flair_cadec_v3_finetune_glove.ipynb

{"cells":[{"cell_type":"code","execution_count":2,"metadata":{"executionInfo":{"elapsed":10826,"status":"ok","timestamp":1682054024191,"user":{"displayName":"Alex He","userId":"06262800374381224408"},"user_tz":240},"id":"xONJpNLKtGie"},"outputs":[],"source":["import pandas as pd\n","import os\n","import pickle"]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":22528,"status":"ok","timestamp":1682054046695,"user":{"displayName":"Alex He","userId":"06262800374381224408"},"user_tz":240},"id":"nw6RM8uotGih","outputId":"eecd19c1-d7e5-41dc-b6a1-58b82cd041fa"},"outputs":[{"name":"stdout","output_type":"stream","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount(\"/content/drive\")\n","\n","os.chdir('/content/drive/MyDrive/SI 630 project')\n","# os.listdir('.')"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1835,"status":"ok","timestamp":1682054048527,"user":{"displayName":"Alex He","userId":"06262800374381224408"},"user_tz":240},"id":"P9LwpBadtGih","outputId":"89ef15bb-f35d-4fc6-b67c-d5d95374f38e"},"outputs":[{"data":{"text/plain":["['sct',\n"," 'text',\n"," 'original - unedited version of edited files',\n"," 'meddra - unedited version of edited files',\n"," 'sct - unedited version of edited files',\n"," 'original',\n"," 'meddra']"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["os.listdir('datasets/CADEC/CADEC.v2/cadec')\n"]},{"cell_type":"code","execution_count":5,"metadata":{"executionInfo":{"elapsed":8,"status":"ok","timestamp":1682054048528,"user":{"displayName":"Alex He","userId":"06262800374381224408"},"user_tz":240},"id":"0RW4oIrnb5Mu"},"outputs":[],"source":["suf = '_v3'\n","\n","data_save_dir = 'data'+suf\n","\n","suf += '_finetune_glove'"]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1682054048528,"user":{"displayName":"Alex He","userId":"06262800374381224408"},"user_tz":240},"id":"BIUl2-W9tGii","outputId":"b08666be-71de-4eab-9a62-904b0384a9e7"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-ee4867b7-4a3b-46fb-afc0-3ad89804b3cb\">\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>ner</th>\n","      <th>meddra</th>\n","      <th>counts_exploded</th>\n","      <th>counts</th>\n","      <th>counts_exploded_rank</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Drug</td>\n","      <td>Lipitor</td>\n","      <td>1081</td>\n","      <td>1073</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>ADR</td>\n","      <td>CONCEPT_LESS</td>\n","      <td>1016</td>\n","      <td>266</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>ADR</td>\n","      <td>Severe_pain</td>\n","      <td>722</td>\n","      <td>247</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>ADR</td>\n","      <td>Myalgia</td>\n","      <td>619</td>\n","      <td>305</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>ADR</td>\n","      <td>Pain</td>\n","      <td>292</td>\n","      <td>260</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1294</th>\n","      <td>Finding</td>\n","      <td>Senility</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1233</td>\n","    </tr>\n","    <tr>\n","      <th>1295</th>\n","      <td>Drug</td>\n","      <td>Seroquel</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1233</td>\n","    </tr>\n","    <tr>\n","      <th>1296</th>\n","      <td>Drug</td>\n","      <td>Solaraze</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1233</td>\n","    </tr>\n","    <tr>\n","      <th>1297</th>\n","      <td>Drug</td>\n","      <td>Tagamet</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1233</td>\n","    </tr>\n","    <tr>\n","      <th>1298</th>\n","      <td>Drug</td>\n","      <td>testosterone</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1233</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1299 rows × 5 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ee4867b7-4a3b-46fb-afc0-3ad89804b3cb')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "],"text/plain":["          ner        meddra  counts_exploded  counts  counts_exploded_rank\n","0        Drug       Lipitor             1081    1073                     1\n","1         ADR  CONCEPT_LESS             1016     266                     2\n","2         ADR   Severe_pain              722     247                     3\n","3         ADR       Myalgia              619     305                     4\n","4         ADR          Pain              292     260                     5\n","...       ...           ...              ...     ...                   ...\n","1294  Finding      Senility                1       1                  1233\n","1295     Drug      Seroquel                1       1                  1233\n","1296     Drug      Solaraze                1       1                  1233\n","1297     Drug       Tagamet                1       1                  1233\n","1298     Drug  testosterone                1       1                  1233\n","\n","[1299 rows x 5 columns]"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["meddra_counts = pd.read_csv(data_save_dir+'/meddra_counts.csv')\n","meddra_counts"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1682054048528,"user":{"displayName":"Alex He","userId":"06262800374381224408"},"user_tz":240},"id":"xzc4Zo0PtGii","outputId":"6c9b61d7-804f-42a8-f2b8-d858ad847ea9"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-0566595d-b1e4-4c69-b3e8-e1cded42d250\">\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>ner</th>\n","      <th>meddra</th>\n","      <th>counts_exploded</th>\n","      <th>counts</th>\n","      <th>counts_exploded_rank</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1</th>\n","      <td>ADR</td>\n","      <td>CONCEPT_LESS</td>\n","      <td>1016</td>\n","      <td>266</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>ADR</td>\n","      <td>Severe_pain</td>\n","      <td>722</td>\n","      <td>247</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>ADR</td>\n","      <td>Myalgia</td>\n","      <td>619</td>\n","      <td>305</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>ADR</td>\n","      <td>Pain</td>\n","      <td>292</td>\n","      <td>260</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>ADR</td>\n","      <td>Arthralgia</td>\n","      <td>280</td>\n","      <td>163</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1268</th>\n","      <td>ADR</td>\n","      <td>Labyrinthitis</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1233</td>\n","    </tr>\n","    <tr>\n","      <th>1269</th>\n","      <td>ADR</td>\n","      <td>Dysphagia</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1233</td>\n","    </tr>\n","    <tr>\n","      <th>1275</th>\n","      <td>ADR</td>\n","      <td>Lupus_erythematosus</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1233</td>\n","    </tr>\n","    <tr>\n","      <th>1278</th>\n","      <td>ADR</td>\n","      <td>Cataract</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1233</td>\n","    </tr>\n","    <tr>\n","      <th>1283</th>\n","      <td>ADR</td>\n","      <td>Crying_associated_with_mood</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1233</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>800 rows × 5 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0566595d-b1e4-4c69-b3e8-e1cded42d250')\"\n","              title=\"Convert this dataframe to an 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "],"text/plain":["      ner                       meddra  counts_exploded  counts  \\\n","1     ADR                 CONCEPT_LESS             1016     266   \n","2     ADR                  Severe_pain              722     247   \n","3     ADR                      Myalgia              619     305   \n","4     ADR                         Pain              292     260   \n","5     ADR                   Arthralgia              280     163   \n","...   ...                          ...              ...     ...   \n","1268  ADR                Labyrinthitis                1       1   \n","1269  ADR                    Dysphagia                1       1   \n","1275  ADR          Lupus_erythematosus                1       1   \n","1278  ADR                     Cataract                1       1   \n","1283  ADR  Crying_associated_with_mood                1       1   \n","\n","      counts_exploded_rank  \n","1                        2  \n","2                        3  \n","3                        4  \n","4                        5  \n","5                        6  \n","...                    ...  \n","1268                  1233  \n","1269                  1233  \n","1275                  1233  \n","1278                  1233  \n","1283                  1233  \n","\n","[800 rows x 5 columns]"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["meddra_counts.loc[meddra_counts.ner == 'ADR',:]"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3110,"status":"ok","timestamp":1682054051633,"user":{"displayName":"Alex He","userId":"06262800374381224408"},"user_tz":240},"id":"hAGPini7tGii","outputId":"89078786-6d4f-4536-b747-92124e2d4e83"},"outputs":[{"data":{"text/plain":["[Sentence[117]: \"i feel a bit drowsy & have a little blurred vision , so far no gastric problems . ive been on arthrotec 50 for over 10 years on and off , only taking it when i needed it . due to my arthritis getting progressively worse , to the point where i am in tears with the agony , gp ' s started me on 75 twice a day and i have to take it . every day for the next month to see how i get on , here goes . so far its been very good , pains almost gone , but i feel a bit weird , didnt have that when on 50 .\" → [\"i\"/O/O, \"feel\"/O/O, \"a\"/O/O, \"bit\"/B-ADR/B-Drowsy, \"drowsy\"/I-ADR/I-Drowsy, \"&\"/O/O, \"have\"/O/O, \"a\"/O/O, \"little\"/B-ADR/B-Blurred_vision_-_hazy, \"blurred\"/I-ADR/I-Blurred_vision_-_hazy, \"vision\"/I-ADR/I-Blurred_vision_-_hazy, \",\"/O/O, \"so\"/O/O, \"far\"/O/O, \"no\"/O/O, \"gastric\"/B-ADR/B-Excessive_upper_gastrointestinal_gas, \"problems\"/I-ADR/I-Excessive_upper_gastrointestinal_gas, \".\"/O/O, \"ive\"/O/O, \"been\"/O/O, \"on\"/O/O, \"arthrotec\"/B-Drug/B-Arthrotec, \"50\"/O/O, \"for\"/O/O, \"over\"/O/O, \"10\"/O/O, \"years\"/O/O, \"on\"/O/O, \"and\"/O/O, \"off\"/O/O, \",\"/O/O, \"only\"/O/O, \"taking\"/O/O, \"it\"/O/O, \"when\"/O/O, \"i\"/O/O, \"needed\"/O/O, \"it\"/O/O, \".\"/O/O, \"due\"/O/O, \"to\"/O/O, \"my\"/O/O, \"arthritis\"/B-Disease/B-Arthritis, \"getting\"/O/O, \"progressively\"/O/O, \"worse\"/O/O, \",\"/O/O, \"to\"/O/O, \"the\"/O/O, \"point\"/O/O, \"where\"/O/O, \"i\"/O/O, \"am\"/O/O, \"in\"/O/O, \"tears\"/O/O, \"with\"/O/O, \"the\"/O/O, \"agony\"/B-Symptom/B-Agony, \",\"/O/O, \"gp\"/O/O, \"'\"/O/O, \"s\"/O/O, \"started\"/O/O, \"me\"/O/O, \"on\"/O/O, \"75\"/O/O, \"twice\"/O/O, \"a\"/O/O, \"day\"/O/O, \"and\"/O/O, \"i\"/O/O, \"have\"/O/O, \"to\"/O/O, \"take\"/O/O, \"it\"/O/O, \".\"/O/O, \"every\"/O/O, \"day\"/O/O, \"for\"/O/O, \"the\"/O/O, \"next\"/O/O, \"month\"/O/O, \"to\"/O/O, \"see\"/O/O, \"how\"/O/O, \"i\"/O/O, \"get\"/O/O, \"on\"/O/O, \",\"/O/O, \"here\"/O/O, \"goes\"/O/O, \".\"/O/O, \"so\"/O/O, \"far\"/O/O, \"its\"/O/O, \"been\"/O/O, \"very\"/O/O, \"good\"/O/O, \",\"/O/O, \"pains\"/B-Symptom/B-Pain, \"almost\"/O/O, \"gone\"/O/O, \",\"/O/O, \"but\"/O/O, \"i\"/O/O, \"feel\"/B-ADR/B-Malaise, \"a\"/I-ADR/I-Malaise, \"bit\"/I-ADR/I-Malaise, \"weird\"/I-ADR/I-Malaise, \",\"/O/O, \"didnt\"/O/O, \"have\"/O/O, \"that\"/O/O, \"when\"/O/O, \"on\"/O/O, \"50\"/O/O, \".\"/O/O],\n"," Sentence[39]: \"hunger pangs . brilliant , i have a new lease of life , i walk up & down steps properly , no longer sideways like a toddler , hip pain as gone other than if i jar it .\" → [\"hunger\"/B-ADR/B-Hunger_pain, \"pangs\"/I-ADR/I-Hunger_pain, \".\"/O/O, \"brilliant\"/O/O, \",\"/O/O, \"i\"/O/O, \"have\"/O/O, \"a\"/O/O, \"new\"/O/O, \"lease\"/O/O, \"of\"/O/O, \"life\"/O/O, \",\"/O/O, \"i\"/O/O, \"walk\"/B-Symptom/B-CONCEPT_LESS, \"up\"/I-Symptom/I-CONCEPT_LESS, \"&\"/I-Symptom/I-CONCEPT_LESS, \"down\"/I-Symptom/I-CONCEPT_LESS, \"steps\"/I-Symptom/I-CONCEPT_LESS, \"properly\"/O/O, \",\"/O/O, \"no\"/O/O, \"longer\"/O/O, \"sideways\"/I-Symptom/I-CONCEPT_LESS, \"like\"/O/O, \"a\"/O/O, \"toddler\"/O/O, \",\"/O/O, \"hip\"/B-Symptom/B-Hip_pain, \"pain\"/I-Symptom/I-Hip_pain, \"as\"/O/O, \"gone\"/O/O, \"other\"/O/O, \"than\"/O/O, \"if\"/O/O, \"i\"/O/O, \"jar\"/O/O, \"it\"/O/O, \".\"/O/O],\n"," Sentence[51]: \"no side effects for the first two months . then vaginal bleeding 2 wks after menstral cycle . stomach pain . canker sores in my mouth . headache . been off for 1 week still have bleeding . helped my pain alot . too scared to take this drug again .\" → [\"no\"/O/O, \"side\"/O/O, \"effects\"/O/O, \"for\"/O/O, \"the\"/O/O, \"first\"/O/O, \"two\"/O/O, \"months\"/O/O, \".\"/O/O, \"then\"/O/O, \"vaginal\"/B-ADR/B-Vaginal_bleeding, \"bleeding\"/I-ADR/I-Vaginal_bleeding, \"2\"/O/O, \"wks\"/O/O, \"after\"/O/O, \"menstral\"/O/O, \"cycle\"/O/O, \".\"/O/O, \"stomach\"/B-ADR/B-Stomach_ache, \"pain\"/I-ADR/I-Stomach_ache, \".\"/O/O, \"canker\"/B-ADR/B-Aphthous_ulcer, \"sores\"/I-ADR/I-Aphthous_ulcer, \"in\"/O/O, \"my\"/O/O, \"mouth\"/O/O, \".\"/O/O, \"headache\"/B-ADR/B-Headache, \".\"/O/O, \"been\"/O/O, \"off\"/O/O, \"for\"/O/O, \"1\"/O/O, \"week\"/O/O, \"still\"/O/O, \"have\"/O/O, \"bleeding\"/O/O, \".\"/O/O, \"helped\"/O/O, \"my\"/O/O, \"pain\"/B-Symptom/B-Pain, \"alot\"/O/O, \".\"/O/O, \"too\"/O/O, \"scared\"/O/O, \"to\"/O/O, \"take\"/O/O, \"this\"/O/O, \"drug\"/O/O, \"again\"/O/O, \".\"/O/O],\n"," Sentence[68]: \"1st pill taken with food , a few hours after i experienced shortness of breath , a sense of depression , cramping , upset stomache will stop taking immediately . honestly can not recommend this drug , i am still experiencing side effects just after 1 pill and im supposed to take twice / day 75mg . i just cant keep going have to try something else .\" → [\"1st\"/O/O, \"pill\"/O/O, \"taken\"/O/O, \"with\"/O/O, \"food\"/O/O, \",\"/O/O, \"a\"/O/O, \"few\"/O/O, \"hours\"/O/O, \"after\"/O/O, \"i\"/O/O, \"experienced\"/O/O, \"shortness\"/B-ADR/B-Dyspnoea, \"of\"/I-ADR/I-Dyspnoea, \"breath\"/I-ADR/I-Dyspnoea, \",\"/O/O, \"a\"/O/O, \"sense\"/O/O, \"of\"/O/O, \"depression\"/B-ADR/B-Depression, \",\"/O/O, \"cramping\"/B-ADR/B-Muscle_cramp, \",\"/O/O, \"upset\"/B-ADR/B-Upset_stomach, \"stomache\"/I-ADR/I-Upset_stomach, \"will\"/O/O, \"stop\"/O/O, \"taking\"/O/O, \"immediately\"/O/O, \".\"/O/O, \"honestly\"/O/O, \"can\"/O/O, \"not\"/O/O, \"recommend\"/O/O, \"this\"/O/O, \"drug\"/O/O, \",\"/O/O, \"i\"/O/O, \"am\"/O/O, \"still\"/O/O, \"experiencing\"/O/O, \"side\"/O/O, \"effects\"/O/O, \"just\"/O/O, \"after\"/O/O, \"1\"/O/O, \"pill\"/O/O, \"and\"/O/O, \"im\"/O/O, \"supposed\"/O/O, \"to\"/O/O, \"take\"/O/O, \"twice\"/O/O, \"/\"/O/O, \"day\"/O/O, \"75mg\"/O/O, \".\"/O/O, \"i\"/O/O, \"just\"/O/O, \"cant\"/O/O, \"keep\"/O/O, \"going\"/O/O, \"have\"/O/O, \"to\"/O/O, \"try\"/O/O, \"something\"/O/O, \"else\"/O/O, \".\"/O/O],\n"," Sentence[50]: \"i have had no side effects been taking arthrotec a little over a year , have not noticed any side effects . it does help alot i noticed that when there are times when i forget to take it i cant stand or walk for any lengths of time .\" → [\"i\"/O/O, \"have\"/O/O, \"had\"/O/O, \"no\"/O/O, \"side\"/O/O, \"effects\"/O/O, \"been\"/O/O, \"taking\"/O/O, \"arthrotec\"/B-Drug/B-Arthrotec, \"a\"/O/O, \"little\"/O/O, \"over\"/O/O, \"a\"/O/O, \"year\"/O/O, \",\"/O/O, \"have\"/O/O, \"not\"/O/O, \"noticed\"/O/O, \"any\"/O/O, \"side\"/O/O, \"effects\"/O/O, \".\"/O/O, \"it\"/O/O, \"does\"/O/O, \"help\"/O/O, \"alot\"/O/O, \"i\"/O/O, \"noticed\"/O/O, \"that\"/O/O, \"when\"/O/O, \"there\"/O/O, \"are\"/O/O, \"times\"/O/O, \"when\"/O/O, \"i\"/O/O, \"forget\"/O/O, \"to\"/O/O, \"take\"/O/O, \"it\"/O/O, \"i\"/B-Symptom/B-Reduced_mobility, \"cant\"/I-Symptom/I-Reduced_mobility, \"stand\"/I-Symptom/I-Reduced_mobility, \"or\"/I-Symptom/I-Reduced_mobility, \"walk\"/I-Symptom/I-Reduced_mobility, \"for\"/I-Symptom/I-Reduced_mobility, \"any\"/I-Symptom/I-Reduced_mobility, \"lengths\"/I-Symptom/I-Reduced_mobility, \"of\"/I-Symptom/I-Reduced_mobility, \"time\"/I-Symptom/I-Reduced_mobility, \".\"/O/O],\n"," Sentence[89]: \"it seems as if bleeding forever is a side effect but doctors tell you that its not the drug . i have been on arthotec for three months i still have pain and stiffness . at first week into this drug i was in so much pain i sat on the floor at work and thought i was gaving a baby . i always feel weak and almost fainted a bunch of times . consider somthing that works i am switching today as this isnt in my head .\" → [\"it\"/O/O, \"seems\"/O/O, \"as\"/O/O, \"if\"/O/O, \"bleeding\"/B-ADR/B-Bleeding, \"forever\"/O/O, \"is\"/O/O, \"a\"/O/O, \"side\"/O/O, \"effect\"/O/O, \"but\"/O/O, \"doctors\"/O/O, \"tell\"/O/O, \"you\"/O/O, \"that\"/O/O, \"its\"/O/O, \"not\"/O/O, \"the\"/O/O, \"drug\"/O/O, \".\"/O/O, \"i\"/O/O, \"have\"/O/O, \"been\"/O/O, \"on\"/O/O, \"arthotec\"/B-Drug/B-Arthrotec, \"for\"/O/O, \"three\"/O/O, \"months\"/O/O, \"i\"/O/O, \"still\"/O/O, \"have\"/O/O, \"pain\"/B-Symptom/B-Pain, \"and\"/I-Symptom/I-Pain, \"stiffness\"/I-Symptom/I-Pain, \".\"/O/O, \"at\"/O/O, \"first\"/O/O, \"week\"/O/O, \"into\"/O/O, \"this\"/O/O, \"drug\"/O/O, \"i\"/O/O, \"was\"/O/O, \"in\"/O/O, \"so\"/B-ADR/B-Pain, \"much\"/I-ADR/I-Pain, \"pain\"/I-ADR/I-Pain, \"i\"/O/O, \"sat\"/O/O, \"on\"/O/O, \"the\"/O/O, \"floor\"/O/O, \"at\"/O/O, \"work\"/O/O, \"and\"/O/O, \"thought\"/O/O, \"i\"/O/O, \"was\"/O/O, \"gaving\"/O/O, \"a\"/O/O, \"baby\"/O/O, \".\"/O/O, \"i\"/O/O, \"always\"/O/O, \"feel\"/B-ADR/B-Malaise_and_fatigue, \"weak\"/I-ADR/I-Malaise_and_fatigue, \"and\"/I-ADR/I-Malaise_and_fatigue, \"almost\"/I-ADR/I-Malaise_and_fatigue, \"fainted\"/I-ADR/I-Malaise_and_fatigue, \"a\"/O/O, \"bunch\"/O/O, \"of\"/O/O, \"times\"/O/O, \".\"/O/O, \"consider\"/O/O, \"somthing\"/O/O, \"that\"/O/O, \"works\"/O/O, \"i\"/O/O, \"am\"/O/O, \"switching\"/O/O, \"today\"/O/O, \"as\"/O/O, \"this\"/O/O, \"isnt\"/O/O, \"in\"/O/O, \"my\"/O/O, \"head\"/O/O, \".\"/O/O],\n"," Sentence[56]: \"some diarrhea , some constipation , fatigue . it works very well for the heel ; i couldnt walk on it very well before taking it . i take 75mg . twice a day . also have severe osteoarthritis in the knees and hands , but it doesnt seem to relieve that pain at all .\" → [\"some\"/O/O, \"diarrhea\"/B-ADR/B-Diarrhoea, \",\"/O/O, \"some\"/O/O, \"constipation\"/B-ADR/B-Constipation, \",\"/O/O, \"fatigue\"/B-ADR/B-Fatigue, \".\"/O/O, \"it\"/O/O, \"works\"/O/O, \"very\"/O/O, \"well\"/O/O, \"for\"/O/O, \"the\"/B-Symptom/B-Walking_disability, \"heel\"/I-Symptom/I-Walking_disability, \";\"/O/O, \"i\"/I-Symptom/I-Walking_disability, \"couldnt\"/I-Symptom/I-Walking_disability, \"walk\"/I-Symptom/I-Walking_disability, \"on\"/I-Symptom/I-Walking_disability, \"it\"/O/O, \"very\"/I-Symptom/I-Walking_disability, \"well\"/I-Symptom/I-Walking_disability, \"before\"/O/O, \"taking\"/O/O, \"it\"/O/O, \".\"/O/O, \"i\"/O/O, \"take\"/O/O, \"75mg\"/O/O, \".\"/O/O, \"twice\"/O/O, \"a\"/O/O, \"day\"/O/O, \".\"/O/O, \"also\"/O/O, \"have\"/O/O, \"severe\"/B-Disease/B-Osteoarthritis_of_knee/B-Osteoarthrosis_of_hand, \"osteoarthritis\"/I-Disease/I-Osteoarthritis_of_knee/I-Osteoarthrosis_of_hand, \"in\"/I-Disease/I-Osteoarthritis_of_knee/I-Osteoarthrosis_of_hand, \"the\"/I-Disease/I-Osteoarthritis_of_knee/I-Osteoarthrosis_of_hand, \"knees\"/I-Disease/I-Osteoarthritis_of_knee, \"and\"/O/O, \"hands\"/I-Disease/I-Osteoarthrosis_of_hand, \",\"/O/O, \"but\"/O/O, \"it\"/O/O, \"doesnt\"/O/O, \"seem\"/O/O, \"to\"/O/O, \"relieve\"/O/O, \"that\"/O/O, \"pain\"/B-Symptom/B-Pain, \"at\"/O/O, \"all\"/O/O, \".\"/O/O],\n"," Sentence[84]: \"ive been on 75mg twice daily for 13 days , with recent body swelling , face , wrists , abdomen , thighs - clothes fit tight . insomina is much worse . i have forgetfulnes and confussion - really bad - almost dangerous . have felt \" empty stomach \" feeling , but not bothersome . have had more loose stools , but tolerable . im looking for a more natural therapy , checking into ha ( hyaluronic acid ) to replace arthrotec .\" → [\"ive\"/O/O, \"been\"/O/O, \"on\"/O/O, \"75mg\"/O/O, \"twice\"/O/O, \"daily\"/O/O, \"for\"/O/O, \"13\"/O/O, \"days\"/O/O, \",\"/O/O, \"with\"/O/O, \"recent\"/O/O, \"body\"/B-ADR/B-Swelling_of_body_region, \"swelling\"/B-ADR/I-ADR/B-Abdominal_swelling/B-Facial_swelling/I-Swelling_of_body_region/B-Swelling_of_lower_limb/B-Swelling_of_wrist_joint, \",\"/O/O, \"face\"/I-ADR/I-Facial_swelling, \",\"/O/O, \"wrists\"/I-ADR/I-Swelling_of_wrist_joint, \",\"/O/O, \"abdomen\"/I-ADR/I-Abdominal_swelling, \",\"/O/O, \"thighs\"/I-ADR/I-Swelling_of_lower_limb, \"-\"/O/O, \"clothes\"/O/O, \"fit\"/O/O, \"tight\"/O/O, \".\"/O/O, \"insomina\"/B-ADR/B-Insomnia, \"is\"/O/O, \"much\"/O/O, \"worse\"/O/O, \".\"/O/O, \"i\"/O/O, \"have\"/O/O, \"forgetfulnes\"/B-ADR/B-Forgetful, \"and\"/I-ADR/I-Forgetful, \"confussion\"/I-ADR/I-Forgetful, \"-\"/O/O, \"really\"/O/O, \"bad\"/O/O, \"-\"/O/O, \"almost\"/O/O, \"dangerous\"/O/O, \".\"/O/O, \"have\"/O/O, \"felt\"/O/O, \"\"\"/B-ADR/B-Feeling_empty, \"empty\"/I-ADR/I-Feeling_empty, \"stomach\"/I-ADR/I-Feeling_empty, \"\"\"/I-ADR/I-Feeling_empty, \"feeling\"/I-ADR/I-Feeling_empty, \",\"/O/O, \"but\"/O/O, \"not\"/O/O, \"bothersome\"/O/O, \".\"/O/O, \"have\"/O/O, \"had\"/O/O, \"more\"/O/O, \"loose\"/B-ADR/B-Loose_stool, \"stools\"/I-ADR/I-Loose_stool, \",\"/O/O, \"but\"/O/O, \"tolerable\"/O/O, \".\"/O/O, \"im\"/O/O, \"looking\"/O/O, \"for\"/O/O, \"a\"/O/O, \"more\"/O/O, \"natural\"/O/O, \"therapy\"/O/O, \",\"/O/O, \"checking\"/O/O, \"into\"/O/O, \"ha\"/B-Drug/B-CONCEPT_LESS, \"(\"/I-Drug/I-CONCEPT_LESS, \"hyaluronic\"/I-Drug/I-CONCEPT_LESS, \"acid\"/I-Drug/I-CONCEPT_LESS, \")\"/I-Drug/I-CONCEPT_LESS, \"to\"/O/O, \"replace\"/O/O, \"arthrotec\"/B-Drug/B-Arthrotec, \".\"/O/O],\n"," Sentence[68]: \"none that i can think of . i recently had bilateral knee replacement . prior to that , arthrotec was the only drug that helped . i tried every drug on the market , but , to no avail . i took 50 mg . 3 times a day of arthrotec and was almost pain free . i highly recommend it to anyone that suffers from arthritis .\" → [\"none\"/O/O, \"that\"/O/O, \"i\"/O/O, \"can\"/O/O, \"think\"/O/O, \"of\"/O/O, \".\"/O/O, \"i\"/O/O, \"recently\"/O/O, \"had\"/O/O, \"bilateral\"/O/O, \"knee\"/O/O, \"replacement\"/O/O, \".\"/O/O, \"prior\"/O/O, \"to\"/O/O, \"that\"/O/O, \",\"/O/O, \"arthrotec\"/B-Drug/B-Arthrotec, \"was\"/O/O, \"the\"/O/O, \"only\"/O/O, \"drug\"/O/O, \"that\"/O/O, \"helped\"/O/O, \".\"/O/O, \"i\"/O/O, \"tried\"/O/O, \"every\"/O/O, \"drug\"/O/O, \"on\"/O/O, \"the\"/O/O, \"market\"/O/O, \",\"/O/O, \"but\"/O/O, \",\"/O/O, \"to\"/O/O, \"no\"/O/O, \"avail\"/O/O, \".\"/O/O, \"i\"/O/O, \"took\"/O/O, \"50\"/O/O, \"mg\"/O/O, \".\"/O/O, \"3\"/O/O, \"times\"/O/O, \"a\"/O/O, \"day\"/O/O, \"of\"/O/O, \"arthrotec\"/B-Drug/B-Arthrotec, \"and\"/O/O, \"was\"/O/O, \"almost\"/O/O, \"pain\"/B-Symptom/B-Pain, \"free\"/O/O, \".\"/O/O, \"i\"/O/O, \"highly\"/O/O, \"recommend\"/O/O, \"it\"/O/O, \"to\"/O/O, \"anyone\"/O/O, \"that\"/O/O, \"suffers\"/O/O, \"from\"/O/O, \"arthritis\"/B-Finding/B-Arthritis, \".\"/O/O],\n"," Sentence[28]: \"hospitalization due extreme rectal bleed that required blood transfusion . i was extremely sick and initially felt i might not survive . should be removed from market .\" → [\"hospitalization\"/O/O, \"due\"/O/O, \"extreme\"/O/O, \"rectal\"/B-ADR/B-Rectal_haemorrhage, \"bleed\"/I-ADR/I-Rectal_haemorrhage, \"that\"/O/O, \"required\"/O/O, \"blood\"/O/O, \"transfusion\"/O/O, \".\"/O/O, \"i\"/O/O, \"was\"/O/O, \"extremely\"/B-ADR/B-Generally_unwell, \"sick\"/I-ADR/I-Generally_unwell, \"and\"/O/O, \"initially\"/O/O, \"felt\"/B-ADR/B-CONCEPT_LESS, \"i\"/I-ADR/I-CONCEPT_LESS, \"might\"/I-ADR/I-CONCEPT_LESS, \"not\"/I-ADR/I-CONCEPT_LESS, \"survive\"/I-ADR/I-CONCEPT_LESS, \".\"/O/O, \"should\"/O/O, \"be\"/O/O, \"removed\"/O/O, \"from\"/O/O, \"market\"/O/O, \".\"/O/O]]"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["with open(data_save_dir+'/cadec_flair_objects.pkl', 'rb') as f:\n","    docs = pickle.load(f)\n","docs[:10]"]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":15,"status":"ok","timestamp":1682054051634,"user":{"displayName":"Alex He","userId":"06262800374381224408"},"user_tz":240},"id":"sW10pPAPtGij","outputId":"1290f93b-1506-41ad-84d0-74037d6bd240"},"outputs":[{"data":{"text/plain":["1186"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["len(docs)"]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1682054051634,"user":{"displayName":"Alex He","userId":"06262800374381224408"},"user_tz":240},"id":"EMT5TsNStGij","outputId":"75c82920-5aad-489a-b3c7-464395fb406c"},"outputs":[{"data":{"text/plain":["Sentence[117]: \"i feel a bit drowsy & have a little blurred vision , so far no gastric problems . ive been on arthrotec 50 for over 10 years on and off , only taking it when i needed it . due to my arthritis getting progressively worse , to the point where i am in tears with the agony , gp ' s started me on 75 twice a day and i have to take it . every day for the next month to see how i get on , here goes . so far its been very good , pains almost gone , but i feel a bit weird , didnt have that when on 50 .\" → [\"i\"/O/O, \"feel\"/O/O, \"a\"/O/O, \"bit\"/B-ADR/B-Drowsy, \"drowsy\"/I-ADR/I-Drowsy, \"&\"/O/O, \"have\"/O/O, \"a\"/O/O, \"little\"/B-ADR/B-Blurred_vision_-_hazy, \"blurred\"/I-ADR/I-Blurred_vision_-_hazy, \"vision\"/I-ADR/I-Blurred_vision_-_hazy, \",\"/O/O, \"so\"/O/O, \"far\"/O/O, \"no\"/O/O, \"gastric\"/B-ADR/B-Excessive_upper_gastrointestinal_gas, \"problems\"/I-ADR/I-Excessive_upper_gastrointestinal_gas, \".\"/O/O, \"ive\"/O/O, \"been\"/O/O, \"on\"/O/O, \"arthrotec\"/B-Drug/B-Arthrotec, \"50\"/O/O, \"for\"/O/O, \"over\"/O/O, \"10\"/O/O, \"years\"/O/O, \"on\"/O/O, \"and\"/O/O, \"off\"/O/O, \",\"/O/O, \"only\"/O/O, \"taking\"/O/O, \"it\"/O/O, \"when\"/O/O, \"i\"/O/O, \"needed\"/O/O, \"it\"/O/O, \".\"/O/O, \"due\"/O/O, \"to\"/O/O, \"my\"/O/O, \"arthritis\"/B-Disease/B-Arthritis, \"getting\"/O/O, \"progressively\"/O/O, \"worse\"/O/O, \",\"/O/O, \"to\"/O/O, \"the\"/O/O, \"point\"/O/O, \"where\"/O/O, \"i\"/O/O, \"am\"/O/O, \"in\"/O/O, \"tears\"/O/O, \"with\"/O/O, \"the\"/O/O, \"agony\"/B-Symptom/B-Agony, \",\"/O/O, \"gp\"/O/O, \"'\"/O/O, \"s\"/O/O, \"started\"/O/O, \"me\"/O/O, \"on\"/O/O, \"75\"/O/O, \"twice\"/O/O, \"a\"/O/O, \"day\"/O/O, \"and\"/O/O, \"i\"/O/O, \"have\"/O/O, \"to\"/O/O, \"take\"/O/O, \"it\"/O/O, \".\"/O/O, \"every\"/O/O, \"day\"/O/O, \"for\"/O/O, \"the\"/O/O, \"next\"/O/O, \"month\"/O/O, \"to\"/O/O, \"see\"/O/O, \"how\"/O/O, \"i\"/O/O, \"get\"/O/O, \"on\"/O/O, \",\"/O/O, \"here\"/O/O, \"goes\"/O/O, \".\"/O/O, \"so\"/O/O, \"far\"/O/O, \"its\"/O/O, \"been\"/O/O, \"very\"/O/O, \"good\"/O/O, \",\"/O/O, \"pains\"/B-Symptom/B-Pain, \"almost\"/O/O, \"gone\"/O/O, \",\"/O/O, \"but\"/O/O, \"i\"/O/O, \"feel\"/B-ADR/B-Malaise, \"a\"/I-ADR/I-Malaise, \"bit\"/I-ADR/I-Malaise, \"weird\"/I-ADR/I-Malaise, \",\"/O/O, \"didnt\"/O/O, \"have\"/O/O, \"that\"/O/O, \"when\"/O/O, \"on\"/O/O, \"50\"/O/O, \".\"/O/O]"]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["temp = docs[0]\n","temp"]},{"cell_type":"code","execution_count":11,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1682054051635,"user":{"displayName":"Alex He","userId":"06262800374381224408"},"user_tz":240},"id":"2EeTdfHstGij","outputId":"389add6d-5fbe-456b-c5e8-09603267a58f"},"outputs":[{"data":{"text/plain":["{'ner': ['Token[0]: \"i\"'/'O' (1.0),\n","  'Token[1]: \"feel\"'/'O' (1.0),\n","  'Token[2]: \"a\"'/'O' (1.0),\n","  'Token[3]: \"bit\"'/'B-ADR' (1.0),\n","  'Token[4]: \"drowsy\"'/'I-ADR' (1.0),\n","  'Token[5]: \"&\"'/'O' (1.0),\n","  'Token[6]: \"have\"'/'O' (1.0),\n","  'Token[7]: \"a\"'/'O' (1.0),\n","  'Token[8]: \"little\"'/'B-ADR' (1.0),\n","  'Token[9]: \"blurred\"'/'I-ADR' (1.0),\n","  'Token[10]: \"vision\"'/'I-ADR' (1.0),\n","  'Token[11]: \",\"'/'O' (1.0),\n","  'Token[12]: \"so\"'/'O' (1.0),\n","  'Token[13]: \"far\"'/'O' (1.0),\n","  'Token[14]: \"no\"'/'O' (1.0),\n","  'Token[15]: \"gastric\"'/'B-ADR' (1.0),\n","  'Token[16]: \"problems\"'/'I-ADR' (1.0),\n","  'Token[17]: \".\"'/'O' (1.0),\n","  'Token[18]: \"ive\"'/'O' (1.0),\n","  'Token[19]: \"been\"'/'O' (1.0),\n","  'Token[20]: \"on\"'/'O' (1.0),\n","  'Token[21]: \"arthrotec\"'/'B-Drug' (1.0),\n","  'Token[22]: \"50\"'/'O' (1.0),\n","  'Token[23]: \"for\"'/'O' (1.0),\n","  'Token[24]: \"over\"'/'O' (1.0),\n","  'Token[25]: \"10\"'/'O' (1.0),\n","  'Token[26]: \"years\"'/'O' (1.0),\n","  'Token[27]: \"on\"'/'O' (1.0),\n","  'Token[28]: \"and\"'/'O' (1.0),\n","  'Token[29]: \"off\"'/'O' (1.0),\n","  'Token[30]: \",\"'/'O' (1.0),\n","  'Token[31]: \"only\"'/'O' (1.0),\n","  'Token[32]: \"taking\"'/'O' (1.0),\n","  'Token[33]: \"it\"'/'O' (1.0),\n","  'Token[34]: \"when\"'/'O' (1.0),\n","  'Token[35]: \"i\"'/'O' (1.0),\n","  'Token[36]: \"needed\"'/'O' (1.0),\n","  'Token[37]: \"it\"'/'O' (1.0),\n","  'Token[38]: \".\"'/'O' (1.0),\n","  'Token[39]: \"due\"'/'O' (1.0),\n","  'Token[40]: \"to\"'/'O' (1.0),\n","  'Token[41]: \"my\"'/'O' (1.0),\n","  'Token[42]: \"arthritis\"'/'B-Disease' (1.0),\n","  'Token[43]: \"getting\"'/'O' (1.0),\n","  'Token[44]: \"progressively\"'/'O' (1.0),\n","  'Token[45]: \"worse\"'/'O' (1.0),\n","  'Token[46]: \",\"'/'O' (1.0),\n","  'Token[47]: \"to\"'/'O' (1.0),\n","  'Token[48]: \"the\"'/'O' (1.0),\n","  'Token[49]: \"point\"'/'O' (1.0),\n","  'Token[50]: \"where\"'/'O' (1.0),\n","  'Token[51]: \"i\"'/'O' (1.0),\n","  'Token[52]: \"am\"'/'O' (1.0),\n","  'Token[53]: \"in\"'/'O' (1.0),\n","  'Token[54]: \"tears\"'/'O' (1.0),\n","  'Token[55]: \"with\"'/'O' (1.0),\n","  'Token[56]: \"the\"'/'O' (1.0),\n","  'Token[57]: \"agony\"'/'B-Symptom' (1.0),\n","  'Token[58]: \",\"'/'O' (1.0),\n","  'Token[59]: \"gp\"'/'O' (1.0),\n","  'Token[60]: \"'\"'/'O' (1.0),\n","  'Token[61]: \"s\"'/'O' (1.0),\n","  'Token[62]: \"started\"'/'O' (1.0),\n","  'Token[63]: \"me\"'/'O' (1.0),\n","  'Token[64]: \"on\"'/'O' (1.0),\n","  'Token[65]: \"75\"'/'O' (1.0),\n","  'Token[66]: \"twice\"'/'O' (1.0),\n","  'Token[67]: \"a\"'/'O' (1.0),\n","  'Token[68]: \"day\"'/'O' (1.0),\n","  'Token[69]: \"and\"'/'O' (1.0),\n","  'Token[70]: \"i\"'/'O' (1.0),\n","  'Token[71]: \"have\"'/'O' (1.0),\n","  'Token[72]: \"to\"'/'O' (1.0),\n","  'Token[73]: \"take\"'/'O' (1.0),\n","  'Token[74]: \"it\"'/'O' (1.0),\n","  'Token[75]: \".\"'/'O' (1.0),\n","  'Token[76]: \"every\"'/'O' (1.0),\n","  'Token[77]: \"day\"'/'O' (1.0),\n","  'Token[78]: \"for\"'/'O' (1.0),\n","  'Token[79]: \"the\"'/'O' (1.0),\n","  'Token[80]: \"next\"'/'O' (1.0),\n","  'Token[81]: \"month\"'/'O' (1.0),\n","  'Token[82]: \"to\"'/'O' (1.0),\n","  'Token[83]: \"see\"'/'O' (1.0),\n","  'Token[84]: \"how\"'/'O' (1.0),\n","  'Token[85]: \"i\"'/'O' (1.0),\n","  'Token[86]: \"get\"'/'O' (1.0),\n","  'Token[87]: \"on\"'/'O' (1.0),\n","  'Token[88]: \",\"'/'O' (1.0),\n","  'Token[89]: \"here\"'/'O' (1.0),\n","  'Token[90]: \"goes\"'/'O' (1.0),\n","  'Token[91]: \".