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hmbench-newseye-fi-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2

Token Classification
Flair
PyTorch
Finnish
sequence-tagger-model
Model card Files Community
hmbench-newseye-fi-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
File size: 23,878 Bytes 
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2023-10-16 18:10:18,197 ----------------------------------------------------------------------------------------------------
2023-10-16 18:10:18,198 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 18:10:18,198 ----------------------------------------------------------------------------------------------------
2023-10-16 18:10:18,198 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 18:10:18,198 ----------------------------------------------------------------------------------------------------
2023-10-16 18:10:18,198 Train:  1166 sentences
2023-10-16 18:10:18,198         (train_with_dev=False, train_with_test=False)
2023-10-16 18:10:18,198 ----------------------------------------------------------------------------------------------------
2023-10-16 18:10:18,198 Training Params:
2023-10-16 18:10:18,198  - learning_rate: "5e-05" 
2023-10-16 18:10:18,199  - mini_batch_size: "4"
2023-10-16 18:10:18,199  - max_epochs: "10"
2023-10-16 18:10:18,199  - shuffle: "True"
2023-10-16 18:10:18,199 ----------------------------------------------------------------------------------------------------
2023-10-16 18:10:18,199 Plugins:
2023-10-16 18:10:18,199  - LinearScheduler | warmup_fraction: '0.1'
2023-10-16 18:10:18,199 ----------------------------------------------------------------------------------------------------
2023-10-16 18:10:18,199 Final evaluation on model from best epoch (best-model.pt)
2023-10-16 18:10:18,199  - metric: "('micro avg', 'f1-score')"
2023-10-16 18:10:18,199 ----------------------------------------------------------------------------------------------------
2023-10-16 18:10:18,199 Computation:
2023-10-16 18:10:18,199  - compute on device: cuda:0
2023-10-16 18:10:18,199  - embedding storage: none
2023-10-16 18:10:18,199 ----------------------------------------------------------------------------------------------------
2023-10-16 18:10:18,199 Model training base path: "hmbench-newseye/fi-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2"
2023-10-16 18:10:18,199 ----------------------------------------------------------------------------------------------------
2023-10-16 18:10:18,199 ----------------------------------------------------------------------------------------------------
2023-10-16 18:10:19,934 epoch 1 - iter 29/292 - loss 2.79895374 - time (sec): 1.73 - samples/sec: 2999.61 - lr: 0.000005 - momentum: 0.000000
2023-10-16 18:10:21,440 epoch 1 - iter 58/292 - loss 2.26112408 - time (sec): 3.24 - samples/sec: 2784.64 - lr: 0.000010 - momentum: 0.000000
2023-10-16 18:10:23,061 epoch 1 - iter 87/292 - loss 1.73551032 - time (sec): 4.86 - samples/sec: 2718.87 - lr: 0.000015 - momentum: 0.000000
2023-10-16 18:10:24,560 epoch 1 - iter 116/292 - loss 1.45381972 - time (sec): 6.36 - samples/sec: 2708.23 - lr: 0.000020 - momentum: 0.000000
