2023-10-15 14:35:18,166 ----------------------------------------------------------------------------------------------------
2023-10-15 14:35:18,167 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=17, bias=True)
  (loss_function): CrossEntropyLoss()
)"
2023-10-15 14:35:18,167 ----------------------------------------------------------------------------------------------------
2023-10-15 14:35:18,167 MultiCorpus: 20847 train + 1123 dev + 3350 test sentences
 - NER_HIPE_2022 Corpus: 20847 train + 1123 dev + 3350 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/de/with_doc_seperator
2023-10-15 14:35:18,167 ----------------------------------------------------------------------------------------------------
2023-10-15 14:35:18,167 Train:  20847 sentences
2023-10-15 14:35:18,167         (train_with_dev=False, train_with_test=False)
2023-10-15 14:35:18,167 ----------------------------------------------------------------------------------------------------
2023-10-15 14:35:18,167 Training Params:
2023-10-15 14:35:18,167  - learning_rate: "5e-05" 
2023-10-15 14:35:18,167  - mini_batch_size: "8"
2023-10-15 14:35:18,167  - max_epochs: "10"
2023-10-15 14:35:18,167  - shuffle: "True"
2023-10-15 14:35:18,167 ----------------------------------------------------------------------------------------------------
2023-10-15 14:35:18,167 Plugins:
2023-10-15 14:35:18,167  - LinearScheduler | warmup_fraction: '0.1'
2023-10-15 14:35:18,167 ----------------------------------------------------------------------------------------------------
2023-10-15 14:35:18,167 Final evaluation on model from best epoch (best-model.pt)
2023-10-15 14:35:18,167  - metric: "('micro avg', 'f1-score')"
2023-10-15 14:35:18,167 ----------------------------------------------------------------------------------------------------
2023-10-15 14:35:18,167 Computation:
2023-10-15 14:35:18,168  - compute on device: cuda:0
2023-10-15 14:35:18,168  - embedding storage: none
2023-10-15 14:35:18,168 ----------------------------------------------------------------------------------------------------
2023-10-15 14:35:18,168 Model training base path: "hmbench-newseye/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2"
2023-10-15 14:35:18,168 ----------------------------------------------------------------------------------------------------
2023-10-15 14:35:18,168 ----------------------------------------------------------------------------------------------------
2023-10-15 14:35:37,481 epoch 1 - iter 260/2606 - loss 1.70398800 - time (sec): 19.31 - samples/sec: 1954.37 - lr: 0.000005 - momentum: 0.000000
2023-10-15 14:35:56,703 epoch 1 - iter 520/2606 - loss 1.04417068 - time (sec): 38.53 - samples/sec: 1936.56 - lr: 0.000010 - momentum: 0.000000
2023-10-15 14:36:16,384 epoch 1 - iter 780/2606 - loss 0.78595655 - time (sec): 58.22 - samples/sec: 1966.31 - lr: 0.000015 - momentum: 0.000000
2023-10-15 14:36:35,005 epoch 1 - iter 1040/2606 - loss 0.66742617 - time (sec): 76.84 - samples/sec: 1944.65 - lr: 0.000020 - momentum: 0.000000
2023-10-15 14:36:53,448 epoch 1 - iter 1300/2606 - loss 0.58275769 - time (sec): 95.28 - samples/sec: 1954.32 - lr: 0.000025 - momentum: 0.000000
