2023-10-16 18:30:41,019 ----------------------------------------------------------------------------------------------------
2023-10-16 18:30:41,020 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()
)"
2023-10-16 18:30:41,020 ----------------------------------------------------------------------------------------------------
2023-10-16 18:30:41,021 MultiCorpus: 1166 train + 165 dev + 415 test sentences
 - NER_HIPE_2022 Corpus: 1166 train + 165 dev + 415 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/fi/with_doc_seperator
2023-10-16 18:30:41,021 ----------------------------------------------------------------------------------------------------
2023-10-16 18:30:41,021 Train:  1166 sentences
2023-10-16 18:30:41,021         (train_with_dev=False, train_with_test=False)
2023-10-16 18:30:41,021 ----------------------------------------------------------------------------------------------------
2023-10-16 18:30:41,021 Training Params:
2023-10-16 18:30:41,021  - learning_rate: "5e-05" 
2023-10-16 18:30:41,021  - mini_batch_size: "8"
2023-10-16 18:30:41,021  - max_epochs: "10"
2023-10-16 18:30:41,021  - shuffle: "True"
2023-10-16 18:30:41,021 ----------------------------------------------------------------------------------------------------
2023-10-16 18:30:41,021 Plugins:
2023-10-16 18:30:41,021  - LinearScheduler | warmup_fraction: '0.1'
2023-10-16 18:30:41,021 ----------------------------------------------------------------------------------------------------
2023-10-16 18:30:41,021 Final evaluation on model from best epoch (best-model.pt)
2023-10-16 18:30:41,021  - metric: "('micro avg', 'f1-score')"
2023-10-16 18:30:41,021 ----------------------------------------------------------------------------------------------------
2023-10-16 18:30:41,021 Computation:
2023-10-16 18:30:41,021  - compute on device: cuda:0
2023-10-16 18:30:41,021  - embedding storage: none
2023-10-16 18:30:41,021 ----------------------------------------------------------------------------------------------------
2023-10-16 18:30:41,021 Model training base path: "hmbench-newseye/fi-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3"
2023-10-16 18:30:41,021 ----------------------------------------------------------------------------------------------------
2023-10-16 18:30:41,021 ----------------------------------------------------------------------------------------------------
2023-10-16 18:30:42,212 epoch 1 - iter 14/146 - loss 2.90073598 - time (sec): 1.19 - samples/sec: 3343.07 - lr: 0.000004 - momentum: 0.000000
2023-10-16 18:30:43,382 epoch 1 - iter 28/146 - loss 2.61009746 - time (sec): 2.36 - samples/sec: 3081.79 - lr: 0.000009 - momentum: 0.000000
2023-10-16 18:30:44,973 epoch 1 - iter 42/146 - loss 1.82097782 - time (sec): 3.95 - samples/sec: 3099.36 - lr: 0.000014 - momentum: 0.000000
2023-10-16 18:30:46,384 epoch 1 - iter 56/146 - loss 1.53880713 - time (sec): 5.36 - samples/sec: 3070.13 - lr: 0.000019 - momentum: 0.000000
2023-10-16 18:30:47,588 epoch 1 - iter 70/146 - loss 1.36752879 - time (sec): 6.57 - samples/sec: 3031.29 - lr: 0.000024 - momentum: 0.000000