2023-10-16 18:10:26,120 epoch 1 - iter 145/292 - loss 1.25529010 - time (sec): 7.92 - samples/sec: 2667.70 - lr: 0.000025 - momentum: 0.000000
2023-10-16 18:10:27,792 epoch 1 - iter 174/292 - loss 1.11428781 - time (sec): 9.59 - samples/sec: 2628.59 - lr: 0.000030 - momentum: 0.000000
2023-10-16 18:10:29,494 epoch 1 - iter 203/292 - loss 0.96722340 - time (sec): 11.29 - samples/sec: 2701.00 - lr: 0.000035 - momentum: 0.000000
2023-10-16 18:10:31,124 epoch 1 - iter 232/292 - loss 0.88860237 - time (sec): 12.92 - samples/sec: 2696.10 - lr: 0.000040 - momentum: 0.000000
2023-10-16 18:10:32,977 epoch 1 - iter 261/292 - loss 0.82506496 - time (sec): 14.78 - samples/sec: 2709.72 - lr: 0.000045 - momentum: 0.000000
2023-10-16 18:10:34,518 epoch 1 - iter 290/292 - loss 0.77331968 - time (sec): 16.32 - samples/sec: 2702.82 - lr: 0.000049 - momentum: 0.000000
2023-10-16 18:10:34,621 ----------------------------------------------------------------------------------------------------
2023-10-16 18:10:34,621 EPOCH 1 done: loss 0.7690 - lr: 0.000049
2023-10-16 18:10:35,854 DEV : loss 0.2004041224718094 - f1-score (micro avg)  0.4475
2023-10-16 18:10:35,860 saving best model
2023-10-16 18:10:36,349 ----------------------------------------------------------------------------------------------------
2023-10-16 18:10:37,956 epoch 2 - iter 29/292 - loss 0.21938164 - time (sec): 1.61 - samples/sec: 2636.68 - lr: 0.000049 - momentum: 0.000000
2023-10-16 18:10:39,534 epoch 2 - iter 58/292 - loss 0.21422136 - time (sec): 3.18 - samples/sec: 2620.74 - lr: 0.000049 - momentum: 0.000000
2023-10-16 18:10:41,136 epoch 2 - iter 87/292 - loss 0.20674539 - time (sec): 4.79 - samples/sec: 2593.64 - lr: 0.000048 - momentum: 0.000000
2023-10-16 18:10:42,738 epoch 2 - iter 116/292 - loss 0.20624764 - time (sec): 6.39 - samples/sec: 2603.98 - lr: 0.000048 - momentum: 0.000000
2023-10-16 18:10:44,384 epoch 2 - iter 145/292 - loss 0.20198539 - time (sec): 8.03 - samples/sec: 2617.16 - lr: 0.000047 - momentum: 0.000000
2023-10-16 18:10:46,189 epoch 2 - iter 174/292 - loss 0.19700909 - time (sec): 9.84 - samples/sec: 2680.82 - lr: 0.000047 - momentum: 0.000000
2023-10-16 18:10:47,868 epoch 2 - iter 203/292 - loss 0.19421637 - time (sec): 11.52 - samples/sec: 2710.40 - lr: 0.000046 - momentum: 0.000000
2023-10-16 18:10:49,486 epoch 2 - iter 232/292 - loss 0.19245480 - time (sec): 13.14 - samples/sec: 2721.24 - lr: 0.000046 - momentum: 0.000000
2023-10-16 18:10:51,068 epoch 2 - iter 261/292 - loss 0.19167829 - time (sec): 14.72 - samples/sec: 2715.53 - lr: 0.000045 - momentum: 0.000000
2023-10-16 18:10:52,706 epoch 2 - iter 290/292 - loss 0.18734658 - time (sec): 16.36 - samples/sec: 2710.77 - lr: 0.000045 - momentum: 0.000000
2023-10-16 18:10:52,798 ----------------------------------------------------------------------------------------------------
2023-10-16 18:10:52,798 EPOCH 2 done: loss 0.1871 - lr: 0.000045
2023-10-16 18:10:54,087 DEV : loss 0.14392311871051788 - f1-score (micro avg)  0.6274
2023-10-16 18:10:54,093 saving best model