2023-10-15 14:37:12,505 epoch 1 - iter 1560/2606 - loss 0.52348496 - time (sec): 114.34 - samples/sec: 1950.56 - lr: 0.000030 - momentum: 0.000000
2023-10-15 14:37:31,309 epoch 1 - iter 1820/2606 - loss 0.47828591 - time (sec): 133.14 - samples/sec: 1943.65 - lr: 0.000035 - momentum: 0.000000
2023-10-15 14:37:48,903 epoch 1 - iter 2080/2606 - loss 0.44960989 - time (sec): 150.73 - samples/sec: 1945.45 - lr: 0.000040 - momentum: 0.000000
2023-10-15 14:38:07,725 epoch 1 - iter 2340/2606 - loss 0.41952533 - time (sec): 169.56 - samples/sec: 1948.33 - lr: 0.000045 - momentum: 0.000000
2023-10-15 14:38:26,224 epoch 1 - iter 2600/2606 - loss 0.39643307 - time (sec): 188.06 - samples/sec: 1948.36 - lr: 0.000050 - momentum: 0.000000
2023-10-15 14:38:26,731 ----------------------------------------------------------------------------------------------------
2023-10-15 14:38:26,731 EPOCH 1 done: loss 0.3961 - lr: 0.000050
2023-10-15 14:38:32,575 DEV : loss 0.1703825443983078 - f1-score (micro avg)  0.2723
2023-10-15 14:38:32,602 saving best model
2023-10-15 14:38:32,932 ----------------------------------------------------------------------------------------------------
2023-10-15 14:38:51,535 epoch 2 - iter 260/2606 - loss 0.15333555 - time (sec): 18.60 - samples/sec: 1881.05 - lr: 0.000049 - momentum: 0.000000
2023-10-15 14:39:10,089 epoch 2 - iter 520/2606 - loss 0.15721909 - time (sec): 37.16 - samples/sec: 1918.36 - lr: 0.000049 - momentum: 0.000000
2023-10-15 14:39:29,112 epoch 2 - iter 780/2606 - loss 0.16754978 - time (sec): 56.18 - samples/sec: 1938.67 - lr: 0.000048 - momentum: 0.000000
2023-10-15 14:39:47,893 epoch 2 - iter 1040/2606 - loss 0.16366363 - time (sec): 74.96 - samples/sec: 1932.23 - lr: 0.000048 - momentum: 0.000000
2023-10-15 14:40:06,293 epoch 2 - iter 1300/2606 - loss 0.15907525 - time (sec): 93.36 - samples/sec: 1946.72 - lr: 0.000047 - momentum: 0.000000
2023-10-15 14:40:25,920 epoch 2 - iter 1560/2606 - loss 0.15929231 - time (sec): 112.99 - samples/sec: 1946.11 - lr: 0.000047 - momentum: 0.000000
2023-10-15 14:40:44,848 epoch 2 - iter 1820/2606 - loss 0.16228991 - time (sec): 131.91 - samples/sec: 1940.93 - lr: 0.000046 - momentum: 0.000000
2023-10-15 14:41:03,759 epoch 2 - iter 2080/2606 - loss 0.15903749 - time (sec): 150.83 - samples/sec: 1951.85 - lr: 0.000046 - momentum: 0.000000
2023-10-15 14:41:22,416 epoch 2 - iter 2340/2606 - loss 0.15780880 - time (sec): 169.48 - samples/sec: 1948.20 - lr: 0.000045 - momentum: 0.000000
2023-10-15 14:41:40,791 epoch 2 - iter 2600/2606 - loss 0.15594055 - time (sec): 187.86 - samples/sec: 1950.59 - lr: 0.000044 - momentum: 0.000000
2023-10-15 14:41:41,236 ----------------------------------------------------------------------------------------------------
2023-10-15 14:41:41,236 EPOCH 2 done: loss 0.1559 - lr: 0.000044
2023-10-15 14:41:50,342 DEV : loss 0.15193568170070648 - f1-score (micro avg)  0.3847
2023-10-15 14:41:50,371 saving best model
2023-10-15 14:41:50,831 ----------------------------------------------------------------------------------------------------
