2023-10-13 12:00:27,065 ----------------------------------------------------------------------------------------------------
2023-10-13 12:00:27,066 Model: "SequenceTagger(
  (embeddings): TransformerWordEmbeddings(
    (model): BertModel(
      (embeddings): BertEmbeddings(
        (word_embeddings): Embedding(32001, 768)
        (position_embeddings): Embedding(512, 768)
        (token_type_embeddings): Embedding(2, 768)
        (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
        (dropout): Dropout(p=0.1, inplace=False)
      )
      (encoder): BertEncoder(
        (layer): ModuleList(
          (0-11): 12 x BertLayer(
            (attention): BertAttention(
              (self): BertSelfAttention(
                (query): Linear(in_features=768, out_features=768, bias=True)
                (key): Linear(in_features=768, out_features=768, bias=True)
                (value): Linear(in_features=768, out_features=768, bias=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (output): BertSelfOutput(
                (dense): Linear(in_features=768, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (intermediate): BertIntermediate(
              (dense): Linear(in_features=768, out_features=3072, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): BertOutput(
              (dense): Linear(in_features=3072, out_features=768, bias=True)
              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
        )
      )
      (pooler): BertPooler(
        (dense): Linear(in_features=768, out_features=768, bias=True)
        (activation): Tanh()
      )
    )
  )
  (locked_dropout): LockedDropout(p=0.5)
  (linear): Linear(in_features=768, out_features=21, bias=True)
  (loss_function): CrossEntropyLoss()
)"
2023-10-13 12:00:27,066 ----------------------------------------------------------------------------------------------------
2023-10-13 12:00:27,066 MultiCorpus: 3575 train + 1235 dev + 1266 test sentences
 - NER_HIPE_2022 Corpus: 3575 train + 1235 dev + 1266 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/hipe2020/de/with_doc_seperator
2023-10-13 12:00:27,066 ----------------------------------------------------------------------------------------------------
2023-10-13 12:00:27,066 Train:  3575 sentences
2023-10-13 12:00:27,066         (train_with_dev=False, train_with_test=False)
2023-10-13 12:00:27,066 ----------------------------------------------------------------------------------------------------
2023-10-13 12:00:27,066 Training Params:
2023-10-13 12:00:27,066  - learning_rate: "5e-05" 
2023-10-13 12:00:27,066  - mini_batch_size: "4"
2023-10-13 12:00:27,066  - max_epochs: "10"
2023-10-13 12:00:27,066  - shuffle: "True"
2023-10-13 12:00:27,066 ----------------------------------------------------------------------------------------------------
2023-10-13 12:00:27,066 Plugins:
2023-10-13 12:00:27,066  - LinearScheduler | warmup_fraction: '0.1'
2023-10-13 12:00:27,066 ----------------------------------------------------------------------------------------------------
2023-10-13 12:00:27,066 Final evaluation on model from best epoch (best-model.pt)
2023-10-13 12:00:27,066  - metric: "('micro avg', 'f1-score')"
2023-10-13 12:00:27,066 ----------------------------------------------------------------------------------------------------
2023-10-13 12:00:27,066 Computation:
2023-10-13 12:00:27,067  - compute on device: cuda:0