2023-10-16 18:10:54,640 ----------------------------------------------------------------------------------------------------
2023-10-16 18:10:56,362 epoch 3 - iter 29/292 - loss 0.12525001 - time (sec): 1.72 - samples/sec: 2533.56 - lr: 0.000044 - momentum: 0.000000
2023-10-16 18:10:57,901 epoch 3 - iter 58/292 - loss 0.10623419 - time (sec): 3.26 - samples/sec: 2703.74 - lr: 0.000043 - momentum: 0.000000
2023-10-16 18:10:59,570 epoch 3 - iter 87/292 - loss 0.11598220 - time (sec): 4.93 - samples/sec: 2733.37 - lr: 0.000043 - momentum: 0.000000
2023-10-16 18:11:01,071 epoch 3 - iter 116/292 - loss 0.10778250 - time (sec): 6.43 - samples/sec: 2688.67 - lr: 0.000042 - momentum: 0.000000
2023-10-16 18:11:02,671 epoch 3 - iter 145/292 - loss 0.10257617 - time (sec): 8.03 - samples/sec: 2689.78 - lr: 0.000042 - momentum: 0.000000
2023-10-16 18:11:04,286 epoch 3 - iter 174/292 - loss 0.10392959 - time (sec): 9.64 - samples/sec: 2673.15 - lr: 0.000041 - momentum: 0.000000
2023-10-16 18:11:06,119 epoch 3 - iter 203/292 - loss 0.10728673 - time (sec): 11.48 - samples/sec: 2706.88 - lr: 0.000041 - momentum: 0.000000
2023-10-16 18:11:07,780 epoch 3 - iter 232/292 - loss 0.10431397 - time (sec): 13.14 - samples/sec: 2705.06 - lr: 0.000040 - momentum: 0.000000
2023-10-16 18:11:09,340 epoch 3 - iter 261/292 - loss 0.10240348 - time (sec): 14.70 - samples/sec: 2705.87 - lr: 0.000040 - momentum: 0.000000
2023-10-16 18:11:11,150 epoch 3 - iter 290/292 - loss 0.10140412 - time (sec): 16.51 - samples/sec: 2682.50 - lr: 0.000039 - momentum: 0.000000
2023-10-16 18:11:11,238 ----------------------------------------------------------------------------------------------------
2023-10-16 18:11:11,239 EPOCH 3 done: loss 0.1013 - lr: 0.000039
2023-10-16 18:11:12,792 DEV : loss 0.15076522529125214 - f1-score (micro avg)  0.6866
2023-10-16 18:11:12,799 saving best model
2023-10-16 18:11:13,343 ----------------------------------------------------------------------------------------------------
2023-10-16 18:11:15,173 epoch 4 - iter 29/292 - loss 0.06366572 - time (sec): 1.83 - samples/sec: 2791.55 - lr: 0.000038 - momentum: 0.000000
2023-10-16 18:11:16,746 epoch 4 - iter 58/292 - loss 0.08419407 - time (sec): 3.40 - samples/sec: 2791.05 - lr: 0.000038 - momentum: 0.000000
2023-10-16 18:11:18,363 epoch 4 - iter 87/292 - loss 0.08091394 - time (sec): 5.02 - samples/sec: 2760.46 - lr: 0.000037 - momentum: 0.000000
2023-10-16 18:11:19,994 epoch 4 - iter 116/292 - loss 0.07811546 - time (sec): 6.65 - samples/sec: 2775.52 - lr: 0.000037 - momentum: 0.000000
2023-10-16 18:11:21,738 epoch 4 - iter 145/292 - loss 0.07285773 - time (sec): 8.39 - samples/sec: 2806.00 - lr: 0.000036 - momentum: 0.000000
2023-10-16 18:11:23,417 epoch 4 - iter 174/292 - loss 0.07228673 - time (sec): 10.07 - samples/sec: 2796.23 - lr: 0.000036 - momentum: 0.000000
2023-10-16 18:11:24,950 epoch 4 - iter 203/292 - loss 0.07173903 - time (sec): 11.61 - samples/sec: 2790.55 - lr: 0.000035 - momentum: 0.000000
2023-10-16 18:11:26,623 epoch 4 - iter 232/292 - loss 0.06922635 - time (sec): 13.28 - samples/sec: 2733.83 - lr: 0.000035 - momentum: 0.000000