2023-10-15 14:42:10,499 epoch 3 - iter 260/2606 - loss 0.10386961 - time (sec): 19.66 - samples/sec: 1969.96 - lr: 0.000044 - momentum: 0.000000
2023-10-15 14:42:29,883 epoch 3 - iter 520/2606 - loss 0.10697405 - time (sec): 39.05 - samples/sec: 1984.47 - lr: 0.000043 - momentum: 0.000000
2023-10-15 14:42:48,549 epoch 3 - iter 780/2606 - loss 0.11066127 - time (sec): 57.71 - samples/sec: 1984.71 - lr: 0.000043 - momentum: 0.000000
2023-10-15 14:43:06,906 epoch 3 - iter 1040/2606 - loss 0.11236558 - time (sec): 76.07 - samples/sec: 1974.96 - lr: 0.000042 - momentum: 0.000000
2023-10-15 14:43:26,140 epoch 3 - iter 1300/2606 - loss 0.10971979 - time (sec): 95.31 - samples/sec: 1975.49 - lr: 0.000042 - momentum: 0.000000
2023-10-15 14:43:44,182 epoch 3 - iter 1560/2606 - loss 0.10942417 - time (sec): 113.35 - samples/sec: 1964.00 - lr: 0.000041 - momentum: 0.000000
2023-10-15 14:44:02,842 epoch 3 - iter 1820/2606 - loss 0.11037765 - time (sec): 132.01 - samples/sec: 1952.43 - lr: 0.000041 - momentum: 0.000000
2023-10-15 14:44:21,244 epoch 3 - iter 2080/2606 - loss 0.11002920 - time (sec): 150.41 - samples/sec: 1947.68 - lr: 0.000040 - momentum: 0.000000
2023-10-15 14:44:40,649 epoch 3 - iter 2340/2606 - loss 0.10906780 - time (sec): 169.81 - samples/sec: 1944.70 - lr: 0.000039 - momentum: 0.000000
2023-10-15 14:44:58,744 epoch 3 - iter 2600/2606 - loss 0.10926208 - time (sec): 187.91 - samples/sec: 1947.44 - lr: 0.000039 - momentum: 0.000000
2023-10-15 14:44:59,407 ----------------------------------------------------------------------------------------------------
2023-10-15 14:44:59,407 EPOCH 3 done: loss 0.1090 - lr: 0.000039
2023-10-15 14:45:08,482 DEV : loss 0.25894129276275635 - f1-score (micro avg)  0.3382
2023-10-15 14:45:08,509 ----------------------------------------------------------------------------------------------------
2023-10-15 14:45:28,158 epoch 4 - iter 260/2606 - loss 0.07515214 - time (sec): 19.65 - samples/sec: 1967.37 - lr: 0.000038 - momentum: 0.000000
2023-10-15 14:45:46,029 epoch 4 - iter 520/2606 - loss 0.07733261 - time (sec): 37.52 - samples/sec: 1927.84 - lr: 0.000038 - momentum: 0.000000
2023-10-15 14:46:04,834 epoch 4 - iter 780/2606 - loss 0.07656362 - time (sec): 56.32 - samples/sec: 1944.62 - lr: 0.000037 - momentum: 0.000000
2023-10-15 14:46:23,015 epoch 4 - iter 1040/2606 - loss 0.07700269 - time (sec): 74.50 - samples/sec: 1947.83 - lr: 0.000037 - momentum: 0.000000
2023-10-15 14:46:41,548 epoch 4 - iter 1300/2606 - loss 0.07567123 - time (sec): 93.04 - samples/sec: 1954.80 - lr: 0.000036 - momentum: 0.000000
2023-10-15 14:47:00,617 epoch 4 - iter 1560/2606 - loss 0.07597083 - time (sec): 112.11 - samples/sec: 1956.79 - lr: 0.000036 - momentum: 0.000000
2023-10-15 14:47:19,328 epoch 4 - iter 1820/2606 - loss 0.07763210 - time (sec): 130.82 - samples/sec: 1952.80 - lr: 0.000035 - momentum: 0.000000
2023-10-15 14:47:37,968 epoch 4 - iter 2080/2606 - loss 0.07854227 - time (sec): 149.46 - samples/sec: 1945.80 - lr: 0.000034 - momentum: 0.000000
2023-10-15 14:47:57,679 epoch 4 - iter 2340/2606 - loss 0.07872444 - time (sec): 169.17 - samples/sec: 1944.39 - lr: 0.000034 - momentum: 0.000000