2023-10-13 12:00:27,067  - embedding storage: none
2023-10-13 12:00:27,067 ----------------------------------------------------------------------------------------------------
2023-10-13 12:00:27,067 Model training base path: "hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1"
2023-10-13 12:00:27,067 ----------------------------------------------------------------------------------------------------
2023-10-13 12:00:27,067 ----------------------------------------------------------------------------------------------------
2023-10-13 12:00:31,356 epoch 1 - iter 89/894 - loss 2.65202089 - time (sec): 4.29 - samples/sec: 2230.44 - lr: 0.000005 - momentum: 0.000000
2023-10-13 12:00:35,325 epoch 1 - iter 178/894 - loss 1.69966045 - time (sec): 8.26 - samples/sec: 2125.46 - lr: 0.000010 - momentum: 0.000000
2023-10-13 12:00:39,217 epoch 1 - iter 267/894 - loss 1.30319450 - time (sec): 12.15 - samples/sec: 2082.04 - lr: 0.000015 - momentum: 0.000000
2023-10-13 12:00:43,289 epoch 1 - iter 356/894 - loss 1.06401462 - time (sec): 16.22 - samples/sec: 2087.13 - lr: 0.000020 - momentum: 0.000000
2023-10-13 12:00:47,424 epoch 1 - iter 445/894 - loss 0.91793285 - time (sec): 20.36 - samples/sec: 2064.05 - lr: 0.000025 - momentum: 0.000000
2023-10-13 12:00:51,643 epoch 1 - iter 534/894 - loss 0.80690184 - time (sec): 24.58 - samples/sec: 2073.01 - lr: 0.000030 - momentum: 0.000000
2023-10-13 12:00:55,771 epoch 1 - iter 623/894 - loss 0.72960178 - time (sec): 28.70 - samples/sec: 2074.20 - lr: 0.000035 - momentum: 0.000000
2023-10-13 12:01:00,063 epoch 1 - iter 712/894 - loss 0.66121317 - time (sec): 33.00 - samples/sec: 2095.23 - lr: 0.000040 - momentum: 0.000000
2023-10-13 12:01:04,132 epoch 1 - iter 801/894 - loss 0.61592548 - time (sec): 37.06 - samples/sec: 2080.65 - lr: 0.000045 - momentum: 0.000000
2023-10-13 12:01:08,647 epoch 1 - iter 890/894 - loss 0.57823314 - time (sec): 41.58 - samples/sec: 2070.70 - lr: 0.000050 - momentum: 0.000000
2023-10-13 12:01:08,863 ----------------------------------------------------------------------------------------------------
2023-10-13 12:01:08,863 EPOCH 1 done: loss 0.5761 - lr: 0.000050
2023-10-13 12:01:14,125 DEV : loss 0.19171087443828583 - f1-score (micro avg)  0.5523
2023-10-13 12:01:14,162 saving best model
2023-10-13 12:01:14,561 ----------------------------------------------------------------------------------------------------
2023-10-13 12:01:19,169 epoch 2 - iter 89/894 - loss 0.16742899 - time (sec): 4.61 - samples/sec: 1876.18 - lr: 0.000049 - momentum: 0.000000
2023-10-13 12:01:24,154 epoch 2 - iter 178/894 - loss 0.18661985 - time (sec): 9.59 - samples/sec: 1799.79 - lr: 0.000049 - momentum: 0.000000
2023-10-13 12:01:28,900 epoch 2 - iter 267/894 - loss 0.18491634 - time (sec): 14.34 - samples/sec: 1810.30 - lr: 0.000048 - momentum: 0.000000
2023-10-13 12:01:33,617 epoch 2 - iter 356/894 - loss 0.17699142 - time (sec): 19.05 - samples/sec: 1803.39 - lr: 0.000048 - momentum: 0.000000
2023-10-13 12:01:38,281 epoch 2 - iter 445/894 - loss 0.17304819 - time (sec): 23.72 - samples/sec: 1787.25 - lr: 0.000047 - momentum: 0.000000
2023-10-13 12:01:43,059 epoch 2 - iter 534/894 - loss 0.16781167 - time (sec): 28.50 - samples/sec: 1795.31 - lr: 0.000047 - momentum: 0.000000
2023-10-13 12:01:47,846 epoch 2 - iter 623/894 - loss 0.16699818 - time (sec): 33.28 - samples/sec: 1814.02 - lr: 0.000046 - momentum: 0.000000