\"'/'O' (1.0),\n","  'Token[92]: \"so\"'/'O' (1.0),\n","  'Token[93]: \"far\"'/'O' (1.0),\n","  'Token[94]: \"its\"'/'O' (1.0),\n","  'Token[95]: \"been\"'/'O' (1.0),\n","  'Token[96]: \"very\"'/'O' (1.0),\n","  'Token[97]: \"good\"'/'O' (1.0),\n","  'Token[98]: \",\"'/'O' (1.0),\n","  'Token[99]: \"pains\"'/'B-Symptom' (1.0),\n","  'Token[100]: \"almost\"'/'O' (1.0),\n","  'Token[101]: \"gone\"'/'O' (1.0),\n","  'Token[102]: \",\"'/'O' (1.0),\n","  'Token[103]: \"but\"'/'O' (1.0),\n","  'Token[104]: \"i\"'/'O' (1.0),\n","  'Token[105]: \"feel\"'/'B-ADR' (1.0),\n","  'Token[106]: \"a\"'/'I-ADR' (1.0),\n","  'Token[107]: \"bit\"'/'I-ADR' (1.0),\n","  'Token[108]: \"weird\"'/'I-ADR' (1.0),\n","  'Token[109]: \",\"'/'O' (1.0),\n","  'Token[110]: \"didnt\"'/'O' (1.0),\n","  'Token[111]: \"have\"'/'O' (1.0),\n","  'Token[112]: \"that\"'/'O' (1.0),\n","  'Token[113]: \"when\"'/'O' (1.0),\n","  'Token[114]: \"on\"'/'O' (1.0),\n","  'Token[115]: \"50\"'/'O' (1.0),\n","  'Token[116]: \".\"'/'O' (1.0)],\n"," 'meddra': ['Token[0]: \"i\"'/'O' (1.0),\n","  'Token[1]: \"feel\"'/'O' (1.0),\n","  'Token[2]: \"a\"'/'O' (1.0),\n","  'Token[3]: \"bit\"'/'B-Drowsy' (1.0),\n","  'Token[4]: \"drowsy\"'/'I-Drowsy' (1.0),\n","  'Token[5]: \"&\"'/'O' (1.0),\n","  'Token[6]: \"have\"'/'O' (1.0),\n","  'Token[7]: \"a\"'/'O' (1.0),\n","  'Token[8]: \"little\"'/'B-Blurred_vision_-_hazy' (1.0),\n","  'Token[9]: \"blurred\"'/'I-Blurred_vision_-_hazy' (1.0),\n","  'Token[10]: \"vision\"'/'I-Blurred_vision_-_hazy' (1.0),\n","  'Token[11]: \",\"'/'O' (1.0),\n","  'Token[12]: \"so\"'/'O' (1.0),\n","  'Token[13]: \"far\"'/'O' (1.0),\n","  'Token[14]: \"no\"'/'O' (1.0),\n","  'Token[15]: \"gastric\"'/'B-Excessive_upper_gastrointestinal_gas' (1.0),\n","  'Token[16]: \"problems\"'/'I-Excessive_upper_gastrointestinal_gas' (1.0),\n","  'Token[17]: \".\"'/'O' (1.0),\n","  'Token[18]: \"ive\"'/'O' (1.0),\n","  'Token[19]: \"been\"'/'O' (1.0),\n","  'Token[20]: \"on\"'/'O' (1.0),\n","  'Token[21]: \"arthrotec\"'/'B-Arthrotec' (1.0),\n","  'Token[22]: \"50\"'/'O' (1.0),\n","  'Token[23]: \"for\"'/'O' (1.0),\n","  'Token[24]: \"over\"'/'O' (1.0),\n","  'Token[25]: \"10\"'/'O' (1.0),\n","  'Token[26]: \"years\"'/'O' (1.0),\n","  'Token[27]: \"on\"'/'O' (1.0),\n","  'Token[28]: \"and\"'/'O' (1.0),\n","  'Token[29]: \"off\"'/'O' (1.0),\n","  'Token[30]: \",\"'/'O' (1.0),\n","  'Token[31]: \"only\"'/'O' (1.0),\n","  'Token[32]: \"taking\"'/'O' (1.0),\n","  'Token[33]: \"it\"'/'O' (1.0),\n","  'Token[34]: \"when\"'/'O' (1.0),\n","  'Token[35]: \"i\"'/'O' (1.0),\n","  'Token[36]: \"needed\"'/'O' (1.0),\n","  'Token[37]: \"it\"'/'O' (1.0),\n","  'Token[38]: \".\"'/'O' (1.0),\n","  'Token[39]: \"due\"'/'O' (1.0),\n","  'Token[40]: \"to\"'/'O' (1.0),\n","  'Token[41]: \"my\"'/'O' (1.0),\n","  'Token[42]: \"arthritis\"'/'B-Arthritis' (1.0),\n","  'Token[43]: \"getting\"'/'O' (1.0),\n","  'Token[44]: \"progressively\"'/'O' (1.0),\n","  'Token[45]: \"worse\"'/'O' (1.0),\n","  'Token[46]: \",\"'/'O' (1.0),\n","  'Token[47]: \"to\"'/'O' (1.0),\n","  'Token[48]: \"the\"'/'O' (1.0),\n","  'Token[49]: \"point\"'/'O' (1.0),\n","  'Token[50]: \"where\"'/'O' (1.0),\n","  'Token[51]: \"i\"'/'O' (1.0),\n","  'Token[52]: \"am\"'/'O' (1.0),\n","  'Token[53]: \"in\"'/'O' (1.0),\n","  'Token[54]: \"tears\"'/'O' (1.0),\n","  'Token[55]: \"with\"'/'O' (1.0),\n","  'Token[56]: \"the\"'/'O' (1.0),\n","  'Token[57]: \"agony\"'/'B-Agony' (1.0),\n","  'Token[58]: \",\"'/'O' (1.0),\n","  'Token[59]: \"gp\"'/'O' (1.0),\n","  'Token[60]: \"'\"'/'O' (1.0),\n","  'Token[61]: \"s\"'/'O' (1.0),\n","  'Token[62]: \"started\"'/'O' (1.0),\n","  'Token[63]: \"me\"'/'O' (1.0),\n","  'Token[64]: \"on\"'/'O' (1.0),\n","  'Token[65]: \"75\"'/'O' (1.0),\n","  'Token[66]: \"twice\"'/'O' (1.0),\n","  'Token[67]: \"a\"'/'O' (1.0),\n","  'Token[68]: \"day\"'/'O' (1.0),\n","  'Token[69]: \"and\"'/'O' (1.0),\n","  'Token[70]: \"i\"'/'O' (1.0),\n","  'Token[71]: \"have\"'/'O' (1.0),\n","  'Token[72]: \"to\"'/'O' (1.0),\n","  'Token[73]: \"take\"'/'O' (1.0),\n","  'Token[74]: \"it\"'/'O' (1.0),\n","  'Token[75]: \".\"'/'O' (1.0),\n","  'Token[76]: \"every\"'/'O' (1.0),\n","  'Token[77]: \"day\"'/'O' (1.0),\n","  'Token[78]: \"for\"'/'O' (1.0),\n","  'Token[79]: \"the\"'/'O' (1.0),\n","  'Token[80]: \"next\"'/'O' (1.0),\n","  'Token[81]: \"month\"'/'O' (1.0),\n","  'Token[82]: \"to\"'/'O' (1.0),\n","  'Token[83]: \"see\"'/'O' (1.0),\n","  'Token[84]: \"how\"'/'O' (1.0),\n","  'Token[85]: \"i\"'/'O' (1.0),\n","  'Token[86]: \"get\"'/'O' (1.0),\n","  'Token[87]: \"on\"'/'O' (1.0),\n","  'Token[88]: \",\"'/'O' (1.0),\n","  'Token[89]: \"here\"'/'O' (1.0),\n","  'Token[90]: \"goes\"'/'O' (1.0),\n","  'Token[91]: \".\"'/'O' (1.0),\n","  'Token[92]: \"so\"'/'O' (1.0),\n","  'Token[93]: \"far\"'/'O' (1.0),\n","  'Token[94]: \"its\"'/'O' (1.0),\n","  'Token[95]: \"been\"'/'O' (1.0),\n","  'Token[96]: \"very\"'/'O' (1.0),\n","  'Token[97]: \"good\"'/'O' (1.0),\n","  'Token[98]: \",\"'/'O' (1.0),\n","  'Token[99]: \"pains\"'/'B-Pain' (1.0),\n","  'Token[100]: \"almost\"'/'O' (1.0),\n","  'Token[101]: \"gone\"'/'O' (1.0),\n","  'Token[102]: \",\"'/'O' (1.0),\n","  'Token[103]: \"but\"'/'O' (1.0),\n","  'Token[104]: \"i\"'/'O' (1.0),\n","  'Token[105]: \"feel\"'/'B-Malaise' (1.0),\n","  'Token[106]: \"a\"'/'I-Malaise' (1.0),\n","  'Token[107]: \"bit\"'/'I-Malaise' (1.0),\n","  'Token[108]: \"weird\"'/'I-Malaise' (1.0),\n","  'Token[109]: \",\"'/'O' (1.0),\n","  'Token[110]: \"didnt\"'/'O' (1.0),\n","  'Token[111]: \"have\"'/'O' (1.0),\n","  'Token[112]: \"that\"'/'O' (1.0),\n","  'Token[113]: \"when\"'/'O' (1.0),\n","  'Token[114]: \"on\"'/'O' (1.0),\n","  'Token[115]: \"50\"'/'O' (1.0),\n","  'Token[116]: \".\"'/'O' (1.0)]}"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["temp.annotation_layers"]},{"cell_type":"code","execution_count":12,"metadata":{"executionInfo":{"elapsed":8,"status":"ok","timestamp":1682054051635,"user":{"displayName":"Alex He","userId":"06262800374381224408"},"user_tz":240},"id":"4CsGKKB6tGik"},"outputs":[],"source":["\n","# define your NER tags\n","label_dict = {\n","    'ADR': 'ADR',\n","    'Drug': 'Drug',\n","    'Finding': 'Finding',\n","    'Symptom': 'Symptom',\n","    'Disease': 'Disease'\n","}"]},{"cell_type":"code","execution_count":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":37060,"status":"ok","timestamp":1682054088688,"user":{"displayName":"Alex He","userId":"06262800374381224408"},"user_tz":240},"id":"QmNsy8AltGik","outputId":"f05a160a-f5ab-4b6f-e8c4-d7fdc9c72c1b"},"outputs":[{"name":"stdout","output_type":"stream","text":["2023-04-21 05:14:11,879 Filtering empty sentences\n","2023-04-21 05:14:11,941 Corpus: 948 train + 119 dev + 119 test sentences\n","2023-04-21 05:14:11,943 Computing label dictionary. Progress:\n"]},{"name":"stderr","output_type":"stream","text":["948it [00:00, 6664.01it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:14:12,090 Dictionary created for label 'ner' with 12 values: O (seen 79057 times), I-ADR (seen 7953 times), B-ADR (seen 4712 times), B-Drug (seen 1396 times), I-Finding (seen 349 times), B-Finding (seen 309 times), B-Disease (seen 231 times), B-Symptom (seen 222 times), I-Symptom (seen 218 times), I-Disease (seen 169 times), I-Drug (seen 161 times)\n","False\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:14:12,646 https://flair.informatik.hu-berlin.de/resources/embeddings/token/glove.gensim.vectors.npy not found in cache, downloading to /tmp/tmpbnjb1o6c\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 153M/153M [00:07<00:00, 20.7MB/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:14:20,775 copying /tmp/tmpbnjb1o6c to cache at /root/.flair/embeddings/glove.gensim.vectors.npy\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:14:20,892 removing temp file /tmp/tmpbnjb1o6c\n","2023-04-21 05:14:21,294 https://flair.informatik.hu-berlin.de/resources/embeddings/token/glove.gensim not found in cache, downloading to /tmp/tmp194uz_l2\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 20.5M/20.5M [00:01<00:00, 12.6MB/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:14:23,402 copying /tmp/tmp194uz_l2 to cache at /root/.flair/embeddings/glove.gensim\n","2023-04-21 05:14:23,420 removing temp file /tmp/tmp194uz_l2\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:14:34,673 https://flair.informatik.hu-berlin.de/resources/embeddings/flair/news-forward-0.4.1.pt not found in cache, downloading to /tmp/tmpdbpj7jhe\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 69.7M/69.7M [00:03<00:00, 18.6MB/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:14:39,008 copying /tmp/tmpdbpj7jhe to cache at /root/.flair/embeddings/news-forward-0.4.1.pt\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:14:39,063 removing temp file /tmp/tmpdbpj7jhe\n","2023-04-21 05:14:44,016 https://flair.informatik.hu-berlin.de/resources/embeddings/flair/news-backward-0.4.1.pt not found in cache, downloading to /tmp/tmpfns0l7k6\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 69.7M/69.7M [00:03<00:00, 18.4MB/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:14:48,384 copying /tmp/tmpfns0l7k6 to cache at /root/.flair/embeddings/news-backward-0.4.1.pt\n","2023-04-21 05:14:48,439 removing temp file /tmp/tmpfns0l7k6\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:14:48,669 SequenceTagger predicts: Dictionary with 12 tags: <unk>, O, I-ADR, B-ADR, B-Drug, I-Finding, B-Finding, B-Disease, B-Symptom, I-Symptom, I-Disease, I-Drug\n","True\n","False\n"]}],"source":["from flair.data import Corpus\n","\n","from flair.embeddings import WordEmbeddings, FlairEmbeddings, StackedEmbeddings, TransformerWordEmbeddings\n","from flair.models import SequenceTagger\n","from flair.trainers import ModelTrainer\n","from sklearn.model_selection import train_test_split\n","\n","from torch.optim import AdamW, SGD\n","\n","from flair.visual.training_curves import Plotter\n","plotter = Plotter()\n","\n","\n","\n","train_docs, test_docs = train_test_split(docs, test_size=0.2, random_state=321)\n","dev_docs, test_docs = train_test_split(test_docs, test_size=0.5, random_state=123)\n","\n","corpus = Corpus(train=train_docs, dev=dev_docs, test=test_docs) # Corpus: 948 train + 119 dev + 119 test sentences\n","corpus.filter_empty_sentences()\n","# # create a tag dictionary\n","labels = ['ADR', 'Drug', 'Finding', 'Symptom', 'Disease']\n","# tag_dictionary = Dictionary(add_unk=True)\n","# for label in labels:\n","#     tag_dictionary.add_item(label)\n","tag_dictionary = corpus.make_label_dictionary(label_type='ner')\n","print(tag_dictionary.span_labels)\n","\n","# create a stacked embedding using GloVe and Flair embeddings\n","stacked_embeddings = StackedEmbeddings([\n","    WordEmbeddings('glove',\n","                   force_cpu=False,\n","                #    embedding_length=1024,\n","                   fine_tune=True,\n","                   ),\n","    # TransformerWordEmbeddings('emilyalsentzer/Bio_ClinicalBERT', layers=\"-1\",\n","    #                                    subtoken_pooling=\"first\",\n","    #                                    is_document_embedding=True,\n","    #                                    fine_tune=True,\n","    #                                    use_context=True,\n","    #                                    max_seq_length=1024,\n","    #                                    ),\n","    FlairEmbeddings('news-forward',\n","                    # fine_tune=True\n","                    ),\n","    FlairEmbeddings('news-backward',\n","                    # fine_tune=True\n","                    ),\n","])\n","# https://github.com/flairNLP/flair/blob/master/resources/docs/embeddings/FLAIR_EMBEDDINGS.md\n","\n","# create a SequenceTagger object and specify the tag dictionary and embeddings to use\n","tagger = SequenceTagger(hidden_size=256,\n","                        embeddings=stacked_embeddings,\n","                        tag_dictionary=tag_dictionary,\n","                        tag_type='ner',\n","                        # use_crf=False,\n","                        # use_rnn=False,\n","                        # reproject_embeddings=False,\n","                        )\n","tagger.predict_spans = False\n","\n","print(tagger._determine_if_span_prediction_problem(tagger.label_dictionary))\n","print(tagger.predict_spans)\n","\n","# https://github.com/flairNLP/flair/blob/master/flair/trainers/trainer.py\n","# create a ModelTrainer object and train the tagger\n","trainer = ModelTrainer(tagger, corpus)"]},{"cell_type":"code","execution_count":14,"metadata":{"executionInfo":{"elapsed":6,"status":"ok","timestamp":1682054088689,"user":{"displayName":"Alex He","userId":"06262800374381224408"},"user_tz":240},"id":"fh0zU2jHAA4J"},"outputs":[],"source":["# !tensorboard --logdir=runs\n","# http://localhost:6006/"]},{"cell_type":"code","execution_count":15,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4227285,"status":"ok","timestamp":1682058315969,"user":{"displayName":"Alex He","userId":"06262800374381224408"},"user_tz":240},"id":"UKTMzxV8zF9o","outputId":"ca76a3f4-8624-4241-cf6e-b7af9a1996bd"},"outputs":[{"name":"stdout","output_type":"stream","text":["2023-04-21 05:14:48,938 tensorboard logging path is runs/tb_logs_v3_finetune_glove\n","2023-04-21 05:14:48,948 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:14:48,950 Model: \"SequenceTagger(\n","  (embeddings): StackedEmbeddings(\n","    (list_embedding_0): WordEmbeddings(\n","      'glove'\n","      (embedding): Embedding(400001, 100)\n","    )\n","    (list_embedding_1): FlairEmbeddings(\n","      (lm): LanguageModel(\n","        (drop): Dropout(p=0.05, inplace=False)\n","        (encoder): Embedding(300, 100)\n","        (rnn): LSTM(100, 2048)\n","      )\n","    )\n","    (list_embedding_2): FlairEmbeddings(\n","      (lm): LanguageModel(\n","        (drop): Dropout(p=0.05, inplace=False)\n","        (encoder): Embedding(300, 100)\n","        (rnn): LSTM(100, 2048)\n","      )\n","    )\n","  )\n","  (word_dropout): WordDropout(p=0.05)\n","  (locked_dropout): LockedDropout(p=0.5)\n","  (embedding2nn): Linear(in_features=4196, out_features=4196, bias=True)\n","  (rnn): LSTM(4196, 256, batch_first=True, bidirectional=True)\n","  (linear): Linear(in_features=512, out_features=14, bias=True)\n","  (loss_function): ViterbiLoss()\n","  (crf): CRF()\n",")\"\n","2023-04-21 05:14:48,951 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:14:48,954 Corpus: \"Corpus: 948 train + 119 dev + 119 test sentences\"\n","2023-04-21 05:14:48,956 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:14:48,958 Parameters:\n","2023-04-21 05:14:48,959  - learning_rate: \"0.100000\"\n","2023-04-21 05:14:48,961  - mini_batch_size: \"12\"\n","2023-04-21 05:14:48,963  - patience: \"1\"\n","2023-04-21 05:14:48,964  - anneal_factor: \"0.5\"\n","2023-04-21 05:14:48,966  - max_epochs: \"100\"\n","2023-04-21 05:14:48,969  - shuffle: \"True\"\n","2023-04-21 05:14:48,972  - train_with_dev: \"False\"\n","2023-04-21 05:14:48,974  - batch_growth_annealing: \"False\"\n","2023-04-21 05:14:48,976 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:14:48,978 Model training base path: \"flair_ner_model_v3_finetune_glove\"\n","2023-04-21 05:14:48,979 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:14:48,981 Device: cuda:0\n","2023-04-21 05:14:48,982 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:14:48,984 Embeddings storage mode: gpu\n","2023-04-21 05:14:48,985 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:14:52,620 epoch 1 - iter 7/79 - loss 3.41104887 - time (sec): 3.63 - samples/sec: 2515.94 - lr: 0.100000\n","2023-04-21 05:14:54,979 epoch 1 - iter 14/79 - loss 2.71693768 - time (sec): 5.99 - samples/sec: 2729.17 - lr: 0.100000\n","2023-04-21 05:14:57,812 epoch 1 - iter 21/79 - loss 2.53836338 - time (sec): 8.82 - samples/sec: 2768.54 - lr: 0.100000\n","2023-04-21 05:15:00,545 epoch 1 - iter 28/79 - loss 2.51749784 - time (sec): 11.56 - samples/sec: 2820.91 - lr: 0.100000\n","2023-04-21 05:15:03,340 epoch 1 - iter 35/79 - loss 2.42248603 - time (sec): 14.35 - samples/sec: 2876.17 - lr: 0.100000\n","2023-04-21 05:15:06,082 epoch 1 - iter 42/79 - loss 2.43928247 - time (sec): 17.09 - samples/sec: 2894.26 - lr: 0.100000\n","2023-04-21 05:15:08,844 epoch 1 - iter 49/79 - loss 2.39890836 - time (sec): 19.86 - samples/sec: 2914.85 - lr: 0.100000\n","2023-04-21 05:15:12,758 epoch 1 - iter 56/79 - loss 2.32366336 - time (sec): 23.77 - samples/sec: 2799.53 - lr: 0.100000\n","2023-04-21 05:15:15,226 epoch 1 - iter 63/79 - loss 2.28316019 - time (sec): 26.24 - samples/sec: 2854.12 - lr: 0.100000\n","2023-04-21 05:15:18,271 epoch 1 - iter 70/79 - loss 2.26804448 - time (sec): 29.28 - samples/sec: 2840.29 - lr: 0.100000\n","2023-04-21 05:15:21,170 epoch 1 - iter 77/79 - loss 2.24215716 - time (sec): 32.18 - samples/sec: 2856.25 - lr: 0.100000\n","2023-04-21 05:15:22,761 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:15:22,762 EPOCH 1 done: loss 2.2240 - lr 0.100000\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.50it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:15:45,468 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:15:47,287 TRAIN : loss 1.346351981163025 - f1-score (weighted avg)  0.23\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:04<00:00,  2.30it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:15:51,783 Evaluating as a multi-label problem: False\n","2023-04-21 05:15:51,845 DEV : loss 1.3821823596954346 - f1-score (weighted avg)  0.241\n","2023-04-21 05:15:51,871 BAD EPOCHS (no improvement): 0\n","2023-04-21 05:15:51,876 saving best model\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:15:54,669 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:15:56,463 epoch 2 - iter 7/79 - loss 1.92555374 - time (sec): 1.79 - samples/sec: 4466.73 - lr: 0.100000\n","2023-04-21 05:15:58,442 epoch 2 - iter 14/79 - loss 1.83595439 - time (sec): 3.77 - samples/sec: 4416.85 - lr: 0.100000\n","2023-04-21 05:16:00,357 epoch 2 - iter 21/79 - loss 1.85039199 - time (sec): 5.68 - samples/sec: 4472.97 - lr: 0.100000\n","2023-04-21 05:16:02,201 epoch 2 - iter 28/79 - loss 1.90925509 - time (sec): 7.53 - samples/sec: 4365.36 - lr: 0.100000\n","2023-04-21 05:16:04,628 epoch 2 - iter 35/79 - loss 1.95371199 - time (sec): 9.96 - samples/sec: 4188.62 - lr: 0.100000\n","2023-04-21 05:16:06,487 epoch 2 - iter 42/79 - loss 1.94622767 - time (sec): 11.82 - samples/sec: 4196.36 - lr: 0.100000\n","2023-04-21 05:16:08,351 epoch 2 - iter 49/79 - loss 1.96358765 - time (sec): 13.68 - samples/sec: 4160.25 - lr: 0.100000\n","2023-04-21 05:16:10,448 epoch 2 - iter 56/79 - loss 1.98067679 - time (sec): 15.78 - samples/sec: 4174.58 - lr: 0.100000\n","2023-04-21 05:16:12,698 epoch 2 - iter 63/79 - loss 2.04160374 - time (sec): 18.03 - samples/sec: 4159.40 - lr: 0.100000\n","2023-04-21 05:16:14,746 epoch 2 - iter 70/79 - loss 2.06466664 - time (sec): 20.07 - samples/sec: 4197.76 - lr: 0.100000\n","2023-04-21 05:16:16,435 epoch 2 - iter 77/79 - loss 2.07696851 - time (sec): 21.76 - samples/sec: 4255.91 - lr: 0.100000\n","2023-04-21 05:16:16,886 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:16:16,888 EPOCH 2 done: loss 2.0764 - lr 0.100000\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.55it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:16:39,245 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:16:41,073 TRAIN : loss 1.5259631872177124 - f1-score (weighted avg)  0.0992\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.67it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:16:43,953 Evaluating as a multi-label problem: False\n","2023-04-21 05:16:44,017 DEV : loss 1.5512405633926392 - f1-score (weighted avg)  0.1093\n","2023-04-21 05:16:44,038 BAD EPOCHS (no improvement): 1\n","2023-04-21 05:16:44,042 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:16:45,881 epoch 3 - iter 7/79 - loss 1.93955722 - time (sec): 1.84 - samples/sec: 5097.03 - lr: 0.100000\n","2023-04-21 05:16:47,826 epoch 3 - iter 14/79 - loss 1.90841998 - time (sec): 3.78 - samples/sec: 4996.52 - lr: 0.100000\n","2023-04-21 05:16:49,698 epoch 3 - iter 21/79 - loss 1.90032854 - time (sec): 5.65 - samples/sec: 4751.75 - lr: 0.100000\n","2023-04-21 05:16:51,481 epoch 3 - iter 28/79 - loss 1.91833384 - time (sec): 7.44 - samples/sec: 4744.55 - lr: 0.100000\n","2023-04-21 05:16:53,340 epoch 3 - iter 35/79 - loss 2.09527665 - time (sec): 9.30 - samples/sec: 4678.40 - lr: 0.100000\n","2023-04-21 05:16:54,899 epoch 3 - iter 42/79 - loss 2.11474998 - time (sec): 10.86 - samples/sec: 4694.33 - lr: 0.100000\n","2023-04-21 05:16:56,713 epoch 3 - iter 49/79 - loss 2.13092565 - time (sec): 12.67 - samples/sec: 4643.09 - lr: 0.100000\n","2023-04-21 05:16:58,424 epoch 3 - iter 56/79 - loss 2.12159176 - time (sec): 14.38 - samples/sec: 4653.58 - lr: 0.100000\n","2023-04-21 05:17:00,502 epoch 3 - iter 63/79 - loss 2.08955015 - time (sec): 16.46 - samples/sec: 4587.27 - lr: 0.100000\n","2023-04-21 05:17:02,086 epoch 3 - iter 70/79 - loss 2.11124148 - time (sec): 18.04 - samples/sec: 4612.87 - lr: 0.100000\n","2023-04-21 05:17:04,516 epoch 3 - iter 77/79 - loss 2.12177226 - time (sec): 20.47 - samples/sec: 4502.98 - lr: 0.100000\n","2023-04-21 05:17:04,970 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:17:04,971 EPOCH 3 done: loss 2.1362 - lr 0.100000\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.55it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:17:27,388 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:17:29,211 TRAIN : loss 1.5675063133239746 - f1-score (weighted avg)  0.2498\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.70it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:17:32,078 Evaluating as a multi-label problem: False\n","2023-04-21 05:17:32,147 DEV : loss 1.5757861137390137 - f1-score (weighted avg)  0.2628\n","2023-04-21 05:17:32,168 Epoch     3: reducing learning rate of group 0 to 5.0000e-02.\n","2023-04-21 05:17:32,170 BAD EPOCHS (no improvement): 2\n","2023-04-21 05:17:32,174 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:17:33,976 epoch 4 - iter 7/79 - loss 1.98717526 - time (sec): 1.80 - samples/sec: 4762.89 - lr: 0.050000\n","2023-04-21 05:17:35,740 epoch 4 - iter 14/79 - loss 1.97315488 - time (sec): 3.56 - samples/sec: 4909.54 - lr: 0.050000\n","2023-04-21 05:17:37,253 epoch 4 - iter 21/79 - loss 1.95176492 - time (sec): 5.08 - samples/sec: 4878.43 - lr: 0.050000\n","2023-04-21 05:17:39,344 epoch 4 - iter 28/79 - loss 2.04920825 - time (sec): 7.17 - samples/sec: 4696.26 - lr: 0.050000\n","2023-04-21 05:17:41,325 epoch 4 - iter 35/79 - loss 1.98389803 - time (sec): 9.15 - samples/sec: 4614.70 - lr: 0.050000\n","2023-04-21 05:17:43,128 epoch 4 - iter 42/79 - loss 1.90218653 - time (sec): 10.95 - samples/sec: 4593.33 - lr: 0.050000\n","2023-04-21 05:17:44,980 epoch 4 - iter 49/79 - loss 1.86047410 - time (sec): 12.80 - samples/sec: 4654.86 - lr: 0.050000\n","2023-04-21 05:17:46,672 epoch 4 - iter 56/79 - loss 1.82158549 - time (sec): 14.50 - samples/sec: 4682.33 - lr: 0.050000\n","2023-04-21 05:17:48,213 epoch 4 - iter 63/79 - loss 1.78655860 - time (sec): 16.04 - samples/sec: 4689.74 - lr: 0.050000\n","2023-04-21 05:17:50,906 epoch 4 - iter 70/79 - loss 1.74830081 - time (sec): 18.73 - samples/sec: 4563.37 - lr: 0.050000\n","2023-04-21 05:17:52,481 epoch 4 - iter 77/79 - loss 1.73541797 - time (sec): 20.31 - samples/sec: 4574.20 - lr: 0.050000\n","2023-04-21 05:17:52,885 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:17:52,887 EPOCH 4 done: loss 1.7391 - lr 0.050000\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.74it/s]\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:18:15,299 Evaluating as a multi-label problem: True\n","2023-04-21 05:18:17,129 TRAIN : loss 1.0348702669143677 - f1-score (weighted avg)  0.0957\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.65it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:18:20,026 Evaluating as a multi-label problem: False\n","2023-04-21 05:18:20,090 DEV : loss 1.0557503700256348 - f1-score (weighted avg)  0.0868\n","2023-04-21 05:18:20,112 BAD EPOCHS (no improvement): 0\n","2023-04-21 05:18:20,116 saving best model\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:18:23,028 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:18:24,830 epoch 5 - iter 7/79 - loss 1.35194641 - time (sec): 1.80 - samples/sec: 4496.82 - lr: 0.050000\n","2023-04-21 05:18:26,775 epoch 5 - iter 14/79 - loss 1.30863901 - time (sec): 3.74 - samples/sec: 4633.32 - lr: 0.050000\n","2023-04-21 05:18:28,557 epoch 5 - iter 21/79 - loss 1.29890958 - time (sec): 5.53 - samples/sec: 4677.19 - lr: 0.050000\n","2023-04-21 05:18:30,660 epoch 5 - iter 28/79 - loss 1.24957704 - time (sec): 7.63 - samples/sec: 4499.29 - lr: 0.050000\n","2023-04-21 05:18:32,443 epoch 5 - iter 35/79 - loss 1.26962275 - time (sec): 9.41 - samples/sec: 4540.12 - lr: 0.050000\n","2023-04-21 05:18:35,070 epoch 5 - iter 42/79 - loss 1.27027462 - time (sec): 12.04 - samples/sec: 4352.30 - lr: 0.050000\n","2023-04-21 05:18:37,557 epoch 5 - iter 49/79 - loss 1.30354364 - time (sec): 14.53 - samples/sec: 4284.78 - lr: 0.050000\n","2023-04-21 05:18:39,074 epoch 5 - iter 56/79 - loss 1.35326079 - time (sec): 16.04 - samples/sec: 4307.09 - lr: 0.050000\n","2023-04-21 05:18:40,643 epoch 5 - iter 63/79 - loss 1.33872687 - time (sec): 17.61 - samples/sec: 4339.23 - lr: 0.050000\n","2023-04-21 05:18:42,393 epoch 5 - iter 70/79 - loss 1.32474497 - time (sec): 19.36 - samples/sec: 4338.05 - lr: 0.050000\n","2023-04-21 05:18:44,326 epoch 5 - iter 77/79 - loss 1.34140432 - time (sec): 21.29 - samples/sec: 4340.85 - lr: 0.050000\n","2023-04-21 05:18:44,742 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:18:44,744 EPOCH 5 done: loss 1.3460 - lr 0.050000\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.75it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:19:05,933 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:19:08,991 TRAIN : loss 1.3516731262207031 - f1-score (weighted avg)  0.1078\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.67it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:19:11,876 Evaluating as a multi-label problem: False\n","2023-04-21 05:19:11,941 DEV : loss 1.3984562158584595 - f1-score (weighted avg)  0.1284\n","2023-04-21 05:19:11,963 BAD EPOCHS (no improvement): 1\n","2023-04-21 05:19:11,968 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:19:13,665 epoch 6 - iter 7/79 - loss 1.85976583 - time (sec): 1.70 - samples/sec: 4442.10 - lr: 0.050000\n","2023-04-21 05:19:15,510 epoch 6 - iter 14/79 - loss 1.67741207 - time (sec): 3.54 - samples/sec: 4946.13 - lr: 0.050000\n","2023-04-21 05:19:17,050 epoch 6 - iter 21/79 - loss 1.70803150 - time (sec): 5.08 - samples/sec: 4957.21 - lr: 0.050000\n","2023-04-21 05:19:18,728 epoch 6 - iter 28/79 - loss 1.58950982 - time (sec): 6.76 - samples/sec: 4894.75 - lr: 0.050000\n","2023-04-21 05:19:21,119 epoch 6 - iter 35/79 - loss 1.49122170 - time (sec): 9.15 - samples/sec: 4690.68 - lr: 0.050000\n","2023-04-21 05:19:23,160 epoch 6 - iter 42/79 - loss 1.41391335 - time (sec): 11.19 - samples/sec: 4664.38 - lr: 0.050000\n","2023-04-21 05:19:24,888 epoch 6 - iter 49/79 - loss 1.40090082 - time (sec): 12.92 - samples/sec: 4657.66 - lr: 0.050000\n","2023-04-21 05:19:26,753 epoch 6 - iter 56/79 - loss 1.45797431 - time (sec): 14.78 - samples/sec: 4585.80 - lr: 0.050000\n","2023-04-21 05:19:28,349 epoch 6 - iter 63/79 - loss 1.47994735 - time (sec): 16.38 - samples/sec: 4619.80 - lr: 0.050000\n","2023-04-21 05:19:30,212 epoch 6 - iter 70/79 - loss 1.47025664 - time (sec): 18.24 - samples/sec: 4580.75 - lr: 0.050000\n","2023-04-21 05:19:32,239 epoch 6 - iter 77/79 - loss 1.52744275 - time (sec): 20.27 - samples/sec: 4567.79 - lr: 0.050000\n","2023-04-21 05:19:32,688 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:19:32,690 EPOCH 6 done: loss 1.5230 - lr 0.050000\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.72it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:19:54,027 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:19:55,837 TRAIN : loss 1.5815060138702393 - f1-score (weighted avg)  0.0686\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.66it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:19:58,727 Evaluating as a multi-label problem: False\n","2023-04-21 05:19:58,797 DEV : loss 1.5791232585906982 - f1-score (weighted avg)  0.0962\n","2023-04-21 05:19:58,819 Epoch     6: reducing learning rate of group 0 to 2.5000e-02.