2023-10-17 15:58:59,059 ----------------------------------------------------------------------------------------------------
2023-10-17 15:58:59,061 Model: "SequenceTagger(
  (embeddings): TransformerWordEmbeddings(
    (model): ElectraModel(
      (embeddings): ElectraEmbeddings(
        (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): ElectraEncoder(
        (layer): ModuleList(
          (0-11): 12 x ElectraLayer(
            (attention): ElectraAttention(
              (self): ElectraSelfAttention(
                (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): ElectraSelfOutput(
                (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): ElectraIntermediate(
              (dense): Linear(in_features=768, out_features=3072, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): ElectraOutput(
              (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)
            )
          )
        )
      )
    )
  )
  (locked_dropout): LockedDropout(p=0.5)
  (linear): Linear(in_features=768, out_features=21, bias=True)
  (loss_function): CrossEntropyLoss()
)"
2023-10-17 15:58:59,061 ----------------------------------------------------------------------------------------------------
2023-10-17 15:58:59,062 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
2023-10-17 15:58:59,062 ----------------------------------------------------------------------------------------------------
2023-10-17 15:58:59,062 Train:  3575 sentences
2023-10-17 15:58:59,062         (train_with_dev=False, train_with_test=False)
2023-10-17 15:58:59,062 ----------------------------------------------------------------------------------------------------
2023-10-17 15:58:59,062 Training Params:
2023-10-17 15:58:59,062  - learning_rate: "3e-05" 
2023-10-17 15:58:59,062  - mini_batch_size: "8"
2023-10-17 15:58:59,062  - max_epochs: "10"
2023-10-17 15:58:59,062  - shuffle: "True"
2023-10-17 15:58:59,062 ----------------------------------------------------------------------------------------------------
2023-10-17 15:58:59,062 Plugins:
2023-10-17 15:58:59,063  - TensorboardLogger
2023-10-17 15:58:59,063  - LinearScheduler | warmup_fraction: '0.1'
2023-10-17 15:58:59,063 ----------------------------------------------------------------------------------------------------
2023-10-17 15:58:59,063 Final evaluation on model from best epoch (best-model.pt)
2023-10-17 15:58:59,063  - metric: "('micro avg', 'f1-score')"
2023-10-17 15:58:59,063 ----------------------------------------------------------------------------------------------------
2023-10-17 15:58:59,063 Computation:
2023-10-17 15:58:59,063  - compute on device: cuda:0
2023-10-17 15:58:59,063  - embedding storage: none
2023-10-17 15:58:59,063 ----------------------------------------------------------------------------------------------------
2023-10-17 15:58:59,063 Model training base path: "hmbench-hipe2020/de-hmteams/teams-base-historic-multilingual-discriminator-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2"
2023-10-17 15:58:59,063 ----------------------------------------------------------------------------------------------------
2023-10-17 15:58:59,063 ----------------------------------------------------------------------------------------------------