2023-10-13 12:01:51,935 epoch 2 - iter 712/894 - loss 0.16546982 - time (sec): 37.37 - samples/sec: 1837.50 - lr: 0.000046 - momentum: 0.000000
2023-10-13 12:01:56,087 epoch 2 - iter 801/894 - loss 0.16583765 - time (sec): 41.52 - samples/sec: 1849.52 - lr: 0.000045 - momentum: 0.000000
2023-10-13 12:02:00,492 epoch 2 - iter 890/894 - loss 0.16396919 - time (sec): 45.93 - samples/sec: 1877.86 - lr: 0.000044 - momentum: 0.000000
2023-10-13 12:02:00,666 ----------------------------------------------------------------------------------------------------
2023-10-13 12:02:00,666 EPOCH 2 done: loss 0.1637 - lr: 0.000044
2023-10-13 12:02:09,259 DEV : loss 0.1673235297203064 - f1-score (micro avg)  0.6731
2023-10-13 12:02:09,287 saving best model
2023-10-13 12:02:09,740 ----------------------------------------------------------------------------------------------------
2023-10-13 12:02:14,071 epoch 3 - iter 89/894 - loss 0.11293528 - time (sec): 4.32 - samples/sec: 1806.55 - lr: 0.000044 - momentum: 0.000000
2023-10-13 12:02:18,083 epoch 3 - iter 178/894 - loss 0.09808312 - time (sec): 8.34 - samples/sec: 1941.24 - lr: 0.000043 - momentum: 0.000000
2023-10-13 12:02:22,186 epoch 3 - iter 267/894 - loss 0.10487395 - time (sec): 12.44 - samples/sec: 1958.54 - lr: 0.000043 - momentum: 0.000000
2023-10-13 12:02:26,519 epoch 3 - iter 356/894 - loss 0.09664391 - time (sec): 16.77 - samples/sec: 1981.81 - lr: 0.000042 - momentum: 0.000000
2023-10-13 12:02:31,084 epoch 3 - iter 445/894 - loss 0.09801920 - time (sec): 21.34 - samples/sec: 1989.82 - lr: 0.000042 - momentum: 0.000000
2023-10-13 12:02:35,161 epoch 3 - iter 534/894 - loss 0.09498912 - time (sec): 25.41 - samples/sec: 2021.14 - lr: 0.000041 - momentum: 0.000000
2023-10-13 12:02:39,416 epoch 3 - iter 623/894 - loss 0.09310128 - time (sec): 29.67 - samples/sec: 2021.67 - lr: 0.000041 - momentum: 0.000000
2023-10-13 12:02:43,736 epoch 3 - iter 712/894 - loss 0.09653710 - time (sec): 33.99 - samples/sec: 2015.81 - lr: 0.000040 - momentum: 0.000000
2023-10-13 12:02:47,930 epoch 3 - iter 801/894 - loss 0.09612514 - time (sec): 38.18 - samples/sec: 2015.52 - lr: 0.000039 - momentum: 0.000000
2023-10-13 12:02:52,340 epoch 3 - iter 890/894 - loss 0.09660086 - time (sec): 42.59 - samples/sec: 2024.95 - lr: 0.000039 - momentum: 0.000000
2023-10-13 12:02:52,514 ----------------------------------------------------------------------------------------------------
2023-10-13 12:02:52,514 EPOCH 3 done: loss 0.0964 - lr: 0.000039
2023-10-13 12:03:01,037 DEV : loss 0.1543554961681366 - f1-score (micro avg)  0.7237
2023-10-13 12:03:01,064 saving best model
2023-10-13 12:03:01,494 ----------------------------------------------------------------------------------------------------
2023-10-13 12:03:05,695 epoch 4 - iter 89/894 - loss 0.06872160 - time (sec): 4.20 - samples/sec: 2147.40 - lr: 0.000038 - momentum: 0.000000
2023-10-13 12:03:09,945 epoch 4 - iter 178/894 - loss 0.06523083 - time (sec): 8.45 - samples/sec: 2031.05 - lr: 0.000038 - momentum: 0.000000
2023-10-13 12:03:14,291 epoch 4 - iter 267/894 - loss 0.06233008 - time (sec): 12.80 - samples/sec: 2053.49 - lr: 0.000037 - momentum: 0.000000
2023-10-13 12:03:18,654 epoch 4 - iter 356/894 - loss 0.06329979 - time (sec): 17.16 - samples/sec: 2106.44 - lr: 0.000037 - momentum: 0.000000
