th>I-Symptom</th>\n",
              "      <th>I-Drug</th>\n",
              "      <th>I-Disease</th>\n",
              "      <th>micro avg</th>\n",
              "      <th>macro avg</th>\n",
              "      <th>weighted avg</th>\n",
              "      <th>samples avg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>train</td>\n",
              "      <td>precision</td>\n",
              "      <td>0.747026</td>\n",
              "      <td>0.808019</td>\n",
              "      <td>0.797532</td>\n",
              "      <td>0.813885</td>\n",
              "      <td>0.899291</td>\n",
              "      <td>0.316129</td>\n",
              "      <td>0.369942</td>\n",
              "      <td>0.227273</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.780286</td>\n",
              "      <td>0.387789</td>\n",
              "      <td>0.735036</td>\n",
              "      <td>0.133143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>train</td>\n",
              "      <td>recall</td>\n",
              "      <td>0.860868</td>\n",
              "      <td>0.896748</td>\n",
              "      <td>0.906838</td>\n",
              "      <td>0.808511</td>\n",
              "      <td>0.986003</td>\n",
              "      <td>0.245000</td>\n",
              "      <td>0.537815</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.802570</td>\n",
              "      <td>0.315768</td>\n",
              "      <td>0.802570</td>\n",
              "      <td>0.133106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>train</td>\n",
              "      <td>f1-score</td>\n",
              "      <td>0.799916</td>\n",
              "      <td>0.850074</td>\n",
              "      <td>0.848680</td>\n",
              "      <td>0.811189</td>\n",
              "      <td>0.940653</td>\n",
              "      <td>0.276056</td>\n",
              "      <td>0.438356</td>\n",
              "      <td>0.081967</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.791271</td>\n",
              "      <td>0.317309</td>\n",
              "      <td>0.761044</td>\n",
              "      <td>0.133119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>train</td>\n",
              "      <td>support</td>\n",
              "      <td>4449.000000</td>\n",
              "      <td>3506.000000</td>\n",
              "      <td>3349.000000</td>\n",
              "      <td>1363.000000</td>\n",
              "      <td>1286.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>119.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>114.0</td>\n",
              "      <td>112.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>104.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>15722.000000</td>\n",
              "      <td>15722.000000</td>\n",
              "      <td>15722.000000</td>\n",
              "      <td>15722.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>dev</td>\n",
              "      <td>precision</td>\n",
              "      <td>0.611801</td>\n",
              "      <td>0.724609</td>\n",
              "      <td>0.722222</td>\n",
              "      <td>0.740566</td>\n",
              "      <td>0.890710</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.699061</td>\n",
              "      <td>0.440177</td>\n",
              "      <td>0.668244</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>dev</td>\n",
              "      <td>recall</td>\n",
              "      <td>0.762089</td>\n",
              "      <td>0.826281</td>\n",
              "      <td>0.852941</td>\n",
              "      <td>0.751196</td>\n",
              "      <td>0.993902</td>\n",
              "      <td>0.117647</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.102564</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.720716</td>\n",
              "      <td>0.277539</td>\n",
              "      <td>0.720716</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>dev</td>\n",
              "      <td>f1-score</td>\n",
              "      <td>0.678725</td>\n",
              "      <td>0.772112</td>\n",
              "      <td>0.782158</td>\n",
              "      <td>0.745843</td>\n",
              "      <td>0.939481</td>\n",
              "      <td>0.177778</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>0.177778</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.709724</td>\n",
              "      <td>0.282199</td>\n",
              "      <td>0.672672</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>dev</td>\n",
              "      <td>support</td>\n",
              "      <td>517.000000</td>\n",
              "      <td>449.000000</td>\n",
              "      <td>442.000000</td>\n",
              "      <td>209.000000</td>\n",
              "      <td>164.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>13.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>2066.000000</td>\n",
