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flair-icdar-nl

Token Classification
Flair
PyTorch
Dutch
sequence-tagger-model
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flair-icdar-nl
File size: 23,735 Bytes 
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2023-10-14 11:08:39,421 ----------------------------------------------------------------------------------------------------
2023-10-14 11:08:39,422 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=13, bias=True)
  (loss_function): CrossEntropyLoss()
)"
2023-10-14 11:08:39,422 ----------------------------------------------------------------------------------------------------
2023-10-14 11:08:39,422 MultiCorpus: 5777 train + 722 dev + 723 test sentences
 - NER_ICDAR_EUROPEANA Corpus: 5777 train + 722 dev + 723 test sentences - /root/.flair/datasets/ner_icdar_europeana/nl
2023-10-14 11:08:39,422 ----------------------------------------------------------------------------------------------------
2023-10-14 11:08:39,422 Train:  5777 sentences
2023-10-14 11:08:39,422         (train_with_dev=False, train_with_test=False)
2023-10-14 11:08:39,423 ----------------------------------------------------------------------------------------------------
2023-10-14 11:08:39,423 Training Params:
2023-10-14 11:08:39,423  - learning_rate: "5e-05" 
2023-10-14 11:08:39,423  - mini_batch_size: "8"
2023-10-14 11:08:39,423  - max_epochs: "10"
2023-10-14 11:08:39,423  - shuffle: "True"
2023-10-14 11:08:39,423 ----------------------------------------------------------------------------------------------------
2023-10-14 11:08:39,423 Plugins:
2023-10-14 11:08:39,423  - LinearScheduler | warmup_fraction: '0.1'
2023-10-14 11:08:39,423 ----------------------------------------------------------------------------------------------------
2023-10-14 11:08:39,423 Final evaluation on model from best epoch (best-model.pt)
2023-10-14 11:08:39,423  - metric: "('micro avg', 'f1-score')"
2023-10-14 11:08:39,423 ----------------------------------------------------------------------------------------------------
2023-10-14 11:08:39,423 Computation:
2023-10-14 11:08:39,423  - compute on device: cuda:0
2023-10-14 11:08:39,423  - embedding storage: none
2023-10-14 11:08:39,423 ----------------------------------------------------------------------------------------------------
2023-10-14 11:08:39,423 Model training base path: "hmbench-icdar/nl-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4"
2023-10-14 11:08:39,423 ----------------------------------------------------------------------------------------------------
2023-10-14 11:08:39,423 ----------------------------------------------------------------------------------------------------
2023-10-14 11:08:45,872 epoch 1 - iter 72/723 - loss 1.95446486 - time (sec): 6.45 - samples/sec: 2891.94 - lr: 0.000005 - momentum: 0.000000
2023-10-14 11:08:51,688 epoch 1 - iter 144/723 - loss 1.15070028 - time (sec): 12.26 - samples/sec: 2945.36 - lr: 0.000010 - momentum: 0.000000
2023-10-14 11:08:57,784 epoch 1 - iter 216/723 - loss 0.84752208 - time (sec): 18.36 - samples/sec: 2902.56 - lr: 0.000015 - momentum: 0.000000
2023-10-14 11:09:03,660 epoch 1 - iter 288/723 - loss 0.68506420 - time (sec): 24.24 - samples/sec: 2896.34 - lr: 0.000020 - momentum: 0.000000
2023-10-14 11:09:09,759 epoch 1 - iter 360/723 - loss 0.57910742 - time (sec): 30.34 - samples/sec: 2910.32 - lr: 0.000025 - momentum: 0.000000
2023-10-14 11:09:15,522 epoch 1 - iter 432/723 - loss 0.50869393 - time (sec): 36.10 - samples/sec: 2939.24 - lr: 0.000030 - momentum: 0.000000
2023-10-14 11:09:21,253 epoch 1 - iter 504/723 - loss 0.45633381 - time (sec): 41.83 - samples/sec: 2955.22 - lr: 0.000035 - momentum: 0.000000
2023-10-14 11:09:27,720 epoch 1 - iter 576/723 - loss 0.41501574 - time (sec): 48.30 - samples/sec: 2953.32 - lr: 0.000040 - momentum: 0.000000
2023-10-14 11:09:33,809 epoch 1 - iter 648/723 - loss 0.38295739 - time (sec): 54.39 - samples/sec: 2940.77 - lr: 0.000045 - momentum: 0.000000
2023-10-14 11:09:38,985 epoch 1 - iter 720/723 - loss 0.36045102 - time (sec): 59.56 - samples/sec: 2949.21 - lr: 0.000050 - momentum: 0.000000
2023-10-14 11:09:39,190 ----------------------------------------------------------------------------------------------------
2023-10-14 11:09:39,190 EPOCH 1 done: loss 0.3598 - lr: 0.000050
2023-10-14 11:09:42,683 DEV : loss 0.11006532609462738 - f1-score (micro avg)  0.7259
2023-10-14 11:09:42,700 saving best model
2023-10-14 11:09:43,090 ----------------------------------------------------------------------------------------------------