2023-10-16 18:30:48,854 epoch 1 - iter 84/146 - loss 1.29136891 - time (sec): 7.83 - samples/sec: 3019.42 - lr: 0.000028 - momentum: 0.000000
2023-10-16 18:30:50,774 epoch 1 - iter 98/146 - loss 1.13604122 - time (sec): 9.75 - samples/sec: 2962.85 - lr: 0.000033 - momentum: 0.000000
2023-10-16 18:30:52,273 epoch 1 - iter 112/146 - loss 1.03226442 - time (sec): 11.25 - samples/sec: 2963.99 - lr: 0.000038 - momentum: 0.000000
2023-10-16 18:30:53,897 epoch 1 - iter 126/146 - loss 0.93905554 - time (sec): 12.87 - samples/sec: 2956.53 - lr: 0.000043 - momentum: 0.000000
2023-10-16 18:30:55,301 epoch 1 - iter 140/146 - loss 0.86688508 - time (sec): 14.28 - samples/sec: 2969.28 - lr: 0.000048 - momentum: 0.000000
2023-10-16 18:30:55,965 ----------------------------------------------------------------------------------------------------
2023-10-16 18:30:55,965 EPOCH 1 done: loss 0.8394 - lr: 0.000048
2023-10-16 18:30:56,802 DEV : loss 0.21242927014827728 - f1-score (micro avg)  0.4782
2023-10-16 18:30:56,806 saving best model
2023-10-16 18:30:57,201 ----------------------------------------------------------------------------------------------------
2023-10-16 18:30:58,684 epoch 2 - iter 14/146 - loss 0.24835850 - time (sec): 1.48 - samples/sec: 3237.36 - lr: 0.000050 - momentum: 0.000000
2023-10-16 18:31:00,328 epoch 2 - iter 28/146 - loss 0.24220847 - time (sec): 3.13 - samples/sec: 3043.72 - lr: 0.000049 - momentum: 0.000000
2023-10-16 18:31:01,559 epoch 2 - iter 42/146 - loss 0.23582406 - time (sec): 4.36 - samples/sec: 3034.13 - lr: 0.000048 - momentum: 0.000000
2023-10-16 18:31:02,973 epoch 2 - iter 56/146 - loss 0.22558961 - time (sec): 5.77 - samples/sec: 3001.51 - lr: 0.000048 - momentum: 0.000000
2023-10-16 18:31:04,305 epoch 2 - iter 70/146 - loss 0.21852986 - time (sec): 7.10 - samples/sec: 2954.56 - lr: 0.000047 - momentum: 0.000000
2023-10-16 18:31:05,999 epoch 2 - iter 84/146 - loss 0.23060054 - time (sec): 8.80 - samples/sec: 2949.37 - lr: 0.000047 - momentum: 0.000000
2023-10-16 18:31:07,551 epoch 2 - iter 98/146 - loss 0.21861190 - time (sec): 10.35 - samples/sec: 2956.61 - lr: 0.000046 - momentum: 0.000000
2023-10-16 18:31:08,738 epoch 2 - iter 112/146 - loss 0.21034426 - time (sec): 11.54 - samples/sec: 2964.15 - lr: 0.000046 - momentum: 0.000000
2023-10-16 18:31:10,020 epoch 2 - iter 126/146 - loss 0.20733549 - time (sec): 12.82 - samples/sec: 3002.18 - lr: 0.000045 - momentum: 0.000000
2023-10-16 18:31:11,627 epoch 2 - iter 140/146 - loss 0.20083590 - time (sec): 14.42 - samples/sec: 2992.18 - lr: 0.000045 - momentum: 0.000000
2023-10-16 18:31:12,084 ----------------------------------------------------------------------------------------------------
2023-10-16 18:31:12,084 EPOCH 2 done: loss 0.1995 - lr: 0.000045
2023-10-16 18:31:13,333 DEV : loss 0.14030463993549347 - f1-score (micro avg)  0.6021
2023-10-16 18:31:13,338 saving best model
2023-10-16 18:31:13,834 ----------------------------------------------------------------------------------------------------