2023-10-17 15:58:59,064 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-17 15:59:03,268 epoch 1 - iter 44/447 - loss 3.75328637 - time (sec): 4.20 - samples/sec: 2112.58 - lr: 0.000003 - momentum: 0.000000
2023-10-17 15:59:07,512 epoch 1 - iter 88/447 - loss 2.79174559 - time (sec): 8.45 - samples/sec: 2030.17 - lr: 0.000006 - momentum: 0.000000
2023-10-17 15:59:11,524 epoch 1 - iter 132/447 - loss 2.08551841 - time (sec): 12.46 - samples/sec: 1987.11 - lr: 0.000009 - momentum: 0.000000
2023-10-17 15:59:16,031 epoch 1 - iter 176/447 - loss 1.63894890 - time (sec): 16.97 - samples/sec: 2016.62 - lr: 0.000012 - momentum: 0.000000
2023-10-17 15:59:20,141 epoch 1 - iter 220/447 - loss 1.39670438 - time (sec): 21.08 - samples/sec: 2016.11 - lr: 0.000015 - momentum: 0.000000
2023-10-17 15:59:24,433 epoch 1 - iter 264/447 - loss 1.22305966 - time (sec): 25.37 - samples/sec: 2016.49 - lr: 0.000018 - momentum: 0.000000
2023-10-17 15:59:28,479 epoch 1 - iter 308/447 - loss 1.10231203 - time (sec): 29.41 - samples/sec: 2005.73 - lr: 0.000021 - momentum: 0.000000
2023-10-17 15:59:32,764 epoch 1 - iter 352/447 - loss 1.00085629 - time (sec): 33.70 - samples/sec: 2007.65 - lr: 0.000024 - momentum: 0.000000
2023-10-17 15:59:37,492 epoch 1 - iter 396/447 - loss 0.91299761 - time (sec): 38.43 - samples/sec: 2004.60 - lr: 0.000027 - momentum: 0.000000
2023-10-17 15:59:41,758 epoch 1 - iter 440/447 - loss 0.84847471 - time (sec): 42.69 - samples/sec: 2001.55 - lr: 0.000029 - momentum: 0.000000
2023-10-17 15:59:42,398 ----------------------------------------------------------------------------------------------------
2023-10-17 15:59:42,398 EPOCH 1 done: loss 0.8401 - lr: 0.000029
2023-10-17 15:59:49,299 DEV : loss 0.1743467152118683 - f1-score (micro avg)  0.5904
2023-10-17 15:59:49,362 saving best model
2023-10-17 15:59:50,000 ----------------------------------------------------------------------------------------------------
2023-10-17 15:59:54,169 epoch 2 - iter 44/447 - loss 0.20202135 - time (sec): 4.17 - samples/sec: 2020.85 - lr: 0.000030 - momentum: 0.000000
2023-10-17 15:59:58,785 epoch 2 - iter 88/447 - loss 0.19013888 - time (sec): 8.78 - samples/sec: 2021.47 - lr: 0.000029 - momentum: 0.000000
2023-10-17 16:00:03,225 epoch 2 - iter 132/447 - loss 0.19608616 - time (sec): 13.22 - samples/sec: 1930.61 - lr: 0.000029 - momentum: 0.000000
2023-10-17 16:00:08,005 epoch 2 - iter 176/447 - loss 0.18596049 - time (sec): 18.00 - samples/sec: 1937.03 - lr: 0.000029 - momentum: 0.000000
2023-10-17 16:00:12,138 epoch 2 - iter 220/447 - loss 0.18035707 - time (sec): 22.13 - samples/sec: 1977.69 - lr: 0.000028 - momentum: 0.000000
2023-10-17 16:00:16,236 epoch 2 - iter 264/447 - loss 0.17461322 - time (sec): 26.23 - samples/sec: 1965.57 - lr: 0.000028 - momentum: 0.000000
2023-10-17 16:00:20,493 epoch 2 - iter 308/447 - loss 0.16847653 - time (sec): 30.49 - samples/sec: 1986.10 - lr: 0.000028 - momentum: 0.000000
2023-10-17 16:00:24,739 epoch 2 - iter 352/447 - loss 0.16607108 - time (sec): 34.74 - samples/sec: 1993.31 - lr: 0.000027 - momentum: 0.000000
2023-10-17 16:00:28,945 epoch 2 - iter 396/447 - loss 0.16349240 - time (sec): 38.94 - samples/sec: 1988.06 - lr: 0.000027 - momentum: 0.000000