2023-10-13 12:03:22,852 epoch 4 - iter 445/894 - loss 0.06037310 - time (sec): 21.36 - samples/sec: 2099.06 - lr: 0.000036 - momentum: 0.000000
2023-10-13 12:03:26,947 epoch 4 - iter 534/894 - loss 0.06290240 - time (sec): 25.45 - samples/sec: 2104.17 - lr: 0.000036 - momentum: 0.000000
2023-10-13 12:03:30,914 epoch 4 - iter 623/894 - loss 0.06194766 - time (sec): 29.42 - samples/sec: 2101.71 - lr: 0.000035 - momentum: 0.000000
2023-10-13 12:03:35,018 epoch 4 - iter 712/894 - loss 0.06159615 - time (sec): 33.52 - samples/sec: 2095.48 - lr: 0.000034 - momentum: 0.000000
2023-10-13 12:03:39,166 epoch 4 - iter 801/894 - loss 0.06291890 - time (sec): 37.67 - samples/sec: 2060.79 - lr: 0.000034 - momentum: 0.000000
2023-10-13 12:03:43,303 epoch 4 - iter 890/894 - loss 0.06248239 - time (sec): 41.81 - samples/sec: 2062.20 - lr: 0.000033 - momentum: 0.000000
2023-10-13 12:03:43,493 ----------------------------------------------------------------------------------------------------
2023-10-13 12:03:43,493 EPOCH 4 done: loss 0.0628 - lr: 0.000033
2023-10-13 12:03:51,962 DEV : loss 0.19826681911945343 - f1-score (micro avg)  0.7608
2023-10-13 12:03:51,990 saving best model
2023-10-13 12:03:52,464 ----------------------------------------------------------------------------------------------------
2023-10-13 12:03:57,042 epoch 5 - iter 89/894 - loss 0.05415502 - time (sec): 4.57 - samples/sec: 2124.39 - lr: 0.000033 - momentum: 0.000000
2023-10-13 12:04:01,786 epoch 5 - iter 178/894 - loss 0.05000802 - time (sec): 9.32 - samples/sec: 1905.31 - lr: 0.000032 - momentum: 0.000000
2023-10-13 12:04:06,621 epoch 5 - iter 267/894 - loss 0.05083210 - time (sec): 14.15 - samples/sec: 1878.52 - lr: 0.000032 - momentum: 0.000000
2023-10-13 12:04:11,213 epoch 5 - iter 356/894 - loss 0.04975196 - time (sec): 18.75 - samples/sec: 1850.66 - lr: 0.000031 - momentum: 0.000000
2023-10-13 12:04:15,475 epoch 5 - iter 445/894 - loss 0.04576638 - time (sec): 23.01 - samples/sec: 1900.36 - lr: 0.000031 - momentum: 0.000000
2023-10-13 12:04:19,525 epoch 5 - iter 534/894 - loss 0.04845092 - time (sec): 27.06 - samples/sec: 1939.73 - lr: 0.000030 - momentum: 0.000000
2023-10-13 12:04:23,737 epoch 5 - iter 623/894 - loss 0.04814231 - time (sec): 31.27 - samples/sec: 1941.02 - lr: 0.000029 - momentum: 0.000000
2023-10-13 12:04:28,002 epoch 5 - iter 712/894 - loss 0.04696082 - time (sec): 35.53 - samples/sec: 1955.02 - lr: 0.000029 - momentum: 0.000000
2023-10-13 12:04:32,196 epoch 5 - iter 801/894 - loss 0.04514786 - time (sec): 39.73 - samples/sec: 1956.95 - lr: 0.000028 - momentum: 0.000000
2023-10-13 12:04:36,300 epoch 5 - iter 890/894 - loss 0.04547050 - time (sec): 43.83 - samples/sec: 1966.57 - lr: 0.000028 - momentum: 0.000000
2023-10-13 12:04:36,493 ----------------------------------------------------------------------------------------------------
2023-10-13 12:04:36,493 EPOCH 5 done: loss 0.0453 - lr: 0.000028
2023-10-13 12:04:44,989 DEV : loss 0.2249317169189453 - f1-score (micro avg)  0.7558
2023-10-13 12:04:45,019 ----------------------------------------------------------------------------------------------------
2023-10-13 12:04:49,517 epoch 6 - iter 89/894 - loss 0.02250297 - time (sec): 4.50 - samples/sec: 1927.38 - lr: 0.000027 - momentum: 0.000000
2023-10-13 12:04:53,620 epoch 6 - iter 178/894 - loss 0.02755065 - time (sec): 8.60 - samples/sec: 1904.80 - lr: 0.000027 - momentum: 0.000000