2023-10-16 18:31:16,105 epoch 3 - iter 14/146 - loss 0.17344869 - time (sec): 2.27 - samples/sec: 2286.20 - lr: 0.000044 - momentum: 0.000000
2023-10-16 18:31:17,368 epoch 3 - iter 28/146 - loss 0.17235774 - time (sec): 3.53 - samples/sec: 2646.16 - lr: 0.000043 - momentum: 0.000000
2023-10-16 18:31:18,893 epoch 3 - iter 42/146 - loss 0.15725543 - time (sec): 5.06 - samples/sec: 2792.00 - lr: 0.000043 - momentum: 0.000000
2023-10-16 18:31:20,349 epoch 3 - iter 56/146 - loss 0.14045524 - time (sec): 6.51 - samples/sec: 2875.19 - lr: 0.000042 - momentum: 0.000000
2023-10-16 18:31:21,940 epoch 3 - iter 70/146 - loss 0.12853378 - time (sec): 8.10 - samples/sec: 2875.20 - lr: 0.000042 - momentum: 0.000000
2023-10-16 18:31:23,206 epoch 3 - iter 84/146 - loss 0.12492428 - time (sec): 9.37 - samples/sec: 2887.87 - lr: 0.000041 - momentum: 0.000000
2023-10-16 18:31:24,635 epoch 3 - iter 98/146 - loss 0.12042291 - time (sec): 10.80 - samples/sec: 2886.03 - lr: 0.000041 - momentum: 0.000000
2023-10-16 18:31:25,852 epoch 3 - iter 112/146 - loss 0.11809638 - time (sec): 12.02 - samples/sec: 2910.19 - lr: 0.000040 - momentum: 0.000000
2023-10-16 18:31:27,358 epoch 3 - iter 126/146 - loss 0.11451646 - time (sec): 13.52 - samples/sec: 2905.95 - lr: 0.000040 - momentum: 0.000000
2023-10-16 18:31:28,584 epoch 3 - iter 140/146 - loss 0.11213648 - time (sec): 14.75 - samples/sec: 2916.08 - lr: 0.000039 - momentum: 0.000000
2023-10-16 18:31:29,040 ----------------------------------------------------------------------------------------------------
2023-10-16 18:31:29,040 EPOCH 3 done: loss 0.1114 - lr: 0.000039
2023-10-16 18:31:30,286 DEV : loss 0.1109694391489029 - f1-score (micro avg)  0.7066
2023-10-16 18:31:30,290 saving best model
2023-10-16 18:31:30,792 ----------------------------------------------------------------------------------------------------
2023-10-16 18:31:32,089 epoch 4 - iter 14/146 - loss 0.06741993 - time (sec): 1.29 - samples/sec: 3016.26 - lr: 0.000038 - momentum: 0.000000
2023-10-16 18:31:33,391 epoch 4 - iter 28/146 - loss 0.07098649 - time (sec): 2.59 - samples/sec: 3070.96 - lr: 0.000038 - momentum: 0.000000
2023-10-16 18:31:34,729 epoch 4 - iter 42/146 - loss 0.08441382 - time (sec): 3.93 - samples/sec: 2968.10 - lr: 0.000037 - momentum: 0.000000
2023-10-16 18:31:36,286 epoch 4 - iter 56/146 - loss 0.07322493 - time (sec): 5.49 - samples/sec: 3004.06 - lr: 0.000037 - momentum: 0.000000
2023-10-16 18:31:37,528 epoch 4 - iter 70/146 - loss 0.07275131 - time (sec): 6.73 - samples/sec: 3010.99 - lr: 0.000036 - momentum: 0.000000
2023-10-16 18:31:38,881 epoch 4 - iter 84/146 - loss 0.07321996 - time (sec): 8.08 - samples/sec: 3010.69 - lr: 0.000036 - momentum: 0.000000
2023-10-16 18:31:40,259 epoch 4 - iter 98/146 - loss 0.07469954 - time (sec): 9.46 - samples/sec: 2992.62 - lr: 0.000035 - momentum: 0.000000
2023-10-16 18:31:41,811 epoch 4 - iter 112/146 - loss 0.07927519 - time (sec): 11.01 - samples/sec: 2989.44 - lr: 0.000035 - momentum: 0.000000
2023-10-16 18:31:43,127 epoch 4 - iter 126/146 - loss 0.07883891 - time (sec): 12.33 - samples/sec: 3010.88 - lr: 0.000034 - momentum: 0.000000