\n","2023-04-21 05:19:58,822 BAD EPOCHS (no improvement): 2\n","2023-04-21 05:19:58,825 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:20:00,976 epoch 7 - iter 7/79 - loss 1.26506507 - time (sec): 2.15 - samples/sec: 4457.74 - lr: 0.025000\n","2023-04-21 05:20:02,593 epoch 7 - iter 14/79 - loss 1.32664658 - time (sec): 3.77 - samples/sec: 4745.05 - lr: 0.025000\n","2023-04-21 05:20:04,386 epoch 7 - iter 21/79 - loss 1.25225289 - time (sec): 5.56 - samples/sec: 4626.18 - lr: 0.025000\n","2023-04-21 05:20:06,103 epoch 7 - iter 28/79 - loss 1.20434772 - time (sec): 7.28 - samples/sec: 4774.67 - lr: 0.025000\n","2023-04-21 05:20:08,463 epoch 7 - iter 35/79 - loss 1.19450551 - time (sec): 9.64 - samples/sec: 4503.68 - lr: 0.025000\n","2023-04-21 05:20:10,439 epoch 7 - iter 42/79 - loss 1.15763957 - time (sec): 11.61 - samples/sec: 4441.05 - lr: 0.025000\n","2023-04-21 05:20:12,051 epoch 7 - iter 49/79 - loss 1.13163260 - time (sec): 13.22 - samples/sec: 4470.35 - lr: 0.025000\n","2023-04-21 05:20:14,455 epoch 7 - iter 56/79 - loss 1.09528602 - time (sec): 15.63 - samples/sec: 4374.42 - lr: 0.025000\n","2023-04-21 05:20:16,131 epoch 7 - iter 63/79 - loss 1.08711481 - time (sec): 17.30 - samples/sec: 4398.14 - lr: 0.025000\n","2023-04-21 05:20:17,621 epoch 7 - iter 70/79 - loss 1.07793225 - time (sec): 18.79 - samples/sec: 4437.29 - lr: 0.025000\n","2023-04-21 05:20:19,606 epoch 7 - iter 77/79 - loss 1.05341280 - time (sec): 20.78 - samples/sec: 4466.62 - lr: 0.025000\n","2023-04-21 05:20:19,984 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:20:19,986 EPOCH 7 done: loss 1.0493 - lr 0.025000\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.51it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:20:42,632 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:20:44,472 TRAIN : loss 0.6906524896621704 - f1-score (weighted avg)  0.1601\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.58it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:20:47,423 Evaluating as a multi-label problem: False\n","2023-04-21 05:20:47,491 DEV : loss 0.7211136221885681 - f1-score (weighted avg)  0.1849\n","2023-04-21 05:20:47,513 BAD EPOCHS (no improvement): 0\n","2023-04-21 05:20:47,518 saving best model\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:20:50,466 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:20:52,437 epoch 8 - iter 7/79 - loss 0.79008160 - time (sec): 1.97 - samples/sec: 3872.80 - lr: 0.025000\n","2023-04-21 05:20:54,217 epoch 8 - iter 14/79 - loss 0.79087915 - time (sec): 3.75 - samples/sec: 4429.05 - lr: 0.025000\n","2023-04-21 05:20:55,828 epoch 8 - iter 21/79 - loss 0.79579754 - time (sec): 5.36 - samples/sec: 4643.17 - lr: 0.025000\n","2023-04-21 05:20:57,772 epoch 8 - iter 28/79 - loss 0.77847492 - time (sec): 7.30 - samples/sec: 4613.09 - lr: 0.025000\n","2023-04-21 05:21:00,113 epoch 8 - iter 35/79 - loss 0.76790377 - time (sec): 9.64 - samples/sec: 4398.41 - lr: 0.025000\n","2023-04-21 05:21:02,231 epoch 8 - iter 42/79 - loss 0.76034618 - time (sec): 11.76 - samples/sec: 4264.54 - lr: 0.025000\n","2023-04-21 05:21:04,064 epoch 8 - iter 49/79 - loss 0.76640260 - time (sec): 13.60 - samples/sec: 4318.87 - lr: 0.025000\n","2023-04-21 05:21:06,216 epoch 8 - iter 56/79 - loss 0.76035906 - time (sec): 15.75 - samples/sec: 4270.78 - lr: 0.025000\n","2023-04-21 05:21:08,192 epoch 8 - iter 63/79 - loss 0.75574116 - time (sec): 17.72 - samples/sec: 4314.31 - lr: 0.025000\n","2023-04-21 05:21:10,138 epoch 8 - iter 70/79 - loss 0.75057463 - time (sec): 19.67 - samples/sec: 4300.25 - lr: 0.025000\n","2023-04-21 05:21:11,687 epoch 8 - iter 77/79 - loss 0.75026138 - time (sec): 21.22 - samples/sec: 4341.45 - lr: 0.025000\n","2023-04-21 05:21:12,240 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:21:12,242 EPOCH 8 done: loss 0.7486 - lr 0.025000\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.54it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:21:34,698 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:21:36,535 TRAIN : loss 0.5678940415382385 - f1-score (weighted avg)  0.2468\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.67it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:21:39,417 Evaluating as a multi-label problem: False\n","2023-04-21 05:21:39,486 DEV : loss 0.6180704236030579 - f1-score (weighted avg)  0.2367\n","2023-04-21 05:21:39,508 BAD EPOCHS (no improvement): 0\n","2023-04-21 05:21:39,513 saving best model\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:21:42,442 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:21:44,638 epoch 9 - iter 7/79 - loss 0.71383335 - time (sec): 2.19 - samples/sec: 3892.68 - lr: 0.025000\n","2023-04-21 05:21:46,559 epoch 9 - iter 14/79 - loss 0.69067307 - time (sec): 4.12 - samples/sec: 4091.08 - lr: 0.025000\n","2023-04-21 05:21:48,097 epoch 9 - iter 21/79 - loss 0.70496313 - time (sec): 5.65 - samples/sec: 4359.88 - lr: 0.025000\n","2023-04-21 05:21:49,884 epoch 9 - iter 28/79 - loss 0.68941820 - time (sec): 7.44 - samples/sec: 4383.01 - lr: 0.025000\n","2023-04-21 05:21:51,756 epoch 9 - iter 35/79 - loss 0.70146855 - time (sec): 9.31 - samples/sec: 4400.95 - lr: 0.025000\n","2023-04-21 05:21:53,755 epoch 9 - iter 42/79 - loss 0.68713944 - time (sec): 11.31 - samples/sec: 4416.53 - lr: 0.025000\n","2023-04-21 05:21:56,172 epoch 9 - iter 49/79 - loss 0.68613782 - time (sec): 13.73 - samples/sec: 4313.48 - lr: 0.025000\n","2023-04-21 05:21:57,949 epoch 9 - iter 56/79 - loss 0.69491205 - time (sec): 15.50 - samples/sec: 4360.59 - lr: 0.025000\n","2023-04-21 05:21:59,615 epoch 9 - iter 63/79 - loss 0.70246408 - time (sec): 17.17 - samples/sec: 4345.56 - lr: 0.025000\n","2023-04-21 05:22:01,355 epoch 9 - iter 70/79 - loss 0.72163083 - time (sec): 18.91 - samples/sec: 4384.70 - lr: 0.025000\n","2023-04-21 05:22:03,405 epoch 9 - iter 77/79 - loss 0.72111388 - time (sec): 20.96 - samples/sec: 4393.75 - lr: 0.025000\n","2023-04-21 05:22:03,871 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:22:03,873 EPOCH 9 done: loss 0.7155 - lr 0.025000\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.55it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:22:26,265 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:22:28,119 TRAIN : loss 0.529806911945343 - f1-score (weighted avg)  0.1547\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.66it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:22:31,012 Evaluating as a multi-label problem: False\n","2023-04-21 05:22:31,077 DEV : loss 0.5616692304611206 - f1-score (weighted avg)  0.1669\n","2023-04-21 05:22:31,099 BAD EPOCHS (no improvement): 0\n","2023-04-21 05:22:31,103 saving best model\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:22:34,039 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:22:35,947 epoch 10 - iter 7/79 - loss 0.63779616 - time (sec): 1.90 - samples/sec: 4713.98 - lr: 0.025000\n","2023-04-21 05:22:37,966 epoch 10 - iter 14/79 - loss 0.63118833 - time (sec): 3.92 - samples/sec: 4367.45 - lr: 0.025000\n","2023-04-21 05:22:39,533 epoch 10 - iter 21/79 - loss 0.63268134 - time (sec): 5.49 - samples/sec: 4429.03 - lr: 0.025000\n","2023-04-21 05:22:41,144 epoch 10 - iter 28/79 - loss 0.67303012 - time (sec): 7.10 - samples/sec: 4511.06 - lr: 0.025000\n","2023-04-21 05:22:43,138 epoch 10 - iter 35/79 - loss 0.66170476 - time (sec): 9.10 - samples/sec: 4434.64 - lr: 0.025000\n","2023-04-21 05:22:45,408 epoch 10 - iter 42/79 - loss 0.68761989 - time (sec): 11.36 - samples/sec: 4320.36 - lr: 0.025000\n","2023-04-21 05:22:47,945 epoch 10 - iter 49/79 - loss 0.69505824 - time (sec): 13.90 - samples/sec: 4245.89 - lr: 0.025000\n","2023-04-21 05:22:50,174 epoch 10 - iter 56/79 - loss 0.69690523 - time (sec): 16.13 - samples/sec: 4221.71 - lr: 0.025000\n","2023-04-21 05:22:51,706 epoch 10 - iter 63/79 - loss 0.68526468 - time (sec): 17.66 - samples/sec: 4277.32 - lr: 0.025000\n","2023-04-21 05:22:53,376 epoch 10 - iter 70/79 - loss 0.68204904 - time (sec): 19.33 - samples/sec: 4327.67 - lr: 0.025000\n","2023-04-21 05:22:55,148 epoch 10 - iter 77/79 - loss 0.67680434 - time (sec): 21.11 - samples/sec: 4372.72 - lr: 0.025000\n","2023-04-21 05:22:55,579 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:22:55,581 EPOCH 10 done: loss 0.6789 - lr 0.025000\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.73it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:23:16,868 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:23:19,931 TRAIN : loss 0.5810723900794983 - f1-score (weighted avg)  0.1663\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.62it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:23:22,854 Evaluating as a multi-label problem: False\n","2023-04-21 05:23:22,920 DEV : loss 0.6086234450340271 - f1-score (weighted avg)  0.1923\n","2023-04-21 05:23:22,941 BAD EPOCHS (no improvement): 1\n","2023-04-21 05:23:22,945 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:23:24,930 epoch 11 - iter 7/79 - loss 0.75959865 - time (sec): 1.98 - samples/sec: 4846.23 - lr: 0.025000\n","2023-04-21 05:23:26,716 epoch 11 - iter 14/79 - loss 0.69835788 - time (sec): 3.77 - samples/sec: 4509.79 - lr: 0.025000\n","2023-04-21 05:23:28,486 epoch 11 - iter 21/79 - loss 0.69237110 - time (sec): 5.54 - samples/sec: 4694.42 - lr: 0.025000\n","2023-04-21 05:23:30,030 epoch 11 - iter 28/79 - loss 0.68577147 - time (sec): 7.08 - samples/sec: 4753.61 - lr: 0.025000\n","2023-04-21 05:23:32,268 epoch 11 - iter 35/79 - loss 0.67047077 - time (sec): 9.32 - samples/sec: 4602.97 - lr: 0.025000\n","2023-04-21 05:23:34,183 epoch 11 - iter 42/79 - loss 0.66588422 - time (sec): 11.24 - samples/sec: 4546.45 - lr: 0.025000\n","2023-04-21 05:23:35,938 epoch 11 - iter 49/79 - loss 0.66342732 - time (sec): 12.99 - samples/sec: 4598.75 - lr: 0.025000\n","2023-04-21 05:23:37,755 epoch 11 - iter 56/79 - loss 0.66231639 - time (sec): 14.81 - samples/sec: 4633.93 - lr: 0.025000\n","2023-04-21 05:23:39,494 epoch 11 - iter 63/79 - loss 0.66834374 - time (sec): 16.55 - samples/sec: 4564.59 - lr: 0.025000\n","2023-04-21 05:23:41,216 epoch 11 - iter 70/79 - loss 0.66745827 - time (sec): 18.27 - samples/sec: 4590.46 - lr: 0.025000\n","2023-04-21 05:23:43,623 epoch 11 - iter 77/79 - loss 0.66561911 - time (sec): 20.68 - samples/sec: 4486.86 - lr: 0.025000\n","2023-04-21 05:23:43,976 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:23:43,978 EPOCH 11 done: loss 0.6649 - lr 0.025000\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.74it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:24:05,252 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:24:07,095 TRAIN : loss 0.5465831756591797 - f1-score (weighted avg)  0.0868\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:03<00:00,  2.64it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:24:11,043 Evaluating as a multi-label problem: False\n","2023-04-21 05:24:11,109 DEV : loss 0.5567585229873657 - f1-score (weighted avg)  0.0773\n","2023-04-21 05:24:11,130 BAD EPOCHS (no improvement): 0\n","2023-04-21 05:24:11,135 saving best model\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:24:14,049 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:24:16,099 epoch 12 - iter 7/79 - loss 0.60836513 - time (sec): 2.05 - samples/sec: 4281.19 - lr: 0.025000\n","2023-04-21 05:24:17,763 epoch 12 - iter 14/79 - loss 0.65703423 - time (sec): 3.71 - samples/sec: 4514.36 - lr: 0.025000\n","2023-04-21 05:24:19,222 epoch 12 - iter 21/79 - loss 0.67113788 - time (sec): 5.17 - samples/sec: 4629.32 - lr: 0.025000\n","2023-04-21 05:24:20,927 epoch 12 - iter 28/79 - loss 0.66537556 - time (sec): 6.88 - samples/sec: 4659.87 - lr: 0.025000\n","2023-04-21 05:24:22,518 epoch 12 - iter 35/79 - loss 0.66074504 - time (sec): 8.47 - samples/sec: 4739.57 - lr: 0.025000\n","2023-04-21 05:24:24,432 epoch 12 - iter 42/79 - loss 0.65452843 - time (sec): 10.38 - samples/sec: 4662.74 - lr: 0.025000\n","2023-04-21 05:24:26,433 epoch 12 - iter 49/79 - loss 0.66362812 - time (sec): 12.38 - samples/sec: 4626.54 - lr: 0.025000\n","2023-04-21 05:24:29,236 epoch 12 - iter 56/79 - loss 0.64769515 - time (sec): 15.18 - samples/sec: 4464.56 - lr: 0.025000\n","2023-04-21 05:24:31,465 epoch 12 - iter 63/79 - loss 0.66568978 - time (sec): 17.41 - samples/sec: 4434.57 - lr: 0.025000\n","2023-04-21 05:24:33,397 epoch 12 - iter 70/79 - loss 0.66734510 - time (sec): 19.35 - samples/sec: 4397.94 - lr: 0.025000\n","2023-04-21 05:24:34,853 epoch 12 - iter 77/79 - loss 0.67830460 - time (sec): 20.80 - samples/sec: 4434.97 - lr: 0.025000\n","2023-04-21 05:24:35,402 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:24:35,404 EPOCH 12 done: loss 0.6763 - lr 0.025000\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.74it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:24:56,650 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:24:58,494 TRAIN : loss 0.5566797256469727 - f1-score (weighted avg)  0.1369\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.64it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:25:01,400 Evaluating as a multi-label problem: False\n","2023-04-21 05:25:01,466 DEV : loss 0.5549883842468262 - f1-score (weighted avg)  0.1631\n","2023-04-21 05:25:01,488 BAD EPOCHS (no improvement): 0\n","2023-04-21 05:25:01,493 saving best model\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:25:04,406 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:25:06,393 epoch 13 - iter 7/79 - loss 0.70895181 - time (sec): 1.98 - samples/sec: 4083.38 - lr: 0.025000\n","2023-04-21 05:25:08,530 epoch 13 - iter 14/79 - loss 0.74516932 - time (sec): 4.12 - samples/sec: 4141.20 - lr: 0.025000\n","2023-04-21 05:25:10,106 epoch 13 - iter 21/79 - loss 0.71964117 - time (sec): 5.70 - samples/sec: 4331.89 - lr: 0.025000\n","2023-04-21 05:25:11,744 epoch 13 - iter 28/79 - loss 0.68888808 - time (sec): 7.33 - samples/sec: 4423.70 - lr: 0.025000\n","2023-04-21 05:25:13,283 epoch 13 - iter 35/79 - loss 0.67811295 - time (sec): 8.87 - samples/sec: 4557.79 - lr: 0.025000\n","2023-04-21 05:25:15,340 epoch 13 - iter 42/79 - loss 0.68674833 - time (sec): 10.93 - samples/sec: 4551.74 - lr: 0.025000\n","2023-04-21 05:25:17,604 epoch 13 - iter 49/79 - loss 0.69165547 - time (sec): 13.19 - samples/sec: 4423.50 - lr: 0.025000\n","2023-04-21 05:25:19,877 epoch 13 - iter 56/79 - loss 0.69198452 - time (sec): 15.47 - samples/sec: 4323.63 - lr: 0.025000\n","2023-04-21 05:25:21,414 epoch 13 - iter 63/79 - loss 0.68690424 - time (sec): 17.00 - samples/sec: 4362.83 - lr: 0.025000\n","2023-04-21 05:25:23,239 epoch 13 - iter 70/79 - loss 0.68991186 - time (sec): 18.83 - samples/sec: 4402.65 - lr: 0.025000\n","2023-04-21 05:25:25,275 epoch 13 - iter 77/79 - loss 0.68663680 - time (sec): 20.87 - samples/sec: 4440.32 - lr: 0.025000\n","2023-04-21 05:25:25,620 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:25:25,621 EPOCH 13 done: loss 0.6847 - lr 0.025000\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.53it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:25:48,110 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:25:49,940 TRAIN : loss 0.4731007218360901 - f1-score (weighted avg)  0.215\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.66it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:25:52,832 Evaluating as a multi-label problem: False\n","2023-04-21 05:25:52,898 DEV : loss 0.4873787760734558 - f1-score (weighted avg)  0.2226\n","2023-04-21 05:25:52,919 BAD EPOCHS (no improvement): 0\n","2023-04-21 05:25:52,923 saving best model\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:25:55,887 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:25:57,641 epoch 14 - iter 7/79 - loss 0.58264102 - time (sec): 1.75 - samples/sec: 4784.87 - lr: 0.025000\n","2023-04-21 05:25:59,403 epoch 14 - iter 14/79 - loss 0.57483903 - time (sec): 3.51 - samples/sec: 4903.44 - lr: 0.025000\n","2023-04-21 05:26:01,374 epoch 14 - iter 21/79 - loss 0.60478844 - time (sec): 5.48 - samples/sec: 4640.81 - lr: 0.025000\n","2023-04-21 05:26:03,109 epoch 14 - iter 28/79 - loss 0.63284078 - time (sec): 7.22 - samples/sec: 4641.01 - lr: 0.025000\n","2023-04-21 05:26:04,876 epoch 14 - iter 35/79 - loss 0.61690913 - time (sec): 8.99 - samples/sec: 4648.83 - lr: 0.025000\n","2023-04-21 05:26:06,582 epoch 14 - iter 42/79 - loss 0.62099353 - time (sec): 10.69 - samples/sec: 4605.12 - lr: 0.025000\n","2023-04-21 05:26:08,645 epoch 14 - iter 49/79 - loss 0.62502090 - time (sec): 12.75 - samples/sec: 4538.46 - lr: 0.025000\n","2023-04-21 05:26:10,554 epoch 14 - iter 56/79 - loss 0.63160526 - time (sec): 14.66 - samples/sec: 4492.29 - lr: 0.025000\n","2023-04-21 05:26:12,988 epoch 14 - iter 63/79 - loss 0.62383338 - time (sec): 17.10 - samples/sec: 4388.37 - lr: 0.025000\n","2023-04-21 05:26:15,559 epoch 14 - iter 70/79 - loss 0.62361965 - time (sec): 19.67 - samples/sec: 4304.94 - lr: 0.025000\n","2023-04-21 05:26:17,273 epoch 14 - iter 77/79 - loss 0.62794715 - time (sec): 21.38 - samples/sec: 4307.33 - lr: 0.025000\n","2023-04-21 05:26:17,826 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:26:17,828 EPOCH 14 done: loss 0.6314 - lr 0.025000\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.52it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:26:40,385 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:26:42,242 TRAIN : loss 0.49840062856674194 - f1-score (weighted avg)  0.2223\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.68it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:26:45,123 Evaluating as a multi-label problem: False\n","2023-04-21 05:26:45,189 DEV : loss 0.5119959712028503 - f1-score (weighted avg)  0.225\n","2023-04-21 05:26:45,211 BAD EPOCHS (no improvement): 1\n","2023-04-21 05:26:45,215 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:26:46,989 epoch 15 - iter 7/79 - loss 0.61984090 - time (sec): 1.77 - samples/sec: 4894.92 - lr: 0.025000\n","2023-04-21 05:26:48,703 epoch 15 - iter 14/79 - loss 0.60939250 - time (sec): 3.49 - samples/sec: 4642.51 - lr: 0.025000\n","2023-04-21 05:26:51,066 epoch 15 - iter 21/79 - loss 0.62567335 - time (sec): 5.85 - samples/sec: 4242.20 - lr: 0.025000\n","2023-04-21 05:26:53,156 epoch 15 - iter 28/79 - loss 0.63827805 - time (sec): 7.94 - samples/sec: 4193.50 - lr: 0.025000\n","2023-04-21 05:26:55,013 epoch 15 - iter 35/79 - loss 0.62802480 - time (sec): 9.80 - samples/sec: 4270.86 - lr: 0.025000\n","2023-04-21 05:26:56,737 epoch 15 - iter 42/79 - loss 0.63348364 - time (sec): 11.52 - samples/sec: 4305.47 - lr: 0.025000\n","2023-04-21 05:26:58,363 epoch 15 - iter 49/79 - loss 0.64292031 - time (sec): 13.15 - samples/sec: 4376.56 - lr: 0.025000\n","2023-04-21 05:27:00,302 epoch 15 - iter 56/79 - loss 0.63527147 - time (sec): 15.08 - samples/sec: 4411.41 - lr: 0.025000\n","2023-04-21 05:27:02,182 epoch 15 - iter 63/79 - loss 0.62950349 - time (sec): 16.96 - samples/sec: 4451.18 - lr: 0.025000\n","2023-04-21 05:27:04,117 epoch 15 - iter 70/79 - loss 0.62475478 - time (sec): 18.90 - samples/sec: 4458.85 - lr: 0.025000\n","2023-04-21 05:27:06,224 epoch 15 - iter 77/79 - loss 0.62146121 - time (sec): 21.01 - samples/sec: 4418.85 - lr: 0.025000\n","2023-04-21 05:27:06,566 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:27:06,568 EPOCH 15 done: loss 0.6226 - lr 0.025000\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.74it/s]\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:27:28,996 Evaluating as a multi-label problem: True\n","2023-04-21 05:27:30,843 TRAIN : loss 0.5975511074066162 - f1-score (weighted avg)  0.1414\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.54it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:27:33,825 Evaluating as a multi-label problem: False\n","2023-04-21 05:27:33,891 DEV : loss 0.5914489030838013 - f1-score (weighted avg)  0.1653\n","2023-04-21 05:27:33,912 Epoch    15: reducing learning rate of group 0 to 1.2500e-02.\n","2023-04-21 05:27:33,915 BAD EPOCHS (no improvement): 2\n","2023-04-21 05:27:33,919 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:27:35,565 epoch 16 - iter 7/79 - loss 0.64807341 - time (sec): 1.65 - samples/sec: 4576.37 - lr: 0.012500\n","2023-04-21 05:27:37,531 epoch 16 - iter 14/79 - loss 0.61014143 - time (sec): 3.61 - samples/sec: 4463.12 - lr: 0.012500\n","2023-04-21 05:27:39,176 epoch 16 - iter 21/79 - loss 0.60455465 - time (sec): 5.26 - samples/sec: 4515.42 - lr: 0.012500\n","2023-04-21 05:27:41,345 epoch 16 - iter 28/79 - loss 0.57850927 - time (sec): 7.42 - samples/sec: 4448.62 - lr: 0.012500\n","2023-04-21 05:27:42,921 epoch 16 - iter 35/79 - loss 0.57299148 - time (sec): 9.00 - samples/sec: 4489.55 - lr: 0.012500\n","2023-04-21 05:27:45,256 epoch 16 - iter 42/79 - loss 0.56384816 - time (sec): 11.34 - samples/sec: 4432.69 - lr: 0.012500\n","2023-04-21 05:27:47,153 epoch 16 - iter 49/79 - loss 0.55895305 - time (sec): 13.23 - samples/sec: 4460.32 - lr: 0.012500\n","2023-04-21 05:27:48,712 epoch 16 - iter 56/79 - loss 0.55986617 - time (sec): 14.79 - samples/sec: 4482.95 - lr: 0.012500\n","2023-04-21 05:27:50,412 epoch 16 - iter 63/79 - loss 0.55595991 - time (sec): 16.49 - samples/sec: 4495.81 - lr: 0.012500\n","2023-04-21 05:27:52,628 epoch 16 - iter 70/79 - loss 0.55710263 - time (sec): 18.71 - samples/sec: 4508.74 - lr: 0.012500\n","2023-04-21 05:27:54,374 epoch 16 - iter 77/79 - loss 0.54877753 - time (sec): 20.45 - samples/sec: 4519.13 - lr: 0.012500\n","2023-04-21 05:27:54,814 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:27:54,816 EPOCH 16 done: loss 0.5477 - lr 0.012500\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.75it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:28:16,027 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:28:19,126 TRAIN : loss 0.40990397334098816 - f1-score (weighted avg)  0.2309\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.64it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:28:22,033 Evaluating as a multi-label problem: False\n","2023-04-21 05:28:22,100 DEV : loss 0.43080785870552063 - f1-score (weighted avg)  0.2258\n","2023-04-21 05:28:22,121 BAD EPOCHS (no improvement): 0\n","2023-04-21 05:28:22,126 saving best model\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:28:25,068 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:28:26,930 epoch 17 - iter 7/79 - loss 0.59255025 - time (sec): 1.86 - samples/sec: 4423.09 - lr: 0.012500\n","2023-04-21 05:28:29,383 epoch 17 - iter 14/79 - loss 0.51156616 - time (sec): 4.31 - samples/sec: 4104.29 - lr: 0.012500\n","2023-04-21 05:28:31,367 epoch 17 - iter 21/79 - loss 0.52374695 - time (sec): 6.30 - samples/sec: 4283.90 - lr: 0.012500\n","2023-04-21 05:28:33,142 epoch 17 - iter 28/79 - loss 0.51992887 - time (sec): 8.07 - samples/sec: 4325.03 - lr: 0.012500\n","2023-04-21 05:28:34,551 epoch 17 - iter 35/79 - loss 0.51712183 - time (sec): 9.48 - samples/sec: 4386.09 - lr: 0.012500\n","2023-04-21 05:28:36,452 epoch 17 - iter 42/79 - loss 0.51773106 - time (sec): 11.38 - samples/sec: 4397.40 - lr: 0.012500\n","2023-04-21 05:28:38,232 epoch 17 - iter 49/79 - loss 0.52147031 - time (sec): 13.16 - samples/sec: 4390.29 - lr: 0.012500\n","2023-04-21 05:28:40,058 epoch 17 - iter 56/79 - loss 0.51255785 - time (sec): 14.99 - samples/sec: 4415.72 - lr: 0.012500\n","2023-04-21 05:28:42,359 epoch 17 - iter 63/79 - loss 0.50643802 - time (sec): 17.29 - samples/sec: 4316.01 - lr: 0.012500\n","2023-04-21 05:28:44,082 epoch 17 - iter 70/79 - loss 0.50230195 - time (sec): 19.01 - samples/sec: 4377.12 - lr: 0.012500\n","2023-04-21 05:28:46,057 epoch 17 - iter 77/79 - loss 0.50251051 - time (sec): 20.99 - samples/sec: 4401.04 - lr: 0.012500\n","2023-04-21 05:28:46,543 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:28:46,545 EPOCH 17 done: loss 0.5008 - lr 0.012500\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.75it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:29:07,751 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:29:09,596 TRAIN : loss 0.40654850006103516 - f1-score (weighted avg)  0.1722\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.58it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:29:12,547 Evaluating as a multi-label problem: False\n","2023-04-21 05:29:12,613 DEV : loss 0.42092251777648926 - f1-score (weighted avg)  0.2057\n","2023-04-21 05:29:12,635 BAD EPOCHS (no improvement): 0\n","2023-04-21 05:29:12,639 saving best model\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:29:15,565 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:29:17,388 epoch 18 - iter 7/79 - loss 0.47870009 - time (sec): 1.80 - samples/sec: 4871.38 - lr: 0.012500\n","2023-04-21 05:29:19,145 epoch 18 - iter 14/79 - loss 0.46685188 - time (sec): 3.56 - samples/sec: 4700.13 - lr: 0.012500\n","2023-04-21 05:29:21,064 epoch 18 - iter 21/79 - loss 0.47307359 - time (sec): 5.48 - samples/sec: 4791.39 - lr: 0.012500\n","2023-04-21 05:29:22,682 epoch 18 - iter 28/79 - loss 0.46887028 - time (sec): 7.10 - samples/sec: 4789.98 - lr: 0.012500\n","2023-04-21 05:29:24,901 epoch 18 - iter 35/79 - loss 0.46369883 - time (sec): 9.31 - samples/sec: 4585.94 - lr: 0.012500\n","2023-04-21 05:29:26,738 epoch 18 - iter 42/79 - loss 0.46846214 - time (sec): 11.15 - samples/sec: 4635.09 - lr: 0.012500\n","2023-04-21 05:29:28,552 epoch 18 - iter 49/79 - loss 0.47315078 - time (sec): 12.96 - samples/sec: 4642.12 - lr: 0.012500\n","2023-04-21 05:29:30,904 epoch 18 - iter 56/79 - loss 0.47271310 - time (sec): 15.32 - samples/sec: 4444.75 - lr: 0.012500\n","2023-04-21 05:29:32,851 epoch 18 - iter 63/79 - loss 0.47307463 - time (sec): 17.26 - samples/sec: 4409.08 - lr: 0.012500\n","2023-04-21 05:29:34,537 epoch 18 - iter 70/79 - loss 0.47031650 - time (sec): 18.95 - samples/sec: 4440.89 - lr: 0.012500\n","2023-04-21 05:29:36,271 epoch 18 - iter 77/79 - loss 0.47043776 - time (sec): 20.68 - samples/sec: 4497.14 - lr: 0.012500\n","2023-04-21 05:29:36,626 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:29:36,628 EPOCH 18 done: loss 0.4710 - lr 0.012500\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.56it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:29:58,969 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:30:00,804 TRAIN : loss 0.43732407689094543 - f1-score (weighted avg)  0.1481\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.58it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:30:03,760 Evaluating as a multi-label problem: False\n","2023-04-21 05:30:03,823 DEV : loss 0.4381190538406372 - f1-score (weighted avg)  0.1616\n","2023-04-21 05:30:03,844 BAD EPOCHS (no improvement): 1\n","2023-04-21 05:30:03,849 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:30:06,017 epoch 19 - iter 7/79 - loss 0.45242602 - time (sec): 2.17 - samples/sec: 3913.29 - lr: 0.012500\n","2023-04-21 05:30:07,620 epoch 19 - iter 14/79 - loss 0.45636010 - time (sec): 3.77 - samples/sec: 4075.68 - lr: 0.012500\n","2023-04-21 05:30:09,549 epoch 19 - iter 21/79 - loss 0.45513048 - time (sec): 5.70 - samples/sec: 4193.58 - lr: 0.012500\n","2023-04-21 05:30:11,906 epoch 19 - iter 28/79 - loss 0.46324005 - time (sec): 8.06 - samples/sec: 4167.25 - lr: 0.012500\n","2023-04-21 05:30:13,811 epoch 19 - iter 35/79 - loss 0.46121822 - time (sec): 9.96 - samples/sec: 4225.39 - lr: 0.012500\n","2023-04-21 05:30:15,507 epoch 19 - iter 42/79 - loss 0.47056795 - time (sec): 11.66 - samples/sec: 4313.29 - lr: 0.012500\n","2023-04-21 05:30:17,276 epoch 19 - iter 49/79 - loss 0.47294715 - time (sec): 13.42 - samples/sec: 4406.99 - lr: 0.012500\n","2023-04-21 05:30:19,176 epoch 19 - iter 56/79 - loss 0.47252392 - time (sec): 15.33 - samples/sec: 4409.58 - lr: 0.012500\n","2023-04-21 05:30:20,952 epoch 19 - iter 63/79 - loss 0.46414238 - time (sec): 17.10 - samples/sec: 4454.50 - lr: 0.012500\n","2023-04-21 05:30:22,651 epoch 19 - iter 70/79 - loss 0.46352570 - time (sec): 18.80 - samples/sec: 4503.91 - lr: 0.012500\n","2023-04-21 05:30:24,240 epoch 19 - iter 77/79 - loss 0.46293454 - time (sec): 20.39 - samples/sec: 4507.56 - lr: 0.012500\n","2023-04-21 05:30:24,809 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:30:24,810 EPOCH 19 done: loss 0.4641 - lr 0.012500\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.56it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:30:47,128 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:30:48,971 TRAIN : loss 0.3687305450439453 - f1-score (weighted avg)  0.2011\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.66it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:30:51,867 Evaluating as a multi-label problem: False\n","2023-04-21 05:30:51,931 DEV : loss 0.3805737793445587 - f1-score (weighted avg)  0.2276\n","2023-04-21 05:30:51,960 BAD EPOCHS (no improvement): 0\n","2023-04-21 05:30:51,964 saving best model\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:30:54,896 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:30:56,885 epoch 20 - iter 7/79 - loss 0.39989954 - time (sec): 1.99 - samples/sec: 4474.18 - lr: 0.012500\n","2023-04-21 05:30:58,825 epoch 20 - iter 14/79 - loss 0.44040302 - time (sec): 3.93 - samples/sec: 4299.54 - lr: 0.012500\n","2023-04-21 05:31:00,454 epoch 20 - iter 21/79 - loss 0.44862633 - time (sec): 5.56 - samples/sec: 4453.01 - lr: 0.012500\n","2023-04-21 05:31:02,379 epoch 20 - iter 28/79 - loss 0.45766073 - time (sec): 7.48 - samples/sec: 4538.31 - lr: 0.012500\n","2023-04-21 05:31:04,112 epoch 20 - iter 35/79 - loss 0.47120497 - time (sec): 9.21 - samples/sec: 4427.25 - lr: 0.012500\n","2023-04-21 05:31:05,866 epoch 20 - iter 42/79 - loss 0.47541959 - time (sec): 10.97 - samples/sec: 4554.37 - lr: 0.012500\n","2023-04-21 05:31:08,192 epoch 20 - iter 49/79 - loss 0.46482815 - time (sec): 13.29 - samples/sec: 4495.82 - lr: 0.012500\n","2023-04-21 05:31:09,726 epoch 20 - iter 56/79 - loss 0.46597098 - time (sec): 14.83 - samples/sec: 4572.44 - lr: 0.012500\n","2023-04-21 05:31:11,484 epoch 20 - iter 63/79 - loss 0.46705778 - time (sec): 16.59 - samples/sec: 4588.31 - lr: 0.012500\n","2023-04-21 05:31:13,135 epoch 20 - iter 70/79 - loss 0.46792778 - time (sec): 18.24 - samples/sec: 4629.25 - lr: 0.012500\n","2023-04-21 05:31:15,481 epoch 20 - iter 77/79 - loss 0.46498853 - time (sec): 20.58 - samples/sec: 4517.28 - lr: 0.012500\n","2023-04-21 05:31:15,865 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:31:15,868 EPOCH 20 done: loss 0.4653 - lr 0.012500\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.54it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:31:38,324 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:31:40,163 TRAIN : loss 0.40053603053092957 - f1-score (weighted avg)  0.2287\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.61it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:31:43,092 Evaluating as a multi-label problem: False\n","2023-04-21 05:31:43,159 DEV : loss 0.41170239448547363 - f1-score (weighted avg)  0.2435\n","2023-04-21 05:31:43,181 BAD EPOCHS (no improvement): 1\n","2023-04-21 05:31:43,186 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:31:44,932 epoch 21 - iter 7/79 - loss 0.50114933 - time (sec): 1.74 - samples/sec: 4410.69 - lr: 0.012500\n","2023-04-21 05:31:46,799 epoch 21 - iter 14/79 - loss 0.57405544 - time (sec): 3.61 - samples/sec: 4461.09 - lr: 0.012500\n","2023-04-21 05:31:48,463 epoch 21 - iter 21/79 - loss 0.53533967 - time (sec): 5.28 - samples/sec: 4521.57 - lr: 0.012500\n","2023-04-21 05:31:50,182 epoch 21 - iter 28/79 - loss 0.52038476 - time (sec): 6.99 - samples/sec: 4616.83 - lr: 0.012500\n","2023-04-21 05:31:51,856 epoch 21 - iter 35/79 - loss 0.50536774 - time (sec): 8.67 - samples/sec: 4687.17 - lr: 0.012500\n","2023-04-21 05:31:53,619 epoch 21 - iter 42/79 - loss 0.50692586 - time (sec): 10.43 - samples/sec: 4669.70 - lr: 0.012500\n","2023-04-21 05:31:55,569 epoch 21 - iter 49/79 - loss 0.50068694 - time (sec): 12.38 - samples/sec: 4554.55 - lr: 0.012500\n","2023-04-21 05:31:58,177 epoch 21 - iter 56/79 - loss 0.49095675 - time (sec): 14.99 - samples/sec: 4438.44 - lr: 0.012500\n","2023-04-21 05:32:00,116 epoch 21 - iter 63/79 - loss 0.48628155 - time (sec): 16.93 - samples/sec: 4434.22 - lr: 0.012500\n","2023-04-21 05:32:01,818 epoch 21 - iter 70/79 - loss 0.47990005 - time (sec): 18.63 - samples/sec: 4478.69 - lr: 0.012500\n","2023-04-21 05:32:03,651 epoch 21 - iter 77/79 - loss 0.47878583 - time (sec): 20.46 - samples/sec: 4504.25 - lr: 0.012500\n","2023-04-21 05:32:04,154 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:32:04,156 EPOCH 21 done: loss 0.4775 - lr 0.012500\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.73it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:32:25,465 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:32:28,536 TRAIN : loss 0.3807082176208496 - f1-score (weighted avg)  0.1474\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.67it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:32:31,420 Evaluating as a multi-label problem: False\n","2023-04-21 05:32:31,485 DEV : loss 0.38495415449142456 - f1-score (weighted avg)  0.1524\n","2023-04-21 05:32:31,507 Epoch    21: reducing learning rate of group 0 to 6.2500e-03.