2023-10-17 16:00:33,205 epoch 2 - iter 440/447 - loss 0.16613347 - time (sec): 43.20 - samples/sec: 1976.45 - lr: 0.000027 - momentum: 0.000000
2023-10-17 16:00:33,854 ----------------------------------------------------------------------------------------------------
2023-10-17 16:00:33,855 EPOCH 2 done: loss 0.1648 - lr: 0.000027
2023-10-17 16:00:45,420 DEV : loss 0.11944183707237244 - f1-score (micro avg)  0.7175
2023-10-17 16:00:45,481 saving best model
2023-10-17 16:00:46,934 ----------------------------------------------------------------------------------------------------
2023-10-17 16:00:51,110 epoch 3 - iter 44/447 - loss 0.08131650 - time (sec): 4.17 - samples/sec: 1891.39 - lr: 0.000026 - momentum: 0.000000
2023-10-17 16:00:55,405 epoch 3 - iter 88/447 - loss 0.08702455 - time (sec): 8.47 - samples/sec: 1967.19 - lr: 0.000026 - momentum: 0.000000
2023-10-17 16:00:59,963 epoch 3 - iter 132/447 - loss 0.07972396 - time (sec): 13.02 - samples/sec: 1964.70 - lr: 0.000026 - momentum: 0.000000
2023-10-17 16:01:04,142 epoch 3 - iter 176/447 - loss 0.07975901 - time (sec): 17.20 - samples/sec: 1939.38 - lr: 0.000025 - momentum: 0.000000
2023-10-17 16:01:08,611 epoch 3 - iter 220/447 - loss 0.08247967 - time (sec): 21.67 - samples/sec: 1967.29 - lr: 0.000025 - momentum: 0.000000
2023-10-17 16:01:12,697 epoch 3 - iter 264/447 - loss 0.08537389 - time (sec): 25.76 - samples/sec: 1978.18 - lr: 0.000025 - momentum: 0.000000
2023-10-17 16:01:17,006 epoch 3 - iter 308/447 - loss 0.08411404 - time (sec): 30.07 - samples/sec: 1987.82 - lr: 0.000024 - momentum: 0.000000
2023-10-17 16:01:21,139 epoch 3 - iter 352/447 - loss 0.08280911 - time (sec): 34.20 - samples/sec: 1990.44 - lr: 0.000024 - momentum: 0.000000
2023-10-17 16:01:25,354 epoch 3 - iter 396/447 - loss 0.08456497 - time (sec): 38.42 - samples/sec: 1993.20 - lr: 0.000024 - momentum: 0.000000
2023-10-17 16:01:29,534 epoch 3 - iter 440/447 - loss 0.08638185 - time (sec): 42.60 - samples/sec: 1984.93 - lr: 0.000023 - momentum: 0.000000
2023-10-17 16:01:30,441 ----------------------------------------------------------------------------------------------------
2023-10-17 16:01:30,442 EPOCH 3 done: loss 0.0860 - lr: 0.000023
2023-10-17 16:01:41,799 DEV : loss 0.13438037037849426 - f1-score (micro avg)  0.7434
2023-10-17 16:01:41,855 saving best model
2023-10-17 16:01:43,314 ----------------------------------------------------------------------------------------------------
2023-10-17 16:01:47,660 epoch 4 - iter 44/447 - loss 0.05444230 - time (sec): 4.34 - samples/sec: 2164.26 - lr: 0.000023 - momentum: 0.000000
2023-10-17 16:01:52,647 epoch 4 - iter 88/447 - loss 0.05193996 - time (sec): 9.33 - samples/sec: 2018.23 - lr: 0.000023 - momentum: 0.000000
2023-10-17 16:01:57,154 epoch 4 - iter 132/447 - loss 0.05306498 - time (sec): 13.83 - samples/sec: 1941.12 - lr: 0.000022 - momentum: 0.000000
2023-10-17 16:02:01,286 epoch 4 - iter 176/447 - loss 0.05252827 - time (sec): 17.97 - samples/sec: 1934.72 - lr: 0.000022 - momentum: 0.000000
2023-10-17 16:02:05,472 epoch 4 - iter 220/447 - loss 0.05261466 - time (sec): 22.15 - samples/sec: 1950.76 - lr: 0.000022 - momentum: 0.000000
2023-10-17 16:02:09,829 epoch 4 - iter 264/447 - loss 0.05182422 - time (sec): 26.51 - samples/sec: 1953.80 - lr: 0.000021 - momentum: 0.000000