2023-10-16 18:31:44,886 epoch 4 - iter 140/146 - loss 0.07438665 - time (sec): 14.09 - samples/sec: 3030.54 - lr: 0.000034 - momentum: 0.000000
2023-10-16 18:31:45,421 ----------------------------------------------------------------------------------------------------
2023-10-16 18:31:45,421 EPOCH 4 done: loss 0.0733 - lr: 0.000034
2023-10-16 18:31:46,715 DEV : loss 0.10175595432519913 - f1-score (micro avg)  0.7583
2023-10-16 18:31:46,719 saving best model
2023-10-16 18:31:47,234 ----------------------------------------------------------------------------------------------------
2023-10-16 18:31:48,760 epoch 5 - iter 14/146 - loss 0.07301139 - time (sec): 1.52 - samples/sec: 2767.38 - lr: 0.000033 - momentum: 0.000000
2023-10-16 18:31:50,262 epoch 5 - iter 28/146 - loss 0.05467576 - time (sec): 3.03 - samples/sec: 2770.85 - lr: 0.000032 - momentum: 0.000000
2023-10-16 18:31:51,886 epoch 5 - iter 42/146 - loss 0.04957886 - time (sec): 4.65 - samples/sec: 2753.58 - lr: 0.000032 - momentum: 0.000000
2023-10-16 18:31:53,160 epoch 5 - iter 56/146 - loss 0.04832781 - time (sec): 5.92 - samples/sec: 2788.29 - lr: 0.000031 - momentum: 0.000000
2023-10-16 18:31:54,659 epoch 5 - iter 70/146 - loss 0.05179242 - time (sec): 7.42 - samples/sec: 2802.73 - lr: 0.000031 - momentum: 0.000000
2023-10-16 18:31:56,055 epoch 5 - iter 84/146 - loss 0.05137597 - time (sec): 8.82 - samples/sec: 2825.33 - lr: 0.000030 - momentum: 0.000000
2023-10-16 18:31:57,544 epoch 5 - iter 98/146 - loss 0.05130334 - time (sec): 10.31 - samples/sec: 2829.89 - lr: 0.000030 - momentum: 0.000000
2023-10-16 18:31:58,970 epoch 5 - iter 112/146 - loss 0.05125951 - time (sec): 11.73 - samples/sec: 2890.23 - lr: 0.000029 - momentum: 0.000000
2023-10-16 18:32:00,395 epoch 5 - iter 126/146 - loss 0.05073713 - time (sec): 13.16 - samples/sec: 2904.17 - lr: 0.000029 - momentum: 0.000000
2023-10-16 18:32:01,716 epoch 5 - iter 140/146 - loss 0.04995567 - time (sec): 14.48 - samples/sec: 2913.06 - lr: 0.000028 - momentum: 0.000000
2023-10-16 18:32:02,426 ----------------------------------------------------------------------------------------------------
2023-10-16 18:32:02,426 EPOCH 5 done: loss 0.0486 - lr: 0.000028
2023-10-16 18:32:03,706 DEV : loss 0.11706184595823288 - f1-score (micro avg)  0.7046
2023-10-16 18:32:03,711 ----------------------------------------------------------------------------------------------------
2023-10-16 18:32:05,386 epoch 6 - iter 14/146 - loss 0.03465601 - time (sec): 1.67 - samples/sec: 2989.96 - lr: 0.000027 - momentum: 0.000000
2023-10-16 18:32:06,997 epoch 6 - iter 28/146 - loss 0.03333665 - time (sec): 3.28 - samples/sec: 2681.08 - lr: 0.000027 - momentum: 0.000000
2023-10-16 18:32:08,390 epoch 6 - iter 42/146 - loss 0.02993117 - time (sec): 4.68 - samples/sec: 2707.80 - lr: 0.000026 - momentum: 0.000000
2023-10-16 18:32:09,988 epoch 6 - iter 56/146 - loss 0.02776494 - time (sec): 6.28 - samples/sec: 2670.20 - lr: 0.000026 - momentum: 0.000000
2023-10-16 18:32:11,382 epoch 6 - iter 70/146 - loss 0.02718342 - time (sec): 7.67 - samples/sec: 2805.28 - lr: 0.000025 - momentum: 0.000000