\n","2023-04-21 05:32:31,509 BAD EPOCHS (no improvement): 2\n","2023-04-21 05:32:31,513 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:32:33,329 epoch 22 - iter 7/79 - loss 0.44565608 - time (sec): 1.81 - samples/sec: 4866.92 - lr: 0.006250\n","2023-04-21 05:32:35,102 epoch 22 - iter 14/79 - loss 0.44303723 - time (sec): 3.59 - samples/sec: 4565.17 - lr: 0.006250\n","2023-04-21 05:32:36,938 epoch 22 - iter 21/79 - loss 0.43107658 - time (sec): 5.42 - samples/sec: 4458.30 - lr: 0.006250\n","2023-04-21 05:32:39,384 epoch 22 - iter 28/79 - loss 0.42100863 - time (sec): 7.87 - samples/sec: 4285.90 - lr: 0.006250\n","2023-04-21 05:32:41,153 epoch 22 - iter 35/79 - loss 0.42123893 - time (sec): 9.64 - samples/sec: 4427.52 - lr: 0.006250\n","2023-04-21 05:32:43,026 epoch 22 - iter 42/79 - loss 0.42558063 - time (sec): 11.51 - samples/sec: 4443.15 - lr: 0.006250\n","2023-04-21 05:32:44,650 epoch 22 - iter 49/79 - loss 0.42555263 - time (sec): 13.14 - samples/sec: 4555.29 - lr: 0.006250\n","2023-04-21 05:32:46,386 epoch 22 - iter 56/79 - loss 0.42412129 - time (sec): 14.87 - samples/sec: 4551.23 - lr: 0.006250\n","2023-04-21 05:32:48,032 epoch 22 - iter 63/79 - loss 0.42529477 - time (sec): 16.52 - samples/sec: 4590.86 - lr: 0.006250\n","2023-04-21 05:32:49,832 epoch 22 - iter 70/79 - loss 0.42248160 - time (sec): 18.32 - samples/sec: 4596.69 - lr: 0.006250\n","2023-04-21 05:32:51,805 epoch 22 - iter 77/79 - loss 0.41940787 - time (sec): 20.29 - samples/sec: 4552.73 - lr: 0.006250\n","2023-04-21 05:32:52,451 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:32:52,452 EPOCH 22 done: loss 0.4188 - lr 0.006250\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.75it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:33:13,664 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:33:15,495 TRAIN : loss 0.35017064213752747 - f1-score (weighted avg)  0.2256\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:03<00:00,  2.56it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:33:19,560 Evaluating as a multi-label problem: False\n","2023-04-21 05:33:19,627 DEV : loss 0.35753852128982544 - f1-score (weighted avg)  0.2281\n","2023-04-21 05:33:19,649 BAD EPOCHS (no improvement): 0\n","2023-04-21 05:33:19,653 saving best model\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:33:22,583 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:33:24,765 epoch 23 - iter 7/79 - loss 0.37519489 - time (sec): 2.17 - samples/sec: 4038.10 - lr: 0.006250\n","2023-04-21 05:33:26,455 epoch 23 - iter 14/79 - loss 0.40488178 - time (sec): 3.86 - samples/sec: 4510.62 - lr: 0.006250\n","2023-04-21 05:33:28,274 epoch 23 - iter 21/79 - loss 0.41412172 - time (sec): 5.67 - samples/sec: 4576.60 - lr: 0.006250\n","2023-04-21 05:33:30,312 epoch 23 - iter 28/79 - loss 0.41209914 - time (sec): 7.71 - samples/sec: 4464.75 - lr: 0.006250\n","2023-04-21 05:33:32,374 epoch 23 - iter 35/79 - loss 0.41238680 - time (sec): 9.77 - samples/sec: 4525.93 - lr: 0.006250\n","2023-04-21 05:33:33,967 epoch 23 - iter 42/79 - loss 0.41091788 - time (sec): 11.37 - samples/sec: 4565.04 - lr: 0.006250\n","2023-04-21 05:33:36,509 epoch 23 - iter 49/79 - loss 0.40891901 - time (sec): 13.91 - samples/sec: 4360.09 - lr: 0.006250\n","2023-04-21 05:33:38,191 epoch 23 - iter 56/79 - loss 0.40796020 - time (sec): 15.59 - samples/sec: 4374.89 - lr: 0.006250\n","2023-04-21 05:33:40,028 epoch 23 - iter 63/79 - loss 0.40755387 - time (sec): 17.43 - samples/sec: 4399.85 - lr: 0.006250\n","2023-04-21 05:33:41,887 epoch 23 - iter 70/79 - loss 0.40781825 - time (sec): 19.29 - samples/sec: 4359.47 - lr: 0.006250\n","2023-04-21 05:33:43,753 epoch 23 - iter 77/79 - loss 0.40263263 - time (sec): 21.15 - samples/sec: 4365.90 - lr: 0.006250\n","2023-04-21 05:33:44,184 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:33:44,185 EPOCH 23 done: loss 0.4037 - lr 0.006250\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.73it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:34:05,494 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:34:07,346 TRAIN : loss 0.34194865822792053 - f1-score (weighted avg)  0.2472\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.62it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:34:10,265 Evaluating as a multi-label problem: False\n","2023-04-21 05:34:10,330 DEV : loss 0.34747615456581116 - f1-score (weighted avg)  0.2655\n","2023-04-21 05:34:10,352 BAD EPOCHS (no improvement): 0\n","2023-04-21 05:34:10,356 saving best model\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:34:13,302 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:34:15,246 epoch 24 - iter 7/79 - loss 0.38179558 - time (sec): 1.94 - samples/sec: 4711.91 - lr: 0.006250\n","2023-04-21 05:34:16,745 epoch 24 - iter 14/79 - loss 0.39448143 - time (sec): 3.44 - samples/sec: 4728.45 - lr: 0.006250\n","2023-04-21 05:34:19,096 epoch 24 - iter 21/79 - loss 0.39474737 - time (sec): 5.79 - samples/sec: 4362.32 - lr: 0.006250\n","2023-04-21 05:34:21,295 epoch 24 - iter 28/79 - loss 0.38898637 - time (sec): 7.99 - samples/sec: 4235.68 - lr: 0.006250\n","2023-04-21 05:34:23,262 epoch 24 - iter 35/79 - loss 0.39147660 - time (sec): 9.96 - samples/sec: 4267.70 - lr: 0.006250\n","2023-04-21 05:34:25,817 epoch 24 - iter 42/79 - loss 0.39549009 - time (sec): 12.51 - samples/sec: 4130.01 - lr: 0.006250\n","2023-04-21 05:34:27,747 epoch 24 - iter 49/79 - loss 0.40065217 - time (sec): 14.44 - samples/sec: 4136.53 - lr: 0.006250\n","2023-04-21 05:34:29,430 epoch 24 - iter 56/79 - loss 0.40286900 - time (sec): 16.13 - samples/sec: 4183.58 - lr: 0.006250\n","2023-04-21 05:34:31,338 epoch 24 - iter 63/79 - loss 0.40112171 - time (sec): 18.03 - samples/sec: 4226.59 - lr: 0.006250\n","2023-04-21 05:34:32,866 epoch 24 - iter 70/79 - loss 0.40123307 - time (sec): 19.56 - samples/sec: 4264.22 - lr: 0.006250\n","2023-04-21 05:34:34,717 epoch 24 - iter 77/79 - loss 0.39946830 - time (sec): 21.41 - samples/sec: 4301.68 - lr: 0.006250\n","2023-04-21 05:34:35,230 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:34:35,232 EPOCH 24 done: loss 0.3988 - lr 0.006250\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.56it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:34:57,557 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:34:59,412 TRAIN : loss 0.3418453633785248 - f1-score (weighted avg)  0.2216\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.66it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:35:02,299 Evaluating as a multi-label problem: False\n","2023-04-21 05:35:02,365 DEV : loss 0.3492474853992462 - f1-score (weighted avg)  0.2369\n","2023-04-21 05:35:02,386 BAD EPOCHS (no improvement): 1\n","2023-04-21 05:35:02,391 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:35:04,025 epoch 25 - iter 7/79 - loss 0.36213255 - time (sec): 1.63 - samples/sec: 5049.49 - lr: 0.006250\n","2023-04-21 05:35:05,613 epoch 25 - iter 14/79 - loss 0.39024732 - time (sec): 3.22 - samples/sec: 5027.19 - lr: 0.006250\n","2023-04-21 05:35:07,321 epoch 25 - iter 21/79 - loss 0.39011601 - time (sec): 4.93 - samples/sec: 5105.90 - lr: 0.006250\n","2023-04-21 05:35:09,214 epoch 25 - iter 28/79 - loss 0.39043171 - time (sec): 6.82 - samples/sec: 4966.23 - lr: 0.006250\n","2023-04-21 05:35:11,174 epoch 25 - iter 35/79 - loss 0.39764105 - time (sec): 8.78 - samples/sec: 4834.83 - lr: 0.006250\n","2023-04-21 05:35:13,428 epoch 25 - iter 42/79 - loss 0.39842592 - time (sec): 11.04 - samples/sec: 4699.84 - lr: 0.006250\n","2023-04-21 05:35:15,324 epoch 25 - iter 49/79 - loss 0.40064364 - time (sec): 12.93 - samples/sec: 4706.19 - lr: 0.006250\n","2023-04-21 05:35:17,174 epoch 25 - iter 56/79 - loss 0.40446375 - time (sec): 14.78 - samples/sec: 4660.93 - lr: 0.006250\n","2023-04-21 05:35:19,270 epoch 25 - iter 63/79 - loss 0.40350317 - time (sec): 16.88 - samples/sec: 4584.08 - lr: 0.006250\n","2023-04-21 05:35:21,529 epoch 25 - iter 70/79 - loss 0.40089998 - time (sec): 19.14 - samples/sec: 4461.59 - lr: 0.006250\n","2023-04-21 05:35:23,099 epoch 25 - iter 77/79 - loss 0.39888462 - time (sec): 20.71 - samples/sec: 4474.73 - lr: 0.006250\n","2023-04-21 05:35:23,535 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:35:23,536 EPOCH 25 done: loss 0.3987 - lr 0.006250\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.55it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:35:45,927 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:35:47,803 TRAIN : loss 0.3436269760131836 - f1-score (weighted avg)  0.2589\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.66it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:35:50,695 Evaluating as a multi-label problem: False\n","2023-04-21 05:35:50,763 DEV : loss 0.3511848747730255 - f1-score (weighted avg)  0.2711\n","2023-04-21 05:35:50,785 Epoch    25: reducing learning rate of group 0 to 3.1250e-03.\n","2023-04-21 05:35:50,788 BAD EPOCHS (no improvement): 2\n","2023-04-21 05:35:50,792 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:35:52,588 epoch 26 - iter 7/79 - loss 0.39955381 - time (sec): 1.79 - samples/sec: 4560.11 - lr: 0.003125\n","2023-04-21 05:35:54,183 epoch 26 - iter 14/79 - loss 0.40368031 - time (sec): 3.39 - samples/sec: 4642.55 - lr: 0.003125\n","2023-04-21 05:35:55,955 epoch 26 - iter 21/79 - loss 0.39854019 - time (sec): 5.16 - samples/sec: 4607.51 - lr: 0.003125\n","2023-04-21 05:35:57,908 epoch 26 - iter 28/79 - loss 0.39480389 - time (sec): 7.11 - samples/sec: 4641.07 - lr: 0.003125\n","2023-04-21 05:35:59,645 epoch 26 - iter 35/79 - loss 0.38627007 - time (sec): 8.85 - samples/sec: 4692.99 - lr: 0.003125\n","2023-04-21 05:36:01,434 epoch 26 - iter 42/79 - loss 0.38749486 - time (sec): 10.64 - samples/sec: 4695.95 - lr: 0.003125\n","2023-04-21 05:36:03,563 epoch 26 - iter 49/79 - loss 0.38542289 - time (sec): 12.77 - samples/sec: 4593.37 - lr: 0.003125\n","2023-04-21 05:36:05,968 epoch 26 - iter 56/79 - loss 0.38165247 - time (sec): 15.18 - samples/sec: 4498.90 - lr: 0.003125\n","2023-04-21 05:36:07,803 epoch 26 - iter 63/79 - loss 0.38056603 - time (sec): 17.01 - samples/sec: 4548.78 - lr: 0.003125\n","2023-04-21 05:36:09,509 epoch 26 - iter 70/79 - loss 0.38102146 - time (sec): 18.72 - samples/sec: 4574.32 - lr: 0.003125\n","2023-04-21 05:36:11,077 epoch 26 - iter 77/79 - loss 0.38517412 - time (sec): 20.28 - samples/sec: 4560.68 - lr: 0.003125\n","2023-04-21 05:36:11,569 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:36:11,570 EPOCH 26 done: loss 0.3837 - lr 0.003125\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.74it/s]\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:36:34,010 Evaluating as a multi-label problem: True\n","2023-04-21 05:36:35,860 TRAIN : loss 0.3476867377758026 - f1-score (weighted avg)  0.1995\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.66it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:36:38,749 Evaluating as a multi-label problem: False\n","2023-04-21 05:36:38,813 DEV : loss 0.35025516152381897 - f1-score (weighted avg)  0.2196\n","2023-04-21 05:36:38,837 BAD EPOCHS (no improvement): 1\n","2023-04-21 05:36:38,841 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:36:40,534 epoch 27 - iter 7/79 - loss 0.38639028 - time (sec): 1.69 - samples/sec: 4574.69 - lr: 0.003125\n","2023-04-21 05:36:41,928 epoch 27 - iter 14/79 - loss 0.41044619 - time (sec): 3.09 - samples/sec: 4865.86 - lr: 0.003125\n","2023-04-21 05:36:44,453 epoch 27 - iter 21/79 - loss 0.38869275 - time (sec): 5.61 - samples/sec: 4369.34 - lr: 0.003125\n","2023-04-21 05:36:46,770 epoch 27 - iter 28/79 - loss 0.38035072 - time (sec): 7.93 - samples/sec: 4119.82 - lr: 0.003125\n","2023-04-21 05:36:48,876 epoch 27 - iter 35/79 - loss 0.37390077 - time (sec): 10.03 - samples/sec: 4126.09 - lr: 0.003125\n","2023-04-21 05:36:50,450 epoch 27 - iter 42/79 - loss 0.37501743 - time (sec): 11.61 - samples/sec: 4199.50 - lr: 0.003125\n","2023-04-21 05:36:52,137 epoch 27 - iter 49/79 - loss 0.37942034 - time (sec): 13.29 - samples/sec: 4276.37 - lr: 0.003125\n","2023-04-21 05:36:53,949 epoch 27 - iter 56/79 - loss 0.37830486 - time (sec): 15.11 - samples/sec: 4321.02 - lr: 0.003125\n","2023-04-21 05:36:55,836 epoch 27 - iter 63/79 - loss 0.37767807 - time (sec): 16.99 - samples/sec: 4355.93 - lr: 0.003125\n","2023-04-21 05:36:57,440 epoch 27 - iter 70/79 - loss 0.37529765 - time (sec): 18.60 - samples/sec: 4416.11 - lr: 0.003125\n","2023-04-21 05:36:59,453 epoch 27 - iter 77/79 - loss 0.37646221 - time (sec): 20.61 - samples/sec: 4458.63 - lr: 0.003125\n","2023-04-21 05:37:00,090 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:37:00,091 EPOCH 27 done: loss 0.3764 - lr 0.003125\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.74it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:37:21,363 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:37:24,468 TRAIN : loss 0.3320097327232361 - f1-score (weighted avg)  0.2437\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.68it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:37:27,348 Evaluating as a multi-label problem: False\n","2023-04-21 05:37:27,416 DEV : loss 0.33983367681503296 - f1-score (weighted avg)  0.2563\n","2023-04-21 05:37:27,440 BAD EPOCHS (no improvement): 0\n","2023-04-21 05:37:27,445 saving best model\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:37:30,391 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:37:32,294 epoch 28 - iter 7/79 - loss 0.37859485 - time (sec): 1.90 - samples/sec: 4597.47 - lr: 0.003125\n","2023-04-21 05:37:34,427 epoch 28 - iter 14/79 - loss 0.38221384 - time (sec): 4.03 - samples/sec: 4298.52 - lr: 0.003125\n","2023-04-21 05:37:36,256 epoch 28 - iter 21/79 - loss 0.37148427 - time (sec): 5.86 - samples/sec: 4517.40 - lr: 0.003125\n","2023-04-21 05:37:38,094 epoch 28 - iter 28/79 - loss 0.36738350 - time (sec): 7.70 - samples/sec: 4474.29 - lr: 0.003125\n","2023-04-21 05:37:39,804 epoch 28 - iter 35/79 - loss 0.37117714 - time (sec): 9.41 - samples/sec: 4488.75 - lr: 0.003125\n","2023-04-21 05:37:41,685 epoch 28 - iter 42/79 - loss 0.37285772 - time (sec): 11.29 - samples/sec: 4479.65 - lr: 0.003125\n","2023-04-21 05:37:43,777 epoch 28 - iter 49/79 - loss 0.37270913 - time (sec): 13.38 - samples/sec: 4372.62 - lr: 0.003125\n","2023-04-21 05:37:45,515 epoch 28 - iter 56/79 - loss 0.37208265 - time (sec): 15.12 - samples/sec: 4423.09 - lr: 0.003125\n","2023-04-21 05:37:47,666 epoch 28 - iter 63/79 - loss 0.37105264 - time (sec): 17.27 - samples/sec: 4413.89 - lr: 0.003125\n","2023-04-21 05:37:49,805 epoch 28 - iter 70/79 - loss 0.37210097 - time (sec): 19.41 - samples/sec: 4341.97 - lr: 0.003125\n","2023-04-21 05:37:51,400 epoch 28 - iter 77/79 - loss 0.37275004 - time (sec): 21.01 - samples/sec: 4374.07 - lr: 0.003125\n","2023-04-21 05:37:51,961 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:37:51,963 EPOCH 28 done: loss 0.3722 - lr 0.003125\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.70it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:38:13,463 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:38:15,306 TRAIN : loss 0.32850584387779236 - f1-score (weighted avg)  0.2578\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.65it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:38:18,205 Evaluating as a multi-label problem: False\n","2023-04-21 05:38:18,273 DEV : loss 0.3366791605949402 - f1-score (weighted avg)  0.2706\n","2023-04-21 05:38:18,294 BAD EPOCHS (no improvement): 0\n","2023-04-21 05:38:18,299 saving best model\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:38:21,230 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:38:22,811 epoch 29 - iter 7/79 - loss 0.38129721 - time (sec): 1.58 - samples/sec: 4519.14 - lr: 0.003125\n","2023-04-21 05:38:24,965 epoch 29 - iter 14/79 - loss 0.37024362 - time (sec): 3.73 - samples/sec: 4475.93 - lr: 0.003125\n","2023-04-21 05:38:26,670 epoch 29 - iter 21/79 - loss 0.36893494 - time (sec): 5.44 - samples/sec: 4558.59 - lr: 0.003125\n","2023-04-21 05:38:28,340 epoch 29 - iter 28/79 - loss 0.37310726 - time (sec): 7.11 - samples/sec: 4580.41 - lr: 0.003125\n","2023-04-21 05:38:30,533 epoch 29 - iter 35/79 - loss 0.37628842 - time (sec): 9.30 - samples/sec: 4421.61 - lr: 0.003125\n","2023-04-21 05:38:32,204 epoch 29 - iter 42/79 - loss 0.37363726 - time (sec): 10.97 - samples/sec: 4557.12 - lr: 0.003125\n","2023-04-21 05:38:34,094 epoch 29 - iter 49/79 - loss 0.37447548 - time (sec): 12.86 - samples/sec: 4539.45 - lr: 0.003125\n","2023-04-21 05:38:36,073 epoch 29 - iter 56/79 - loss 0.37594666 - time (sec): 14.84 - samples/sec: 4513.16 - lr: 0.003125\n","2023-04-21 05:38:37,704 epoch 29 - iter 63/79 - loss 0.37630602 - time (sec): 16.47 - samples/sec: 4525.13 - lr: 0.003125\n","2023-04-21 05:38:40,320 epoch 29 - iter 70/79 - loss 0.37471670 - time (sec): 19.09 - samples/sec: 4424.20 - lr: 0.003125\n","2023-04-21 05:38:42,192 epoch 29 - iter 77/79 - loss 0.37295951 - time (sec): 20.96 - samples/sec: 4429.08 - lr: 0.003125\n","2023-04-21 05:38:42,611 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:38:42,613 EPOCH 29 done: loss 0.3736 - lr 0.003125\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.55it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:39:04,990 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:39:06,868 TRAIN : loss 0.327297568321228 - f1-score (weighted avg)  0.2874\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.67it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:39:09,753 Evaluating as a multi-label problem: False\n","2023-04-21 05:39:09,820 DEV : loss 0.3374345600605011 - f1-score (weighted avg)  0.2982\n","2023-04-21 05:39:09,844 BAD EPOCHS (no improvement): 1\n","2023-04-21 05:39:09,856 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:39:11,532 epoch 30 - iter 7/79 - loss 0.38587826 - time (sec): 1.68 - samples/sec: 5032.98 - lr: 0.003125\n","2023-04-21 05:39:14,066 epoch 30 - iter 14/79 - loss 0.37589586 - time (sec): 4.21 - samples/sec: 4107.73 - lr: 0.003125\n","2023-04-21 05:39:15,872 epoch 30 - iter 21/79 - loss 0.37522867 - time (sec): 6.01 - samples/sec: 4282.72 - lr: 0.003125\n","2023-04-21 05:39:17,295 epoch 30 - iter 28/79 - loss 0.37578077 - time (sec): 7.44 - samples/sec: 4473.76 - lr: 0.003125\n","2023-04-21 05:39:19,064 epoch 30 - iter 35/79 - loss 0.37834829 - time (sec): 9.21 - samples/sec: 4564.06 - lr: 0.003125\n","2023-04-21 05:39:20,784 epoch 30 - iter 42/79 - loss 0.37353169 - time (sec): 10.93 - samples/sec: 4627.13 - lr: 0.003125\n","2023-04-21 05:39:23,027 epoch 30 - iter 49/79 - loss 0.37312536 - time (sec): 13.17 - samples/sec: 4530.12 - lr: 0.003125\n","2023-04-21 05:39:24,732 epoch 30 - iter 56/79 - loss 0.36998676 - time (sec): 14.88 - samples/sec: 4538.62 - lr: 0.003125\n","2023-04-21 05:39:26,676 epoch 30 - iter 63/79 - loss 0.36939841 - time (sec): 16.82 - samples/sec: 4530.07 - lr: 0.003125\n","2023-04-21 05:39:28,469 epoch 30 - iter 70/79 - loss 0.36848323 - time (sec): 18.61 - samples/sec: 4536.90 - lr: 0.003125\n","2023-04-21 05:39:30,175 epoch 30 - iter 77/79 - loss 0.36982318 - time (sec): 20.32 - samples/sec: 4531.77 - lr: 0.003125\n","2023-04-21 05:39:30,734 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:39:30,735 EPOCH 30 done: loss 0.3714 - lr 0.003125\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.52it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:39:53,327 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:39:55,167 TRAIN : loss 0.3293810486793518 - f1-score (weighted avg)  0.2614\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.61it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:39:58,099 Evaluating as a multi-label problem: False\n","2023-04-21 05:39:58,167 DEV : loss 0.3348151743412018 - f1-score (weighted avg)  0.2741\n","2023-04-21 05:39:58,191 BAD EPOCHS (no improvement): 0\n","2023-04-21 05:39:58,196 saving best model\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:40:01,134 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:40:03,018 epoch 31 - iter 7/79 - loss 0.34811856 - time (sec): 1.88 - samples/sec: 4264.37 - lr: 0.003125\n","2023-04-21 05:40:06,003 epoch 31 - iter 14/79 - loss 0.33766115 - time (sec): 4.87 - samples/sec: 3841.98 - lr: 0.003125\n","2023-04-21 05:40:07,879 epoch 31 - iter 21/79 - loss 0.34929164 - time (sec): 6.74 - samples/sec: 4022.65 - lr: 0.003125\n","2023-04-21 05:40:09,709 epoch 31 - iter 28/79 - loss 0.35162236 - time (sec): 8.57 - samples/sec: 4163.81 - lr: 0.003125\n","2023-04-21 05:40:11,097 epoch 31 - iter 35/79 - loss 0.35859202 - time (sec): 9.96 - samples/sec: 4241.33 - lr: 0.003125\n","2023-04-21 05:40:12,865 epoch 31 - iter 42/79 - loss 0.36673303 - time (sec): 11.73 - samples/sec: 4306.46 - lr: 0.003125\n","2023-04-21 05:40:14,546 epoch 31 - iter 49/79 - loss 0.36496618 - time (sec): 13.41 - samples/sec: 4340.11 - lr: 0.003125\n","2023-04-21 05:40:16,360 epoch 31 - iter 56/79 - loss 0.36640262 - time (sec): 15.22 - samples/sec: 4391.18 - lr: 0.003125\n","2023-04-21 05:40:18,177 epoch 31 - iter 63/79 - loss 0.36585122 - time (sec): 17.04 - samples/sec: 4389.84 - lr: 0.003125\n","2023-04-21 05:40:19,877 epoch 31 - iter 70/79 - loss 0.36755721 - time (sec): 18.74 - samples/sec: 4426.31 - lr: 0.003125\n","2023-04-21 05:40:21,698 epoch 31 - iter 77/79 - loss 0.36747138 - time (sec): 20.56 - samples/sec: 4455.45 - lr: 0.003125\n","2023-04-21 05:40:22,456 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:40:22,458 EPOCH 31 done: loss 0.3681 - lr 0.003125\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.54it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:40:44,933 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:40:46,778 TRAIN : loss 0.3259187638759613 - f1-score (weighted avg)  0.2606\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.69it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:40:49,652 Evaluating as a multi-label problem: False\n","2023-04-21 05:40:49,717 DEV : loss 0.3350030779838562 - f1-score (weighted avg)  0.2676\n","2023-04-21 05:40:49,740 BAD EPOCHS (no improvement): 1\n","2023-04-21 05:40:49,744 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:40:51,497 epoch 32 - iter 7/79 - loss 0.37327066 - time (sec): 1.75 - samples/sec: 4555.97 - lr: 0.003125\n","2023-04-21 05:40:53,219 epoch 32 - iter 14/79 - loss 0.37545636 - time (sec): 3.47 - samples/sec: 4906.19 - lr: 0.003125\n","2023-04-21 05:40:55,180 epoch 32 - iter 21/79 - loss 0.36924483 - time (sec): 5.43 - samples/sec: 4758.35 - lr: 0.003125\n","2023-04-21 05:40:57,005 epoch 32 - iter 28/79 - loss 0.36705121 - time (sec): 7.26 - samples/sec: 4644.50 - lr: 0.003125\n","2023-04-21 05:40:58,860 epoch 32 - iter 35/79 - loss 0.36557607 - time (sec): 9.11 - samples/sec: 4615.15 - lr: 0.003125\n","2023-04-21 05:41:00,573 epoch 32 - iter 42/79 - loss 0.36846955 - time (sec): 10.83 - samples/sec: 4590.15 - lr: 0.003125\n","2023-04-21 05:41:02,544 epoch 32 - iter 49/79 - loss 0.36743444 - time (sec): 12.80 - samples/sec: 4599.20 - lr: 0.003125\n","2023-04-21 05:41:04,226 epoch 32 - iter 56/79 - loss 0.36773977 - time (sec): 14.48 - samples/sec: 4632.01 - lr: 0.003125\n","2023-04-21 05:41:06,401 epoch 32 - iter 63/79 - loss 0.36798483 - time (sec): 16.66 - samples/sec: 4535.46 - lr: 0.003125\n","2023-04-21 05:41:07,941 epoch 32 - iter 70/79 - loss 0.37073960 - time (sec): 18.20 - samples/sec: 4542.43 - lr: 0.003125\n","2023-04-21 05:41:10,188 epoch 32 - iter 77/79 - loss 0.36909208 - time (sec): 20.44 - samples/sec: 4476.49 - lr: 0.003125\n","2023-04-21 05:41:10,767 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:41:10,769 EPOCH 32 done: loss 0.3684 - lr 0.003125\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.74it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:41:32,044 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:41:35,135 TRAIN : loss 0.32557064294815063 - f1-score (weighted avg)  0.264\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.63it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:41:38,059 Evaluating as a multi-label problem: False\n","2023-04-21 05:41:38,127 DEV : loss 0.3338547348976135 - f1-score (weighted avg)  0.277\n","2023-04-21 05:41:38,148 BAD EPOCHS (no improvement): 0\n","2023-04-21 05:41:38,153 saving best model\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:41:41,082 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:41:42,679 epoch 33 - iter 7/79 - loss 0.37691344 - time (sec): 1.59 - samples/sec: 4366.58 - lr: 0.003125\n","2023-04-21 05:41:44,652 epoch 33 - iter 14/79 - loss 0.35747460 - time (sec): 3.57 - samples/sec: 4158.09 - lr: 0.003125\n","2023-04-21 05:41:46,519 epoch 33 - iter 21/79 - loss 0.35575971 - time (sec): 5.44 - samples/sec: 4422.12 - lr: 0.003125\n","2023-04-21 05:41:48,254 epoch 33 - iter 28/79 - loss 0.36104404 - time (sec): 7.17 - samples/sec: 4443.96 - lr: 0.003125\n","2023-04-21 05:41:50,695 epoch 33 - iter 35/79 - loss 0.35913878 - time (sec): 9.61 - samples/sec: 4286.41 - lr: 0.003125\n","2023-04-21 05:41:52,511 epoch 33 - iter 42/79 - loss 0.36025277 - time (sec): 11.43 - samples/sec: 4395.65 - lr: 0.003125\n","2023-04-21 05:41:54,505 epoch 33 - iter 49/79 - loss 0.36183456 - time (sec): 13.42 - samples/sec: 4378.08 - lr: 0.003125\n","2023-04-21 05:41:56,667 epoch 33 - iter 56/79 - loss 0.36329716 - time (sec): 15.58 - samples/sec: 4353.68 - lr: 0.003125\n","2023-04-21 05:41:58,642 epoch 33 - iter 63/79 - loss 0.36772492 - time (sec): 17.56 - samples/sec: 4340.60 - lr: 0.003125\n","2023-04-21 05:42:00,446 epoch 33 - iter 70/79 - loss 0.36701674 - time (sec): 19.36 - samples/sec: 4365.20 - lr: 0.003125\n","2023-04-21 05:42:02,113 epoch 33 - iter 77/79 - loss 0.36760352 - time (sec): 21.03 - samples/sec: 4384.14 - lr: 0.003125\n","2023-04-21 05:42:02,744 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:42:02,746 EPOCH 33 done: loss 0.3668 - lr 0.003125\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.73it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:42:24,046 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:42:25,905 TRAIN : loss 0.32269325852394104 - f1-score (weighted avg)  0.2577\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:03<00:00,  2.62it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:42:29,885 Evaluating as a multi-label problem: False\n","2023-04-21 05:42:29,952 DEV : loss 0.33055010437965393 - f1-score (weighted avg)  0.2666\n","2023-04-21 05:42:29,975 BAD EPOCHS (no improvement): 0\n","2023-04-21 05:42:29,981 saving best model\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:42:32,901 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:42:34,994 epoch 34 - iter 7/79 - loss 0.33932856 - time (sec): 2.09 - samples/sec: 4095.85 - lr: 0.003125\n","2023-04-21 05:42:37,248 epoch 34 - iter 14/79 - loss 0.34826077 - time (sec): 4.34 - samples/sec: 4029.74 - lr: 0.003125\n","2023-04-21 05:42:39,265 epoch 34 - iter 21/79 - loss 0.35806893 - time (sec): 6.36 - samples/sec: 4130.21 - lr: 0.003125\n","2023-04-21 05:42:41,044 epoch 34 - iter 28/79 - loss 0.36226972 - time (sec): 8.14 - samples/sec: 4199.24 - lr: 0.003125\n","2023-04-21 05:42:42,981 epoch 34 - iter 35/79 - loss 0.36185378 - time (sec): 10.08 - samples/sec: 4247.07 - lr: 0.003125\n","2023-04-21 05:42:45,194 epoch 34 - iter 42/79 - loss 0.36437328 - time (sec): 12.29 - samples/sec: 4239.52 - lr: 0.003125\n","2023-04-21 05:42:46,777 epoch 34 - iter 49/79 - loss 0.36522369 - time (sec): 13.87 - samples/sec: 4307.18 - lr: 0.003125\n","2023-04-21 05:42:48,686 epoch 34 - iter 56/79 - loss 0.36716334 - time (sec): 15.78 - samples/sec: 4307.18 - lr: 0.003125\n","2023-04-21 05:42:50,681 epoch 34 - iter 63/79 - loss 0.36544335 - time (sec): 17.78 - samples/sec: 4258.97 - lr: 0.003125\n","2023-04-21 05:42:52,647 epoch 34 - iter 70/79 - loss 0.36954974 - time (sec): 19.74 - samples/sec: 4275.01 - lr: 0.003125\n","2023-04-21 05:42:54,275 epoch 34 - iter 77/79 - loss 0.37168233 - time (sec): 21.37 - samples/sec: 4339.68 - lr: 0.003125\n","2023-04-21 05:42:54,752 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:42:54,754 EPOCH 34 done: loss 0.3710 - lr 0.003125\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:20<00:00,  3.76it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:43:15,883 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:43:17,729 TRAIN : loss 0.32109764218330383 - f1-score (weighted avg)  0.2296\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.57it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:43:20,698 Evaluating as a multi-label problem: False\n","2023-04-21 05:43:20,763 DEV : loss 0.32746240496635437 - f1-score (weighted avg)  0.2438\n","2023-04-21 05:43:20,785 BAD EPOCHS (no improvement): 0\n","2023-04-21 05:43:20,790 saving best model\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:43:23,723 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:43:26,058 epoch 35 - iter 7/79 - loss 0.36381676 - time (sec): 2.33 - samples/sec: 3805.25 - lr: 0.003125\n","2023-04-21 05:43:28,169 epoch 35 - iter 14/79 - loss 0.35325240 - time (sec): 4.44 - samples/sec: 4039.00 - lr: 0.003125\n","2023-04-21 05:43:29,749 epoch 35 - iter 21/79 - loss 0.36356855 - time (sec): 6.02 - samples/sec: 4256.83 - lr: 0.003125\n","2023-04-21 05:43:32,291 epoch 35 - iter 28/79 - loss 0.35816501 - time (sec): 8.57 - samples/sec: 4189.43 - lr: 0.003125\n","2023-04-21 05:43:34,306 epoch 35 - iter 35/79 - loss 0.36228776 - time (sec): 10.58 - samples/sec: 4121.54 - lr: 0.003125\n","2023-04-21 05:43:36,167 epoch 35 - iter 42/79 - loss 0.36554236 - time (sec): 12.44 - samples/sec: 4135.90 - lr: 0.003125\n","2023-04-21 05:43:37,923 epoch 35 - iter 49/79 - loss 0.36455863 - time (sec): 14.20 - samples/sec: 4177.92 - lr: 0.003125\n","2023-04-21 05:43:39,767 epoch 35 - iter 56/79 - loss 0.36231825 - time (sec): 16.04 - samples/sec: 4202.11 - lr: 0.003125\n","2023-04-21 05:43:41,465 epoch 35 - iter 63/79 - loss 0.36183715 - time (sec): 17.74 - samples/sec: 4281.48 - lr: 0.003125\n","2023-04-21 05:43:43,524 epoch 35 - iter 70/79 - loss 0.36415427 - time (sec): 19.80 - samples/sec: 4234.63 - lr: 0.003125\n","2023-04-21 05:43:45,289 epoch 35 - iter 77/79 - loss 0.36423470 - time (sec): 21.56 - samples/sec: 4303.90 - lr: 0.003125\n","2023-04-21 05:43:45,757 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:43:45,759 EPOCH 35 done: loss 0.3641 - lr 0.003125\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.55it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:44:08,133 