2023-10-13 12:04:57,991 epoch 6 - iter 267/894 - loss 0.02537520 - time (sec): 12.97 - samples/sec: 1969.77 - lr: 0.000026 - momentum: 0.000000
2023-10-13 12:05:02,077 epoch 6 - iter 356/894 - loss 0.02778131 - time (sec): 17.06 - samples/sec: 2019.16 - lr: 0.000026 - momentum: 0.000000
2023-10-13 12:05:06,083 epoch 6 - iter 445/894 - loss 0.02565103 - time (sec): 21.06 - samples/sec: 1994.56 - lr: 0.000025 - momentum: 0.000000
2023-10-13 12:05:10,138 epoch 6 - iter 534/894 - loss 0.02415612 - time (sec): 25.12 - samples/sec: 2001.89 - lr: 0.000024 - momentum: 0.000000
2023-10-13 12:05:14,184 epoch 6 - iter 623/894 - loss 0.02615286 - time (sec): 29.16 - samples/sec: 1993.85 - lr: 0.000024 - momentum: 0.000000
2023-10-13 12:05:18,598 epoch 6 - iter 712/894 - loss 0.02606805 - time (sec): 33.58 - samples/sec: 2035.76 - lr: 0.000023 - momentum: 0.000000
2023-10-13 12:05:22,821 epoch 6 - iter 801/894 - loss 0.02757777 - time (sec): 37.80 - samples/sec: 2034.05 - lr: 0.000023 - momentum: 0.000000
2023-10-13 12:05:27,484 epoch 6 - iter 890/894 - loss 0.02680885 - time (sec): 42.46 - samples/sec: 2027.87 - lr: 0.000022 - momentum: 0.000000
2023-10-13 12:05:27,693 ----------------------------------------------------------------------------------------------------
2023-10-13 12:05:27,693 EPOCH 6 done: loss 0.0267 - lr: 0.000022
2023-10-13 12:05:36,241 DEV : loss 0.2265176773071289 - f1-score (micro avg)  0.761
2023-10-13 12:05:36,268 saving best model
2023-10-13 12:05:36,722 ----------------------------------------------------------------------------------------------------
2023-10-13 12:05:41,169 epoch 7 - iter 89/894 - loss 0.01241443 - time (sec): 4.45 - samples/sec: 1976.84 - lr: 0.000022 - momentum: 0.000000
2023-10-13 12:05:45,326 epoch 7 - iter 178/894 - loss 0.01280006 - time (sec): 8.60 - samples/sec: 1988.62 - lr: 0.000021 - momentum: 0.000000
2023-10-13 12:05:50,011 epoch 7 - iter 267/894 - loss 0.01410320 - time (sec): 13.29 - samples/sec: 2051.18 - lr: 0.000021 - momentum: 0.000000
2023-10-13 12:05:54,218 epoch 7 - iter 356/894 - loss 0.01506068 - time (sec): 17.50 - samples/sec: 2036.46 - lr: 0.000020 - momentum: 0.000000
2023-10-13 12:05:58,380 epoch 7 - iter 445/894 - loss 0.01802183 - time (sec): 21.66 - samples/sec: 2050.15 - lr: 0.000019 - momentum: 0.000000
2023-10-13 12:06:02,559 epoch 7 - iter 534/894 - loss 0.01957567 - time (sec): 25.84 - samples/sec: 2036.06 - lr: 0.000019 - momentum: 0.000000
2023-10-13 12:06:06,819 epoch 7 - iter 623/894 - loss 0.01864068 - time (sec): 30.10 - samples/sec: 2025.40 - lr: 0.000018 - momentum: 0.000000
2023-10-13 12:06:10,824 epoch 7 - iter 712/894 - loss 0.01838519 - time (sec): 34.10 - samples/sec: 2028.91 - lr: 0.000018 - momentum: 0.000000
2023-10-13 12:06:14,873 epoch 7 - iter 801/894 - loss 0.01897641 - time (sec): 38.15 - samples/sec: 2020.45 - lr: 0.000017 - momentum: 0.000000
2023-10-13 12:06:19,125 epoch 7 - iter 890/894 - loss 0.01885509 - time (sec): 42.40 - samples/sec: 2034.65 - lr: 0.000017 - momentum: 0.000000
2023-10-13 12:06:19,303 ----------------------------------------------------------------------------------------------------
2023-10-13 12:06:19,304 EPOCH 7 done: loss 0.0188 - lr: 0.000017
2023-10-13 12:06:27,833 DEV : loss 0.23773737251758575 - f1-score (micro avg)  0.7709
2023-10-13 12:06:27,862 saving best model