              "      <td>2066.000000</td>\n",
              "      <td>2066.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>test</td>\n",
              "      <td>precision</td>\n",
              "      <td>0.675485</td>\n",
              "      <td>0.783951</td>\n",
              "      <td>0.748414</td>\n",
              "      <td>0.704142</td>\n",
              "      <td>0.857143</td>\n",
              "      <td>0.428571</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.732026</td>\n",
              "      <td>0.330362</td>\n",
              "      <td>0.679839</td>\n",
              "      <td>0.123035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>test</td>\n",
              "      <td>recall</td>\n",
              "      <td>0.729524</td>\n",
              "      <td>0.861991</td>\n",
              "      <td>0.846890</td>\n",
              "      <td>0.753165</td>\n",
              "      <td>0.968750</td>\n",
              "      <td>0.222222</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>0.041667</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.732026</td>\n",
              "      <td>0.300609</td>\n",
              "      <td>0.732026</td>\n",
              "      <td>0.122951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>test</td>\n",
              "      <td>f1-score</td>\n",
              "      <td>0.701465</td>\n",
              "      <td>0.821121</td>\n",
              "      <td>0.794613</td>\n",
              "      <td>0.727829</td>\n",
              "      <td>0.909535</td>\n",
              "      <td>0.292683</td>\n",
              "      <td>0.303030</td>\n",
              "      <td>0.064516</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.732026</td>\n",
              "      <td>0.296012</td>\n",
              "      <td>0.700571</td>\n",
              "      <td>0.122979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>test</td>\n",
              "      <td>support</td>\n",
              "      <td>525.000000</td>\n",
              "      <td>442.000000</td>\n",
              "      <td>418.000000</td>\n",
              "      <td>158.000000</td>\n",
              "      <td>192.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>13.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1989.000000</td>\n",
              "      <td>1989.000000</td>\n",
              "      <td>1989.000000</td>\n",
              "      <td>1989.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>12 rows × 26 columns</p>\n",
              "</div>\n",
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            ],
            "text/plain": [
              "    split      index        I-ADR        E-ADR        B-ADR        S-ADR  \\\n",
              "0   train  precision     0.747026     0.808019     0.797532     0.813885   \n",
              "1   train     recall     0.860868     0.896748     0.906838     0.808511   \n",
              "2   train   f1-score     0.799916     0.850074     0.848680     0.811189   \n",
              "3   train    support  4449.000000  3506.000000  3349.000000  1363.000000   \n",
              "4     dev  precision     0.611801     0.724609     0.722222     0.740566   \n",
              "5     dev     recall     0.762089     0.826281     0.852941     0.751196   \n",
              "6     dev   f1-score     0.678725     0.772112     0.782158     0.745843   \n",
              "7     dev    support   517.000000   449.000000   442.000000   209.000000   \n",
              "8    test  precision     0.675485     0.783951     0.748414     0.704142   \n",
              "9    test     recall     0.729524     0.861991     0.846890     0.753165   \n",
              "10   test   f1-score     0.701465     0.821121     0.794613     0.727829   \n",
              "11   test    support   525.000000   442.000000   418.000000   158.000000   \n",
              "\n",
              "         S-Drug   E-Finding   B-Disease   B-Finding  ...  E-Symptom  \\\n",
              "0      0.899291    0.316129    0.369942    0.227273  ...        0.0   \n",
              "1      0.986003    0.245000    0.537815    0.050000  ...        0.0   \n",
              "2      0.940653    0.276056    0.438356    0.081967  ...        0.0   \n",
              "3   1286.000000  200.000000  119.000000  200.000000  ...      114.0   \n",
              "4      0.890710    0.363636    0.333333    0.666667  ...        0.0   \n",
              "5      0.993902    0.117647    0.400000    0.102564  ...        0.0   \n",
              "6      0.939481    0.177778    0.363636    0.177778  ...        0.0   \n",
              "7    164.000000   34.000000   15.000000   39.000000  ...       13.0   \n",
              "8      0.857143    0.428571    0.200000    0.142857  ...        0.0   \n",
              "9      0.968750    0.222222    0.625000    0.041667  ...        0.0   \n",