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:44:10,000 TRAIN : loss 0.3233334422111511 - f1-score (weighted avg)  0.247\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.58it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:44:12,954 Evaluating as a multi-label problem: False\n","2023-04-21 05:44:13,024 DEV : loss 0.3270644247531891 - f1-score (weighted avg)  0.2646\n","2023-04-21 05:44:13,049 BAD EPOCHS (no improvement): 0\n","2023-04-21 05:44:13,054 saving best model\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:44:16,004 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:44:17,986 epoch 36 - iter 7/79 - loss 0.35880522 - time (sec): 1.96 - samples/sec: 4389.08 - lr: 0.003125\n","2023-04-21 05:44:19,641 epoch 36 - iter 14/79 - loss 0.36599704 - time (sec): 3.62 - samples/sec: 4534.88 - lr: 0.003125\n","2023-04-21 05:44:21,474 epoch 36 - iter 21/79 - loss 0.38212972 - time (sec): 5.45 - samples/sec: 4607.87 - lr: 0.003125\n","2023-04-21 05:44:24,110 epoch 36 - iter 28/79 - loss 0.37628224 - time (sec): 8.09 - samples/sec: 4209.89 - lr: 0.003125\n","2023-04-21 05:44:25,936 epoch 36 - iter 35/79 - loss 0.37530470 - time (sec): 9.91 - samples/sec: 4225.03 - lr: 0.003125\n","2023-04-21 05:44:28,015 epoch 36 - iter 42/79 - loss 0.37409760 - time (sec): 11.99 - samples/sec: 4204.07 - lr: 0.003125\n","2023-04-21 05:44:29,938 epoch 36 - iter 49/79 - loss 0.37091001 - time (sec): 13.92 - samples/sec: 4233.32 - lr: 0.003125\n","2023-04-21 05:44:31,922 epoch 36 - iter 56/79 - loss 0.36697800 - time (sec): 15.90 - samples/sec: 4290.12 - lr: 0.003125\n","2023-04-21 05:44:33,397 epoch 36 - iter 63/79 - loss 0.36417931 - time (sec): 17.38 - samples/sec: 4376.32 - lr: 0.003125\n","2023-04-21 05:44:35,267 epoch 36 - iter 70/79 - loss 0.36354912 - time (sec): 19.25 - samples/sec: 4406.55 - lr: 0.003125\n","2023-04-21 05:44:36,880 epoch 36 - iter 77/79 - loss 0.36094775 - time (sec): 20.86 - samples/sec: 4450.58 - lr: 0.003125\n","2023-04-21 05:44:37,351 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:44:37,352 EPOCH 36 done: loss 0.3617 - lr 0.003125\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.52it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:44:59,924 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:45:01,783 TRAIN : loss 0.3136407136917114 - f1-score (weighted avg)  0.2719\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.65it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:45:04,687 Evaluating as a multi-label problem: False\n","2023-04-21 05:45:04,755 DEV : loss 0.32032856345176697 - f1-score (weighted avg)  0.2782\n","2023-04-21 05:45:04,776 BAD EPOCHS (no improvement): 0\n","2023-04-21 05:45:04,781 saving best model\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:45:07,757 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:45:09,390 epoch 37 - iter 7/79 - loss 0.32781788 - time (sec): 1.63 - samples/sec: 4816.15 - lr: 0.003125\n","2023-04-21 05:45:11,393 epoch 37 - iter 14/79 - loss 0.35563532 - time (sec): 3.63 - samples/sec: 4604.72 - lr: 0.003125\n","2023-04-21 05:45:13,059 epoch 37 - iter 21/79 - loss 0.36048186 - time (sec): 5.30 - samples/sec: 4534.71 - lr: 0.003125\n","2023-04-21 05:45:14,941 epoch 37 - iter 28/79 - loss 0.36741767 - time (sec): 7.18 - samples/sec: 4510.39 - lr: 0.003125\n","2023-04-21 05:45:16,547 epoch 37 - iter 35/79 - loss 0.36838197 - time (sec): 8.79 - samples/sec: 4568.84 - lr: 0.003125\n","2023-04-21 05:45:18,591 epoch 37 - iter 42/79 - loss 0.37376213 - time (sec): 10.83 - samples/sec: 4498.91 - lr: 0.003125\n","2023-04-21 05:45:20,832 epoch 37 - iter 49/79 - loss 0.37071468 - time (sec): 13.07 - samples/sec: 4397.38 - lr: 0.003125\n","2023-04-21 05:45:23,342 epoch 37 - iter 56/79 - loss 0.36473894 - time (sec): 15.58 - samples/sec: 4299.36 - lr: 0.003125\n","2023-04-21 05:45:25,249 epoch 37 - iter 63/79 - loss 0.36192547 - time (sec): 17.49 - samples/sec: 4289.50 - lr: 0.003125\n","2023-04-21 05:45:27,295 epoch 37 - iter 70/79 - loss 0.35879741 - time (sec): 19.54 - samples/sec: 4324.43 - lr: 0.003125\n","2023-04-21 05:45:28,899 epoch 37 - iter 77/79 - loss 0.35958388 - time (sec): 21.14 - samples/sec: 4365.71 - lr: 0.003125\n","2023-04-21 05:45:29,345 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:45:29,346 EPOCH 37 done: loss 0.3597 - lr 0.003125\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.71it/s]\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:45:51,939 Evaluating as a multi-label problem: True\n","2023-04-21 05:45:53,805 TRAIN : loss 0.3192870616912842 - f1-score (weighted avg)  0.2809\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.67it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:45:56,691 Evaluating as a multi-label problem: False\n","2023-04-21 05:45:56,756 DEV : loss 0.32689717411994934 - f1-score (weighted avg)  0.2817\n","2023-04-21 05:45:56,778 BAD EPOCHS (no improvement): 1\n","2023-04-21 05:45:56,782 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:45:58,591 epoch 38 - iter 7/79 - loss 0.36034061 - time (sec): 1.81 - samples/sec: 4293.37 - lr: 0.003125\n","2023-04-21 05:46:00,346 epoch 38 - iter 14/79 - loss 0.36012608 - time (sec): 3.56 - samples/sec: 4200.98 - lr: 0.003125\n","2023-04-21 05:46:02,091 epoch 38 - iter 21/79 - loss 0.36122521 - time (sec): 5.31 - samples/sec: 4415.37 - lr: 0.003125\n","2023-04-21 05:46:04,020 epoch 38 - iter 28/79 - loss 0.35717619 - time (sec): 7.24 - samples/sec: 4468.56 - lr: 0.003125\n","2023-04-21 05:46:05,771 epoch 38 - iter 35/79 - loss 0.35958290 - time (sec): 8.99 - samples/sec: 4555.14 - lr: 0.003125\n","2023-04-21 05:46:07,650 epoch 38 - iter 42/79 - loss 0.35791107 - time (sec): 10.87 - samples/sec: 4555.14 - lr: 0.003125\n","2023-04-21 05:46:09,563 epoch 38 - iter 49/79 - loss 0.35996839 - time (sec): 12.78 - samples/sec: 4538.42 - lr: 0.003125\n","2023-04-21 05:46:11,440 epoch 38 - iter 56/79 - loss 0.35988460 - time (sec): 14.66 - samples/sec: 4536.08 - lr: 0.003125\n","2023-04-21 05:46:12,952 epoch 38 - iter 63/79 - loss 0.36251796 - time (sec): 16.17 - samples/sec: 4643.70 - lr: 0.003125\n","2023-04-21 05:46:14,792 epoch 38 - iter 70/79 - loss 0.36361971 - time (sec): 18.01 - samples/sec: 4668.05 - lr: 0.003125\n","2023-04-21 05:46:17,245 epoch 38 - iter 77/79 - loss 0.36101432 - time (sec): 20.46 - samples/sec: 4519.93 - lr: 0.003125\n","2023-04-21 05:46:17,794 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:46:17,795 EPOCH 38 done: loss 0.3594 - lr 0.003125\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.74it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:46:39,038 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:46:42,140 TRAIN : loss 0.32760635018348694 - f1-score (weighted avg)  0.2582\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.61it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:46:45,072 Evaluating as a multi-label problem: False\n","2023-04-21 05:46:45,140 DEV : loss 0.3331727087497711 - f1-score (weighted avg)  0.2683\n","2023-04-21 05:46:45,161 Epoch    38: reducing learning rate of group 0 to 1.5625e-03.\n","2023-04-21 05:46:45,164 BAD EPOCHS (no improvement): 2\n","2023-04-21 05:46:45,168 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:46:46,913 epoch 39 - iter 7/79 - loss 0.36240392 - time (sec): 1.74 - samples/sec: 4773.57 - lr: 0.001563\n","2023-04-21 05:46:48,455 epoch 39 - iter 14/79 - loss 0.35442249 - time (sec): 3.29 - samples/sec: 5232.20 - lr: 0.001563\n","2023-04-21 05:46:50,610 epoch 39 - iter 21/79 - loss 0.34738281 - time (sec): 5.44 - samples/sec: 4768.58 - lr: 0.001563\n","2023-04-21 05:46:52,523 epoch 39 - iter 28/79 - loss 0.35107917 - time (sec): 7.35 - samples/sec: 4655.64 - lr: 0.001563\n","2023-04-21 05:46:54,310 epoch 39 - iter 35/79 - loss 0.35067597 - time (sec): 9.14 - samples/sec: 4744.20 - lr: 0.001563\n","2023-04-21 05:46:56,630 epoch 39 - iter 42/79 - loss 0.34784630 - time (sec): 11.46 - samples/sec: 4598.64 - lr: 0.001563\n","2023-04-21 05:46:58,352 epoch 39 - iter 49/79 - loss 0.34579902 - time (sec): 13.18 - samples/sec: 4639.14 - lr: 0.001563\n","2023-04-21 05:47:00,018 epoch 39 - iter 56/79 - loss 0.34458009 - time (sec): 14.85 - samples/sec: 4666.45 - lr: 0.001563\n","2023-04-21 05:47:01,749 epoch 39 - iter 63/79 - loss 0.34981269 - time (sec): 16.58 - samples/sec: 4663.04 - lr: 0.001563\n","2023-04-21 05:47:03,465 epoch 39 - iter 70/79 - loss 0.35080789 - time (sec): 18.30 - samples/sec: 4633.75 - lr: 0.001563\n","2023-04-21 05:47:05,435 epoch 39 - iter 77/79 - loss 0.35371776 - time (sec): 20.27 - samples/sec: 4569.15 - lr: 0.001563\n","2023-04-21 05:47:06,026 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:47:06,028 EPOCH 39 done: loss 0.3534 - lr 0.001563\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.73it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:47:27,348 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:47:29,215 TRAIN : loss 0.3065740764141083 - f1-score (weighted avg)  0.3153\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.55it/s]\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:47:33,311 Evaluating as a multi-label problem: False\n","2023-04-21 05:47:33,379 DEV : loss 0.31484267115592957 - f1-score (weighted avg)  0.3044\n","2023-04-21 05:47:33,402 BAD EPOCHS (no improvement): 0\n","2023-04-21 05:47:33,407 saving best model\n","2023-04-21 05:47:36,319 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:47:38,278 epoch 40 - iter 7/79 - loss 0.34003953 - time (sec): 1.94 - samples/sec: 4710.38 - lr: 0.001563\n","2023-04-21 05:47:40,211 epoch 40 - iter 14/79 - loss 0.33117786 - time (sec): 3.88 - samples/sec: 4864.25 - lr: 0.001563\n","2023-04-21 05:47:41,538 epoch 40 - iter 21/79 - loss 0.33993644 - time (sec): 5.20 - samples/sec: 4903.63 - lr: 0.001563\n","2023-04-21 05:47:43,561 epoch 40 - iter 28/79 - loss 0.35125432 - time (sec): 7.23 - samples/sec: 4856.92 - lr: 0.001563\n","2023-04-21 05:47:45,439 epoch 40 - iter 35/79 - loss 0.35155910 - time (sec): 9.11 - samples/sec: 4799.16 - lr: 0.001563\n","2023-04-21 05:47:47,352 epoch 40 - iter 42/79 - loss 0.35127930 - time (sec): 11.02 - samples/sec: 4682.39 - lr: 0.001563\n","2023-04-21 05:47:49,124 epoch 40 - iter 49/79 - loss 0.35407820 - time (sec): 12.79 - samples/sec: 4617.32 - lr: 0.001563\n","2023-04-21 05:47:51,063 epoch 40 - iter 56/79 - loss 0.35061942 - time (sec): 14.73 - samples/sec: 4567.86 - lr: 0.001563\n","2023-04-21 05:47:53,260 epoch 40 - iter 63/79 - loss 0.34991095 - time (sec): 16.93 - samples/sec: 4487.87 - lr: 0.001563\n","2023-04-21 05:47:55,501 epoch 40 - iter 70/79 - loss 0.34976113 - time (sec): 19.17 - samples/sec: 4398.40 - lr: 0.001563\n","2023-04-21 05:47:57,210 epoch 40 - iter 77/79 - loss 0.35061014 - time (sec): 20.88 - samples/sec: 4417.03 - lr: 0.001563\n","2023-04-21 05:47:57,661 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:47:57,664 EPOCH 40 done: loss 0.3501 - lr 0.001563\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.72it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:48:19,031 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:48:20,906 TRAIN : loss 0.30952194333076477 - f1-score (weighted avg)  0.3191\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.62it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:48:23,832 Evaluating as a multi-label problem: False\n","2023-04-21 05:48:23,903 DEV : loss 0.320065438747406 - f1-score (weighted avg)  0.3044\n","2023-04-21 05:48:23,926 BAD EPOCHS (no improvement): 1\n","2023-04-21 05:48:23,930 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:48:25,609 epoch 41 - iter 7/79 - loss 0.36161974 - time (sec): 1.68 - samples/sec: 4629.91 - lr: 0.001563\n","2023-04-21 05:48:27,523 epoch 41 - iter 14/79 - loss 0.35267324 - time (sec): 3.59 - samples/sec: 4670.83 - lr: 0.001563\n","2023-04-21 05:48:29,348 epoch 41 - iter 21/79 - loss 0.35159047 - time (sec): 5.42 - samples/sec: 4616.15 - lr: 0.001563\n","2023-04-21 05:48:31,303 epoch 41 - iter 28/79 - loss 0.34758921 - time (sec): 7.37 - samples/sec: 4513.75 - lr: 0.001563\n","2023-04-21 05:48:33,007 epoch 41 - iter 35/79 - loss 0.35340712 - time (sec): 9.07 - samples/sec: 4545.36 - lr: 0.001563\n","2023-04-21 05:48:34,831 epoch 41 - iter 42/79 - loss 0.35442623 - time (sec): 10.90 - samples/sec: 4575.01 - lr: 0.001563\n","2023-04-21 05:48:36,954 epoch 41 - iter 49/79 - loss 0.35334763 - time (sec): 13.02 - samples/sec: 4513.13 - lr: 0.001563\n","2023-04-21 05:48:38,733 epoch 41 - iter 56/79 - loss 0.35054454 - time (sec): 14.80 - samples/sec: 4526.35 - lr: 0.001563\n","2023-04-21 05:48:40,560 epoch 41 - iter 63/79 - loss 0.35186595 - time (sec): 16.63 - samples/sec: 4497.27 - lr: 0.001563\n","2023-04-21 05:48:42,239 epoch 41 - iter 70/79 - loss 0.34933715 - time (sec): 18.31 - samples/sec: 4518.03 - lr: 0.001563\n","2023-04-21 05:48:44,777 epoch 41 - iter 77/79 - loss 0.34928310 - time (sec): 20.85 - samples/sec: 4466.53 - lr: 0.001563\n","2023-04-21 05:48:45,075 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:48:45,076 EPOCH 41 done: loss 0.3485 - lr 0.001563\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.53it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:49:07,574 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:49:09,438 TRAIN : loss 0.30542486906051636 - f1-score (weighted avg)  0.3023\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.64it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:49:12,348 Evaluating as a multi-label problem: False\n","2023-04-21 05:49:12,416 DEV : loss 0.3119701147079468 - f1-score (weighted avg)  0.2977\n","2023-04-21 05:49:12,438 BAD EPOCHS (no improvement): 0\n","2023-04-21 05:49:12,442 saving best model\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:49:15,374 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:49:17,119 epoch 42 - iter 7/79 - loss 0.33792732 - time (sec): 1.74 - samples/sec: 4733.64 - lr: 0.001563\n","2023-04-21 05:49:18,923 epoch 42 - iter 14/79 - loss 0.34153847 - time (sec): 3.55 - samples/sec: 4679.73 - lr: 0.001563\n","2023-04-21 05:49:20,791 epoch 42 - iter 21/79 - loss 0.34442018 - time (sec): 5.41 - samples/sec: 4679.70 - lr: 0.001563\n","2023-04-21 05:49:22,897 epoch 42 - iter 28/79 - loss 0.34665028 - time (sec): 7.52 - samples/sec: 4562.17 - lr: 0.001563\n","2023-04-21 05:49:24,688 epoch 42 - iter 35/79 - loss 0.35244904 - time (sec): 9.31 - samples/sec: 4602.74 - lr: 0.001563\n","2023-04-21 05:49:27,293 epoch 42 - iter 42/79 - loss 0.34888110 - time (sec): 11.92 - samples/sec: 4298.32 - lr: 0.001563\n","2023-04-21 05:49:29,273 epoch 42 - iter 49/79 - loss 0.35184717 - time (sec): 13.90 - samples/sec: 4267.88 - lr: 0.001563\n","2023-04-21 05:49:31,047 epoch 42 - iter 56/79 - loss 0.35064473 - time (sec): 15.67 - samples/sec: 4284.07 - lr: 0.001563\n","2023-04-21 05:49:32,730 epoch 42 - iter 63/79 - loss 0.34948217 - time (sec): 17.35 - samples/sec: 4334.06 - lr: 0.001563\n","2023-04-21 05:49:34,801 epoch 42 - iter 70/79 - loss 0.34435892 - time (sec): 19.42 - samples/sec: 4348.95 - lr: 0.001563\n","2023-04-21 05:49:36,796 epoch 42 - iter 77/79 - loss 0.34855659 - time (sec): 21.42 - samples/sec: 4333.38 - lr: 0.001563\n","2023-04-21 05:49:37,263 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:49:37,264 EPOCH 42 done: loss 0.3494 - lr 0.001563\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.55it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:49:59,664 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:50:01,543 TRAIN : loss 0.3033792972564697 - f1-score (weighted avg)  0.306\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.62it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:50:04,469 Evaluating as a multi-label problem: False\n","2023-04-21 05:50:04,539 DEV : loss 0.31179213523864746 - f1-score (weighted avg)  0.2929\n","2023-04-21 05:50:04,560 BAD EPOCHS (no improvement): 0\n","2023-04-21 05:50:04,565 saving best model\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:50:07,508 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:50:09,174 epoch 43 - iter 7/79 - loss 0.33872186 - time (sec): 1.66 - samples/sec: 4640.18 - lr: 0.001563\n","2023-04-21 05:50:10,920 epoch 43 - iter 14/79 - loss 0.34714487 - time (sec): 3.41 - samples/sec: 4718.70 - lr: 0.001563\n","2023-04-21 05:50:12,935 epoch 43 - iter 21/79 - loss 0.35883854 - time (sec): 5.42 - samples/sec: 4530.71 - lr: 0.001563\n","2023-04-21 05:50:14,463 epoch 43 - iter 28/79 - loss 0.35599741 - time (sec): 6.95 - samples/sec: 4660.97 - lr: 0.001563\n","2023-04-21 05:50:16,913 epoch 43 - iter 35/79 - loss 0.34820995 - time (sec): 9.40 - samples/sec: 4366.49 - lr: 0.001563\n","2023-04-21 05:50:18,642 epoch 43 - iter 42/79 - loss 0.35338075 - time (sec): 11.13 - samples/sec: 4397.45 - lr: 0.001563\n","2023-04-21 05:50:20,384 epoch 43 - iter 49/79 - loss 0.34966802 - time (sec): 12.87 - samples/sec: 4486.88 - lr: 0.001563\n","2023-04-21 05:50:22,426 epoch 43 - iter 56/79 - loss 0.34914873 - time (sec): 14.92 - samples/sec: 4452.12 - lr: 0.001563\n","2023-04-21 05:50:24,157 epoch 43 - iter 63/79 - loss 0.34918681 - time (sec): 16.65 - samples/sec: 4461.11 - lr: 0.001563\n","2023-04-21 05:50:26,041 epoch 43 - iter 70/79 - loss 0.34893190 - time (sec): 18.53 - samples/sec: 4507.07 - lr: 0.001563\n","2023-04-21 05:50:28,191 epoch 43 - iter 77/79 - loss 0.35027239 - time (sec): 20.68 - samples/sec: 4459.21 - lr: 0.001563\n","2023-04-21 05:50:28,783 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:50:28,785 EPOCH 43 done: loss 0.3484 - lr 0.001563\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.52it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:50:51,338 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:50:53,195 TRAIN : loss 0.3027087152004242 - f1-score (weighted avg)  0.2909\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.63it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:50:56,122 Evaluating as a multi-label problem: False\n","2023-04-21 05:50:56,191 DEV : loss 0.3090602159500122 - f1-score (weighted avg)  0.2872\n","2023-04-21 05:50:56,214 BAD EPOCHS (no improvement): 0\n","2023-04-21 05:50:56,218 saving best model\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:50:59,189 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:51:00,979 epoch 44 - iter 7/79 - loss 0.34200937 - time (sec): 1.79 - samples/sec: 4217.49 - lr: 0.001563\n","2023-04-21 05:51:02,977 epoch 44 - iter 14/79 - loss 0.34017923 - time (sec): 3.79 - samples/sec: 4350.38 - lr: 0.001563\n","2023-04-21 05:51:04,670 epoch 44 - iter 21/79 - loss 0.35537405 - time (sec): 5.48 - samples/sec: 4362.97 - lr: 0.001563\n","2023-04-21 05:51:06,598 epoch 44 - iter 28/79 - loss 0.35696594 - time (sec): 7.41 - samples/sec: 4476.22 - lr: 0.001563\n","2023-04-21 05:51:08,950 epoch 44 - iter 35/79 - loss 0.35585825 - time (sec): 9.76 - samples/sec: 4323.48 - lr: 0.001563\n","2023-04-21 05:51:11,107 epoch 44 - iter 42/79 - loss 0.35228673 - time (sec): 11.92 - samples/sec: 4275.53 - lr: 0.001563\n","2023-04-21 05:51:12,982 epoch 44 - iter 49/79 - loss 0.35157605 - time (sec): 13.79 - samples/sec: 4327.05 - lr: 0.001563\n","2023-04-21 05:51:14,995 epoch 44 - iter 56/79 - loss 0.34786459 - time (sec): 15.80 - samples/sec: 4298.48 - lr: 0.001563\n","2023-04-21 05:51:17,029 epoch 44 - iter 63/79 - loss 0.35035019 - time (sec): 17.84 - samples/sec: 4296.85 - lr: 0.001563\n","2023-04-21 05:51:18,776 epoch 44 - iter 70/79 - loss 0.34696856 - time (sec): 19.58 - samples/sec: 4341.58 - lr: 0.001563\n","2023-04-21 05:51:20,595 epoch 44 - iter 77/79 - loss 0.34814021 - time (sec): 21.40 - samples/sec: 4344.29 - lr: 0.001563\n","2023-04-21 05:51:21,053 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:51:21,055 EPOCH 44 done: loss 0.3483 - lr 0.001563\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.71it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:51:42,506 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:51:45,605 TRAIN : loss 0.30222728848457336 - f1-score (weighted avg)  0.3126\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.67it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:51:48,490 Evaluating as a multi-label problem: False\n","2023-04-21 05:51:48,563 DEV : loss 0.31110385060310364 - f1-score (weighted avg)  0.295\n","2023-04-21 05:51:48,585 BAD EPOCHS (no improvement): 1\n","2023-04-21 05:51:48,590 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:51:50,396 epoch 45 - iter 7/79 - loss 0.35621257 - time (sec): 1.80 - samples/sec: 5117.66 - lr: 0.001563\n","2023-04-21 05:51:52,197 epoch 45 - iter 14/79 - loss 0.34897559 - time (sec): 3.61 - samples/sec: 4833.09 - lr: 0.001563\n","2023-04-21 05:51:54,571 epoch 45 - iter 21/79 - loss 0.34791703 - time (sec): 5.98 - samples/sec: 4609.08 - lr: 0.001563\n","2023-04-21 05:51:56,399 epoch 45 - iter 28/79 - loss 0.34095758 - time (sec): 7.81 - samples/sec: 4620.30 - lr: 0.001563\n","2023-04-21 05:51:58,014 epoch 45 - iter 35/79 - loss 0.34501928 - time (sec): 9.42 - samples/sec: 4617.74 - lr: 0.001563\n","2023-04-21 05:51:59,843 epoch 45 - iter 42/79 - loss 0.34458256 - time (sec): 11.25 - samples/sec: 4571.24 - lr: 0.001563\n","2023-04-21 05:52:01,571 epoch 45 - iter 49/79 - loss 0.34585340 - time (sec): 12.98 - samples/sec: 4572.01 - lr: 0.001563\n","2023-04-21 05:52:03,462 epoch 45 - iter 56/79 - loss 0.34480370 - time (sec): 14.87 - samples/sec: 4561.86 - lr: 0.001563\n","2023-04-21 05:52:05,291 epoch 45 - iter 63/79 - loss 0.34274598 - time (sec): 16.70 - samples/sec: 4552.83 - lr: 0.001563\n","2023-04-21 05:52:07,328 epoch 45 - iter 70/79 - loss 0.34355967 - time (sec): 18.74 - samples/sec: 4515.95 - lr: 0.001563\n","2023-04-21 05:52:08,870 epoch 45 - iter 77/79 - loss 0.34428503 - time (sec): 20.28 - samples/sec: 4554.94 - lr: 0.001563\n","2023-04-21 05:52:09,362 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:52:09,364 EPOCH 45 done: loss 0.3446 - lr 0.001563\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.75it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:52:30,554 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:52:33,734 TRAIN : loss 0.30652186274528503 - f1-score (weighted avg)  0.289\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.67it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:52:36,621 Evaluating as a multi-label problem: False\n","2023-04-21 05:52:36,687 DEV : loss 0.31173235177993774 - f1-score (weighted avg)  0.3013\n","2023-04-21 05:52:36,709 Epoch    45: reducing learning rate of group 0 to 7.8125e-04.\n","2023-04-21 05:52:36,712 BAD EPOCHS (no improvement): 2\n","2023-04-21 05:52:36,716 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:52:38,487 epoch 46 - iter 7/79 - loss 0.34667918 - time (sec): 1.77 - samples/sec: 4313.46 - lr: 0.000781\n","2023-04-21 05:52:39,869 epoch 46 - iter 14/79 - loss 0.36008717 - time (sec): 3.15 - samples/sec: 4747.29 - lr: 0.000781\n","2023-04-21 05:52:41,675 epoch 46 - iter 21/79 - loss 0.36006911 - time (sec): 4.96 - samples/sec: 4609.42 - lr: 0.000781\n","2023-04-21 05:52:43,534 epoch 46 - iter 28/79 - loss 0.35260402 - time (sec): 6.82 - samples/sec: 4598.26 - lr: 0.000781\n","2023-04-21 05:52:45,562 epoch 46 - iter 35/79 - loss 0.34622310 - time (sec): 8.85 - samples/sec: 4535.42 - lr: 0.000781\n","2023-04-21 05:52:47,459 epoch 46 - iter 42/79 - loss 0.34424482 - time (sec): 10.74 - samples/sec: 4588.12 - lr: 0.000781\n","2023-04-21 05:52:49,064 epoch 46 - iter 49/79 - loss 0.34766744 - time (sec): 12.35 - samples/sec: 4606.93 - lr: 0.000781\n","2023-04-21 05:52:50,840 epoch 46 - iter 56/79 - loss 0.35002360 - time (sec): 14.12 - samples/sec: 4575.19 - lr: 0.000781\n","2023-04-21 05:52:53,001 epoch 46 - iter 63/79 - loss 0.34982931 - time (sec): 16.28 - samples/sec: 4560.66 - lr: 0.000781\n","2023-04-21 05:52:55,457 epoch 46 - iter 70/79 - loss 0.34708905 - time (sec): 18.74 - samples/sec: 4472.45 - lr: 0.000781\n","2023-04-21 05:52:57,321 epoch 46 - iter 77/79 - loss 0.34734841 - time (sec): 20.60 - samples/sec: 4483.45 - lr: 0.000781\n","2023-04-21 05:52:57,759 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:52:57,761 EPOCH 46 done: loss 0.3460 - lr 0.000781\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.72it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:53:19,154 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:53:20,998 TRAIN : loss 0.30434805154800415 - f1-score (weighted avg)  0.2634\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.66it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:53:23,897 Evaluating as a multi-label problem: False\n","2023-04-21 05:53:23,963 DEV : loss 0.308689147233963 - f1-score (weighted avg)  0.2787\n","2023-04-21 05:53:23,984 BAD EPOCHS (no improvement): 0\n","2023-04-21 05:53:23,989 saving best model\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:53:26,928 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:53:28,616 epoch 47 - iter 7/79 - loss 0.33516273 - time (sec): 1.69 - samples/sec: 5154.62 - lr: 0.000781\n","2023-04-21 05:53:30,957 epoch 47 - iter 14/79 - loss 0.33012679 - time (sec): 4.03 - samples/sec: 4186.42 - lr: 0.000781\n","2023-04-21 05:53:32,611 epoch 47 - iter 21/79 - loss 0.33365742 - time (sec): 5.68 - samples/sec: 4327.18 - lr: 0.000781\n","2023-04-21 05:53:34,360 epoch 47 - iter 28/79 - loss 0.33539571 - time (sec): 7.43 - samples/sec: 4541.74 - lr: 0.000781\n","2023-04-21 05:53:36,292 epoch 47 - iter 35/79 - loss 0.33856212 - time (sec): 9.36 - samples/sec: 4516.97 - lr: 0.000781\n","2023-04-21 05:53:38,107 epoch 47 - iter 42/79 - loss 0.33608541 - time (sec): 11.18 - samples/sec: 4529.20 - lr: 0.000781\n","2023-04-21 05:53:40,016 epoch 47 - iter 49/79 - loss 0.33358852 - time (sec): 13.09 - samples/sec: 4489.47 - lr: 0.000781\n","2023-04-21 05:53:41,983 epoch 47 - iter 56/79 - loss 0.33799959 - time (sec): 15.05 - samples/sec: 4463.75 - lr: 0.000781\n","2023-04-21 05:53:44,440 epoch 47 - iter 63/79 - loss 0.34132397 - time (sec): 17.51 - samples/sec: 4367.99 - lr: 0.000781\n","2023-04-21 05:53:46,448 epoch 47 - iter 70/79 - loss 0.34074815 - time (sec): 19.52 - samples/sec: 4314.84 - lr: 0.000781\n","2023-04-21 05:53:48,197 epoch 47 - iter 77/79 - loss 0.34300428 - time (sec): 21.27 - samples/sec: 4357.47 - lr: 0.000781\n","2023-04-21 05:53:48,791 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:53:48,793 EPOCH 47 done: loss 0.3417 - lr 0.000781\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.54it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:54:11,268 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:54:13,136 TRAIN : loss 0.3015443980693817 - f1-score (weighted avg)  0.2868\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.61it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:54:16,074 Evaluating as a multi-label problem: False\n","2023-04-21 05:54:16,140 DEV : loss 0.30668359994888306 - f1-score (weighted avg)  0.2917\n","2023-04-21 05:54:16,162 BAD EPOCHS (no improvement): 0\n","2023-04-21 05:54:16,167 saving best model\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:54:19,096 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:54:21,032 epoch 48 - iter 7/79 - loss 0.34752372 - time (sec): 1.92 - samples/sec: 4702.72 - lr: 0.000781\n","2023-04-21 05:54:22,756 epoch 48 - iter 14/79 - loss 0.34944534 - time (sec): 3.64 - samples/sec: 4812.95 - lr: 0.000781\n","2023-04-21 05:54:24,598 epoch 48 - iter 21/79 - loss 0.34194628 - time (sec): 5.49 - samples/sec: 4665.67 - lr: 0.000781\n","2023-04-21 05:54:26,453 epoch 48 - iter 28/79 - loss 0.34493077 - time (sec): 7.34 - samples/sec: 4789.86 - lr: 0.000781\n","2023-04-21 05:54:28,628 epoch 48 - iter 35/79 - loss 0.34243820 - time (sec): 9.52 - samples/sec: 4538.04 - lr: 0.000781\n","2023-04-21 05:54:30,511 epoch 48 - iter 42/79 - loss 0.34440300 - time (sec): 11.40 - samples/sec: 4467.95 - lr: 0.000781\n","2023-04-21 05:54:32,239 epoch 48 - iter 49/79 - loss 0.34731137 - time (sec): 13.13 - samples/sec: 4435.51 - lr: 0.000781\n","2023-04-21 05:54:34,978 epoch 48 - iter 56/79 - loss 0.34411035 - time (sec): 15.87 - samples/sec: 4259.98 - lr: 0.000781\n","2023-04-21 05:54:36,756 epoch 48 - iter 63/79 - loss 0.34429123 - time (sec): 17.64 - samples/sec: 4292.25 - lr: 0.000781\n","2023-04-21 05:54:38,721 epoch 48 - iter 70/79 - loss 0.34372236 - time (sec): 19.61 - samples/sec: 4308.07 - lr: 0.000781\n","2023-04-21 05:54:40,584 epoch 48 - iter 77/79 - loss 0.34335150 - time (sec): 21.47 - samples/sec: 4315.69 - lr: 0.000781\n","2023-04-21 05:54:41,143 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:54:41,144 EPOCH 48 done: loss 0.3429 - lr 0.000781\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.73it/s]\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:55:03,662 Evaluating as a multi-label problem: True\n","2023-04-21 05:55:05,531 TRAIN : loss 0.30080002546310425 - f1-score (weighted avg)  0.2941\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.62it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:55:08,459 Evaluating as a multi-label problem: False\n","2023-04-21 05:55:08,527 DEV : loss 0.3065962791442871 - f1-score (weighted avg)  0.3015\n","2023-04-21 05:55:08,549 BAD EPOCHS (no improvement): 0\n","2023-04-21 05:55:08,554 saving best model\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:55:11,504 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:55:13,397 epoch 49 - iter 7/79 - loss 0.34188116 - time (sec): 1.89 - samples/sec: 4363.28 - lr: 0.000781\n","2023-04-21 05:55:15,637 epoch 49 - iter 14/79 - loss 0.34766007 - time (sec): 4.13 - samples/sec: 4026.91 - lr: 0.000781\n","2023-04-21 05:55:17,263 epoch 49 - iter 21/79 - loss 0.35142199 - time (sec): 5.76 - samples/sec: 4248.78 - lr: 0.000781\n","2023-04-21 05:55:19,039 epoch 49 - iter 28/79 - loss 0.34917321 - time (sec): 7.53 - samples/sec: 4225.52 - lr: 0.000781\n","2023-04-21 05:55:20,802 epoch 49 - iter 35/79 - loss 0.34730844 - time (sec): 9.30 - samples/sec: 4302.89 - lr: 0.000781\n","2023-04-21 05:55:23,111 epoch 49 - iter 42/79 - loss 0.34381728 - time (sec): 11.60 - samples/sec: 4247.80 - lr: 0.000781\n","2023-04-21 05:55:25,212 epoch 49 - iter 49/79 - loss 0.34264932 - time (sec): 13.71 - samples/sec: 4178.67 - lr: 0.000781\n","2023-04-21 05:55:27,239 epoch 49 - iter 56/79 - loss 0.34052538 - time (sec): 15.73 - samples/sec: 4148.39 - lr: 0.000781\n","2023-04-21 05:55:29,072 epoch 49 - iter 63/79 - loss 0.34187303 - time (sec): 17.57 - samples/sec: 4167.96 - lr: 0.000781\n","2023-04-21 05:55:30,897 epoch 49 - iter 70/79 - loss 0.34264007 - time (sec): 19.39 - samples/sec: 4282.81 - lr: 0.000781\n","2023-04-21 05:55:32,863 epoch 49 - iter 77/79 - loss 0.34203326 - time (sec): 21.36 - samples/sec: 4298.77 - lr: 0.000781\n","2023-04-21 05:55:33,385 