2023-10-13 12:06:28,338 ----------------------------------------------------------------------------------------------------
2023-10-13 12:06:32,884 epoch 8 - iter 89/894 - loss 0.01758360 - time (sec): 4.54 - samples/sec: 1907.50 - lr: 0.000016 - momentum: 0.000000
2023-10-13 12:06:37,227 epoch 8 - iter 178/894 - loss 0.01300323 - time (sec): 8.89 - samples/sec: 2000.63 - lr: 0.000016 - momentum: 0.000000
2023-10-13 12:06:41,456 epoch 8 - iter 267/894 - loss 0.01026527 - time (sec): 13.12 - samples/sec: 2049.43 - lr: 0.000015 - momentum: 0.000000
2023-10-13 12:06:45,592 epoch 8 - iter 356/894 - loss 0.00865146 - time (sec): 17.25 - samples/sec: 2112.95 - lr: 0.000014 - momentum: 0.000000
2023-10-13 12:06:49,491 epoch 8 - iter 445/894 - loss 0.01113562 - time (sec): 21.15 - samples/sec: 2087.22 - lr: 0.000014 - momentum: 0.000000
2023-10-13 12:06:53,684 epoch 8 - iter 534/894 - loss 0.01103381 - time (sec): 25.34 - samples/sec: 2076.68 - lr: 0.000013 - momentum: 0.000000
2023-10-13 12:06:58,224 epoch 8 - iter 623/894 - loss 0.01078598 - time (sec): 29.88 - samples/sec: 2063.56 - lr: 0.000013 - momentum: 0.000000
2023-10-13 12:07:02,633 epoch 8 - iter 712/894 - loss 0.01053832 - time (sec): 34.29 - samples/sec: 2039.31 - lr: 0.000012 - momentum: 0.000000
2023-10-13 12:07:06,832 epoch 8 - iter 801/894 - loss 0.01089148 - time (sec): 38.49 - samples/sec: 2028.88 - lr: 0.000012 - momentum: 0.000000
2023-10-13 12:07:10,958 epoch 8 - iter 890/894 - loss 0.01109678 - time (sec): 42.62 - samples/sec: 2022.12 - lr: 0.000011 - momentum: 0.000000
2023-10-13 12:07:11,139 ----------------------------------------------------------------------------------------------------
2023-10-13 12:07:11,139 EPOCH 8 done: loss 0.0110 - lr: 0.000011
2023-10-13 12:07:19,962 DEV : loss 0.23593451082706451 - f1-score (micro avg)  0.7892
2023-10-13 12:07:19,992 saving best model
2023-10-13 12:07:20,478 ----------------------------------------------------------------------------------------------------
2023-10-13 12:07:24,789 epoch 9 - iter 89/894 - loss 0.00628948 - time (sec): 4.30 - samples/sec: 1921.98 - lr: 0.000011 - momentum: 0.000000
2023-10-13 12:07:29,139 epoch 9 - iter 178/894 - loss 0.00390042 - time (sec): 8.65 - samples/sec: 2031.56 - lr: 0.000010 - momentum: 0.000000
2023-10-13 12:07:33,428 epoch 9 - iter 267/894 - loss 0.00644408 - time (sec): 12.94 - samples/sec: 2000.87 - lr: 0.000009 - momentum: 0.000000
2023-10-13 12:07:37,671 epoch 9 - iter 356/894 - loss 0.00542230 - time (sec): 17.18 - samples/sec: 2057.31 - lr: 0.000009 - momentum: 0.000000
2023-10-13 12:07:41,968 epoch 9 - iter 445/894 - loss 0.00725202 - time (sec): 21.48 - samples/sec: 2057.49 - lr: 0.000008 - momentum: 0.000000
2023-10-13 12:07:46,266 epoch 9 - iter 534/894 - loss 0.00698739 - time (sec): 25.78 - samples/sec: 2073.97 - lr: 0.000008 - momentum: 0.000000
2023-10-13 12:07:50,551 epoch 9 - iter 623/894 - loss 0.00633533 - time (sec): 30.06 - samples/sec: 2062.87 - lr: 0.000007 - momentum: 0.000000
2023-10-13 12:07:54,589 epoch 9 - iter 712/894 - loss 0.00588233 - time (sec): 34.10 - samples/sec: 2053.11 - lr: 0.000007 - momentum: 0.000000
2023-10-13 12:07:58,682 epoch 9 - iter 801/894 - loss 0.00614205 - time (sec): 38.19 - samples/sec: 2047.19 - lr: 0.000006 - momentum: 0.000000