2023-10-16 18:32:12,568 epoch 6 - iter 84/146 - loss 0.02843722 - time (sec): 8.86 - samples/sec: 2845.93 - lr: 0.000025 - momentum: 0.000000
2023-10-16 18:32:14,033 epoch 6 - iter 98/146 - loss 0.02633427 - time (sec): 10.32 - samples/sec: 2876.24 - lr: 0.000024 - momentum: 0.000000
2023-10-16 18:32:15,297 epoch 6 - iter 112/146 - loss 0.02898382 - time (sec): 11.58 - samples/sec: 2877.91 - lr: 0.000024 - momentum: 0.000000
2023-10-16 18:32:17,080 epoch 6 - iter 126/146 - loss 0.03285964 - time (sec): 13.37 - samples/sec: 2923.22 - lr: 0.000023 - momentum: 0.000000
2023-10-16 18:32:18,215 epoch 6 - iter 140/146 - loss 0.03401398 - time (sec): 14.50 - samples/sec: 2920.24 - lr: 0.000023 - momentum: 0.000000
2023-10-16 18:32:19,114 ----------------------------------------------------------------------------------------------------
2023-10-16 18:32:19,114 EPOCH 6 done: loss 0.0359 - lr: 0.000023
2023-10-16 18:32:20,340 DEV : loss 0.12150020152330399 - f1-score (micro avg)  0.7409
2023-10-16 18:32:20,344 ----------------------------------------------------------------------------------------------------
2023-10-16 18:32:21,970 epoch 7 - iter 14/146 - loss 0.03183064 - time (sec): 1.62 - samples/sec: 3256.19 - lr: 0.000022 - momentum: 0.000000
2023-10-16 18:32:23,197 epoch 7 - iter 28/146 - loss 0.02451667 - time (sec): 2.85 - samples/sec: 3220.52 - lr: 0.000021 - momentum: 0.000000
2023-10-16 18:32:24,790 epoch 7 - iter 42/146 - loss 0.02191339 - time (sec): 4.44 - samples/sec: 3101.58 - lr: 0.000021 - momentum: 0.000000
2023-10-16 18:32:26,257 epoch 7 - iter 56/146 - loss 0.02125095 - time (sec): 5.91 - samples/sec: 3003.69 - lr: 0.000020 - momentum: 0.000000
2023-10-16 18:32:27,775 epoch 7 - iter 70/146 - loss 0.02706119 - time (sec): 7.43 - samples/sec: 2964.28 - lr: 0.000020 - momentum: 0.000000
2023-10-16 18:32:29,322 epoch 7 - iter 84/146 - loss 0.02505213 - time (sec): 8.98 - samples/sec: 2957.87 - lr: 0.000019 - momentum: 0.000000
2023-10-16 18:32:30,488 epoch 7 - iter 98/146 - loss 0.02643594 - time (sec): 10.14 - samples/sec: 2975.44 - lr: 0.000019 - momentum: 0.000000
2023-10-16 18:32:31,886 epoch 7 - iter 112/146 - loss 0.02500584 - time (sec): 11.54 - samples/sec: 2962.65 - lr: 0.000018 - momentum: 0.000000
2023-10-16 18:32:33,268 epoch 7 - iter 126/146 - loss 0.02596879 - time (sec): 12.92 - samples/sec: 3004.97 - lr: 0.000018 - momentum: 0.000000
2023-10-16 18:32:34,527 epoch 7 - iter 140/146 - loss 0.02553034 - time (sec): 14.18 - samples/sec: 3010.56 - lr: 0.000017 - momentum: 0.000000
2023-10-16 18:32:35,244 ----------------------------------------------------------------------------------------------------
2023-10-16 18:32:35,244 EPOCH 7 done: loss 0.0251 - lr: 0.000017
2023-10-16 18:32:36,485 DEV : loss 0.11602330207824707 - f1-score (micro avg)  0.7699
2023-10-16 18:32:36,489 saving best model
2023-10-16 18:32:37,056 ----------------------------------------------------------------------------------------------------
2023-10-16 18:32:38,367 epoch 8 - iter 14/146 - loss 0.03129626 - time (sec): 1.31 - samples/sec: 3195.87 - lr: 0.000016 - momentum: 0.000000