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:55:33,387 EPOCH 49 done: loss 0.3426 - lr 0.000781\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.71it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:55:54,824 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:55:57,926 TRAIN : loss 0.30064883828163147 - f1-score (weighted avg)  0.2924\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.60it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:56:00,878 Evaluating as a multi-label problem: False\n","2023-04-21 05:56:00,946 DEV : loss 0.3066248893737793 - f1-score (weighted avg)  0.2966\n","2023-04-21 05:56:00,968 BAD EPOCHS (no improvement): 1\n","2023-04-21 05:56:00,974 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:56:03,096 epoch 50 - iter 7/79 - loss 0.34123109 - time (sec): 2.12 - samples/sec: 3906.51 - lr: 0.000781\n","2023-04-21 05:56:05,712 epoch 50 - iter 14/79 - loss 0.32495834 - time (sec): 4.74 - samples/sec: 3970.96 - lr: 0.000781\n","2023-04-21 05:56:07,517 epoch 50 - iter 21/79 - loss 0.33258374 - time (sec): 6.54 - samples/sec: 4113.02 - lr: 0.000781\n","2023-04-21 05:56:09,182 epoch 50 - iter 28/79 - loss 0.33135725 - time (sec): 8.21 - samples/sec: 4283.85 - lr: 0.000781\n","2023-04-21 05:56:10,994 epoch 50 - iter 35/79 - loss 0.33674118 - time (sec): 10.02 - samples/sec: 4287.12 - lr: 0.000781\n","2023-04-21 05:56:12,772 epoch 50 - iter 42/79 - loss 0.33869811 - time (sec): 11.80 - samples/sec: 4392.20 - lr: 0.000781\n","2023-04-21 05:56:14,627 epoch 50 - iter 49/79 - loss 0.33631242 - time (sec): 13.65 - samples/sec: 4383.49 - lr: 0.000781\n","2023-04-21 05:56:16,284 epoch 50 - iter 56/79 - loss 0.34003648 - time (sec): 15.31 - samples/sec: 4417.31 - lr: 0.000781\n","2023-04-21 05:56:17,934 epoch 50 - iter 63/79 - loss 0.34233537 - time (sec): 16.96 - samples/sec: 4486.09 - lr: 0.000781\n","2023-04-21 05:56:19,599 epoch 50 - iter 70/79 - loss 0.34145275 - time (sec): 18.62 - samples/sec: 4564.82 - lr: 0.000781\n","2023-04-21 05:56:21,231 epoch 50 - iter 77/79 - loss 0.34065673 - time (sec): 20.26 - samples/sec: 4538.00 - lr: 0.000781\n","2023-04-21 05:56:22,006 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:56:22,008 EPOCH 50 done: loss 0.3404 - lr 0.000781\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.74it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:56:43,293 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:56:45,158 TRAIN : loss 0.2988983988761902 - f1-score (weighted avg)  0.3034\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.62it/s]\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:56:49,181 Evaluating as a multi-label problem: False\n","2023-04-21 05:56:49,249 DEV : loss 0.30643072724342346 - f1-score (weighted avg)  0.2917\n","2023-04-21 05:56:49,272 BAD EPOCHS (no improvement): 0\n","2023-04-21 05:56:49,277 saving best model\n","2023-04-21 05:56:52,200 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:56:53,879 epoch 51 - iter 7/79 - loss 0.32400727 - time (sec): 1.68 - samples/sec: 4925.87 - lr: 0.000781\n","2023-04-21 05:56:55,532 epoch 51 - iter 14/79 - loss 0.33138228 - time (sec): 3.33 - samples/sec: 4775.21 - lr: 0.000781\n","2023-04-21 05:56:57,249 epoch 51 - iter 21/79 - loss 0.33874120 - time (sec): 5.05 - samples/sec: 4753.08 - lr: 0.000781\n","2023-04-21 05:56:58,926 epoch 51 - iter 28/79 - loss 0.34326342 - time (sec): 6.72 - samples/sec: 4823.72 - lr: 0.000781\n","2023-04-21 05:57:00,713 epoch 51 - iter 35/79 - loss 0.34143733 - time (sec): 8.51 - samples/sec: 4747.73 - lr: 0.000781\n","2023-04-21 05:57:02,751 epoch 51 - iter 42/79 - loss 0.33851914 - time (sec): 10.55 - samples/sec: 4661.24 - lr: 0.000781\n","2023-04-21 05:57:04,722 epoch 51 - iter 49/79 - loss 0.33828403 - time (sec): 12.52 - samples/sec: 4685.56 - lr: 0.000781\n","2023-04-21 05:57:06,443 epoch 51 - iter 56/79 - loss 0.33817521 - time (sec): 14.24 - samples/sec: 4661.81 - lr: 0.000781\n","2023-04-21 05:57:08,398 epoch 51 - iter 63/79 - loss 0.34026614 - time (sec): 16.20 - samples/sec: 4625.53 - lr: 0.000781\n","2023-04-21 05:57:10,091 epoch 51 - iter 70/79 - loss 0.33820906 - time (sec): 17.89 - samples/sec: 4656.25 - lr: 0.000781\n","2023-04-21 05:57:12,651 epoch 51 - iter 77/79 - loss 0.34088978 - time (sec): 20.45 - samples/sec: 4507.54 - lr: 0.000781\n","2023-04-21 05:57:13,262 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:57:13,264 EPOCH 51 done: loss 0.3407 - lr 0.000781\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.75it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:57:34,450 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:57:36,313 TRAIN : loss 0.30027395486831665 - f1-score (weighted avg)  0.3001\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.65it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:57:39,219 Evaluating as a multi-label problem: False\n","2023-04-21 05:57:39,287 DEV : loss 0.30633944272994995 - f1-score (weighted avg)  0.306\n","2023-04-21 05:57:39,311 BAD EPOCHS (no improvement): 0\n","2023-04-21 05:57:39,315 saving best model\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:57:42,260 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:57:44,115 epoch 52 - iter 7/79 - loss 0.33789785 - time (sec): 1.84 - samples/sec: 4702.13 - lr: 0.000781\n","2023-04-21 05:57:45,795 epoch 52 - iter 14/79 - loss 0.34649808 - time (sec): 3.52 - samples/sec: 4601.70 - lr: 0.000781\n","2023-04-21 05:57:47,449 epoch 52 - iter 21/79 - loss 0.34625743 - time (sec): 5.17 - samples/sec: 4742.71 - lr: 0.000781\n","2023-04-21 05:57:49,503 epoch 52 - iter 28/79 - loss 0.34446593 - time (sec): 7.23 - samples/sec: 4501.61 - lr: 0.000781\n","2023-04-21 05:57:51,244 epoch 52 - iter 35/79 - loss 0.34985851 - time (sec): 8.97 - samples/sec: 4544.49 - lr: 0.000781\n","2023-04-21 05:57:52,815 epoch 52 - iter 42/79 - loss 0.34790932 - time (sec): 10.54 - samples/sec: 4697.12 - lr: 0.000781\n","2023-04-21 05:57:54,788 epoch 52 - iter 49/79 - loss 0.34644172 - time (sec): 12.51 - samples/sec: 4608.03 - lr: 0.000781\n","2023-04-21 05:57:56,795 epoch 52 - iter 56/79 - loss 0.34367413 - time (sec): 14.52 - samples/sec: 4559.46 - lr: 0.000781\n","2023-04-21 05:57:58,891 epoch 52 - iter 63/79 - loss 0.34255073 - time (sec): 16.61 - samples/sec: 4504.64 - lr: 0.000781\n","2023-04-21 05:58:00,829 epoch 52 - iter 70/79 - loss 0.34325406 - time (sec): 18.55 - samples/sec: 4521.66 - lr: 0.000781\n","2023-04-21 05:58:03,299 epoch 52 - iter 77/79 - loss 0.34234254 - time (sec): 21.02 - samples/sec: 4397.69 - lr: 0.000781\n","2023-04-21 05:58:03,836 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:58:03,838 EPOCH 52 done: loss 0.3422 - lr 0.000781\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.55it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:58:26,201 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:58:28,042 TRAIN : loss 0.30182480812072754 - f1-score (weighted avg)  0.2799\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.63it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:58:30,972 Evaluating as a multi-label problem: False\n","2023-04-21 05:58:31,037 DEV : loss 0.3062654435634613 - f1-score (weighted avg)  0.2888\n","2023-04-21 05:58:31,059 BAD EPOCHS (no improvement): 0\n","2023-04-21 05:58:31,063 saving best model\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:58:34,013 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:58:35,779 epoch 53 - iter 7/79 - loss 0.34925424 - time (sec): 1.76 - samples/sec: 4426.32 - lr: 0.000781\n","2023-04-21 05:58:37,864 epoch 53 - iter 14/79 - loss 0.33483911 - time (sec): 3.85 - samples/sec: 4113.85 - lr: 0.000781\n","2023-04-21 05:58:39,774 epoch 53 - iter 21/79 - loss 0.32990512 - time (sec): 5.76 - samples/sec: 4311.55 - lr: 0.000781\n","2023-04-21 05:58:41,545 epoch 53 - iter 28/79 - loss 0.33314878 - time (sec): 7.53 - samples/sec: 4345.44 - lr: 0.000781\n","2023-04-21 05:58:43,613 epoch 53 - iter 35/79 - loss 0.33550856 - time (sec): 9.60 - samples/sec: 4329.87 - lr: 0.000781\n","2023-04-21 05:58:45,391 epoch 53 - iter 42/79 - loss 0.34028782 - time (sec): 11.38 - samples/sec: 4355.56 - lr: 0.000781\n","2023-04-21 05:58:46,952 epoch 53 - iter 49/79 - loss 0.34369386 - time (sec): 12.94 - samples/sec: 4434.36 - lr: 0.000781\n","2023-04-21 05:58:48,709 epoch 53 - iter 56/79 - loss 0.34287051 - time (sec): 14.69 - samples/sec: 4507.05 - lr: 0.000781\n","2023-04-21 05:58:50,800 epoch 53 - iter 63/79 - loss 0.34036255 - time (sec): 16.78 - samples/sec: 4495.77 - lr: 0.000781\n","2023-04-21 05:58:53,164 epoch 53 - iter 70/79 - loss 0.33905288 - time (sec): 19.15 - samples/sec: 4391.43 - lr: 0.000781\n","2023-04-21 05:58:54,917 epoch 53 - iter 77/79 - loss 0.33730033 - time (sec): 20.90 - samples/sec: 4440.77 - lr: 0.000781\n","2023-04-21 05:58:55,284 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:58:55,286 EPOCH 53 done: loss 0.3371 - lr 0.000781\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.54it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:59:17,736 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:59:19,596 TRAIN : loss 0.3002476692199707 - f1-score (weighted avg)  0.2818\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.64it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:59:22,500 Evaluating as a multi-label problem: False\n","2023-04-21 05:59:22,566 DEV : loss 0.30510029196739197 - f1-score (weighted avg)  0.2885\n","2023-04-21 05:59:22,587 BAD EPOCHS (no improvement): 0\n","2023-04-21 05:59:22,592 saving best model\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 05:59:25,531 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:59:27,027 epoch 54 - iter 7/79 - loss 0.38330010 - time (sec): 1.48 - samples/sec: 4853.63 - lr: 0.000781\n","2023-04-21 05:59:29,197 epoch 54 - iter 14/79 - loss 0.35012495 - time (sec): 3.65 - samples/sec: 4343.36 - lr: 0.000781\n","2023-04-21 05:59:31,119 epoch 54 - iter 21/79 - loss 0.34717754 - time (sec): 5.57 - samples/sec: 4432.72 - lr: 0.000781\n","2023-04-21 05:59:32,909 epoch 54 - iter 28/79 - loss 0.34601307 - time (sec): 7.36 - samples/sec: 4561.72 - lr: 0.000781\n","2023-04-21 05:59:35,389 epoch 54 - iter 35/79 - loss 0.34487769 - time (sec): 9.84 - samples/sec: 4278.11 - lr: 0.000781\n","2023-04-21 05:59:37,353 epoch 54 - iter 42/79 - loss 0.34374971 - time (sec): 11.81 - samples/sec: 4217.90 - lr: 0.000781\n","2023-04-21 05:59:39,329 epoch 54 - iter 49/79 - loss 0.34436316 - time (sec): 13.78 - samples/sec: 4231.66 - lr: 0.000781\n","2023-04-21 05:59:41,089 epoch 54 - iter 56/79 - loss 0.34496378 - time (sec): 15.54 - samples/sec: 4248.35 - lr: 0.000781\n","2023-04-21 05:59:42,935 epoch 54 - iter 63/79 - loss 0.34428386 - time (sec): 17.39 - samples/sec: 4321.85 - lr: 0.000781\n","2023-04-21 05:59:45,130 epoch 54 - iter 70/79 - loss 0.34264420 - time (sec): 19.58 - samples/sec: 4260.21 - lr: 0.000781\n","2023-04-21 05:59:47,104 epoch 54 - iter 77/79 - loss 0.34082742 - time (sec): 21.56 - samples/sec: 4279.15 - lr: 0.000781\n","2023-04-21 05:59:47,743 ----------------------------------------------------------------------------------------------------\n","2023-04-21 05:59:47,744 EPOCH 54 done: loss 0.3409 - lr 0.000781\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.73it/s]\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:00:10,249 Evaluating as a multi-label problem: True\n","2023-04-21 06:00:12,088 TRAIN : loss 0.30630794167518616 - f1-score (weighted avg)  0.2512\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.63it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:00:15,007 Evaluating as a multi-label problem: False\n","2023-04-21 06:00:15,075 DEV : loss 0.30888959765434265 - f1-score (weighted avg)  0.271\n","2023-04-21 06:00:15,098 BAD EPOCHS (no improvement): 1\n","2023-04-21 06:00:15,103 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:00:17,176 epoch 55 - iter 7/79 - loss 0.34547303 - time (sec): 2.07 - samples/sec: 4052.08 - lr: 0.000781\n","2023-04-21 06:00:18,565 epoch 55 - iter 14/79 - loss 0.35175519 - time (sec): 3.46 - samples/sec: 4686.29 - lr: 0.000781\n","2023-04-21 06:00:20,206 epoch 55 - iter 21/79 - loss 0.35058590 - time (sec): 5.10 - samples/sec: 4641.13 - lr: 0.000781\n","2023-04-21 06:00:22,085 epoch 55 - iter 28/79 - loss 0.35078669 - time (sec): 6.98 - samples/sec: 4594.74 - lr: 0.000781\n","2023-04-21 06:00:24,140 epoch 55 - iter 35/79 - loss 0.34654201 - time (sec): 9.04 - samples/sec: 4518.62 - lr: 0.000781\n","2023-04-21 06:00:25,812 epoch 55 - iter 42/79 - loss 0.34449420 - time (sec): 10.71 - samples/sec: 4590.54 - lr: 0.000781\n","2023-04-21 06:00:27,601 epoch 55 - iter 49/79 - loss 0.34857683 - time (sec): 12.50 - samples/sec: 4617.76 - lr: 0.000781\n","2023-04-21 06:00:29,920 epoch 55 - iter 56/79 - loss 0.34424205 - time (sec): 14.82 - samples/sec: 4500.48 - lr: 0.000781\n","2023-04-21 06:00:31,650 epoch 55 - iter 63/79 - loss 0.34019926 - time (sec): 16.55 - samples/sec: 4490.87 - lr: 0.000781\n","2023-04-21 06:00:33,541 epoch 55 - iter 70/79 - loss 0.33975466 - time (sec): 18.44 - samples/sec: 4492.51 - lr: 0.000781\n","2023-04-21 06:00:35,670 epoch 55 - iter 77/79 - loss 0.34081482 - time (sec): 20.57 - samples/sec: 4503.94 - lr: 0.000781\n","2023-04-21 06:00:36,091 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:00:36,093 EPOCH 55 done: loss 0.3400 - lr 0.000781\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.73it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:00:57,408 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:01:00,543 TRAIN : loss 0.29685160517692566 - f1-score (weighted avg)  0.3052\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.67it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:01:03,426 Evaluating as a multi-label problem: False\n","2023-04-21 06:01:03,493 DEV : loss 0.30187302827835083 - f1-score (weighted avg)  0.3055\n","2023-04-21 06:01:03,514 BAD EPOCHS (no improvement): 0\n","2023-04-21 06:01:03,519 saving best model\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:01:06,457 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:01:08,338 epoch 56 - iter 7/79 - loss 0.33461622 - time (sec): 1.88 - samples/sec: 4216.77 - lr: 0.000781\n","2023-04-21 06:01:09,908 epoch 56 - iter 14/79 - loss 0.34074074 - time (sec): 3.45 - samples/sec: 4576.64 - lr: 0.000781\n","2023-04-21 06:01:12,454 epoch 56 - iter 21/79 - loss 0.33425098 - time (sec): 5.99 - samples/sec: 4260.45 - lr: 0.000781\n","2023-04-21 06:01:14,934 epoch 56 - iter 28/79 - loss 0.33396173 - time (sec): 8.47 - samples/sec: 4081.28 - lr: 0.000781\n","2023-04-21 06:01:16,989 epoch 56 - iter 35/79 - loss 0.33531110 - time (sec): 10.53 - samples/sec: 4085.34 - lr: 0.000781\n","2023-04-21 06:01:18,924 epoch 56 - iter 42/79 - loss 0.33366709 - time (sec): 12.46 - samples/sec: 4189.54 - lr: 0.000781\n","2023-04-21 06:01:20,787 epoch 56 - iter 49/79 - loss 0.33352345 - time (sec): 14.33 - samples/sec: 4240.09 - lr: 0.000781\n","2023-04-21 06:01:22,720 epoch 56 - iter 56/79 - loss 0.33454150 - time (sec): 16.26 - samples/sec: 4248.54 - lr: 0.000781\n","2023-04-21 06:01:24,559 epoch 56 - iter 63/79 - loss 0.33622937 - time (sec): 18.10 - samples/sec: 4271.48 - lr: 0.000781\n","2023-04-21 06:01:26,142 epoch 56 - iter 70/79 - loss 0.33746966 - time (sec): 19.68 - samples/sec: 4310.28 - lr: 0.000781\n","2023-04-21 06:01:27,690 epoch 56 - iter 77/79 - loss 0.33931023 - time (sec): 21.23 - samples/sec: 4367.71 - lr: 0.000781\n","2023-04-21 06:01:28,126 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:01:28,129 EPOCH 56 done: loss 0.3394 - lr 0.000781\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.74it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:01:49,410 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:01:51,257 TRAIN : loss 0.30056366324424744 - f1-score (weighted avg)  0.2926\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.59it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:01:54,203 Evaluating as a multi-label problem: False\n","2023-04-21 06:01:54,268 DEV : loss 0.3058890402317047 - f1-score (weighted avg)  0.2963\n","2023-04-21 06:01:54,290 BAD EPOCHS (no improvement): 1\n","2023-04-21 06:01:54,295 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:01:56,051 epoch 57 - iter 7/79 - loss 0.36604550 - time (sec): 1.75 - samples/sec: 4571.60 - lr: 0.000781\n","2023-04-21 06:01:57,913 epoch 57 - iter 14/79 - loss 0.35335795 - time (sec): 3.62 - samples/sec: 4635.25 - lr: 0.000781\n","2023-04-21 06:01:59,653 epoch 57 - iter 21/79 - loss 0.34754075 - time (sec): 5.36 - samples/sec: 4647.11 - lr: 0.000781\n","2023-04-21 06:02:01,554 epoch 57 - iter 28/79 - loss 0.33831022 - time (sec): 7.26 - samples/sec: 4571.50 - lr: 0.000781\n","2023-04-21 06:02:03,463 epoch 57 - iter 35/79 - loss 0.33673086 - time (sec): 9.17 - samples/sec: 4606.90 - lr: 0.000781\n","2023-04-21 06:02:05,307 epoch 57 - iter 42/79 - loss 0.33865105 - time (sec): 11.01 - samples/sec: 4592.95 - lr: 0.000781\n","2023-04-21 06:02:07,192 epoch 57 - iter 49/79 - loss 0.34261201 - time (sec): 12.90 - samples/sec: 4570.13 - lr: 0.000781\n","2023-04-21 06:02:09,084 epoch 57 - iter 56/79 - loss 0.34153940 - time (sec): 14.79 - samples/sec: 4613.98 - lr: 0.000781\n","2023-04-21 06:02:10,802 epoch 57 - iter 63/79 - loss 0.33764138 - time (sec): 16.51 - samples/sec: 4636.53 - lr: 0.000781\n","2023-04-21 06:02:12,911 epoch 57 - iter 70/79 - loss 0.33765240 - time (sec): 18.62 - samples/sec: 4544.10 - lr: 0.000781\n","2023-04-21 06:02:14,347 epoch 57 - iter 77/79 - loss 0.33900108 - time (sec): 20.05 - samples/sec: 4591.82 - lr: 0.000781\n","2023-04-21 06:02:14,963 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:02:14,964 EPOCH 57 done: loss 0.3375 - lr 0.000781\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.53it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:02:37,479 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:02:39,328 TRAIN : loss 0.30057594180107117 - f1-score (weighted avg)  0.3143\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.60it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:02:42,267 Evaluating as a multi-label problem: False\n","2023-04-21 06:02:42,336 DEV : loss 0.3099925220012665 - f1-score (weighted avg)  0.3046\n","2023-04-21 06:02:42,358 Epoch    57: reducing learning rate of group 0 to 3.9063e-04.\n","2023-04-21 06:02:42,360 BAD EPOCHS (no improvement): 2\n","2023-04-21 06:02:42,364 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:02:43,789 epoch 58 - iter 7/79 - loss 0.37397855 - time (sec): 1.42 - samples/sec: 4289.23 - lr: 0.000391\n","2023-04-21 06:02:45,627 epoch 58 - iter 14/79 - loss 0.34847087 - time (sec): 3.26 - samples/sec: 4627.10 - lr: 0.000391\n","2023-04-21 06:02:47,535 epoch 58 - iter 21/79 - loss 0.34877985 - time (sec): 5.17 - samples/sec: 4756.73 - lr: 0.000391\n","2023-04-21 06:02:49,196 epoch 58 - iter 28/79 - loss 0.34599673 - time (sec): 6.83 - samples/sec: 4767.34 - lr: 0.000391\n","2023-04-21 06:02:50,966 epoch 58 - iter 35/79 - loss 0.34160800 - time (sec): 8.60 - samples/sec: 4845.14 - lr: 0.000391\n","2023-04-21 06:02:53,112 epoch 58 - iter 42/79 - loss 0.33982463 - time (sec): 10.75 - samples/sec: 4712.48 - lr: 0.000391\n","2023-04-21 06:02:54,833 epoch 58 - iter 49/79 - loss 0.34045294 - time (sec): 12.47 - samples/sec: 4715.31 - lr: 0.000391\n","2023-04-21 06:02:56,953 epoch 58 - iter 56/79 - loss 0.33903432 - time (sec): 14.59 - samples/sec: 4685.19 - lr: 0.000391\n","2023-04-21 06:02:58,897 epoch 58 - iter 63/79 - loss 0.33902639 - time (sec): 16.53 - samples/sec: 4687.18 - lr: 0.000391\n","2023-04-21 06:03:00,411 epoch 58 - iter 70/79 - loss 0.34090367 - time (sec): 18.05 - samples/sec: 4715.09 - lr: 0.000391\n","2023-04-21 06:03:02,038 epoch 58 - iter 77/79 - loss 0.34005228 - time (sec): 19.67 - samples/sec: 4682.09 - lr: 0.000391\n","2023-04-21 06:03:02,738 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:03:02,740 EPOCH 58 done: loss 0.3397 - lr 0.000391\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.54it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:03:25,185 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:03:27,028 TRAIN : loss 0.30225875973701477 - f1-score (weighted avg)  0.2918\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.54it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:03:30,021 Evaluating as a multi-label problem: False\n","2023-04-21 06:03:30,091 DEV : loss 0.3093827962875366 - f1-score (weighted avg)  0.3021\n","2023-04-21 06:03:30,115 BAD EPOCHS (no improvement): 1\n","2023-04-21 06:03:30,120 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:03:31,942 epoch 59 - iter 7/79 - loss 0.33719871 - time (sec): 1.82 - samples/sec: 4857.43 - lr: 0.000391\n","2023-04-21 06:03:33,644 epoch 59 - iter 14/79 - loss 0.33890881 - time (sec): 3.52 - samples/sec: 4623.11 - lr: 0.000391\n","2023-04-21 06:03:35,645 epoch 59 - iter 21/79 - loss 0.33927071 - time (sec): 5.52 - samples/sec: 4362.71 - lr: 0.000391\n","2023-04-21 06:03:37,838 epoch 59 - iter 28/79 - loss 0.34133171 - time (sec): 7.72 - samples/sec: 4179.58 - lr: 0.000391\n","2023-04-21 06:03:39,530 epoch 59 - iter 35/79 - loss 0.34087662 - time (sec): 9.41 - samples/sec: 4289.80 - lr: 0.000391\n","2023-04-21 06:03:41,536 epoch 59 - iter 42/79 - loss 0.33729954 - time (sec): 11.41 - samples/sec: 4379.35 - lr: 0.000391\n","2023-04-21 06:03:43,879 epoch 59 - iter 49/79 - loss 0.33548652 - time (sec): 13.76 - samples/sec: 4314.53 - lr: 0.000391\n","2023-04-21 06:03:45,753 epoch 59 - iter 56/79 - loss 0.33603632 - time (sec): 15.63 - samples/sec: 4330.32 - lr: 0.000391\n","2023-04-21 06:03:47,511 epoch 59 - iter 63/79 - loss 0.33781924 - time (sec): 17.39 - samples/sec: 4412.49 - lr: 0.000391\n","2023-04-21 06:03:49,188 epoch 59 - iter 70/79 - loss 0.33766502 - time (sec): 19.07 - samples/sec: 4457.42 - lr: 0.000391\n","2023-04-21 06:03:50,731 epoch 59 - iter 77/79 - loss 0.33755344 - time (sec): 20.61 - samples/sec: 4498.73 - lr: 0.000391\n","2023-04-21 06:03:51,164 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:03:51,166 EPOCH 59 done: loss 0.3385 - lr 0.000391\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.74it/s]\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:04:13,649 Evaluating as a multi-label problem: True\n","2023-04-21 06:04:15,513 TRAIN : loss 0.3009488880634308 - f1-score (weighted avg)  0.2848\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.66it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:04:18,404 Evaluating as a multi-label problem: False\n","2023-04-21 06:04:18,470 DEV : loss 0.3072778582572937 - f1-score (weighted avg)  0.2902\n","2023-04-21 06:04:18,491 Epoch    59: reducing learning rate of group 0 to 1.9531e-04.\n","2023-04-21 06:04:18,494 BAD EPOCHS (no improvement): 2\n","2023-04-21 06:04:18,497 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:04:20,720 epoch 60 - iter 7/79 - loss 0.34805206 - time (sec): 2.22 - samples/sec: 3902.30 - lr: 0.000195\n","2023-04-21 06:04:22,820 epoch 60 - iter 14/79 - loss 0.32761662 - time (sec): 4.32 - samples/sec: 4222.00 - lr: 0.000195\n","2023-04-21 06:04:24,688 epoch 60 - iter 21/79 - loss 0.33264476 - time (sec): 6.19 - samples/sec: 4396.75 - lr: 0.000195\n","2023-04-21 06:04:26,250 epoch 60 - iter 28/79 - loss 0.33280476 - time (sec): 7.75 - samples/sec: 4516.26 - lr: 0.000195\n","2023-04-21 06:04:28,150 epoch 60 - iter 35/79 - loss 0.33829182 - time (sec): 9.65 - samples/sec: 4485.65 - lr: 0.000195\n","2023-04-21 06:04:30,252 epoch 60 - iter 42/79 - loss 0.33762131 - time (sec): 11.75 - samples/sec: 4487.11 - lr: 0.000195\n","2023-04-21 06:04:31,980 epoch 60 - iter 49/79 - loss 0.34137817 - time (sec): 13.48 - samples/sec: 4541.72 - lr: 0.000195\n","2023-04-21 06:04:33,525 epoch 60 - iter 56/79 - loss 0.33953714 - time (sec): 15.03 - samples/sec: 4590.03 - lr: 0.000195\n","2023-04-21 06:04:35,680 epoch 60 - iter 63/79 - loss 0.33880698 - time (sec): 17.18 - samples/sec: 4511.34 - lr: 0.000195\n","2023-04-21 06:04:37,573 epoch 60 - iter 70/79 - loss 0.33806614 - time (sec): 19.07 - samples/sec: 4446.99 - lr: 0.000195\n","2023-04-21 06:04:39,255 epoch 60 - iter 77/79 - loss 0.33686959 - time (sec): 20.76 - samples/sec: 4460.77 - lr: 0.000195\n","2023-04-21 06:04:39,658 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:04:39,660 EPOCH 60 done: loss 0.3381 - lr 0.000195\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.74it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:05:00,921 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:05:04,009 TRAIN : loss 0.30068865418434143 - f1-score (weighted avg)  0.2929\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.66it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:05:06,910 Evaluating as a multi-label problem: False\n","2023-04-21 06:05:06,978 DEV : loss 0.30701810121536255 - f1-score (weighted avg)  0.2959\n","2023-04-21 06:05:07,000 BAD EPOCHS (no improvement): 1\n","2023-04-21 06:05:07,005 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:05:08,618 epoch 61 - iter 7/79 - loss 0.35199324 - time (sec): 1.61 - samples/sec: 4834.64 - lr: 0.000195\n","2023-04-21 06:05:10,208 epoch 61 - iter 14/79 - loss 0.33890468 - time (sec): 3.20 - samples/sec: 5061.52 - lr: 0.000195\n","2023-04-21 06:05:12,138 epoch 61 - iter 21/79 - loss 0.33844112 - time (sec): 5.13 - samples/sec: 4792.12 - lr: 0.000195\n","2023-04-21 06:05:13,782 epoch 61 - iter 28/79 - loss 0.33597059 - time (sec): 6.78 - samples/sec: 4821.80 - lr: 0.000195\n","2023-04-21 06:05:15,490 epoch 61 - iter 35/79 - loss 0.33734478 - time (sec): 8.48 - samples/sec: 4812.28 - lr: 0.000195\n","2023-04-21 06:05:17,405 epoch 61 - iter 42/79 - loss 0.33581242 - time (sec): 10.40 - samples/sec: 4717.76 - lr: 0.000195\n","2023-04-21 06:05:19,038 epoch 61 - iter 49/79 - loss 0.33490433 - time (sec): 12.03 - samples/sec: 4766.10 - lr: 0.000195\n","2023-04-21 06:05:21,175 epoch 61 - iter 56/79 - loss 0.33491384 - time (sec): 14.17 - samples/sec: 4703.60 - lr: 0.000195\n","2023-04-21 06:05:23,246 epoch 61 - iter 63/79 - loss 0.33453136 - time (sec): 16.24 - samples/sec: 4638.91 - lr: 0.000195\n","2023-04-21 06:05:25,050 epoch 61 - iter 70/79 - loss 0.33669639 - time (sec): 18.04 - samples/sec: 4619.90 - lr: 0.000195\n","2023-04-21 06:05:27,333 epoch 61 - iter 77/79 - loss 0.33804748 - time (sec): 20.33 - samples/sec: 4529.86 - lr: 0.000195\n","2023-04-21 06:05:27,875 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:05:27,876 EPOCH 61 done: loss 0.3380 - lr 0.000195\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.74it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:05:49,118 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:05:50,978 TRAIN : loss 0.29936230182647705 - f1-score (weighted avg)  0.2992\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.68it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:05:53,863 Evaluating as a multi-label problem: False\n","2023-04-21 06:05:53,929 DEV : loss 0.30608412623405457 - f1-score (weighted avg)  0.3014\n","2023-04-21 06:05:53,952 Epoch    61: reducing learning rate of group 0 to 9.7656e-05.\n","2023-04-21 06:05:53,954 BAD EPOCHS (no improvement): 2\n","2023-04-21 06:05:53,958 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:05:56,215 epoch 62 - iter 7/79 - loss 0.35028128 - time (sec): 2.26 - samples/sec: 4192.40 - lr: 0.000098\n","2023-04-21 06:05:58,539 epoch 62 - iter 14/79 - loss 0.34853947 - time (sec): 4.58 - samples/sec: 4143.79 - lr: 0.000098\n","2023-04-21 06:06:00,490 epoch 62 - iter 21/79 - loss 0.34161873 - time (sec): 6.53 - samples/sec: 4238.79 - lr: 0.000098\n","2023-04-21 06:06:02,347 epoch 62 - iter 28/79 - loss 0.34831462 - time (sec): 8.39 - samples/sec: 4298.53 - lr: 0.000098\n","2023-04-21 06:06:05,232 epoch 62 - iter 35/79 - loss 0.34233757 - time (sec): 11.27 - samples/sec: 3905.37 - lr: 0.000098\n","2023-04-21 06:06:06,874 epoch 62 - iter 42/79 - loss 0.34218468 - time (sec): 12.91 - samples/sec: 3991.17 - lr: 0.000098\n","2023-04-21 06:06:08,527 epoch 62 - iter 49/79 - loss 0.33897197 - time (sec): 14.57 - samples/sec: 4094.08 - lr: 0.000098\n","2023-04-21 06:06:10,312 epoch 62 - iter 56/79 - loss 0.33981602 - time (sec): 16.35 - samples/sec: 4188.51 - lr: 0.000098\n","2023-04-21 06:06:12,102 epoch 62 - iter 63/79 - loss 0.34304859 - time (sec): 18.14 - samples/sec: 4249.74 - lr: 0.000098\n","2023-04-21 06:06:13,635 epoch 62 - iter 70/79 - loss 0.34002335 - time (sec): 19.68 - samples/sec: 4294.61 - lr: 0.000098\n","2023-04-21 06:06:15,318 epoch 62 - iter 77/79 - loss 0.33857618 - time (sec): 21.36 - samples/sec: 4298.14 - lr: 0.000098\n","2023-04-21 06:06:16,006 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:06:16,008 EPOCH 62 done: loss 0.3382 - lr 0.000098\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.71it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:06:37,456 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:06:39,328 TRAIN : loss 0.30082446336746216 - f1-score (weighted avg)  0.2919\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.62it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:06:42,253 Evaluating as a multi-label problem: False\n","2023-04-21 06:06:42,322 DEV : loss 0.3071078658103943 - f1-score (weighted avg)  0.2985\n","2023-04-21 06:06:42,344 BAD EPOCHS (no improvement): 1\n","2023-04-21 06:06:42,348 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:06:44,714 epoch 63 - iter 7/79 - loss 0.33703728 - time (sec): 2.36 - samples/sec: 3866.18 - lr: 0.000098\n","2023-04-21 06:06:46,182 epoch 63 - iter 14/79 - loss 0.35265556 - time (sec): 3.83 - samples/sec: 4205.02 - lr: 0.000098\n","2023-04-21 06:06:47,850 epoch 63 - iter 21/79 - loss 0.34505360 - time (sec): 5.50 - samples/sec: 4451.73 - lr: 0.000098\n","2023-04-21 06:06:50,363 epoch 63 - iter 28/79 - loss 0.34147722 - time (sec): 8.01 - samples/sec: 4182.86 - lr: 0.000098\n","2023-04-21 06:06:52,143 epoch 63 - iter 35/79 - loss 0.34126035 - time (sec): 9.79 - samples/sec: 4280.83 - lr: 0.000098\n","2023-04-21 06:06:53,909 epoch 63 - iter 42/79 - loss 0.33948470 - time (sec): 11.56 - samples/sec: 4378.07 - lr: 0.000098\n","2023-04-21 06:06:55,533 epoch 63 - iter 49/79 - loss 0.33916294 - time (sec): 13.18 - samples/sec: 4442.49 - lr: 0.000098\n","2023-04-21 06:06:57,274 epoch 63 - iter 56/79 - loss 0.33986015 - time (sec): 14.92 - samples/sec: 4485.68 - lr: 0.000098\n","2023-04-21 06:06:59,080 epoch 63 - iter 63/79 - loss 0.33864564 - time (sec): 16.73 - samples/sec: 4458.14 - lr: 0.000098\n","2023-04-21 06:07:00,816 epoch 63 - iter 70/79 - loss 0.33945384 - time (sec): 18.47 - samples/sec: 4472.59 - lr: 0.000098\n","2023-04-21 06:07:02,802 epoch 63 - iter 77/79 - loss 0.33781194 - time (sec): 20.45 - samples/sec: 4503.35 - lr: 0.000098\n","2023-04-21 06:07:03,314 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:07:03,316 EPOCH 63 done: loss 0.3368 - lr 0.000098\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.55it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:07:25,693 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:07:27,564 TRAIN : loss 0.29972705245018005 - f1-score (weighted avg)  0.2978\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.51it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:07:30,577 Evaluating as a multi-label problem: False\n","2023-04-21 06:07:30,643 DEV : loss 0.30626773834228516 - f1-score (weighted avg)  0.3012\n","2023-04-21 06:07:30,664 Epoch    63: reducing learning rate of group 0 to 4.8828e-05.