2023-10-17 16:02:13,882 epoch 4 - iter 308/447 - loss 0.05086636 - time (sec): 30.56 - samples/sec: 1955.14 - lr: 0.000021 - momentum: 0.000000
2023-10-17 16:02:18,196 epoch 4 - iter 352/447 - loss 0.05167051 - time (sec): 34.88 - samples/sec: 1968.66 - lr: 0.000021 - momentum: 0.000000
2023-10-17 16:02:22,250 epoch 4 - iter 396/447 - loss 0.05200802 - time (sec): 38.93 - samples/sec: 1971.49 - lr: 0.000020 - momentum: 0.000000
2023-10-17 16:02:26,437 epoch 4 - iter 440/447 - loss 0.05402605 - time (sec): 43.12 - samples/sec: 1980.43 - lr: 0.000020 - momentum: 0.000000
2023-10-17 16:02:27,080 ----------------------------------------------------------------------------------------------------
2023-10-17 16:02:27,080 EPOCH 4 done: loss 0.0547 - lr: 0.000020
2023-10-17 16:02:38,092 DEV : loss 0.15765978395938873 - f1-score (micro avg)  0.7564
2023-10-17 16:02:38,149 saving best model
2023-10-17 16:02:39,959 ----------------------------------------------------------------------------------------------------
2023-10-17 16:02:44,066 epoch 5 - iter 44/447 - loss 0.02163230 - time (sec): 4.10 - samples/sec: 2014.16 - lr: 0.000020 - momentum: 0.000000
2023-10-17 16:02:48,624 epoch 5 - iter 88/447 - loss 0.02328428 - time (sec): 8.66 - samples/sec: 2063.37 - lr: 0.000019 - momentum: 0.000000
2023-10-17 16:02:52,824 epoch 5 - iter 132/447 - loss 0.02815843 - time (sec): 12.86 - samples/sec: 2055.05 - lr: 0.000019 - momentum: 0.000000
2023-10-17 16:02:57,091 epoch 5 - iter 176/447 - loss 0.03162369 - time (sec): 17.13 - samples/sec: 2030.52 - lr: 0.000019 - momentum: 0.000000
2023-10-17 16:03:01,212 epoch 5 - iter 220/447 - loss 0.03390401 - time (sec): 21.25 - samples/sec: 2016.40 - lr: 0.000018 - momentum: 0.000000
2023-10-17 16:03:05,489 epoch 5 - iter 264/447 - loss 0.03510618 - time (sec): 25.53 - samples/sec: 1996.63 - lr: 0.000018 - momentum: 0.000000
2023-10-17 16:03:09,836 epoch 5 - iter 308/447 - loss 0.03570858 - time (sec): 29.87 - samples/sec: 2011.06 - lr: 0.000018 - momentum: 0.000000
2023-10-17 16:03:14,158 epoch 5 - iter 352/447 - loss 0.03496530 - time (sec): 34.19 - samples/sec: 2017.95 - lr: 0.000017 - momentum: 0.000000
2023-10-17 16:03:18,233 epoch 5 - iter 396/447 - loss 0.03442000 - time (sec): 38.27 - samples/sec: 2012.70 - lr: 0.000017 - momentum: 0.000000
2023-10-17 16:03:22,296 epoch 5 - iter 440/447 - loss 0.03343545 - time (sec): 42.33 - samples/sec: 2012.39 - lr: 0.000017 - momentum: 0.000000
2023-10-17 16:03:22,912 ----------------------------------------------------------------------------------------------------
2023-10-17 16:03:22,912 EPOCH 5 done: loss 0.0336 - lr: 0.000017
2023-10-17 16:03:34,146 DEV : loss 0.18739798665046692 - f1-score (micro avg)  0.7602
2023-10-17 16:03:34,211 saving best model
2023-10-17 16:03:35,654 ----------------------------------------------------------------------------------------------------
2023-10-17 16:03:40,239 epoch 6 - iter 44/447 - loss 0.02567182 - time (sec): 4.58 - samples/sec: 1915.30 - lr: 0.000016 - momentum: 0.000000
2023-10-17 16:03:44,559 epoch 6 - iter 88/447 - loss 0.02386413 - time (sec): 8.90 - samples/sec: 1963.68 - lr: 0.000016 - momentum: 0.000000
2023-10-17 16:03:48,830 epoch 6 - iter 132/447 - loss 0.02665298 - time (sec): 13.17 - samples/sec: 1938.11 - lr: 0.000016 - momentum: 0.000000