2023-10-16 18:11:28,237 epoch 4 - iter 261/292 - loss 0.06934773 - time (sec): 14.89 - samples/sec: 2736.98 - lr: 0.000034 - momentum: 0.000000
2023-10-16 18:11:29,735 epoch 4 - iter 290/292 - loss 0.06709901 - time (sec): 16.39 - samples/sec: 2704.16 - lr: 0.000033 - momentum: 0.000000
2023-10-16 18:11:29,823 ----------------------------------------------------------------------------------------------------
2023-10-16 18:11:29,823 EPOCH 4 done: loss 0.0669 - lr: 0.000033
2023-10-16 18:11:31,187 DEV : loss 0.12698547542095184 - f1-score (micro avg)  0.7442
2023-10-16 18:11:31,194 saving best model
2023-10-16 18:11:31,738 ----------------------------------------------------------------------------------------------------
2023-10-16 18:11:33,348 epoch 5 - iter 29/292 - loss 0.03971967 - time (sec): 1.61 - samples/sec: 2971.03 - lr: 0.000033 - momentum: 0.000000
2023-10-16 18:11:35,043 epoch 5 - iter 58/292 - loss 0.04349609 - time (sec): 3.30 - samples/sec: 2862.25 - lr: 0.000032 - momentum: 0.000000
2023-10-16 18:11:36,866 epoch 5 - iter 87/292 - loss 0.04345885 - time (sec): 5.13 - samples/sec: 2838.22 - lr: 0.000032 - momentum: 0.000000
2023-10-16 18:11:38,470 epoch 5 - iter 116/292 - loss 0.04052249 - time (sec): 6.73 - samples/sec: 2806.53 - lr: 0.000031 - momentum: 0.000000
2023-10-16 18:11:39,989 epoch 5 - iter 145/292 - loss 0.04240124 - time (sec): 8.25 - samples/sec: 2770.39 - lr: 0.000031 - momentum: 0.000000
2023-10-16 18:11:41,530 epoch 5 - iter 174/292 - loss 0.04137216 - time (sec): 9.79 - samples/sec: 2722.46 - lr: 0.000030 - momentum: 0.000000
2023-10-16 18:11:43,122 epoch 5 - iter 203/292 - loss 0.04406993 - time (sec): 11.38 - samples/sec: 2693.47 - lr: 0.000030 - momentum: 0.000000
2023-10-16 18:11:44,829 epoch 5 - iter 232/292 - loss 0.04965846 - time (sec): 13.09 - samples/sec: 2687.74 - lr: 0.000029 - momentum: 0.000000
2023-10-16 18:11:46,495 epoch 5 - iter 261/292 - loss 0.04992373 - time (sec): 14.75 - samples/sec: 2651.24 - lr: 0.000028 - momentum: 0.000000
2023-10-16 18:11:48,221 epoch 5 - iter 290/292 - loss 0.04923535 - time (sec): 16.48 - samples/sec: 2684.64 - lr: 0.000028 - momentum: 0.000000
2023-10-16 18:11:48,307 ----------------------------------------------------------------------------------------------------
2023-10-16 18:11:48,307 EPOCH 5 done: loss 0.0490 - lr: 0.000028
2023-10-16 18:11:49,647 DEV : loss 0.12098459899425507 - f1-score (micro avg)  0.7489
2023-10-16 18:11:49,654 saving best model
2023-10-16 18:11:50,251 ----------------------------------------------------------------------------------------------------
2023-10-16 18:11:51,792 epoch 6 - iter 29/292 - loss 0.02893335 - time (sec): 1.54 - samples/sec: 2489.86 - lr: 0.000027 - momentum: 0.000000
2023-10-16 18:11:53,344 epoch 6 - iter 58/292 - loss 0.02571484 - time (sec): 3.09 - samples/sec: 2674.75 - lr: 0.000027 - momentum: 0.000000
2023-10-16 18:11:54,891 epoch 6 - iter 87/292 - loss 0.02231029 - time (sec): 4.64 - samples/sec: 2649.66 - lr: 0.000026 - momentum: 0.000000
2023-10-16 18:11:56,383 epoch 6 - iter 116/292 - loss 0.02316900 - time (sec): 6.13 - samples/sec: 2686.67 - lr: 0.000026 - momentum: 0.000000