2023-10-16 18:32:39,770 epoch 8 - iter 28/146 - loss 0.02105207 - time (sec): 2.71 - samples/sec: 3170.88 - lr: 0.000016 - momentum: 0.000000
2023-10-16 18:32:41,336 epoch 8 - iter 42/146 - loss 0.02054404 - time (sec): 4.28 - samples/sec: 2981.94 - lr: 0.000015 - momentum: 0.000000
2023-10-16 18:32:42,828 epoch 8 - iter 56/146 - loss 0.01985796 - time (sec): 5.77 - samples/sec: 2902.09 - lr: 0.000015 - momentum: 0.000000
2023-10-16 18:32:44,319 epoch 8 - iter 70/146 - loss 0.02096278 - time (sec): 7.26 - samples/sec: 2925.26 - lr: 0.000014 - momentum: 0.000000
2023-10-16 18:32:45,534 epoch 8 - iter 84/146 - loss 0.02060282 - time (sec): 8.48 - samples/sec: 2958.97 - lr: 0.000014 - momentum: 0.000000
2023-10-16 18:32:47,293 epoch 8 - iter 98/146 - loss 0.02043961 - time (sec): 10.23 - samples/sec: 2920.58 - lr: 0.000013 - momentum: 0.000000
2023-10-16 18:32:48,752 epoch 8 - iter 112/146 - loss 0.01967954 - time (sec): 11.69 - samples/sec: 2945.27 - lr: 0.000013 - momentum: 0.000000
2023-10-16 18:32:49,995 epoch 8 - iter 126/146 - loss 0.01978582 - time (sec): 12.94 - samples/sec: 2944.65 - lr: 0.000012 - momentum: 0.000000
2023-10-16 18:32:51,387 epoch 8 - iter 140/146 - loss 0.02004748 - time (sec): 14.33 - samples/sec: 2972.78 - lr: 0.000012 - momentum: 0.000000
2023-10-16 18:32:52,022 ----------------------------------------------------------------------------------------------------
2023-10-16 18:32:52,022 EPOCH 8 done: loss 0.0199 - lr: 0.000012
2023-10-16 18:32:53,288 DEV : loss 0.13683120906352997 - f1-score (micro avg)  0.778
2023-10-16 18:32:53,292 saving best model
2023-10-16 18:32:53,797 ----------------------------------------------------------------------------------------------------
2023-10-16 18:32:55,044 epoch 9 - iter 14/146 - loss 0.01036090 - time (sec): 1.24 - samples/sec: 3399.56 - lr: 0.000011 - momentum: 0.000000
2023-10-16 18:32:56,924 epoch 9 - iter 28/146 - loss 0.01412081 - time (sec): 3.12 - samples/sec: 2766.19 - lr: 0.000010 - momentum: 0.000000
2023-10-16 18:32:58,399 epoch 9 - iter 42/146 - loss 0.01439111 - time (sec): 4.59 - samples/sec: 2804.03 - lr: 0.000010 - momentum: 0.000000
2023-10-16 18:32:59,870 epoch 9 - iter 56/146 - loss 0.01249084 - time (sec): 6.06 - samples/sec: 2911.17 - lr: 0.000009 - momentum: 0.000000
2023-10-16 18:33:01,541 epoch 9 - iter 70/146 - loss 0.01166056 - time (sec): 7.74 - samples/sec: 2893.95 - lr: 0.000009 - momentum: 0.000000
2023-10-16 18:33:03,013 epoch 9 - iter 84/146 - loss 0.01200218 - time (sec): 9.21 - samples/sec: 2897.96 - lr: 0.000008 - momentum: 0.000000
2023-10-16 18:33:04,344 epoch 9 - iter 98/146 - loss 0.01474939 - time (sec): 10.54 - samples/sec: 2925.67 - lr: 0.000008 - momentum: 0.000000
2023-10-16 18:33:05,766 epoch 9 - iter 112/146 - loss 0.01486486 - time (sec): 11.96 - samples/sec: 2933.90 - lr: 0.000007 - momentum: 0.000000
2023-10-16 18:33:07,032 epoch 9 - iter 126/146 - loss 0.01464348 - time (sec): 13.23 - samples/sec: 2938.00 - lr: 0.000007 - momentum: 0.000000
2023-10-16 18:33:08,471 epoch 9 - iter 140/146 - loss 0.01441580 - time (sec): 14.67 - samples/sec: 2921.37 - lr: 0.000006 - momentum: 0.000000