2023-10-15 14:48:16,986 epoch 4 - iter 2600/2606 - loss 0.07824603 - time (sec): 188.48 - samples/sec: 1945.35 - lr: 0.000033 - momentum: 0.000000
2023-10-15 14:48:17,398 ----------------------------------------------------------------------------------------------------
2023-10-15 14:48:17,398 EPOCH 4 done: loss 0.0782 - lr: 0.000033
2023-10-15 14:48:26,390 DEV : loss 0.2620859742164612 - f1-score (micro avg)  0.3624
2023-10-15 14:48:26,417 ----------------------------------------------------------------------------------------------------
2023-10-15 14:48:44,955 epoch 5 - iter 260/2606 - loss 0.05400958 - time (sec): 18.54 - samples/sec: 1982.06 - lr: 0.000033 - momentum: 0.000000
2023-10-15 14:49:05,767 epoch 5 - iter 520/2606 - loss 0.05211167 - time (sec): 39.35 - samples/sec: 1984.42 - lr: 0.000032 - momentum: 0.000000
2023-10-15 14:49:24,232 epoch 5 - iter 780/2606 - loss 0.05301335 - time (sec): 57.81 - samples/sec: 1966.71 - lr: 0.000032 - momentum: 0.000000
2023-10-15 14:49:43,982 epoch 5 - iter 1040/2606 - loss 0.05569211 - time (sec): 77.56 - samples/sec: 1963.80 - lr: 0.000031 - momentum: 0.000000
2023-10-15 14:50:02,076 epoch 5 - iter 1300/2606 - loss 0.05528294 - time (sec): 95.66 - samples/sec: 1962.93 - lr: 0.000031 - momentum: 0.000000
2023-10-15 14:50:21,420 epoch 5 - iter 1560/2606 - loss 0.05556370 - time (sec): 115.00 - samples/sec: 1962.65 - lr: 0.000030 - momentum: 0.000000
2023-10-15 14:50:40,077 epoch 5 - iter 1820/2606 - loss 0.05564560 - time (sec): 133.66 - samples/sec: 1948.82 - lr: 0.000029 - momentum: 0.000000
2023-10-15 14:50:58,218 epoch 5 - iter 2080/2606 - loss 0.05629388 - time (sec): 151.80 - samples/sec: 1948.56 - lr: 0.000029 - momentum: 0.000000
2023-10-15 14:51:17,603 epoch 5 - iter 2340/2606 - loss 0.05874671 - time (sec): 171.18 - samples/sec: 1947.78 - lr: 0.000028 - momentum: 0.000000
2023-10-15 14:51:35,349 epoch 5 - iter 2600/2606 - loss 0.05895855 - time (sec): 188.93 - samples/sec: 1941.44 - lr: 0.000028 - momentum: 0.000000
2023-10-15 14:51:35,736 ----------------------------------------------------------------------------------------------------
2023-10-15 14:51:35,737 EPOCH 5 done: loss 0.0589 - lr: 0.000028
2023-10-15 14:51:43,968 DEV : loss 0.3398889899253845 - f1-score (micro avg)  0.3363
2023-10-15 14:51:43,998 ----------------------------------------------------------------------------------------------------
2023-10-15 14:52:04,548 epoch 6 - iter 260/2606 - loss 0.03389900 - time (sec): 20.55 - samples/sec: 1875.95 - lr: 0.000027 - momentum: 0.000000
2023-10-15 14:52:22,918 epoch 6 - iter 520/2606 - loss 0.04212432 - time (sec): 38.92 - samples/sec: 1912.60 - lr: 0.000027 - momentum: 0.000000
2023-10-15 14:52:41,566 epoch 6 - iter 780/2606 - loss 0.04368331 - time (sec): 57.57 - samples/sec: 1937.32 - lr: 0.000026 - momentum: 0.000000
2023-10-15 14:52:59,861 epoch 6 - iter 1040/2606 - loss 0.04514083 - time (sec): 75.86 - samples/sec: 1920.95 - lr: 0.000026 - momentum: 0.000000
2023-10-15 14:53:19,505 epoch 6 - iter 1300/2606 - loss 0.04442191 - time (sec): 95.51 - samples/sec: 1925.06 - lr: 0.000025 - momentum: 0.000000