2023-10-16 18:11:58,105 epoch 6 - iter 145/292 - loss 0.02370091 - time (sec): 7.85 - samples/sec: 2692.09 - lr: 0.000025 - momentum: 0.000000
2023-10-16 18:11:59,533 epoch 6 - iter 174/292 - loss 0.02575667 - time (sec): 9.28 - samples/sec: 2668.33 - lr: 0.000025 - momentum: 0.000000
2023-10-16 18:12:01,236 epoch 6 - iter 203/292 - loss 0.02844555 - time (sec): 10.98 - samples/sec: 2674.47 - lr: 0.000024 - momentum: 0.000000
2023-10-16 18:12:02,979 epoch 6 - iter 232/292 - loss 0.02883653 - time (sec): 12.73 - samples/sec: 2708.12 - lr: 0.000023 - momentum: 0.000000
2023-10-16 18:12:04,682 epoch 6 - iter 261/292 - loss 0.03382284 - time (sec): 14.43 - samples/sec: 2738.11 - lr: 0.000023 - momentum: 0.000000
2023-10-16 18:12:06,372 epoch 6 - iter 290/292 - loss 0.03316595 - time (sec): 16.12 - samples/sec: 2743.02 - lr: 0.000022 - momentum: 0.000000
2023-10-16 18:12:06,460 ----------------------------------------------------------------------------------------------------
2023-10-16 18:12:06,461 EPOCH 6 done: loss 0.0333 - lr: 0.000022
2023-10-16 18:12:07,758 DEV : loss 0.1667584925889969 - f1-score (micro avg)  0.7421
2023-10-16 18:12:07,763 ----------------------------------------------------------------------------------------------------
2023-10-16 18:12:09,375 epoch 7 - iter 29/292 - loss 0.03171726 - time (sec): 1.61 - samples/sec: 2546.40 - lr: 0.000022 - momentum: 0.000000
2023-10-16 18:12:11,125 epoch 7 - iter 58/292 - loss 0.02587482 - time (sec): 3.36 - samples/sec: 2771.90 - lr: 0.000021 - momentum: 0.000000
2023-10-16 18:12:12,838 epoch 7 - iter 87/292 - loss 0.02438651 - time (sec): 5.07 - samples/sec: 2792.89 - lr: 0.000021 - momentum: 0.000000
2023-10-16 18:12:14,619 epoch 7 - iter 116/292 - loss 0.02407190 - time (sec): 6.85 - samples/sec: 2743.58 - lr: 0.000020 - momentum: 0.000000
2023-10-16 18:12:16,281 epoch 7 - iter 145/292 - loss 0.02700205 - time (sec): 8.52 - samples/sec: 2689.03 - lr: 0.000020 - momentum: 0.000000
2023-10-16 18:12:17,822 epoch 7 - iter 174/292 - loss 0.02378621 - time (sec): 10.06 - samples/sec: 2678.06 - lr: 0.000019 - momentum: 0.000000
2023-10-16 18:12:19,362 epoch 7 - iter 203/292 - loss 0.02200729 - time (sec): 11.60 - samples/sec: 2665.80 - lr: 0.000018 - momentum: 0.000000
2023-10-16 18:12:21,020 epoch 7 - iter 232/292 - loss 0.02317887 - time (sec): 13.26 - samples/sec: 2696.89 - lr: 0.000018 - momentum: 0.000000
2023-10-16 18:12:22,569 epoch 7 - iter 261/292 - loss 0.02212980 - time (sec): 14.80 - samples/sec: 2682.01 - lr: 0.000017 - momentum: 0.000000
2023-10-16 18:12:24,250 epoch 7 - iter 290/292 - loss 0.02749476 - time (sec): 16.49 - samples/sec: 2678.18 - lr: 0.000017 - momentum: 0.000000
2023-10-16 18:12:24,360 ----------------------------------------------------------------------------------------------------
2023-10-16 18:12:24,360 EPOCH 7 done: loss 0.0273 - lr: 0.000017
2023-10-16 18:12:25,635 DEV : loss 0.1895621120929718 - f1-score (micro avg)  0.6967
2023-10-16 18:12:25,640 ----------------------------------------------------------------------------------------------------