              "10     0.909535    0.292683    0.303030    0.064516  ...        0.0   \n",
              "11   192.000000   27.000000    8.000000   24.000000  ...       13.0   \n",
              "\n",
              "    S-Symptom  B-Symptom  I-Symptom  I-Drug  I-Disease     micro avg  \\\n",
              "0         0.0        0.0        0.0     0.0        0.0      0.780286   \n",
              "1         0.0        0.0        0.0     0.0        0.0      0.802570   \n",
              "2         0.0        0.0        0.0     0.0        0.0      0.791271   \n",
              "3       112.0      110.0      104.0    50.0       50.0  15722.000000   \n",
              "4         0.0        0.0        0.0     0.0        0.0      0.699061   \n",
              "5         0.0        0.0        0.0     0.0        0.0      0.720716   \n",
              "6         0.0        0.0        0.0     0.0        0.0      0.709724   \n",
              "7        13.0       12.0       23.0     7.0       13.0   2066.000000   \n",
              "8         0.0        0.0        0.0     0.0        0.0      0.732026   \n",
              "9         0.0        0.0        0.0     0.0        0.0      0.732026   \n",
              "10        0.0        0.0        0.0     0.0        0.0      0.732026   \n",
              "11        9.0       13.0       20.0    25.0        3.0   1989.000000   \n",
              "\n",
              "       macro avg  weighted avg   samples avg  \n",
              "0       0.387789      0.735036      0.133143  \n",
              "1       0.315768      0.802570      0.133106  \n",
              "2       0.317309      0.761044      0.133119  \n",
              "3   15722.000000  15722.000000  15722.000000  \n",
              "4       0.440177      0.668244           NaN  \n",
              "5       0.277539      0.720716           NaN  \n",
              "6       0.282199      0.672672           NaN  \n",
              "7    2066.000000   2066.000000           NaN  \n",
              "8       0.330362      0.679839      0.123035  \n",
              "9       0.300609      0.732026      0.122951  \n",
              "10      0.296012      0.700571      0.122979  \n",
              "11   1989.000000   1989.000000   1989.000000  \n",
              "\n",
              "[12 rows x 26 columns]"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(suf)\n",
        "\n",
        "os.mkdir('flair_ner_model'+suf+'/classification_report')\n",
        "plotter.plot_training_curves('flair_ner_model'+suf+'/loss.tsv')\n",
        "\n",
        "# evaluate the tagger on the test set\n",
        "result = tagger.evaluate(corpus.train, \n",
        "                         embeddings_storage_mode='gpu',\n",
        "                         mini_batch_size=12,\n",
        "                         main_evaluation_metric = (\"weighted avg\", \"f1-score\"),\n",
        "                         gold_label_type='ner',\n",
        "                         gold_label_dictionary=tag_dictionary)\n",
        "open('flair_ner_model'+suf+'/classification_report/train.txt', 'w').write(str(result))\n",
        "res1 = pd.DataFrame.from_dict(result.classification_report).reset_index()\n",
        "res1.insert(loc=0, column='split', value='train')\n",
        "\n",
        "result = tagger.evaluate(corpus.dev, \n",
        "                         embeddings_storage_mode='gpu',\n",
        "                         mini_batch_size=12,\n",
        "                         main_evaluation_metric = (\"weighted avg\", \"f1-score\"),\n",
        "                         gold_label_type='ner',\n",
        "                         gold_label_dictionary=tag_dictionary)\n",
        "open('flair_ner_model'+suf+'/classification_report/dev.txt', 'w').write(str(result))\n",
        "res2 = pd.DataFrame.from_dict(result.classification_report).reset_index()\n",
        "res2.insert(loc=0, column='split', value='dev')\n",
        "\n",
        "result = tagger.evaluate(corpus.test, \n",
        "                         embeddings_storage_mode='gpu',\n",
        "                         mini_batch_size=12,\n",
        "                         main_evaluation_metric = (\"weighted avg\", \"f1-score\"),\n",
        "                         gold_label_type='ner',\n",
        "                         gold_label_dictionary=tag_dictionary)\n",
        "open('flair_ner_model'+suf+'/classification_report/test.txt', 'w').write(str(result))\n",
        "res3 = pd.DataFrame.from_dict(result.classification_report).reset_index()\n",
        "res3.insert(loc=0, column='split', value='test')\n",
        "\n",
        "res = pd.concat([res1,res2,res3],ignore_index=True)\n",
        "res.to_csv('flair_ner_model'+suf+'/classification_report.csv',index=False)\n",
        "res"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "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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