2023-10-15 14:53:38,680 epoch 6 - iter 1560/2606 - loss 0.04517608 - time (sec): 114.68 - samples/sec: 1930.25 - lr: 0.000024 - momentum: 0.000000
2023-10-15 14:53:59,139 epoch 6 - iter 1820/2606 - loss 0.04366513 - time (sec): 135.14 - samples/sec: 1934.69 - lr: 0.000024 - momentum: 0.000000
2023-10-15 14:54:17,058 epoch 6 - iter 2080/2606 - loss 0.04407085 - time (sec): 153.06 - samples/sec: 1930.85 - lr: 0.000023 - momentum: 0.000000
2023-10-15 14:54:35,685 epoch 6 - iter 2340/2606 - loss 0.04345890 - time (sec): 171.69 - samples/sec: 1930.29 - lr: 0.000023 - momentum: 0.000000
2023-10-15 14:54:54,040 epoch 6 - iter 2600/2606 - loss 0.04335818 - time (sec): 190.04 - samples/sec: 1928.19 - lr: 0.000022 - momentum: 0.000000
2023-10-15 14:54:54,516 ----------------------------------------------------------------------------------------------------
2023-10-15 14:54:54,517 EPOCH 6 done: loss 0.0432 - lr: 0.000022
2023-10-15 14:55:03,040 DEV : loss 0.3315297067165375 - f1-score (micro avg)  0.3722
2023-10-15 14:55:03,072 ----------------------------------------------------------------------------------------------------
2023-10-15 14:55:21,053 epoch 7 - iter 260/2606 - loss 0.02363314 - time (sec): 17.98 - samples/sec: 1876.09 - lr: 0.000022 - momentum: 0.000000
2023-10-15 14:55:39,507 epoch 7 - iter 520/2606 - loss 0.02903783 - time (sec): 36.43 - samples/sec: 1901.90 - lr: 0.000021 - momentum: 0.000000
2023-10-15 14:55:59,059 epoch 7 - iter 780/2606 - loss 0.03146854 - time (sec): 55.99 - samples/sec: 1930.42 - lr: 0.000021 - momentum: 0.000000
2023-10-15 14:56:17,964 epoch 7 - iter 1040/2606 - loss 0.03278017 - time (sec): 74.89 - samples/sec: 1903.68 - lr: 0.000020 - momentum: 0.000000
2023-10-15 14:56:35,796 epoch 7 - iter 1300/2606 - loss 0.03388944 - time (sec): 92.72 - samples/sec: 1906.56 - lr: 0.000019 - momentum: 0.000000
2023-10-15 14:56:54,161 epoch 7 - iter 1560/2606 - loss 0.03255298 - time (sec): 111.09 - samples/sec: 1924.34 - lr: 0.000019 - momentum: 0.000000
2023-10-15 14:57:13,152 epoch 7 - iter 1820/2606 - loss 0.03196894 - time (sec): 130.08 - samples/sec: 1927.44 - lr: 0.000018 - momentum: 0.000000
2023-10-15 14:57:32,652 epoch 7 - iter 2080/2606 - loss 0.03065448 - time (sec): 149.58 - samples/sec: 1936.22 - lr: 0.000018 - momentum: 0.000000
2023-10-15 14:57:52,137 epoch 7 - iter 2340/2606 - loss 0.02973814 - time (sec): 169.06 - samples/sec: 1942.74 - lr: 0.000017 - momentum: 0.000000
2023-10-15 14:58:11,366 epoch 7 - iter 2600/2606 - loss 0.03090218 - time (sec): 188.29 - samples/sec: 1943.06 - lr: 0.000017 - momentum: 0.000000
2023-10-15 14:58:12,154 ----------------------------------------------------------------------------------------------------
2023-10-15 14:58:12,154 EPOCH 7 done: loss 0.0309 - lr: 0.000017
2023-10-15 14:58:20,444 DEV : loss 0.4454517364501953 - f1-score (micro avg)  0.362
2023-10-15 14:58:20,472 ----------------------------------------------------------------------------------------------------
2023-10-15 14:58:39,045 epoch 8 - iter 260/2606 - loss 0.01715014 - time (sec): 18.57 - samples/sec: 2053.83 - lr: 0.000016 - momentum: 0.000000
2023-10-15 14:58:57,659 epoch 8 - iter 520/2606 - loss 0.01871143 - time (sec): 37.19 - samples/sec: 2016.24 - lr: 0.000016 - momentum: 0.000000