2023-10-16 18:12:27,412 epoch 8 - iter 29/292 - loss 0.02580784 - time (sec): 1.77 - samples/sec: 2688.35 - lr: 0.000016 - momentum: 0.000000
2023-10-16 18:12:28,839 epoch 8 - iter 58/292 - loss 0.02256540 - time (sec): 3.20 - samples/sec: 2496.98 - lr: 0.000016 - momentum: 0.000000
2023-10-16 18:12:30,653 epoch 8 - iter 87/292 - loss 0.02010110 - time (sec): 5.01 - samples/sec: 2508.74 - lr: 0.000015 - momentum: 0.000000
2023-10-16 18:12:32,605 epoch 8 - iter 116/292 - loss 0.02032594 - time (sec): 6.96 - samples/sec: 2490.68 - lr: 0.000015 - momentum: 0.000000
2023-10-16 18:12:34,168 epoch 8 - iter 145/292 - loss 0.02114831 - time (sec): 8.53 - samples/sec: 2560.79 - lr: 0.000014 - momentum: 0.000000
2023-10-16 18:12:35,818 epoch 8 - iter 174/292 - loss 0.01900158 - time (sec): 10.18 - samples/sec: 2625.35 - lr: 0.000013 - momentum: 0.000000
2023-10-16 18:12:37,611 epoch 8 - iter 203/292 - loss 0.01807571 - time (sec): 11.97 - samples/sec: 2670.01 - lr: 0.000013 - momentum: 0.000000
2023-10-16 18:12:39,215 epoch 8 - iter 232/292 - loss 0.02007551 - time (sec): 13.57 - samples/sec: 2687.76 - lr: 0.000012 - momentum: 0.000000
2023-10-16 18:12:40,645 epoch 8 - iter 261/292 - loss 0.01934112 - time (sec): 15.00 - samples/sec: 2669.54 - lr: 0.000012 - momentum: 0.000000
2023-10-16 18:12:42,184 epoch 8 - iter 290/292 - loss 0.01808974 - time (sec): 16.54 - samples/sec: 2673.29 - lr: 0.000011 - momentum: 0.000000
2023-10-16 18:12:42,278 ----------------------------------------------------------------------------------------------------
2023-10-16 18:12:42,279 EPOCH 8 done: loss 0.0181 - lr: 0.000011
2023-10-16 18:12:43,523 DEV : loss 0.15801307559013367 - f1-score (micro avg)  0.7722
2023-10-16 18:12:43,527 saving best model
2023-10-16 18:12:44,050 ----------------------------------------------------------------------------------------------------
2023-10-16 18:12:45,848 epoch 9 - iter 29/292 - loss 0.00653241 - time (sec): 1.79 - samples/sec: 3042.49 - lr: 0.000011 - momentum: 0.000000
2023-10-16 18:12:47,403 epoch 9 - iter 58/292 - loss 0.01637186 - time (sec): 3.35 - samples/sec: 2787.34 - lr: 0.000010 - momentum: 0.000000
2023-10-16 18:12:49,039 epoch 9 - iter 87/292 - loss 0.02178710 - time (sec): 4.98 - samples/sec: 2806.55 - lr: 0.000010 - momentum: 0.000000
2023-10-16 18:12:50,583 epoch 9 - iter 116/292 - loss 0.01938170 - time (sec): 6.53 - samples/sec: 2840.27 - lr: 0.000009 - momentum: 0.000000
2023-10-16 18:12:52,201 epoch 9 - iter 145/292 - loss 0.01644012 - time (sec): 8.15 - samples/sec: 2786.41 - lr: 0.000008 - momentum: 0.000000
2023-10-16 18:12:53,750 epoch 9 - iter 174/292 - loss 0.01589825 - time (sec): 9.70 - samples/sec: 2747.03 - lr: 0.000008 - momentum: 0.000000
2023-10-16 18:12:55,369 epoch 9 - iter 203/292 - loss 0.01495786 - time (sec): 11.31 - samples/sec: 2724.12 - lr: 0.000007 - momentum: 0.000000
2023-10-16 18:12:57,101 epoch 9 - iter 232/292 - loss 0.01534385 - time (sec): 13.05 - samples/sec: 2714.09 - lr: 0.000007 - momentum: 0.000000
2023-10-16 18:12:58,778 epoch 9 - iter 261/292 - loss 0.01426417 - time (sec): 14.72 - samples/sec: 2712.16 - lr: 0.000006 - momentum: 0.000000