2023-10-17 16:03:52,872 epoch 6 - iter 176/447 - loss 0.02638426 - time (sec): 17.21 - samples/sec: 1966.56 - lr: 0.000015 - momentum: 0.000000
2023-10-17 16:03:56,972 epoch 6 - iter 220/447 - loss 0.02455531 - time (sec): 21.31 - samples/sec: 2002.06 - lr: 0.000015 - momentum: 0.000000
2023-10-17 16:04:01,089 epoch 6 - iter 264/447 - loss 0.02439035 - time (sec): 25.43 - samples/sec: 1989.32 - lr: 0.000015 - momentum: 0.000000
2023-10-17 16:04:05,048 epoch 6 - iter 308/447 - loss 0.02384438 - time (sec): 29.39 - samples/sec: 1990.74 - lr: 0.000014 - momentum: 0.000000
2023-10-17 16:04:09,461 epoch 6 - iter 352/447 - loss 0.02340501 - time (sec): 33.80 - samples/sec: 1998.99 - lr: 0.000014 - momentum: 0.000000
2023-10-17 16:04:13,567 epoch 6 - iter 396/447 - loss 0.02300984 - time (sec): 37.91 - samples/sec: 1999.25 - lr: 0.000014 - momentum: 0.000000
2023-10-17 16:04:18,493 epoch 6 - iter 440/447 - loss 0.02335350 - time (sec): 42.83 - samples/sec: 1992.49 - lr: 0.000013 - momentum: 0.000000
2023-10-17 16:04:19,112 ----------------------------------------------------------------------------------------------------
2023-10-17 16:04:19,113 EPOCH 6 done: loss 0.0233 - lr: 0.000013
2023-10-17 16:04:30,550 DEV : loss 0.1992470622062683 - f1-score (micro avg)  0.7886
2023-10-17 16:04:30,611 saving best model
2023-10-17 16:04:32,023 ----------------------------------------------------------------------------------------------------
2023-10-17 16:04:36,035 epoch 7 - iter 44/447 - loss 0.01165349 - time (sec): 4.01 - samples/sec: 2154.32 - lr: 0.000013 - momentum: 0.000000
2023-10-17 16:04:39,982 epoch 7 - iter 88/447 - loss 0.01076700 - time (sec): 7.95 - samples/sec: 2043.10 - lr: 0.000013 - momentum: 0.000000
2023-10-17 16:04:43,960 epoch 7 - iter 132/447 - loss 0.01479762 - time (sec): 11.93 - samples/sec: 2072.77 - lr: 0.000012 - momentum: 0.000000
2023-10-17 16:04:48,204 epoch 7 - iter 176/447 - loss 0.01368042 - time (sec): 16.18 - samples/sec: 2089.53 - lr: 0.000012 - momentum: 0.000000
2023-10-17 16:04:52,249 epoch 7 - iter 220/447 - loss 0.01535559 - time (sec): 20.22 - samples/sec: 2089.04 - lr: 0.000012 - momentum: 0.000000
2023-10-17 16:04:56,520 epoch 7 - iter 264/447 - loss 0.01497302 - time (sec): 24.49 - samples/sec: 2078.72 - lr: 0.000011 - momentum: 0.000000
2023-10-17 16:05:00,828 epoch 7 - iter 308/447 - loss 0.01480877 - time (sec): 28.80 - samples/sec: 2071.01 - lr: 0.000011 - momentum: 0.000000
2023-10-17 16:05:05,246 epoch 7 - iter 352/447 - loss 0.01534251 - time (sec): 33.22 - samples/sec: 2063.40 - lr: 0.000011 - momentum: 0.000000
2023-10-17 16:05:09,457 epoch 7 - iter 396/447 - loss 0.01577918 - time (sec): 37.43 - samples/sec: 2062.23 - lr: 0.000010 - momentum: 0.000000
2023-10-17 16:05:13,493 epoch 7 - iter 440/447 - loss 0.01506408 - time (sec): 41.47 - samples/sec: 2055.56 - lr: 0.000010 - momentum: 0.000000
2023-10-17 16:05:14,130 ----------------------------------------------------------------------------------------------------
2023-10-17 16:05:14,130 EPOCH 7 done: loss 0.0151 - lr: 0.000010
2023-10-17 16:05:25,738 DEV : loss 0.19981977343559265 - f1-score (micro avg)  0.7962
2023-10-17 16:05:25,795 saving best model