\n","2023-04-21 06:07:30,666 BAD EPOCHS (no improvement): 2\n","2023-04-21 06:07:30,669 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:07:32,365 epoch 64 - iter 7/79 - loss 0.35571132 - time (sec): 1.69 - samples/sec: 4952.71 - lr: 0.000049\n","2023-04-21 06:07:34,118 epoch 64 - iter 14/79 - loss 0.34206943 - time (sec): 3.45 - samples/sec: 4924.76 - lr: 0.000049\n","2023-04-21 06:07:35,725 epoch 64 - iter 21/79 - loss 0.34545580 - time (sec): 5.05 - samples/sec: 4924.95 - lr: 0.000049\n","2023-04-21 06:07:37,594 epoch 64 - iter 28/79 - loss 0.34257308 - time (sec): 6.92 - samples/sec: 4948.19 - lr: 0.000049\n","2023-04-21 06:07:39,590 epoch 64 - iter 35/79 - loss 0.33695657 - time (sec): 8.92 - samples/sec: 4844.51 - lr: 0.000049\n","2023-04-21 06:07:42,197 epoch 64 - iter 42/79 - loss 0.33343996 - time (sec): 11.53 - samples/sec: 4591.07 - lr: 0.000049\n","2023-04-21 06:07:43,951 epoch 64 - iter 49/79 - loss 0.32969283 - time (sec): 13.28 - samples/sec: 4629.19 - lr: 0.000049\n","2023-04-21 06:07:45,726 epoch 64 - iter 56/79 - loss 0.33658475 - time (sec): 15.05 - samples/sec: 4580.59 - lr: 0.000049\n","2023-04-21 06:07:47,414 epoch 64 - iter 63/79 - loss 0.33789561 - time (sec): 16.74 - samples/sec: 4654.46 - lr: 0.000049\n","2023-04-21 06:07:48,982 epoch 64 - iter 70/79 - loss 0.33765522 - time (sec): 18.31 - samples/sec: 4660.49 - lr: 0.000049\n","2023-04-21 06:07:50,727 epoch 64 - iter 77/79 - loss 0.33807893 - time (sec): 20.06 - samples/sec: 4622.97 - lr: 0.000049\n","2023-04-21 06:07:51,128 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:07:51,130 EPOCH 64 done: loss 0.3379 - lr 0.000049\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.55it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:08:13,546 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:08:15,393 TRAIN : loss 0.30037692189216614 - f1-score (weighted avg)  0.2922\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.57it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:08:18,359 Evaluating as a multi-label problem: False\n","2023-04-21 06:08:18,427 DEV : loss 0.30675652623176575 - f1-score (weighted avg)  0.298\n","2023-04-21 06:08:18,449 BAD EPOCHS (no improvement): 1\n","2023-04-21 06:08:18,454 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:08:20,036 epoch 65 - iter 7/79 - loss 0.36044923 - time (sec): 1.58 - samples/sec: 4930.95 - lr: 0.000049\n","2023-04-21 06:08:21,875 epoch 65 - iter 14/79 - loss 0.36330768 - time (sec): 3.42 - samples/sec: 4583.59 - lr: 0.000049\n","2023-04-21 06:08:23,843 epoch 65 - iter 21/79 - loss 0.34530518 - time (sec): 5.39 - samples/sec: 4655.62 - lr: 0.000049\n","2023-04-21 06:08:25,374 epoch 65 - iter 28/79 - loss 0.34732403 - time (sec): 6.92 - samples/sec: 4675.57 - lr: 0.000049\n","2023-04-21 06:08:27,024 epoch 65 - iter 35/79 - loss 0.34788814 - time (sec): 8.57 - samples/sec: 4664.99 - lr: 0.000049\n","2023-04-21 06:08:28,802 epoch 65 - iter 42/79 - loss 0.34454627 - time (sec): 10.35 - samples/sec: 4737.96 - lr: 0.000049\n","2023-04-21 06:08:31,601 epoch 65 - iter 49/79 - loss 0.34358952 - time (sec): 13.14 - samples/sec: 4485.66 - lr: 0.000049\n","2023-04-21 06:08:33,397 epoch 65 - iter 56/79 - loss 0.34078071 - time (sec): 14.94 - samples/sec: 4459.62 - lr: 0.000049\n","2023-04-21 06:08:35,207 epoch 65 - iter 63/79 - loss 0.33938151 - time (sec): 16.75 - samples/sec: 4481.59 - lr: 0.000049\n","2023-04-21 06:08:36,786 epoch 65 - iter 70/79 - loss 0.33691378 - time (sec): 18.33 - samples/sec: 4516.61 - lr: 0.000049\n","2023-04-21 06:08:38,630 epoch 65 - iter 77/79 - loss 0.33693645 - time (sec): 20.17 - samples/sec: 4579.13 - lr: 0.000049\n","2023-04-21 06:08:39,168 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:08:39,170 EPOCH 65 done: loss 0.3382 - lr 0.000049\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.73it/s]\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:09:01,717 Evaluating as a multi-label problem: True\n","2023-04-21 06:09:03,582 TRAIN : loss 0.3007950782775879 - f1-score (weighted avg)  0.2909\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.65it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:09:06,486 Evaluating as a multi-label problem: False\n","2023-04-21 06:09:06,553 DEV : loss 0.3068401515483856 - f1-score (weighted avg)  0.2961\n","2023-04-21 06:09:06,575 Epoch    65: reducing learning rate of group 0 to 2.4414e-05.\n","2023-04-21 06:09:06,577 BAD EPOCHS (no improvement): 2\n","2023-04-21 06:09:06,581 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:09:08,384 epoch 66 - iter 7/79 - loss 0.33370109 - time (sec): 1.80 - samples/sec: 4423.73 - lr: 0.000024\n","2023-04-21 06:09:10,118 epoch 66 - iter 14/79 - loss 0.34848987 - time (sec): 3.54 - samples/sec: 4520.26 - lr: 0.000024\n","2023-04-21 06:09:11,663 epoch 66 - iter 21/79 - loss 0.34356779 - time (sec): 5.08 - samples/sec: 4806.19 - lr: 0.000024\n","2023-04-21 06:09:13,493 epoch 66 - iter 28/79 - loss 0.34480553 - time (sec): 6.91 - samples/sec: 4756.47 - lr: 0.000024\n","2023-04-21 06:09:15,655 epoch 66 - iter 35/79 - loss 0.34357246 - time (sec): 9.07 - samples/sec: 4503.47 - lr: 0.000024\n","2023-04-21 06:09:17,829 epoch 66 - iter 42/79 - loss 0.33702085 - time (sec): 11.25 - samples/sec: 4434.22 - lr: 0.000024\n","2023-04-21 06:09:19,560 epoch 66 - iter 49/79 - loss 0.33550209 - time (sec): 12.98 - samples/sec: 4427.06 - lr: 0.000024\n","2023-04-21 06:09:22,041 epoch 66 - iter 56/79 - loss 0.33177363 - time (sec): 15.46 - samples/sec: 4311.70 - lr: 0.000024\n","2023-04-21 06:09:23,811 epoch 66 - iter 63/79 - loss 0.33315463 - time (sec): 17.23 - samples/sec: 4368.31 - lr: 0.000024\n","2023-04-21 06:09:25,701 epoch 66 - iter 70/79 - loss 0.33487039 - time (sec): 19.12 - samples/sec: 4414.03 - lr: 0.000024\n","2023-04-21 06:09:27,268 epoch 66 - iter 77/79 - loss 0.33443630 - time (sec): 20.69 - samples/sec: 4445.12 - lr: 0.000024\n","2023-04-21 06:09:27,786 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:09:27,788 EPOCH 66 done: loss 0.3347 - lr 0.000024\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.73it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:09:49,098 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:09:52,211 TRAIN : loss 0.3002484142780304 - f1-score (weighted avg)  0.2931\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.57it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:09:55,176 Evaluating as a multi-label problem: False\n","2023-04-21 06:09:55,243 DEV : loss 0.3065083920955658 - f1-score (weighted avg)  0.3005\n","2023-04-21 06:09:55,266 BAD EPOCHS (no improvement): 1\n","2023-04-21 06:09:55,271 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:09:57,074 epoch 67 - iter 7/79 - loss 0.34311319 - time (sec): 1.80 - samples/sec: 5004.40 - lr: 0.000024\n","2023-04-21 06:09:58,710 epoch 67 - iter 14/79 - loss 0.34486851 - time (sec): 3.44 - samples/sec: 4827.37 - lr: 0.000024\n","2023-04-21 06:10:00,638 epoch 67 - iter 21/79 - loss 0.34117153 - time (sec): 5.37 - samples/sec: 4873.82 - lr: 0.000024\n","2023-04-21 06:10:02,822 epoch 67 - iter 28/79 - loss 0.33860013 - time (sec): 7.55 - samples/sec: 4602.47 - lr: 0.000024\n","2023-04-21 06:10:05,046 epoch 67 - iter 35/79 - loss 0.33583840 - time (sec): 9.77 - samples/sec: 4402.41 - lr: 0.000024\n","2023-04-21 06:10:06,678 epoch 67 - iter 42/79 - loss 0.33467695 - time (sec): 11.41 - samples/sec: 4426.10 - lr: 0.000024\n","2023-04-21 06:10:08,362 epoch 67 - iter 49/79 - loss 0.33734650 - time (sec): 13.09 - samples/sec: 4459.18 - lr: 0.000024\n","2023-04-21 06:10:10,035 epoch 67 - iter 56/79 - loss 0.33679173 - time (sec): 14.76 - samples/sec: 4526.92 - lr: 0.000024\n","2023-04-21 06:10:11,663 epoch 67 - iter 63/79 - loss 0.33699667 - time (sec): 16.39 - samples/sec: 4556.99 - lr: 0.000024\n","2023-04-21 06:10:13,608 epoch 67 - iter 70/79 - loss 0.33883734 - time (sec): 18.34 - samples/sec: 4526.78 - lr: 0.000024\n","2023-04-21 06:10:15,659 epoch 67 - iter 77/79 - loss 0.33815045 - time (sec): 20.39 - samples/sec: 4545.02 - lr: 0.000024\n","2023-04-21 06:10:16,112 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:10:16,114 EPOCH 67 done: loss 0.3388 - lr 0.000024\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.75it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:10:37,337 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:10:39,183 TRAIN : loss 0.2999713122844696 - f1-score (weighted avg)  0.2943\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.55it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:10:42,170 Evaluating as a multi-label problem: False\n","2023-04-21 06:10:42,240 DEV : loss 0.30631574988365173 - f1-score (weighted avg)  0.3011\n","2023-04-21 06:10:42,264 Epoch    67: reducing learning rate of group 0 to 1.2207e-05.\n","2023-04-21 06:10:42,267 BAD EPOCHS (no improvement): 2\n","2023-04-21 06:10:42,271 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:10:43,805 epoch 68 - iter 7/79 - loss 0.35190265 - time (sec): 1.53 - samples/sec: 4938.64 - lr: 0.000012\n","2023-04-21 06:10:45,400 epoch 68 - iter 14/79 - loss 0.34374502 - time (sec): 3.13 - samples/sec: 4768.49 - lr: 0.000012\n","2023-04-21 06:10:47,690 epoch 68 - iter 21/79 - loss 0.33575999 - time (sec): 5.42 - samples/sec: 4543.46 - lr: 0.000012\n","2023-04-21 06:10:49,869 epoch 68 - iter 28/79 - loss 0.33739318 - time (sec): 7.60 - samples/sec: 4459.76 - lr: 0.000012\n","2023-04-21 06:10:51,439 epoch 68 - iter 35/79 - loss 0.33986587 - time (sec): 9.17 - samples/sec: 4473.59 - lr: 0.000012\n","2023-04-21 06:10:53,438 epoch 68 - iter 42/79 - loss 0.33479976 - time (sec): 11.16 - samples/sec: 4473.84 - lr: 0.000012\n","2023-04-21 06:10:55,238 epoch 68 - iter 49/79 - loss 0.33652526 - time (sec): 12.96 - samples/sec: 4478.15 - lr: 0.000012\n","2023-04-21 06:10:57,097 epoch 68 - iter 56/79 - loss 0.33417886 - time (sec): 14.82 - samples/sec: 4446.19 - lr: 0.000012\n","2023-04-21 06:10:58,948 epoch 68 - iter 63/79 - loss 0.33378827 - time (sec): 16.67 - samples/sec: 4518.66 - lr: 0.000012\n","2023-04-21 06:11:00,677 epoch 68 - iter 70/79 - loss 0.33309805 - time (sec): 18.40 - samples/sec: 4541.15 - lr: 0.000012\n","2023-04-21 06:11:02,362 epoch 68 - iter 77/79 - loss 0.33554751 - time (sec): 20.09 - samples/sec: 4559.90 - lr: 0.000012\n","2023-04-21 06:11:03,025 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:11:03,027 EPOCH 68 done: loss 0.3356 - lr 0.000012\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.56it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:11:25,349 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:11:27,205 TRAIN : loss 0.30010786652565 - f1-score (weighted avg)  0.2927\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.67it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:11:30,096 Evaluating as a multi-label problem: False\n","2023-04-21 06:11:30,161 DEV : loss 0.30633744597435 - f1-score (weighted avg)  0.3018\n","2023-04-21 06:11:30,184 BAD EPOCHS (no improvement): 1\n","2023-04-21 06:11:30,188 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:11:31,685 epoch 69 - iter 7/79 - loss 0.34077083 - time (sec): 1.50 - samples/sec: 5212.96 - lr: 0.000012\n","2023-04-21 06:11:33,858 epoch 69 - iter 14/79 - loss 0.34313395 - time (sec): 3.67 - samples/sec: 4475.50 - lr: 0.000012\n","2023-04-21 06:11:35,515 epoch 69 - iter 21/79 - loss 0.34110797 - time (sec): 5.33 - samples/sec: 4598.42 - lr: 0.000012\n","2023-04-21 06:11:37,148 epoch 69 - iter 28/79 - loss 0.34170858 - time (sec): 6.96 - samples/sec: 4705.05 - lr: 0.000012\n","2023-04-21 06:11:39,133 epoch 69 - iter 35/79 - loss 0.33915719 - time (sec): 8.94 - samples/sec: 4579.31 - lr: 0.000012\n","2023-04-21 06:11:41,085 epoch 69 - iter 42/79 - loss 0.33878365 - time (sec): 10.90 - samples/sec: 4616.62 - lr: 0.000012\n","2023-04-21 06:11:42,622 epoch 69 - iter 49/79 - loss 0.33840877 - time (sec): 12.43 - samples/sec: 4622.03 - lr: 0.000012\n","2023-04-21 06:11:45,158 epoch 69 - iter 56/79 - loss 0.33463867 - time (sec): 14.97 - samples/sec: 4460.56 - lr: 0.000012\n","2023-04-21 06:11:46,898 epoch 69 - iter 63/79 - loss 0.33448107 - time (sec): 16.71 - samples/sec: 4499.05 - lr: 0.000012\n","2023-04-21 06:11:48,905 epoch 69 - iter 70/79 - loss 0.33471806 - time (sec): 18.72 - samples/sec: 4520.80 - lr: 0.000012\n","2023-04-21 06:11:50,723 epoch 69 - iter 77/79 - loss 0.33528913 - time (sec): 20.53 - samples/sec: 4517.17 - lr: 0.000012\n","2023-04-21 06:11:51,147 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:11:51,148 EPOCH 69 done: loss 0.3354 - lr 0.000012\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.54it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:12:13,582 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:12:15,429 TRAIN : loss 0.2998535931110382 - f1-score (weighted avg)  0.2965\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.61it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:12:18,366 Evaluating as a multi-label problem: False\n","2023-04-21 06:12:18,433 DEV : loss 0.3062562644481659 - f1-score (weighted avg)  0.3008\n","2023-04-21 06:12:18,456 Epoch    69: reducing learning rate of group 0 to 6.1035e-06.\n","2023-04-21 06:12:18,459 BAD EPOCHS (no improvement): 2\n","2023-04-21 06:12:18,463 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:12:20,139 epoch 70 - iter 7/79 - loss 0.36280399 - time (sec): 1.67 - samples/sec: 4577.06 - lr: 0.000006\n","2023-04-21 06:12:21,769 epoch 70 - iter 14/79 - loss 0.34124012 - time (sec): 3.30 - samples/sec: 4851.30 - lr: 0.000006\n","2023-04-21 06:12:23,610 epoch 70 - iter 21/79 - loss 0.34180575 - time (sec): 5.15 - samples/sec: 4682.40 - lr: 0.000006\n","2023-04-21 06:12:25,433 epoch 70 - iter 28/79 - loss 0.34691689 - time (sec): 6.97 - samples/sec: 4647.92 - lr: 0.000006\n","2023-04-21 06:12:27,483 epoch 70 - iter 35/79 - loss 0.34419682 - time (sec): 9.02 - samples/sec: 4617.83 - lr: 0.000006\n","2023-04-21 06:12:29,756 epoch 70 - iter 42/79 - loss 0.33651860 - time (sec): 11.29 - samples/sec: 4542.85 - lr: 0.000006\n","2023-04-21 06:12:31,259 epoch 70 - iter 49/79 - loss 0.33646612 - time (sec): 12.79 - samples/sec: 4584.00 - lr: 0.000006\n","2023-04-21 06:12:32,777 epoch 70 - iter 56/79 - loss 0.33817496 - time (sec): 14.31 - samples/sec: 4665.12 - lr: 0.000006\n","2023-04-21 06:12:34,888 epoch 70 - iter 63/79 - loss 0.33745400 - time (sec): 16.42 - samples/sec: 4620.13 - lr: 0.000006\n","2023-04-21 06:12:36,797 epoch 70 - iter 70/79 - loss 0.33814050 - time (sec): 18.33 - samples/sec: 4611.45 - lr: 0.000006\n","2023-04-21 06:12:38,595 epoch 70 - iter 77/79 - loss 0.33705695 - time (sec): 20.13 - samples/sec: 4615.92 - lr: 0.000006\n","2023-04-21 06:12:39,010 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:12:39,013 EPOCH 70 done: loss 0.3372 - lr 0.000006\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.53it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:13:01,531 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:13:03,401 TRAIN : loss 0.29990100860595703 - f1-score (weighted avg)  0.2941\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.68it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:13:06,282 Evaluating as a multi-label problem: False\n","2023-04-21 06:13:06,353 DEV : loss 0.30622202157974243 - f1-score (weighted avg)  0.3008\n","2023-04-21 06:13:06,376 BAD EPOCHS (no improvement): 1\n","2023-04-21 06:13:06,381 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:13:08,105 epoch 71 - iter 7/79 - loss 0.34384181 - time (sec): 1.72 - samples/sec: 5156.98 - lr: 0.000006\n","2023-04-21 06:13:09,761 epoch 71 - iter 14/79 - loss 0.34672897 - time (sec): 3.38 - samples/sec: 5136.95 - lr: 0.000006\n","2023-04-21 06:13:11,497 epoch 71 - iter 21/79 - loss 0.34750171 - time (sec): 5.11 - samples/sec: 4960.79 - lr: 0.000006\n","2023-04-21 06:13:13,532 epoch 71 - iter 28/79 - loss 0.33702380 - time (sec): 7.15 - samples/sec: 4926.56 - lr: 0.000006\n","2023-04-21 06:13:15,588 epoch 71 - iter 35/79 - loss 0.34021217 - time (sec): 9.21 - samples/sec: 4743.72 - lr: 0.000006\n","2023-04-21 06:13:17,558 epoch 71 - iter 42/79 - loss 0.34276391 - time (sec): 11.18 - samples/sec: 4601.97 - lr: 0.000006\n","2023-04-21 06:13:19,228 epoch 71 - iter 49/79 - loss 0.34328580 - time (sec): 12.85 - samples/sec: 4567.64 - lr: 0.000006\n","2023-04-21 06:13:20,909 epoch 71 - iter 56/79 - loss 0.34103120 - time (sec): 14.53 - samples/sec: 4616.12 - lr: 0.000006\n","2023-04-21 06:13:22,807 epoch 71 - iter 63/79 - loss 0.34010457 - time (sec): 16.42 - samples/sec: 4637.80 - lr: 0.000006\n","2023-04-21 06:13:25,127 epoch 71 - iter 70/79 - loss 0.33884872 - time (sec): 18.74 - samples/sec: 4542.66 - lr: 0.000006\n","2023-04-21 06:13:26,628 epoch 71 - iter 77/79 - loss 0.33914342 - time (sec): 20.25 - samples/sec: 4580.98 - lr: 0.000006\n","2023-04-21 06:13:27,193 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:13:27,195 EPOCH 71 done: loss 0.3385 - lr 0.000006\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.74it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:13:48,440 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:13:51,560 TRAIN : loss 0.2999405264854431 - f1-score (weighted avg)  0.2932\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.64it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:13:54,473 Evaluating as a multi-label problem: False\n","2023-04-21 06:13:54,540 DEV : loss 0.3062182068824768 - f1-score (weighted avg)  0.3008\n","2023-04-21 06:13:54,561 Epoch    71: reducing learning rate of group 0 to 3.0518e-06.\n","2023-04-21 06:13:54,564 BAD EPOCHS (no improvement): 2\n","2023-04-21 06:13:54,567 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:13:56,394 epoch 72 - iter 7/79 - loss 0.33268999 - time (sec): 1.83 - samples/sec: 4585.83 - lr: 0.000003\n","2023-04-21 06:13:58,056 epoch 72 - iter 14/79 - loss 0.33690168 - time (sec): 3.49 - samples/sec: 4588.93 - lr: 0.000003\n","2023-04-21 06:13:59,857 epoch 72 - iter 21/79 - loss 0.33480581 - time (sec): 5.29 - samples/sec: 4618.86 - lr: 0.000003\n","2023-04-21 06:14:01,444 epoch 72 - iter 28/79 - loss 0.33515591 - time (sec): 6.88 - samples/sec: 4654.93 - lr: 0.000003\n","2023-04-21 06:14:03,369 epoch 72 - iter 35/79 - loss 0.32999363 - time (sec): 8.80 - samples/sec: 4607.18 - lr: 0.000003\n","2023-04-21 06:14:04,988 epoch 72 - iter 42/79 - loss 0.33341147 - time (sec): 10.42 - samples/sec: 4661.77 - lr: 0.000003\n","2023-04-21 06:14:07,117 epoch 72 - iter 49/79 - loss 0.33400297 - time (sec): 12.55 - samples/sec: 4558.63 - lr: 0.000003\n","2023-04-21 06:14:09,256 epoch 72 - iter 56/79 - loss 0.33378559 - time (sec): 14.69 - samples/sec: 4469.64 - lr: 0.000003\n","2023-04-21 06:14:11,406 epoch 72 - iter 63/79 - loss 0.33701901 - time (sec): 16.84 - samples/sec: 4454.28 - lr: 0.000003\n","2023-04-21 06:14:13,481 epoch 72 - iter 70/79 - loss 0.33665824 - time (sec): 18.91 - samples/sec: 4461.23 - lr: 0.000003\n","2023-04-21 06:14:15,079 epoch 72 - iter 77/79 - loss 0.33729938 - time (sec): 20.51 - samples/sec: 4479.74 - lr: 0.000003\n","2023-04-21 06:14:15,556 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:14:15,557 EPOCH 72 done: loss 0.3380 - lr 0.000003\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.75it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:14:36,747 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:14:38,619 TRAIN : loss 0.2998974323272705 - f1-score (weighted avg)  0.2938\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:03<00:00,  2.62it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:14:42,599 Evaluating as a multi-label problem: False\n","2023-04-21 06:14:42,665 DEV : loss 0.30621302127838135 - f1-score (weighted avg)  0.3008\n","2023-04-21 06:14:42,687 BAD EPOCHS (no improvement): 1\n","2023-04-21 06:14:42,700 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:14:44,489 epoch 73 - iter 7/79 - loss 0.35424399 - time (sec): 1.79 - samples/sec: 4685.24 - lr: 0.000003\n","2023-04-21 06:14:46,177 epoch 73 - iter 14/79 - loss 0.34034880 - time (sec): 3.47 - samples/sec: 4936.69 - lr: 0.000003\n","2023-04-21 06:14:48,080 epoch 73 - iter 21/79 - loss 0.34666986 - time (sec): 5.38 - samples/sec: 4654.24 - lr: 0.000003\n","2023-04-21 06:14:50,438 epoch 73 - iter 28/79 - loss 0.34820780 - time (sec): 7.74 - samples/sec: 4336.68 - lr: 0.000003\n","2023-04-21 06:14:52,193 epoch 73 - iter 35/79 - loss 0.34327551 - time (sec): 9.49 - samples/sec: 4394.31 - lr: 0.000003\n","2023-04-21 06:14:54,259 epoch 73 - iter 42/79 - loss 0.33815367 - time (sec): 11.56 - samples/sec: 4351.88 - lr: 0.000003\n","2023-04-21 06:14:56,361 epoch 73 - iter 49/79 - loss 0.33817795 - time (sec): 13.66 - samples/sec: 4285.36 - lr: 0.000003\n","2023-04-21 06:14:58,243 epoch 73 - iter 56/79 - loss 0.33977749 - time (sec): 15.54 - samples/sec: 4434.38 - lr: 0.000003\n","2023-04-21 06:15:00,086 epoch 73 - iter 63/79 - loss 0.33885954 - time (sec): 17.38 - samples/sec: 4427.19 - lr: 0.000003\n","2023-04-21 06:15:01,641 epoch 73 - iter 70/79 - loss 0.33880871 - time (sec): 18.94 - samples/sec: 4460.24 - lr: 0.000003\n","2023-04-21 06:15:03,583 epoch 73 - iter 77/79 - loss 0.33744149 - time (sec): 20.88 - samples/sec: 4443.77 - lr: 0.000003\n","2023-04-21 06:15:04,054 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:15:04,056 EPOCH 73 done: loss 0.3370 - lr 0.000003\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.70it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:15:25,548 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:15:27,403 TRAIN : loss 0.29987046122550964 - f1-score (weighted avg)  0.2937\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.66it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:15:30,299 Evaluating as a multi-label problem: False\n","2023-04-21 06:15:30,366 DEV : loss 0.3062053620815277 - f1-score (weighted avg)  0.3008\n","2023-04-21 06:15:30,387 Epoch    73: reducing learning rate of group 0 to 1.5259e-06.\n","2023-04-21 06:15:30,390 BAD EPOCHS (no improvement): 2\n","2023-04-21 06:15:30,393 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:15:32,278 epoch 74 - iter 7/79 - loss 0.36358276 - time (sec): 1.88 - samples/sec: 4182.83 - lr: 0.000002\n","2023-04-21 06:15:34,194 epoch 74 - iter 14/79 - loss 0.35337252 - time (sec): 3.80 - samples/sec: 4244.52 - lr: 0.000002\n","2023-04-21 06:15:36,095 epoch 74 - iter 21/79 - loss 0.35065777 - time (sec): 5.70 - samples/sec: 4279.06 - lr: 0.000002\n","2023-04-21 06:15:37,874 epoch 74 - iter 28/79 - loss 0.34639687 - time (sec): 7.48 - samples/sec: 4336.26 - lr: 0.000002\n","2023-04-21 06:15:39,840 epoch 74 - iter 35/79 - loss 0.34301454 - time (sec): 9.45 - samples/sec: 4413.44 - lr: 0.000002\n","2023-04-21 06:15:41,675 epoch 74 - iter 42/79 - loss 0.34240734 - time (sec): 11.28 - samples/sec: 4413.98 - lr: 0.000002\n","2023-04-21 06:15:43,815 epoch 74 - iter 49/79 - loss 0.33811896 - time (sec): 13.42 - samples/sec: 4363.26 - lr: 0.000002\n","2023-04-21 06:15:45,462 epoch 74 - iter 56/79 - loss 0.33691620 - time (sec): 15.07 - samples/sec: 4386.72 - lr: 0.000002\n","2023-04-21 06:15:47,842 epoch 74 - iter 63/79 - loss 0.33394585 - time (sec): 17.45 - samples/sec: 4322.47 - lr: 0.000002\n","2023-04-21 06:15:49,778 epoch 74 - iter 70/79 - loss 0.33481087 - time (sec): 19.38 - samples/sec: 4362.82 - lr: 0.000002\n","2023-04-21 06:15:51,389 epoch 74 - iter 77/79 - loss 0.33678895 - time (sec): 20.99 - samples/sec: 4405.91 - lr: 0.000002\n","2023-04-21 06:15:51,911 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:15:51,912 EPOCH 74 done: loss 0.3364 - lr 0.000002\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.56it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:16:14,263 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:16:16,135 TRAIN : loss 0.29983973503112793 - f1-score (weighted avg)  0.2945\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.68it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:16:19,032 Evaluating as a multi-label problem: False\n","2023-04-21 06:16:19,098 DEV : loss 0.3061952590942383 - f1-score (weighted avg)  0.3008\n","2023-04-21 06:16:19,120 BAD EPOCHS (no improvement): 1\n","2023-04-21 06:16:19,124 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:16:21,386 epoch 75 - iter 7/79 - loss 0.34396647 - time (sec): 2.26 - samples/sec: 3641.93 - lr: 0.000002\n","2023-04-21 06:16:22,950 epoch 75 - iter 14/79 - loss 0.34545755 - time (sec): 3.82 - samples/sec: 4093.24 - lr: 0.000002\n","2023-04-21 06:16:24,704 epoch 75 - iter 21/79 - loss 0.35034563 - time (sec): 5.58 - samples/sec: 4242.70 - lr: 0.000002\n","2023-04-21 06:16:26,465 epoch 75 - iter 28/79 - loss 0.34472303 - time (sec): 7.34 - samples/sec: 4408.81 - lr: 0.000002\n","2023-04-21 06:16:28,100 epoch 75 - iter 35/79 - loss 0.34429750 - time (sec): 8.97 - samples/sec: 4604.00 - lr: 0.000002\n","2023-04-21 06:16:29,743 epoch 75 - iter 42/79 - loss 0.34283899 - time (sec): 10.62 - samples/sec: 4567.36 - lr: 0.000002\n","2023-04-21 06:16:32,083 epoch 75 - iter 49/79 - loss 0.34018451 - time (sec): 12.96 - samples/sec: 4517.18 - lr: 0.000002\n","2023-04-21 06:16:33,959 epoch 75 - iter 56/79 - loss 0.33964581 - time (sec): 14.83 - samples/sec: 4494.40 - lr: 0.000002\n","2023-04-21 06:16:35,869 epoch 75 - iter 63/79 - loss 0.33855987 - time (sec): 16.74 - samples/sec: 4515.86 - lr: 0.000002\n","2023-04-21 06:16:37,680 epoch 75 - iter 70/79 - loss 0.33956809 - time (sec): 18.55 - samples/sec: 4559.32 - lr: 0.000002\n","2023-04-21 06:16:39,424 epoch 75 - iter 77/79 - loss 0.33701658 - time (sec): 20.30 - samples/sec: 4554.48 - lr: 0.000002\n","2023-04-21 06:16:39,963 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:16:39,965 EPOCH 75 done: loss 0.3371 - lr 0.000002\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.54it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:17:02,441 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:17:04,285 TRAIN : loss 0.29984042048454285 - f1-score (weighted avg)  0.2945\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.66it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:17:07,178 Evaluating as a multi-label problem: False\n","2023-04-21 06:17:07,245 DEV : loss 0.30620017647743225 - f1-score (weighted avg)  0.3\n","2023-04-21 06:17:07,268 Epoch    75: reducing learning rate of group 0 to 7.6294e-07.\n","2023-04-21 06:17:07,270 BAD EPOCHS (no improvement): 2\n","2023-04-21 06:17:07,274 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:17:09,035 epoch 76 - iter 7/79 - loss 0.35552706 - time (sec): 1.76 - samples/sec: 4664.31 - lr: 0.000001\n","2023-04-21 06:17:10,952 epoch 76 - iter 14/79 - loss 0.33878345 - time (sec): 3.68 - samples/sec: 4710.16 - lr: 0.000001\n","2023-04-21 06:17:12,790 epoch 76 - iter 21/79 - loss 0.33705116 - time (sec): 5.51 - samples/sec: 4473.58 - lr: 0.000001\n","2023-04-21 06:17:14,606 epoch 76 - iter 28/79 - loss 0.33734161 - time (sec): 7.33 - samples/sec: 4453.39 - lr: 0.000001\n","2023-04-21 06:17:16,330 epoch 76 - iter 35/79 - loss 0.33497565 - time (sec): 9.05 - samples/sec: 4522.24 - lr: 0.000001\n","2023-04-21 06:17:18,045 epoch 76 - iter 42/79 - loss 0.33844429 - time (sec): 10.77 - samples/sec: 4577.10 - lr: 0.000001\n","2023-04-21 06:17:20,204 epoch 76 - iter 49/79 - loss 0.33718882 - time (sec): 12.93 - samples/sec: 4511.54 - lr: 0.000001\n","2023-04-21 06:17:21,763 epoch 76 - iter 56/79 - loss 0.33443526 - time (sec): 14.49 - samples/sec: 4562.58 - lr: 0.000001\n","2023-04-21 06:17:23,439 epoch 76 - iter 63/79 - loss 0.33584050 - time (sec): 16.16 - samples/sec: 4592.02 - lr: 0.000001\n","2023-04-21 06:17:25,970 epoch 76 - iter 70/79 - loss 0.33377185 - time (sec): 18.69 - samples/sec: 4534.49 - lr: 0.000001\n","2023-04-21 06:17:27,709 epoch 76 - iter 77/79 - loss 0.33609098 - time (sec): 20.43 - samples/sec: 4517.45 - lr: 0.000001\n","2023-04-21 06:17:28,295 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:17:28,297 EPOCH 76 done: loss 0.3358 - lr 0.000001\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.75it/s]\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:17:50,682 Evaluating as a multi-label problem: True\n","2023-04-21 06:17:52,552 TRAIN : loss 0.29985514283180237 - f1-score (weighted avg)  0.294\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.61it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:17:55,487 Evaluating as a multi-label problem: False\n","2023-04-21 06:17:55,554 DEV : loss 0.30620551109313965 - f1-score (weighted avg)  0.3\n","2023-04-21 06:17:55,576 BAD EPOCHS (no improvement): 1\n","2023-04-21 06:17:55,580 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:17:56,925 epoch 77 - iter 7/79 - loss 0.34850681 - time (sec): 1.34 - samples/sec: 5043.31 - lr: 0.000001\n","2023-04-21 06:17:58,587 epoch 77 - iter 14/79 - loss 0.34543684 - time (sec): 3.01 - samples/sec: 4956.07 - lr: 0.000001\n","2023-04-21 06:18:00,166 epoch 77 - iter 21/79 - loss 0.34527693 - time (sec): 4.58 - samples/sec: 5006.01 - lr: 0.000001\n","2023-04-21 06:18:01,900 epoch 77 - iter 28/79 - loss 0.34726075 - time (sec): 6.32 - samples/sec: 5083.83 - lr: 0.000001\n","2023-04-21 06:18:04,140 epoch 77 - iter 35/79 - loss 0.34460925 - time (sec): 8.56 - samples/sec: 4747.66 - lr: 0.000001\n","2023-04-21 06:18:06,148 epoch 77 - iter 42/79 - loss 0.33729538 - time (sec): 10.57 - samples/sec: 4721.60 - lr: 0.000001\n","2023-04-21 06:18:08,488 epoch 77 - iter 49/79 - loss 0.33126734 - time (sec): 12.91 - samples/sec: 4602.39 - lr: 0.000001\n","2023-04-21 06:18:10,057 epoch 77 - iter 56/79 - loss 0.33186448 - time (sec): 14.48 - samples/sec: 4662.04 - lr: 0.000001\n","2023-04-21 06:18:12,007 epoch 77 - iter 63/79 - loss 0.33449405 - time (sec): 16.43 - samples/sec: 4592.87 - lr: 0.000001\n","2023-04-21 06:18:13,984 epoch 77 - iter 70/79 - loss 0.33421324 - time (sec): 18.40 - samples/sec: 4575.65 - lr: 0.000001\n","2023-04-21 06:18:15,725 epoch 77 - iter 77/79 - loss 0.33741991 - time (sec): 20.14 - samples/sec: 4572.06 - lr: 0.000001\n","2023-04-21 06:18:16,209 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:18:16,210 EPOCH 77 done: loss 0.3363 - lr 0.000001\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.74it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:18:37,447 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:18:40,560 TRAIN : loss 0.29985079169273376 - f1-score (weighted avg)  0.2943\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.69it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:18:43,441 Evaluating as a multi-label problem: False\n","2023-04-21 06:18:43,507 DEV : loss 0.30620628595352173 - f1-score (weighted avg)  0.3\n","2023-04-21 06:18:43,528 Epoch    77: reducing learning rate of group 0 to 3.8147e-07.