2023-10-16 18:13:00,480 epoch 9 - iter 290/292 - loss 0.01426577 - time (sec): 16.43 - samples/sec: 2698.84 - lr: 0.000006 - momentum: 0.000000
2023-10-16 18:13:00,568 ----------------------------------------------------------------------------------------------------
2023-10-16 18:13:00,568 EPOCH 9 done: loss 0.0142 - lr: 0.000006
2023-10-16 18:13:01,879 DEV : loss 0.1797230988740921 - f1-score (micro avg)  0.7197
2023-10-16 18:13:01,888 ----------------------------------------------------------------------------------------------------
2023-10-16 18:13:03,569 epoch 10 - iter 29/292 - loss 0.01425186 - time (sec): 1.68 - samples/sec: 2499.41 - lr: 0.000005 - momentum: 0.000000
2023-10-16 18:13:05,122 epoch 10 - iter 58/292 - loss 0.00873565 - time (sec): 3.23 - samples/sec: 2542.70 - lr: 0.000005 - momentum: 0.000000
2023-10-16 18:13:06,726 epoch 10 - iter 87/292 - loss 0.00939751 - time (sec): 4.84 - samples/sec: 2603.80 - lr: 0.000004 - momentum: 0.000000
2023-10-16 18:13:08,464 epoch 10 - iter 116/292 - loss 0.00986969 - time (sec): 6.57 - samples/sec: 2639.89 - lr: 0.000003 - momentum: 0.000000
2023-10-16 18:13:10,085 epoch 10 - iter 145/292 - loss 0.00847025 - time (sec): 8.20 - samples/sec: 2600.30 - lr: 0.000003 - momentum: 0.000000
2023-10-16 18:13:11,755 epoch 10 - iter 174/292 - loss 0.00994205 - time (sec): 9.87 - samples/sec: 2626.36 - lr: 0.000002 - momentum: 0.000000
2023-10-16 18:13:13,283 epoch 10 - iter 203/292 - loss 0.01008661 - time (sec): 11.39 - samples/sec: 2646.48 - lr: 0.000002 - momentum: 0.000000
2023-10-16 18:13:15,084 epoch 10 - iter 232/292 - loss 0.00916347 - time (sec): 13.19 - samples/sec: 2628.55 - lr: 0.000001 - momentum: 0.000000
2023-10-16 18:13:16,666 epoch 10 - iter 261/292 - loss 0.00946923 - time (sec): 14.78 - samples/sec: 2637.62 - lr: 0.000001 - momentum: 0.000000
2023-10-16 18:13:18,415 epoch 10 - iter 290/292 - loss 0.01083818 - time (sec): 16.53 - samples/sec: 2675.26 - lr: 0.000000 - momentum: 0.000000
2023-10-16 18:13:18,514 ----------------------------------------------------------------------------------------------------
2023-10-16 18:13:18,514 EPOCH 10 done: loss 0.0114 - lr: 0.000000
2023-10-16 18:13:19,807 DEV : loss 0.1740553081035614 - f1-score (micro avg)  0.7426
2023-10-16 18:13:20,260 ----------------------------------------------------------------------------------------------------
2023-10-16 18:13:20,261 Loading model from best epoch ...
2023-10-16 18:13:21,923 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 18:13:24,313 
Results:
- F-score (micro) 0.7541
- F-score (macro) 0.6943
- Accuracy 0.6298

By class:
              precision    recall  f1-score   support

         PER     0.8169    0.8075    0.8121       348
         LOC     0.6333    0.8736    0.7343       261
         ORG     0.4865    0.3462    0.4045        52
   HumanProd     0.7917    0.8636    0.8261        22

   micro avg     0.7137    0.7994    0.7541       683
   macro avg     0.6821    0.7227    0.6943       683
weighted avg     0.7208    0.7994    0.7518       683

2023-10-16 18:13:24,313 ----------------------------------------------------------------------------------------------------