2023-10-17 16:05:27,205 ----------------------------------------------------------------------------------------------------
2023-10-17 16:05:31,392 epoch 8 - iter 44/447 - loss 0.00747848 - time (sec): 4.18 - samples/sec: 2091.78 - lr: 0.000010 - momentum: 0.000000
2023-10-17 16:05:35,357 epoch 8 - iter 88/447 - loss 0.00942650 - time (sec): 8.15 - samples/sec: 2073.55 - lr: 0.000009 - momentum: 0.000000
2023-10-17 16:05:39,452 epoch 8 - iter 132/447 - loss 0.01019952 - time (sec): 12.24 - samples/sec: 2051.86 - lr: 0.000009 - momentum: 0.000000
2023-10-17 16:05:43,674 epoch 8 - iter 176/447 - loss 0.01051999 - time (sec): 16.46 - samples/sec: 2018.14 - lr: 0.000009 - momentum: 0.000000
2023-10-17 16:05:48,447 epoch 8 - iter 220/447 - loss 0.00988506 - time (sec): 21.24 - samples/sec: 2029.40 - lr: 0.000008 - momentum: 0.000000
2023-10-17 16:05:52,743 epoch 8 - iter 264/447 - loss 0.00923766 - time (sec): 25.53 - samples/sec: 2028.90 - lr: 0.000008 - momentum: 0.000000
2023-10-17 16:05:56,918 epoch 8 - iter 308/447 - loss 0.00896950 - time (sec): 29.71 - samples/sec: 2045.61 - lr: 0.000008 - momentum: 0.000000
2023-10-17 16:06:01,147 epoch 8 - iter 352/447 - loss 0.00877691 - time (sec): 33.94 - samples/sec: 2033.23 - lr: 0.000007 - momentum: 0.000000
2023-10-17 16:06:05,239 epoch 8 - iter 396/447 - loss 0.00839354 - time (sec): 38.03 - samples/sec: 2027.39 - lr: 0.000007 - momentum: 0.000000
2023-10-17 16:06:09,389 epoch 8 - iter 440/447 - loss 0.00837721 - time (sec): 42.18 - samples/sec: 2020.17 - lr: 0.000007 - momentum: 0.000000
2023-10-17 16:06:10,039 ----------------------------------------------------------------------------------------------------
2023-10-17 16:06:10,039 EPOCH 8 done: loss 0.0085 - lr: 0.000007
2023-10-17 16:06:21,780 DEV : loss 0.21822908520698547 - f1-score (micro avg)  0.7915
2023-10-17 16:06:21,841 ----------------------------------------------------------------------------------------------------
2023-10-17 16:06:25,974 epoch 9 - iter 44/447 - loss 0.00971530 - time (sec): 4.13 - samples/sec: 2046.00 - lr: 0.000006 - momentum: 0.000000
2023-10-17 16:06:30,128 epoch 9 - iter 88/447 - loss 0.00589797 - time (sec): 8.28 - samples/sec: 2051.38 - lr: 0.000006 - momentum: 0.000000
2023-10-17 16:06:34,084 epoch 9 - iter 132/447 - loss 0.00489136 - time (sec): 12.24 - samples/sec: 2019.89 - lr: 0.000006 - momentum: 0.000000
2023-10-17 16:06:38,278 epoch 9 - iter 176/447 - loss 0.00468170 - time (sec): 16.43 - samples/sec: 2041.42 - lr: 0.000005 - momentum: 0.000000
2023-10-17 16:06:42,419 epoch 9 - iter 220/447 - loss 0.00474446 - time (sec): 20.58 - samples/sec: 2047.85 - lr: 0.000005 - momentum: 0.000000
2023-10-17 16:06:46,728 epoch 9 - iter 264/447 - loss 0.00529921 - time (sec): 24.88 - samples/sec: 2054.82 - lr: 0.000005 - momentum: 0.000000
2023-10-17 16:06:50,744 epoch 9 - iter 308/447 - loss 0.00534902 - time (sec): 28.90 - samples/sec: 2056.82 - lr: 0.000004 - momentum: 0.000000
2023-10-17 16:06:54,817 epoch 9 - iter 352/447 - loss 0.00532626 - time (sec): 32.97 - samples/sec: 2060.60 - lr: 0.000004 - momentum: 0.000000
2023-10-17 16:06:59,232 epoch 9 - iter 396/447 - loss 0.00517003 - time (sec): 37.39 - samples/sec: 2059.50 - lr: 0.000004 - momentum: 0.000000