2023-10-16 18:33:08,944 ----------------------------------------------------------------------------------------------------
2023-10-16 18:33:08,944 EPOCH 9 done: loss 0.0142 - lr: 0.000006
2023-10-16 18:33:10,208 DEV : loss 0.14194026589393616 - f1-score (micro avg)  0.7773
2023-10-16 18:33:10,212 ----------------------------------------------------------------------------------------------------
2023-10-16 18:33:11,637 epoch 10 - iter 14/146 - loss 0.01016943 - time (sec): 1.42 - samples/sec: 3092.25 - lr: 0.000005 - momentum: 0.000000
2023-10-16 18:33:13,234 epoch 10 - iter 28/146 - loss 0.01164300 - time (sec): 3.02 - samples/sec: 3152.92 - lr: 0.000005 - momentum: 0.000000
2023-10-16 18:33:14,590 epoch 10 - iter 42/146 - loss 0.01529058 - time (sec): 4.38 - samples/sec: 3063.14 - lr: 0.000004 - momentum: 0.000000
2023-10-16 18:33:15,902 epoch 10 - iter 56/146 - loss 0.01388744 - time (sec): 5.69 - samples/sec: 3098.56 - lr: 0.000004 - momentum: 0.000000
2023-10-16 18:33:17,357 epoch 10 - iter 70/146 - loss 0.01378762 - time (sec): 7.14 - samples/sec: 3016.66 - lr: 0.000003 - momentum: 0.000000
2023-10-16 18:33:19,018 epoch 10 - iter 84/146 - loss 0.01335628 - time (sec): 8.80 - samples/sec: 3048.03 - lr: 0.000003 - momentum: 0.000000
2023-10-16 18:33:20,320 epoch 10 - iter 98/146 - loss 0.01221901 - time (sec): 10.11 - samples/sec: 3063.87 - lr: 0.000002 - momentum: 0.000000
2023-10-16 18:33:21,629 epoch 10 - iter 112/146 - loss 0.01168636 - time (sec): 11.42 - samples/sec: 3029.87 - lr: 0.000002 - momentum: 0.000000
2023-10-16 18:33:23,148 epoch 10 - iter 126/146 - loss 0.01059421 - time (sec): 12.94 - samples/sec: 2999.30 - lr: 0.000001 - momentum: 0.000000
2023-10-16 18:33:24,515 epoch 10 - iter 140/146 - loss 0.01057943 - time (sec): 14.30 - samples/sec: 3013.35 - lr: 0.000000 - momentum: 0.000000
2023-10-16 18:33:24,998 ----------------------------------------------------------------------------------------------------
2023-10-16 18:33:24,998 EPOCH 10 done: loss 0.0108 - lr: 0.000000
2023-10-16 18:33:26,284 DEV : loss 0.14382334053516388 - f1-score (micro avg)  0.7676
2023-10-16 18:33:26,696 ----------------------------------------------------------------------------------------------------
2023-10-16 18:33:26,697 Loading model from best epoch ...
2023-10-16 18:33:28,341 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
2023-10-16 18:33:30,789 
Results:
- F-score (micro) 0.743
- F-score (macro) 0.6497
- Accuracy 0.6158

By class:
              precision    recall  f1-score   support

         PER     0.7849    0.8391    0.8111       348
         LOC     0.6614    0.8084    0.7276       261
         ORG     0.4194    0.5000    0.4561        52
   HumanProd     0.5161    0.7273    0.6038        22

   micro avg     0.6952    0.7980    0.7430       683
   macro avg     0.5955    0.7187    0.6497       683
weighted avg     0.7013    0.7980    0.7455       683

2023-10-16 18:33:30,789 ----------------------------------------------------------------------------------------------------