\n","2023-04-21 06:18:43,531 BAD EPOCHS (no improvement): 2\n","2023-04-21 06:18:43,534 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:18:45,468 epoch 78 - iter 7/79 - loss 0.33568198 - time (sec): 1.93 - samples/sec: 4374.91 - lr: 0.000000\n","2023-04-21 06:18:47,200 epoch 78 - iter 14/79 - loss 0.33761244 - time (sec): 3.66 - samples/sec: 4577.98 - lr: 0.000000\n","2023-04-21 06:18:49,331 epoch 78 - iter 21/79 - loss 0.33039261 - time (sec): 5.80 - samples/sec: 4309.28 - lr: 0.000000\n","2023-04-21 06:18:50,983 epoch 78 - iter 28/79 - loss 0.32786839 - time (sec): 7.45 - samples/sec: 4441.34 - lr: 0.000000\n","2023-04-21 06:18:53,446 epoch 78 - iter 35/79 - loss 0.32637686 - time (sec): 9.91 - samples/sec: 4215.62 - lr: 0.000000\n","2023-04-21 06:18:55,240 epoch 78 - iter 42/79 - loss 0.32844648 - time (sec): 11.70 - samples/sec: 4353.10 - lr: 0.000000\n","2023-04-21 06:18:56,793 epoch 78 - iter 49/79 - loss 0.32955159 - time (sec): 13.26 - samples/sec: 4429.17 - lr: 0.000000\n","2023-04-21 06:18:58,626 epoch 78 - iter 56/79 - loss 0.33308529 - time (sec): 15.09 - samples/sec: 4401.71 - lr: 0.000000\n","2023-04-21 06:19:00,539 epoch 78 - iter 63/79 - loss 0.33164351 - time (sec): 17.00 - samples/sec: 4429.13 - lr: 0.000000\n","2023-04-21 06:19:02,305 epoch 78 - iter 70/79 - loss 0.33211761 - time (sec): 18.77 - samples/sec: 4449.29 - lr: 0.000000\n","2023-04-21 06:19:04,048 epoch 78 - iter 77/79 - loss 0.33274014 - time (sec): 20.51 - samples/sec: 4518.95 - lr: 0.000000\n","2023-04-21 06:19:04,458 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:19:04,459 EPOCH 78 done: loss 0.3354 - lr 0.000000\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.74it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:19:25,718 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:19:27,568 TRAIN : loss 0.2998567521572113 - f1-score (weighted avg)  0.294\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.63it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:19:30,491 Evaluating as a multi-label problem: False\n","2023-04-21 06:19:30,558 DEV : loss 0.30620765686035156 - f1-score (weighted avg)  0.3\n","2023-04-21 06:19:30,580 BAD EPOCHS (no improvement): 1\n","2023-04-21 06:19:30,585 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:19:32,454 epoch 79 - iter 7/79 - loss 0.34647769 - time (sec): 1.87 - samples/sec: 4112.15 - lr: 0.000000\n","2023-04-21 06:19:35,428 epoch 79 - iter 14/79 - loss 0.32621861 - time (sec): 4.84 - samples/sec: 3431.12 - lr: 0.000000\n","2023-04-21 06:19:37,297 epoch 79 - iter 21/79 - loss 0.33063903 - time (sec): 6.71 - samples/sec: 3788.83 - lr: 0.000000\n","2023-04-21 06:19:39,425 epoch 79 - iter 28/79 - loss 0.32700102 - time (sec): 8.84 - samples/sec: 3887.45 - lr: 0.000000\n","2023-04-21 06:19:41,113 epoch 79 - iter 35/79 - loss 0.32764112 - time (sec): 10.53 - samples/sec: 4066.66 - lr: 0.000000\n","2023-04-21 06:19:43,065 epoch 79 - iter 42/79 - loss 0.32783090 - time (sec): 12.48 - samples/sec: 4106.62 - lr: 0.000000\n","2023-04-21 06:19:44,738 epoch 79 - iter 49/79 - loss 0.33040826 - time (sec): 14.15 - samples/sec: 4202.52 - lr: 0.000000\n","2023-04-21 06:19:46,797 epoch 79 - iter 56/79 - loss 0.33509800 - time (sec): 16.21 - samples/sec: 4144.18 - lr: 0.000000\n","2023-04-21 06:19:48,629 epoch 79 - iter 63/79 - loss 0.33413597 - time (sec): 18.04 - samples/sec: 4184.66 - lr: 0.000000\n","2023-04-21 06:19:50,341 epoch 79 - iter 70/79 - loss 0.33780607 - time (sec): 19.75 - samples/sec: 4224.05 - lr: 0.000000\n","2023-04-21 06:19:52,032 epoch 79 - iter 77/79 - loss 0.33860767 - time (sec): 21.45 - samples/sec: 4312.58 - lr: 0.000000\n","2023-04-21 06:19:52,515 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:19:52,516 EPOCH 79 done: loss 0.3384 - lr 0.000000\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.72it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:20:13,882 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:20:15,736 TRAIN : loss 0.29985785484313965 - f1-score (weighted avg)  0.294\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.66it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:20:18,630 Evaluating as a multi-label problem: False\n","2023-04-21 06:20:18,695 DEV : loss 0.3062056601047516 - f1-score (weighted avg)  0.3\n","2023-04-21 06:20:18,715 Epoch    79: reducing learning rate of group 0 to 1.9073e-07.\n","2023-04-21 06:20:18,718 BAD EPOCHS (no improvement): 2\n","2023-04-21 06:20:18,721 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:20:20,224 epoch 80 - iter 7/79 - loss 0.34560441 - time (sec): 1.50 - samples/sec: 5349.18 - lr: 0.000000\n","2023-04-21 06:20:21,855 epoch 80 - iter 14/79 - loss 0.34600304 - time (sec): 3.13 - samples/sec: 5093.00 - lr: 0.000000\n","2023-04-21 06:20:23,425 epoch 80 - iter 21/79 - loss 0.34407934 - time (sec): 4.70 - samples/sec: 4984.81 - lr: 0.000000\n","2023-04-21 06:20:25,523 epoch 80 - iter 28/79 - loss 0.34806197 - time (sec): 6.80 - samples/sec: 4706.75 - lr: 0.000000\n","2023-04-21 06:20:27,380 epoch 80 - iter 35/79 - loss 0.34653034 - time (sec): 8.66 - samples/sec: 4666.04 - lr: 0.000000\n","2023-04-21 06:20:29,025 epoch 80 - iter 42/79 - loss 0.34384235 - time (sec): 10.30 - samples/sec: 4605.16 - lr: 0.000000\n","2023-04-21 06:20:31,141 epoch 80 - iter 49/79 - loss 0.34200384 - time (sec): 12.42 - samples/sec: 4510.19 - lr: 0.000000\n","2023-04-21 06:20:32,862 epoch 80 - iter 56/79 - loss 0.34189855 - time (sec): 14.14 - samples/sec: 4547.18 - lr: 0.000000\n","2023-04-21 06:20:34,890 epoch 80 - iter 63/79 - loss 0.34134081 - time (sec): 16.17 - samples/sec: 4613.70 - lr: 0.000000\n","2023-04-21 06:20:37,111 epoch 80 - iter 70/79 - loss 0.33940927 - time (sec): 18.39 - samples/sec: 4562.61 - lr: 0.000000\n","2023-04-21 06:20:38,838 epoch 80 - iter 77/79 - loss 0.33722960 - time (sec): 20.12 - samples/sec: 4580.14 - lr: 0.000000\n","2023-04-21 06:20:39,368 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:20:39,370 EPOCH 80 done: loss 0.3370 - lr 0.000000\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.51it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:21:02,022 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:21:03,861 TRAIN : loss 0.2998558580875397 - f1-score (weighted avg)  0.294\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.63it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:21:06,781 Evaluating as a multi-label problem: False\n","2023-04-21 06:21:06,850 DEV : loss 0.3062032163143158 - f1-score (weighted avg)  0.3\n","2023-04-21 06:21:06,873 BAD EPOCHS (no improvement): 1\n","2023-04-21 06:21:06,877 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:21:08,581 epoch 81 - iter 7/79 - loss 0.33983145 - time (sec): 1.70 - samples/sec: 5040.04 - lr: 0.000000\n","2023-04-21 06:21:10,773 epoch 81 - iter 14/79 - loss 0.34474322 - time (sec): 3.89 - samples/sec: 4666.32 - lr: 0.000000\n","2023-04-21 06:21:12,467 epoch 81 - iter 21/79 - loss 0.34466545 - time (sec): 5.59 - samples/sec: 4690.14 - lr: 0.000000\n","2023-04-21 06:21:14,419 epoch 81 - iter 28/79 - loss 0.34248362 - time (sec): 7.54 - samples/sec: 4627.58 - lr: 0.000000\n","2023-04-21 06:21:16,598 epoch 81 - iter 35/79 - loss 0.34159714 - time (sec): 9.72 - samples/sec: 4489.06 - lr: 0.000000\n","2023-04-21 06:21:18,491 epoch 81 - iter 42/79 - loss 0.33627309 - time (sec): 11.61 - samples/sec: 4388.66 - lr: 0.000000\n","2023-04-21 06:21:20,410 epoch 81 - iter 49/79 - loss 0.33663440 - time (sec): 13.53 - samples/sec: 4424.61 - lr: 0.000000\n","2023-04-21 06:21:22,158 epoch 81 - iter 56/79 - loss 0.33844723 - time (sec): 15.28 - samples/sec: 4443.41 - lr: 0.000000\n","2023-04-21 06:21:23,846 epoch 81 - iter 63/79 - loss 0.33923068 - time (sec): 16.97 - samples/sec: 4522.38 - lr: 0.000000\n","2023-04-21 06:21:25,690 epoch 81 - iter 70/79 - loss 0.33763256 - time (sec): 18.81 - samples/sec: 4506.01 - lr: 0.000000\n","2023-04-21 06:21:27,715 epoch 81 - iter 77/79 - loss 0.33672577 - time (sec): 20.84 - samples/sec: 4469.27 - lr: 0.000000\n","2023-04-21 06:21:28,046 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:21:28,048 EPOCH 81 done: loss 0.3375 - lr 0.000000\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.54it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:21:50,532 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:21:52,389 TRAIN : loss 0.2998602092266083 - f1-score (weighted avg)  0.294\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.62it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:21:55,319 Evaluating as a multi-label problem: False\n","2023-04-21 06:21:55,386 DEV : loss 0.30620765686035156 - f1-score (weighted avg)  0.3\n","2023-04-21 06:21:55,408 Epoch    81: reducing learning rate of group 0 to 9.5367e-08.\n","2023-04-21 06:21:55,411 BAD EPOCHS (no improvement): 2\n","2023-04-21 06:21:55,414 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:21:57,327 epoch 82 - iter 7/79 - loss 0.35405945 - time (sec): 1.91 - samples/sec: 4586.20 - lr: 0.000000\n","2023-04-21 06:21:59,168 epoch 82 - iter 14/79 - loss 0.35225160 - time (sec): 3.75 - samples/sec: 4594.75 - lr: 0.000000\n","2023-04-21 06:22:01,046 epoch 82 - iter 21/79 - loss 0.34340987 - time (sec): 5.63 - samples/sec: 4480.30 - lr: 0.000000\n","2023-04-21 06:22:02,710 epoch 82 - iter 28/79 - loss 0.33915981 - time (sec): 7.29 - samples/sec: 4610.87 - lr: 0.000000\n","2023-04-21 06:22:04,520 epoch 82 - iter 35/79 - loss 0.34164833 - time (sec): 9.10 - samples/sec: 4676.09 - lr: 0.000000\n","2023-04-21 06:22:06,308 epoch 82 - iter 42/79 - loss 0.34212318 - time (sec): 10.89 - samples/sec: 4643.04 - lr: 0.000000\n","2023-04-21 06:22:08,093 epoch 82 - iter 49/79 - loss 0.33927242 - time (sec): 12.68 - samples/sec: 4608.60 - lr: 0.000000\n","2023-04-21 06:22:10,076 epoch 82 - iter 56/79 - loss 0.33585477 - time (sec): 14.66 - samples/sec: 4569.05 - lr: 0.000000\n","2023-04-21 06:22:11,783 epoch 82 - iter 63/79 - loss 0.33858638 - time (sec): 16.37 - samples/sec: 4578.43 - lr: 0.000000\n","2023-04-21 06:22:14,030 epoch 82 - iter 70/79 - loss 0.33630416 - time (sec): 18.61 - samples/sec: 4510.86 - lr: 0.000000\n","2023-04-21 06:22:15,762 epoch 82 - iter 77/79 - loss 0.33642917 - time (sec): 20.35 - samples/sec: 4545.35 - lr: 0.000000\n","2023-04-21 06:22:16,225 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:22:16,227 EPOCH 82 done: loss 0.3365 - lr 0.000000\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.73it/s]\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:22:38,729 Evaluating as a multi-label problem: True\n","2023-04-21 06:22:40,590 TRAIN : loss 0.29986056685447693 - f1-score (weighted avg)  0.294\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.63it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:22:43,511 Evaluating as a multi-label problem: False\n","2023-04-21 06:22:43,581 DEV : loss 0.3062068819999695 - f1-score (weighted avg)  0.3\n","2023-04-21 06:22:43,604 BAD EPOCHS (no improvement): 1\n","2023-04-21 06:22:43,609 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:22:45,593 epoch 83 - iter 7/79 - loss 0.34222696 - time (sec): 1.98 - samples/sec: 3969.01 - lr: 0.000000\n","2023-04-21 06:22:47,261 epoch 83 - iter 14/79 - loss 0.33298646 - time (sec): 3.65 - samples/sec: 4535.64 - lr: 0.000000\n","2023-04-21 06:22:49,443 epoch 83 - iter 21/79 - loss 0.33923950 - time (sec): 5.83 - samples/sec: 4735.36 - lr: 0.000000\n","2023-04-21 06:22:51,420 epoch 83 - iter 28/79 - loss 0.33079793 - time (sec): 7.81 - samples/sec: 4601.51 - lr: 0.000000\n","2023-04-21 06:22:52,833 epoch 83 - iter 35/79 - loss 0.33755739 - time (sec): 9.22 - samples/sec: 4669.52 - lr: 0.000000\n","2023-04-21 06:22:54,798 epoch 83 - iter 42/79 - loss 0.33405933 - time (sec): 11.19 - samples/sec: 4585.35 - lr: 0.000000\n","2023-04-21 06:22:56,470 epoch 83 - iter 49/79 - loss 0.33891915 - time (sec): 12.86 - samples/sec: 4565.64 - lr: 0.000000\n","2023-04-21 06:22:58,217 epoch 83 - iter 56/79 - loss 0.33700275 - time (sec): 14.61 - samples/sec: 4633.66 - lr: 0.000000\n","2023-04-21 06:23:00,073 epoch 83 - iter 63/79 - loss 0.33638642 - time (sec): 16.46 - samples/sec: 4631.77 - lr: 0.000000\n","2023-04-21 06:23:02,084 epoch 83 - iter 70/79 - loss 0.33735838 - time (sec): 18.47 - samples/sec: 4557.39 - lr: 0.000000\n","2023-04-21 06:23:03,648 epoch 83 - iter 77/79 - loss 0.33845159 - time (sec): 20.04 - samples/sec: 4612.45 - lr: 0.000000\n","2023-04-21 06:23:04,130 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:23:04,132 EPOCH 83 done: loss 0.3374 - lr 0.000000\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.72it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:23:25,490 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:23:28,644 TRAIN : loss 0.29986128211021423 - f1-score (weighted avg)  0.294\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.64it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:23:31,562 Evaluating as a multi-label problem: False\n","2023-04-21 06:23:31,632 DEV : loss 0.30620598793029785 - f1-score (weighted avg)  0.3\n","2023-04-21 06:23:31,653 Epoch    83: reducing learning rate of group 0 to 4.7684e-08.\n","2023-04-21 06:23:31,656 BAD EPOCHS (no improvement): 2\n","2023-04-21 06:23:31,661 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:23:33,326 epoch 84 - iter 7/79 - loss 0.34738472 - time (sec): 1.66 - samples/sec: 4558.10 - lr: 0.000000\n","2023-04-21 06:23:34,993 epoch 84 - iter 14/79 - loss 0.34648453 - time (sec): 3.33 - samples/sec: 4798.99 - lr: 0.000000\n","2023-04-21 06:23:36,672 epoch 84 - iter 21/79 - loss 0.35107912 - time (sec): 5.01 - samples/sec: 4907.93 - lr: 0.000000\n","2023-04-21 06:23:38,559 epoch 84 - iter 28/79 - loss 0.35068907 - time (sec): 6.90 - samples/sec: 4696.41 - lr: 0.000000\n","2023-04-21 06:23:40,737 epoch 84 - iter 35/79 - loss 0.34779607 - time (sec): 9.07 - samples/sec: 4447.87 - lr: 0.000000\n","2023-04-21 06:23:42,309 epoch 84 - iter 42/79 - loss 0.34471078 - time (sec): 10.65 - samples/sec: 4521.81 - lr: 0.000000\n","2023-04-21 06:23:43,919 epoch 84 - iter 49/79 - loss 0.34206515 - time (sec): 12.26 - samples/sec: 4630.63 - lr: 0.000000\n","2023-04-21 06:23:45,845 epoch 84 - iter 56/79 - loss 0.34077516 - time (sec): 14.18 - samples/sec: 4654.47 - lr: 0.000000\n","2023-04-21 06:23:47,497 epoch 84 - iter 63/79 - loss 0.34051625 - time (sec): 15.83 - samples/sec: 4662.74 - lr: 0.000000\n","2023-04-21 06:23:49,362 epoch 84 - iter 70/79 - loss 0.33915035 - time (sec): 17.70 - samples/sec: 4636.90 - lr: 0.000000\n","2023-04-21 06:23:51,079 epoch 84 - iter 77/79 - loss 0.33938217 - time (sec): 19.42 - samples/sec: 4701.00 - lr: 0.000000\n","2023-04-21 06:23:51,980 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:23:51,982 EPOCH 84 done: loss 0.3384 - lr 0.000000\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:21<00:00,  3.73it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:24:13,294 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:24:15,139 TRAIN : loss 0.29986080527305603 - f1-score (weighted avg)  0.294\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.65it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:24:18,046 Evaluating as a multi-label problem: False\n","2023-04-21 06:24:18,113 DEV : loss 0.3062061369419098 - f1-score (weighted avg)  0.3\n","2023-04-21 06:24:18,135 BAD EPOCHS (no improvement): 1\n","2023-04-21 06:24:18,140 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:24:20,561 epoch 85 - iter 7/79 - loss 0.32403441 - time (sec): 2.42 - samples/sec: 3897.56 - lr: 0.000000\n","2023-04-21 06:24:22,471 epoch 85 - iter 14/79 - loss 0.32389733 - time (sec): 4.33 - samples/sec: 4016.22 - lr: 0.000000\n","2023-04-21 06:24:24,171 epoch 85 - iter 21/79 - loss 0.32460808 - time (sec): 6.03 - samples/sec: 4269.48 - lr: 0.000000\n","2023-04-21 06:24:25,833 epoch 85 - iter 28/79 - loss 0.33414238 - time (sec): 7.69 - samples/sec: 4307.34 - lr: 0.000000\n","2023-04-21 06:24:27,703 epoch 85 - iter 35/79 - loss 0.33682576 - time (sec): 9.56 - samples/sec: 4434.59 - lr: 0.000000\n","2023-04-21 06:24:29,423 epoch 85 - iter 42/79 - loss 0.33721451 - time (sec): 11.28 - samples/sec: 4532.58 - lr: 0.000000\n","2023-04-21 06:24:31,188 epoch 85 - iter 49/79 - loss 0.33645155 - time (sec): 13.05 - samples/sec: 4555.38 - lr: 0.000000\n","2023-04-21 06:24:32,994 epoch 85 - iter 56/79 - loss 0.33868001 - time (sec): 14.85 - samples/sec: 4535.69 - lr: 0.000000\n","2023-04-21 06:24:35,026 epoch 85 - iter 63/79 - loss 0.33720792 - time (sec): 16.88 - samples/sec: 4499.30 - lr: 0.000000\n","2023-04-21 06:24:36,919 epoch 85 - iter 70/79 - loss 0.33878342 - time (sec): 18.78 - samples/sec: 4457.11 - lr: 0.000000\n","2023-04-21 06:24:38,627 epoch 85 - iter 77/79 - loss 0.33703965 - time (sec): 20.49 - samples/sec: 4523.28 - lr: 0.000000\n","2023-04-21 06:24:39,050 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:24:39,051 EPOCH 85 done: loss 0.3373 - lr 0.000000\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:22<00:00,  3.56it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:25:01,385 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:25:03,244 TRAIN : loss 0.2998594343662262 - f1-score (weighted avg)  0.294\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:02<00:00,  3.58it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:25:06,204 Evaluating as a multi-label problem: False\n","2023-04-21 06:25:06,270 DEV : loss 0.3062049150466919 - f1-score (weighted avg)  0.3\n","2023-04-21 06:25:06,294 Epoch    85: reducing learning rate of group 0 to 2.3842e-08.\n","2023-04-21 06:25:06,296 BAD EPOCHS (no improvement): 2\n","2023-04-21 06:25:06,299 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:25:06,301 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:25:06,303 learning rate too small - quitting training!\n","2023-04-21 06:25:06,305 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:25:09,142 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:25:11,760 SequenceTagger predicts: Dictionary with 14 tags: <unk>, O, I-ADR, B-ADR, B-Drug, I-Finding, B-Finding, B-Disease, B-Symptom, I-Symptom, I-Disease, I-Drug, <START>, <STOP>\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 10/10 [00:03<00:00,  2.69it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:25:15,881 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:25:16,110 0.6243\t0.2323\t0.3386\t0.8581\n","2023-04-21 06:25:16,112 \n","Results:\n","- F-score (micro) 0.3386\n","- F-score (macro) 0.0806\n","- Accuracy 0.8581\n","\n","By class:\n","              precision    recall  f1-score   support\n","\n","       I-ADR     0.6000    0.3102    0.4090       967\n","       B-ADR     0.6750    0.2812    0.3971       576\n","      B-Drug     0.0000    0.0000    0.0000       219\n","   B-Finding     0.0000    0.0000    0.0000        52\n","      I-Drug     0.0000    0.0000    0.0000        52\n","   I-Finding     0.0000    0.0000    0.0000        36\n","   I-Symptom     0.0000    0.0000    0.0000        33\n","   B-Symptom     0.0000    0.0000    0.0000        22\n","   B-Disease     0.0000    0.0000    0.0000        20\n","   I-Disease     0.0000    0.0000    0.0000        12\n","\n","   micro avg     0.6243    0.2323    0.3386      1989\n","   macro avg     0.1275    0.0591    0.0806      1989\n","weighted avg     0.4872    0.2323    0.3138      1989\n"," samples avg     0.0390    0.0390    0.0390      1989\n","\n","2023-04-21 06:25:16,113 ----------------------------------------------------------------------------------------------------\n"]}],"source":["hist = trainer.train('flair_ner_model'+suf,\n","              embeddings_storage_mode='gpu',\n","              gold_label_dictionary_for_eval=tag_dictionary,\n","              mini_batch_size = 12,\n","              eval_batch_size = 12,\n","              patience=1,\n","              anneal_factor = 0.5,\n","              anneal_against_dev_loss=True,\n","              main_evaluation_metric = (\"weighted avg\", \"f1-score\"),\n","              metrics_for_tensorboard=[(\"weighted avg\", 'precision'), (\"weighted avg\", 'recall'), (\"macro avg\", 'f1-score'), (\"macro avg\", 'precision'), (\"macro avg\", 'recall')],\n","              use_tensorboard=True,\n","              tensorboard_comment='Flair NER',\n","              tensorboard_log_dir='runs/tb_logs'+suf,\n","              min_learning_rate=3e-8,\n","              optimizer=AdamW,\n","              learning_rate=1e-1,\n","              weight_decay=0.001,\n","              max_epochs=100,\n","              monitor_train=True,\n","              save_optimizer_state=True,)\n","# hist"]},{"cell_type":"code","execution_count":16,"metadata":{"executionInfo":{"elapsed":16,"status":"ok","timestamp":1682058315970,"user":{"displayName":"Alex He","userId":"06262800374381224408"},"user_tz":240},"id":"ExiLRreqXsDJ"},"outputs":[],"source":["# print(hist.keys())\n","# hist"]},{"cell_type":"code","execution_count":17,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":35562,"status":"ok","timestamp":1682058351517,"user":{"displayName":"Alex He","userId":"06262800374381224408"},"user_tz":240},"id":"P5Uu5qkN0pia","outputId":"58311aa2-6d93-43ce-b44d-b66b5965bd47"},"outputs":[{"name":"stdout","output_type":"stream","text":["_v3_finetune_glove\n","2023-04-21 06:25:16,142 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:25:16,143 WARNING: No LOSS found for test split in this data.\n","2023-04-21 06:25:16,144 Are you sure you want to plot LOSS and not another value?\n","2023-04-21 06:25:16,145 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:25:16,170 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:25:16,171 WARNING: No F1 found for test split in this data.\n","2023-04-21 06:25:16,173 Are you sure you want to plot F1 and not another value?\n","2023-04-21 06:25:16,174 ----------------------------------------------------------------------------------------------------\n","2023-04-21 06:25:17,410 Loss and F1 plots are saved in 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size 1500x1000 with 2 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["100%|██████████| 79/79 [00:23<00:00,  3.39it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:25:41,207 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n","100%|██████████| 10/10 [00:02<00:00,  3.76it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:25:45,743 Evaluating as a multi-label problem: False\n"]},{"name":"stderr","output_type":"stream","text":["\n","100%|██████████| 10/10 [00:04<00:00,  2.23it/s]"]},{"name":"stdout","output_type":"stream","text":["2023-04-21 06:25:50,322 Evaluating as a multi-label problem: True\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"data":{"text/html":["\n","  <div id=\"df-fac6d64d-c459-4f45-83aa-aa4dd30ac6eb\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr 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<td>1.000000</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>0.0</td>\n","      <td>0.577215</td>\n","      <td>0.216211</td>\n","      <td>0.554591</td>\n","      <td>0.037744</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>train</td>\n","      <td>recall</td>\n","      <td>0.298126</td>\n","      <td>0.255730</td>\n","      <td>0.000716</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>0.0</td>\n","      <td>0.227545</td>\n","      <td>0.055457</td>\n","      <td>0.227545</td>\n","      <td>0.037739</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>train</td>\n","      <td>f1-score</td>\n","      <td>0.391416</td>\n","      <td>0.357249</td>\n","      <td>0.001432</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>0.0</td>\n","      <td>0.326413</td>\n","      <td>0.075010</td>\n","      <td>0.305234</td>\n","      <td>0.037740</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>train</td>\n","      <td>support</td>\n","      <td>7953.000000</td>\n","      <td>4712.000000</td>\n","      <td>1396.000000</td>\n","      <td>349.0</td>\n","      <td>309.0</td>\n","      <td>231.0</td>\n","      <td>222.0</td>\n","      <td>218.0</td>\n","      <td>169.0</td>\n","      <td>161.0</td>\n","      <td>15720.000000</td>\n","      <td>15720.000000</td>\n","      <td>15720.000000</td>\n","      <td>15720.000000</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>dev</td>\n","      <td>precision</td>\n","      <td>0.530000</td>\n","      <td>0.599303</td>\n","      <td>0.000000</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>0.0</td>\n","      <td>0.552424</td>\n","      <td>0.112930</td>\n","      <td>0.436654</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>dev</td>\n","      <td>recall</td>\n","      <td>0.329193</td>\n","      <td>0.264209</td>\n","      <td>0.000000</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>0.0</td>\n","      <td>0.237173</td>\n","      <td>0.059340</td>\n","      <td>0.237173</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>dev</td>\n","      <td>f1-score</td>\n","      <td>0.406130</td>\n","      <td>0.366738</td>\n","      <td>0.000000</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>0.0</td>\n","      <td>0.331866</td>\n","      <td>0.077287</td>\n","      <td>0.305454</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>dev</td>\n","      <td>support</td>\n","      <td>966.000000</td>\n","      <td>651.000000</td>\n","      <td>184.000000</td>\n","      <td>54.0</td>\n","      <td>64.0</td>\n","      <td>31.0</td>\n","      <td>25.0</td>\n","      <td>36.0</td>\n","      <td>28.0</td>\n","      <td>27.0</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>0.600000</td>\n","      <td>0.675000</td>\n","      <td>0.000000</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>0.0</td>\n","      <td>0.624324</td>\n","      <td>0.127500</td>\n","      <td>0.487179</td>\n","      <td>0.039040</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>test</td>\n","      <td>recall</td>\n","      <td>0.310238</td>\n","      <td>0.281250</td>\n","      <td>0.000000</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>0.0</td>\n","      <td>0.232278</td>\n","      <td>0.059149</td>\n","      <td>0.232278</td>\n","      <td>0.038998</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>test</td>\n","      <td>f1-score</td>\n","      <td>0.408998</td>\n","      <td>0.397059</td>\n","      <td>0.000000</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>0.0</td>\n","      <td>0.338586</td>\n","      <td>0.080606</td>\n","      <td>0.313830</td>\n","      <td>0.039012</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      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\n","4     dev  precision     0.530000     0.599303     0.000000        0.0   \n","5     dev     recall     0.329193     0.264209     0.000000        0.0   \n","6     dev   f1-score     0.406130     0.366738     0.000000        0.0   \n","7     dev    support   966.000000   651.000000   184.000000       54.0   \n","8    test  precision     0.600000     0.675000     0.000000        0.0   \n","9    test     recall     0.310238     0.281250     0.000000        0.0   \n","10   test   f1-score     0.408998     0.397059     0.000000        0.0   \n","11   test    support   967.000000   576.000000   219.000000       36.0   \n","\n","    B-Finding  B-Disease  B-Symptom  I-Symptom  I-Disease  I-Drug  \\\n","0         0.0        0.0        0.0        0.0        0.0     0.0   \n","1         0.0        0.0        0.0        0.0        0.0     0.0   \n","2         0.0        0.0        0.0        0.0        0.0     0.0   \n","3       309.0      231.0      222.0      218.0      169.0   161.0   \n","4         0.0        0.0        0.0        0.0        0.0     0.0   \n","5         0.0        0.0        0.0        0.0        0.0     0.0   \n","6         0.0        0.0        0.0        0.0        0.0     0.0   \n","7        64.0       31.0       25.0       36.0       28.0    27.0   \n","8         0.0        0.0        0.0        0.0        0.0     0.0   \n","9         0.0        0.0        0.0        0.0        0.0     0.0   \n","10        0.0        0.0        0.0        0.0        0.0     0.0   \n","11       52.0       20.0       22.0       33.0       12.0    52.0   \n","\n","       micro avg     macro avg  weighted avg   samples avg  \n","0       0.577215      0.216211      0.554591      0.037744  \n","1       0.227545      0.055457      0.227545      0.037739  \n","2       0.326413      0.075010      0.305234      0.037740  \n","3   15720.000000  15720.000000  15720.000000  15720.000000  \n","4       0.552424      0.112930      0.436654           NaN  \n","5       0.237173      0.059340      0.237173           NaN  \n","6       0.331866      0.077287      0.305454           NaN  \n","7    2066.000000   2066.000000   2066.000000           NaN  \n","8       0.624324      0.127500      0.487179      0.039040  \n","9       0.232278      0.059149      0.232278      0.038998  \n","10      0.338586      0.080606      0.313830      0.039012  \n","11   1989.000000   1989.000000   1989.000000   1989.000000  "]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["print(suf)\n","\n","os.mkdir('flair_ner_model'+suf+'/classification_report')\n","plotter.plot_training_curves('flair_ner_model'+suf+'/loss.tsv')\n","\n","# evaluate the tagger on the test set\n","result = tagger.evaluate(corpus.train, \n","                         embeddings_storage_mode='gpu',\n","                         mini_batch_size=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":18,"metadata":{"executionInfo":{"elapsed":5,"status":"ok","timestamp":1682058351517,"user":{"displayName":"Alex He","userId":"06262800374381224408"},"user_tz":240},"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."]}],"metadata":{"accelerator":"GPU","colab":{"gpuType":"A100","provenance":[]},"gpuClass":"standard","kernelspec":{"display_name":"flair","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.16"},"vscode":{"interpreter":{"hash":"381c8abc3392c5f20473858f182d9ac4a53ef053fb1f276fcb4936938616c3f3"}}},"nbformat":4,"nbformat_minor":0}