2023-10-13 12:08:03,257 epoch 9 - iter 890/894 - loss 0.00729369 - time (sec): 42.77 - samples/sec: 2013.71 - lr: 0.000006 - momentum: 0.000000
2023-10-13 12:08:03,460 ----------------------------------------------------------------------------------------------------
2023-10-13 12:08:03,461 EPOCH 9 done: loss 0.0073 - lr: 0.000006
2023-10-13 12:08:11,860 DEV : loss 0.24528658390045166 - f1-score (micro avg)  0.7827
2023-10-13 12:08:11,887 ----------------------------------------------------------------------------------------------------
2023-10-13 12:08:15,936 epoch 10 - iter 89/894 - loss 0.00803708 - time (sec): 4.05 - samples/sec: 2170.54 - lr: 0.000005 - momentum: 0.000000
2023-10-13 12:08:20,177 epoch 10 - iter 178/894 - loss 0.00744826 - time (sec): 8.29 - samples/sec: 2017.30 - lr: 0.000004 - momentum: 0.000000
2023-10-13 12:08:24,447 epoch 10 - iter 267/894 - loss 0.00540126 - time (sec): 12.56 - samples/sec: 2013.04 - lr: 0.000004 - momentum: 0.000000
2023-10-13 12:08:28,584 epoch 10 - iter 356/894 - loss 0.00463210 - time (sec): 16.69 - samples/sec: 2041.58 - lr: 0.000003 - momentum: 0.000000
2023-10-13 12:08:32,819 epoch 10 - iter 445/894 - loss 0.00503189 - time (sec): 20.93 - samples/sec: 2066.56 - lr: 0.000003 - momentum: 0.000000
2023-10-13 12:08:37,002 epoch 10 - iter 534/894 - loss 0.00461758 - time (sec): 25.11 - samples/sec: 2079.27 - lr: 0.000002 - momentum: 0.000000
2023-10-13 12:08:41,295 epoch 10 - iter 623/894 - loss 0.00437106 - time (sec): 29.41 - samples/sec: 2072.49 - lr: 0.000002 - momentum: 0.000000
2023-10-13 12:08:45,312 epoch 10 - iter 712/894 - loss 0.00448286 - time (sec): 33.42 - samples/sec: 2070.88 - lr: 0.000001 - momentum: 0.000000
2023-10-13 12:08:49,472 epoch 10 - iter 801/894 - loss 0.00428854 - time (sec): 37.58 - samples/sec: 2051.98 - lr: 0.000001 - momentum: 0.000000
2023-10-13 12:08:53,712 epoch 10 - iter 890/894 - loss 0.00454052 - time (sec): 41.82 - samples/sec: 2060.73 - lr: 0.000000 - momentum: 0.000000
2023-10-13 12:08:53,895 ----------------------------------------------------------------------------------------------------
2023-10-13 12:08:53,895 EPOCH 10 done: loss 0.0045 - lr: 0.000000
2023-10-13 12:09:02,408 DEV : loss 0.25100380182266235 - f1-score (micro avg)  0.7943
2023-10-13 12:09:02,436 saving best model
2023-10-13 12:09:03,246 ----------------------------------------------------------------------------------------------------
2023-10-13 12:09:03,247 Loading model from best epoch ...
2023-10-13 12:09:04,728 SequenceTagger predicts: Dictionary with 21 tags: O, S-loc, B-loc, E-loc, I-loc, S-pers, B-pers, E-pers, I-pers, S-org, B-org, E-org, I-org, S-prod, B-prod, E-prod, I-prod, S-time, B-time, E-time, I-time
2023-10-13 12:09:09,207 
Results:
- F-score (micro) 0.7421
- F-score (macro) 0.6478
- Accuracy 0.6085

By class:
              precision    recall  f1-score   support

         loc     0.8375    0.8473    0.8424       596
        pers     0.6768    0.7357    0.7050       333
         org     0.5392    0.4167    0.4701       132
        prod     0.5490    0.4242    0.4786        66
        time     0.6964    0.7959    0.7429        49

   micro avg     0.7428    0.7415    0.7421      1176
   macro avg     0.6598    0.6440    0.6478      1176
weighted avg     0.7364    0.7415    0.7371      1176

2023-10-13 12:09:09,207 ----------------------------------------------------------------------------------------------------