2023-10-15 14:59:15,612 epoch 8 - iter 780/2606 - loss 0.02054380 - time (sec): 55.14 - samples/sec: 1967.69 - lr: 0.000015 - momentum: 0.000000
2023-10-15 14:59:34,079 epoch 8 - iter 1040/2606 - loss 0.02016721 - time (sec): 73.61 - samples/sec: 1969.24 - lr: 0.000014 - momentum: 0.000000
2023-10-15 14:59:51,734 epoch 8 - iter 1300/2606 - loss 0.02166010 - time (sec): 91.26 - samples/sec: 1959.92 - lr: 0.000014 - momentum: 0.000000
2023-10-15 15:00:10,857 epoch 8 - iter 1560/2606 - loss 0.02170611 - time (sec): 110.38 - samples/sec: 1952.41 - lr: 0.000013 - momentum: 0.000000
2023-10-15 15:00:31,030 epoch 8 - iter 1820/2606 - loss 0.02156229 - time (sec): 130.56 - samples/sec: 1959.10 - lr: 0.000013 - momentum: 0.000000
2023-10-15 15:00:50,568 epoch 8 - iter 2080/2606 - loss 0.02342631 - time (sec): 150.09 - samples/sec: 1959.29 - lr: 0.000012 - momentum: 0.000000
2023-10-15 15:01:08,797 epoch 8 - iter 2340/2606 - loss 0.02326894 - time (sec): 168.32 - samples/sec: 1958.13 - lr: 0.000012 - momentum: 0.000000
2023-10-15 15:01:27,579 epoch 8 - iter 2600/2606 - loss 0.02326670 - time (sec): 187.11 - samples/sec: 1958.63 - lr: 0.000011 - momentum: 0.000000
2023-10-15 15:01:28,005 ----------------------------------------------------------------------------------------------------
2023-10-15 15:01:28,005 EPOCH 8 done: loss 0.0233 - lr: 0.000011
2023-10-15 15:01:36,293 DEV : loss 0.5098573565483093 - f1-score (micro avg)  0.3418
2023-10-15 15:01:36,321 ----------------------------------------------------------------------------------------------------
2023-10-15 15:01:53,995 epoch 9 - iter 260/2606 - loss 0.01668464 - time (sec): 17.67 - samples/sec: 1958.94 - lr: 0.000011 - momentum: 0.000000
2023-10-15 15:02:11,655 epoch 9 - iter 520/2606 - loss 0.01529131 - time (sec): 35.33 - samples/sec: 1937.99 - lr: 0.000010 - momentum: 0.000000
2023-10-15 15:02:31,960 epoch 9 - iter 780/2606 - loss 0.01417848 - time (sec): 55.64 - samples/sec: 1944.47 - lr: 0.000009 - momentum: 0.000000
2023-10-15 15:02:51,522 epoch 9 - iter 1040/2606 - loss 0.01420376 - time (sec): 75.20 - samples/sec: 1930.86 - lr: 0.000009 - momentum: 0.000000
2023-10-15 15:03:11,085 epoch 9 - iter 1300/2606 - loss 0.01442784 - time (sec): 94.76 - samples/sec: 1931.52 - lr: 0.000008 - momentum: 0.000000
2023-10-15 15:03:30,402 epoch 9 - iter 1560/2606 - loss 0.01622053 - time (sec): 114.08 - samples/sec: 1925.97 - lr: 0.000008 - momentum: 0.000000
2023-10-15 15:03:49,697 epoch 9 - iter 1820/2606 - loss 0.01569716 - time (sec): 133.37 - samples/sec: 1929.46 - lr: 0.000007 - momentum: 0.000000
2023-10-15 15:04:08,230 epoch 9 - iter 2080/2606 - loss 0.01571005 - time (sec): 151.91 - samples/sec: 1933.51 - lr: 0.000007 - momentum: 0.000000
2023-10-15 15:04:28,322 epoch 9 - iter 2340/2606 - loss 0.01553269 - time (sec): 172.00 - samples/sec: 1921.80 - lr: 0.000006 - momentum: 0.000000
2023-10-15 15:04:47,584 epoch 9 - iter 2600/2606 - loss 0.01532827 - time (sec): 191.26 - samples/sec: 1919.20 - lr: 0.000006 - momentum: 0.000000