2023-10-17 16:07:03,570 epoch 9 - iter 440/447 - loss 0.00496894 - time (sec): 41.73 - samples/sec: 2052.54 - lr: 0.000003 - momentum: 0.000000
2023-10-17 16:07:04,188 ----------------------------------------------------------------------------------------------------
2023-10-17 16:07:04,188 EPOCH 9 done: loss 0.0049 - lr: 0.000003
2023-10-17 16:07:15,489 DEV : loss 0.2285258173942566 - f1-score (micro avg)  0.7966
2023-10-17 16:07:15,545 saving best model
2023-10-17 16:07:16,964 ----------------------------------------------------------------------------------------------------
2023-10-17 16:07:21,376 epoch 10 - iter 44/447 - loss 0.00131630 - time (sec): 4.41 - samples/sec: 2067.92 - lr: 0.000003 - momentum: 0.000000
2023-10-17 16:07:25,410 epoch 10 - iter 88/447 - loss 0.00319788 - time (sec): 8.44 - samples/sec: 2035.90 - lr: 0.000003 - momentum: 0.000000
2023-10-17 16:07:29,655 epoch 10 - iter 132/447 - loss 0.00353846 - time (sec): 12.69 - samples/sec: 1978.90 - lr: 0.000002 - momentum: 0.000000
2023-10-17 16:07:34,051 epoch 10 - iter 176/447 - loss 0.00330267 - time (sec): 17.08 - samples/sec: 1977.05 - lr: 0.000002 - momentum: 0.000000
2023-10-17 16:07:38,665 epoch 10 - iter 220/447 - loss 0.00339446 - time (sec): 21.70 - samples/sec: 1945.15 - lr: 0.000002 - momentum: 0.000000
2023-10-17 16:07:43,234 epoch 10 - iter 264/447 - loss 0.00404962 - time (sec): 26.27 - samples/sec: 1971.40 - lr: 0.000001 - momentum: 0.000000
2023-10-17 16:07:47,304 epoch 10 - iter 308/447 - loss 0.00401999 - time (sec): 30.34 - samples/sec: 1956.79 - lr: 0.000001 - momentum: 0.000000
2023-10-17 16:07:51,685 epoch 10 - iter 352/447 - loss 0.00372843 - time (sec): 34.72 - samples/sec: 1959.83 - lr: 0.000001 - momentum: 0.000000
2023-10-17 16:07:55,746 epoch 10 - iter 396/447 - loss 0.00351848 - time (sec): 38.78 - samples/sec: 1963.55 - lr: 0.000000 - momentum: 0.000000
2023-10-17 16:07:59,987 epoch 10 - iter 440/447 - loss 0.00359443 - time (sec): 43.02 - samples/sec: 1975.68 - lr: 0.000000 - momentum: 0.000000
2023-10-17 16:08:00,668 ----------------------------------------------------------------------------------------------------
2023-10-17 16:08:00,669 EPOCH 10 done: loss 0.0035 - lr: 0.000000
2023-10-17 16:08:11,622 DEV : loss 0.22872760891914368 - f1-score (micro avg)  0.7952
2023-10-17 16:08:12,224 ----------------------------------------------------------------------------------------------------
2023-10-17 16:08:12,226 Loading model from best epoch ...
2023-10-17 16:08:14,951 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
2023-10-17 16:08:21,279 
Results:
- F-score (micro) 0.764
- F-score (macro) 0.6761
- Accuracy 0.6388

By class:
              precision    recall  f1-score   support

         loc     0.8315    0.8775    0.8539       596
        pers     0.7314    0.7688    0.7496       333
         org     0.4892    0.5152    0.5018       132
        prod     0.6154    0.4848    0.5424        66
        time     0.7115    0.7551    0.7327        49

   micro avg     0.7496    0.7789    0.7640      1176
   macro avg     0.6758    0.6803    0.6761      1176
weighted avg     0.7476    0.7789    0.7623      1176

2023-10-17 16:08:21,280 ----------------------------------------------------------------------------------------------------