2023-10-15 15:04:47,880 ----------------------------------------------------------------------------------------------------
2023-10-15 15:04:47,880 EPOCH 9 done: loss 0.0153 - lr: 0.000006
2023-10-15 15:04:56,163 DEV : loss 0.4624152183532715 - f1-score (micro avg)  0.3618
2023-10-15 15:04:56,192 ----------------------------------------------------------------------------------------------------
2023-10-15 15:05:15,338 epoch 10 - iter 260/2606 - loss 0.00882181 - time (sec): 19.14 - samples/sec: 1930.56 - lr: 0.000005 - momentum: 0.000000
2023-10-15 15:05:34,619 epoch 10 - iter 520/2606 - loss 0.01164627 - time (sec): 38.43 - samples/sec: 1921.16 - lr: 0.000004 - momentum: 0.000000
2023-10-15 15:05:54,353 epoch 10 - iter 780/2606 - loss 0.01191395 - time (sec): 58.16 - samples/sec: 1945.73 - lr: 0.000004 - momentum: 0.000000
2023-10-15 15:06:13,183 epoch 10 - iter 1040/2606 - loss 0.01088257 - time (sec): 76.99 - samples/sec: 1947.12 - lr: 0.000003 - momentum: 0.000000
2023-10-15 15:06:30,946 epoch 10 - iter 1300/2606 - loss 0.01110488 - time (sec): 94.75 - samples/sec: 1943.64 - lr: 0.000003 - momentum: 0.000000
2023-10-15 15:06:49,375 epoch 10 - iter 1560/2606 - loss 0.01164780 - time (sec): 113.18 - samples/sec: 1944.76 - lr: 0.000002 - momentum: 0.000000
2023-10-15 15:07:09,170 epoch 10 - iter 1820/2606 - loss 0.01131708 - time (sec): 132.98 - samples/sec: 1941.93 - lr: 0.000002 - momentum: 0.000000
2023-10-15 15:07:27,445 epoch 10 - iter 2080/2606 - loss 0.01073956 - time (sec): 151.25 - samples/sec: 1940.29 - lr: 0.000001 - momentum: 0.000000
2023-10-15 15:07:46,629 epoch 10 - iter 2340/2606 - loss 0.01037212 - time (sec): 170.44 - samples/sec: 1938.05 - lr: 0.000001 - momentum: 0.000000
2023-10-15 15:08:05,232 epoch 10 - iter 2600/2606 - loss 0.01025554 - time (sec): 189.04 - samples/sec: 1937.16 - lr: 0.000000 - momentum: 0.000000
2023-10-15 15:08:05,747 ----------------------------------------------------------------------------------------------------
2023-10-15 15:08:05,747 EPOCH 10 done: loss 0.0102 - lr: 0.000000
2023-10-15 15:08:14,814 DEV : loss 0.4725865125656128 - f1-score (micro avg)  0.3743
2023-10-15 15:08:15,190 ----------------------------------------------------------------------------------------------------
2023-10-15 15:08:15,191 Loading model from best epoch ...
2023-10-15 15:08:16,646 SequenceTagger predicts: Dictionary with 17 tags: O, S-LOC, B-LOC, E-LOC, I-LOC, S-PER, B-PER, E-PER, I-PER, S-ORG, B-ORG, E-ORG, I-ORG, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd
2023-10-15 15:08:31,788 
Results:
- F-score (micro) 0.4127
- F-score (macro) 0.2811
- Accuracy 0.2633

By class:
              precision    recall  f1-score   support

         LOC     0.5280    0.4745    0.4998      1214
         PER     0.3436    0.4295    0.3817       808
         ORG     0.2143    0.2805    0.2429       353
   HumanProd     0.0000    0.0000    0.0000        15

   micro avg     0.3988    0.4276    0.4127      2390
   macro avg     0.2715    0.2961    0.2811      2390
weighted avg     0.4160    0.4276    0.4188      2390

2023-10-15 15:08:31,788 ----------------------------------------------------------------------------------------------------