2023-10-14 11:09:48,823 epoch 2 - iter 72/723 - loss 0.11097378 - time (sec): 5.73 - samples/sec: 2830.47 - lr: 0.000049 - momentum: 0.000000
2023-10-14 11:09:54,889 epoch 2 - iter 144/723 - loss 0.10276963 - time (sec): 11.80 - samples/sec: 2864.35 - lr: 0.000049 - momentum: 0.000000
2023-10-14 11:10:01,074 epoch 2 - iter 216/723 - loss 0.10943754 - time (sec): 17.98 - samples/sec: 2878.27 - lr: 0.000048 - momentum: 0.000000
2023-10-14 11:10:07,800 epoch 2 - iter 288/723 - loss 0.10336155 - time (sec): 24.71 - samples/sec: 2868.76 - lr: 0.000048 - momentum: 0.000000
2023-10-14 11:10:13,888 epoch 2 - iter 360/723 - loss 0.09910848 - time (sec): 30.80 - samples/sec: 2884.35 - lr: 0.000047 - momentum: 0.000000
2023-10-14 11:10:19,661 epoch 2 - iter 432/723 - loss 0.09836247 - time (sec): 36.57 - samples/sec: 2887.37 - lr: 0.000047 - momentum: 0.000000
2023-10-14 11:10:25,255 epoch 2 - iter 504/723 - loss 0.09901695 - time (sec): 42.16 - samples/sec: 2896.44 - lr: 0.000046 - momentum: 0.000000
2023-10-14 11:10:30,985 epoch 2 - iter 576/723 - loss 0.09643764 - time (sec): 47.89 - samples/sec: 2911.46 - lr: 0.000046 - momentum: 0.000000
2023-10-14 11:10:37,114 epoch 2 - iter 648/723 - loss 0.09508996 - time (sec): 54.02 - samples/sec: 2910.76 - lr: 0.000045 - momentum: 0.000000
2023-10-14 11:10:43,191 epoch 2 - iter 720/723 - loss 0.09515557 - time (sec): 60.10 - samples/sec: 2923.45 - lr: 0.000044 - momentum: 0.000000
2023-10-14 11:10:43,424 ----------------------------------------------------------------------------------------------------
2023-10-14 11:10:43,425 EPOCH 2 done: loss 0.0950 - lr: 0.000044
2023-10-14 11:10:46,938 DEV : loss 0.11145603656768799 - f1-score (micro avg)  0.6008
2023-10-14 11:10:46,954 ----------------------------------------------------------------------------------------------------
2023-10-14 11:10:53,078 epoch 3 - iter 72/723 - loss 0.05697663 - time (sec): 6.12 - samples/sec: 2960.64 - lr: 0.000044 - momentum: 0.000000
2023-10-14 11:10:59,115 epoch 3 - iter 144/723 - loss 0.05590903 - time (sec): 12.16 - samples/sec: 2928.48 - lr: 0.000043 - momentum: 0.000000
2023-10-14 11:11:04,869 epoch 3 - iter 216/723 - loss 0.06028807 - time (sec): 17.91 - samples/sec: 2898.16 - lr: 0.000043 - momentum: 0.000000
2023-10-14 11:11:10,528 epoch 3 - iter 288/723 - loss 0.06014774 - time (sec): 23.57 - samples/sec: 2937.38 - lr: 0.000042 - momentum: 0.000000
2023-10-14 11:11:16,547 epoch 3 - iter 360/723 - loss 0.05942668 - time (sec): 29.59 - samples/sec: 2963.42 - lr: 0.000042 - momentum: 0.000000
2023-10-14 11:11:22,397 epoch 3 - iter 432/723 - loss 0.06112432 - time (sec): 35.44 - samples/sec: 2966.69 - lr: 0.000041 - momentum: 0.000000
2023-10-14 11:11:28,720 epoch 3 - iter 504/723 - loss 0.06151410 - time (sec): 41.77 - samples/sec: 2965.99 - lr: 0.000041 - momentum: 0.000000
2023-10-14 11:11:34,166 epoch 3 - iter 576/723 - loss 0.06139185 - time (sec): 47.21 - samples/sec: 2972.35 - lr: 0.000040 - momentum: 0.000000
2023-10-14 11:11:40,094 epoch 3 - iter 648/723 - loss 0.06076136 - time (sec): 53.14 - samples/sec: 2963.14 - lr: 0.000039 - momentum: 0.000000
2023-10-14 11:11:46,678 epoch 3 - iter 720/723 - loss 0.06160248 - time (sec): 59.72 - samples/sec: 2936.77 - lr: 0.000039 - momentum: 0.000000
2023-10-14 11:11:47,005 ----------------------------------------------------------------------------------------------------
2023-10-14 11:11:47,005 EPOCH 3 done: loss 0.0615 - lr: 0.000039
2023-10-14 11:11:50,496 DEV : loss 0.09180538356304169 - f1-score (micro avg)  0.8029
2023-10-14 11:11:50,515 saving best model
2023-10-14 11:11:50,997 ----------------------------------------------------------------------------------------------------
2023-10-14 11:11:57,031 epoch 4 - iter 72/723 - loss 0.03286789 - time (sec): 6.03 - samples/sec: 2911.68 - lr: 0.000038 - momentum: 0.000000
2023-10-14 11:12:03,391 epoch 4 - iter 144/723 - loss 0.04890866 - time (sec): 12.39 - samples/sec: 2893.01 - lr: 0.000038 - momentum: 0.000000
2023-10-14 11:12:09,237 epoch 4 - iter 216/723 - loss 0.04888331 - time (sec): 18.24 - samples/sec: 2894.47 - lr: 0.000037 - momentum: 0.000000
2023-10-14 11:12:15,546 epoch 4 - iter 288/723 - loss 0.04495173 - time (sec): 24.55 - samples/sec: 2878.44 - lr: 0.000037 - momentum: 0.000000
2023-10-14 11:12:21,075 epoch 4 - iter 360/723 - loss 0.04443698 - time (sec): 30.08 - samples/sec: 2897.91 - lr: 0.000036 - momentum: 0.000000
2023-10-14 11:12:27,144 epoch 4 - iter 432/723 - loss 0.04236909 - time (sec): 36.15 - samples/sec: 2924.18 - lr: 0.000036 - momentum: 0.000000
2023-10-14 11:12:33,112 epoch 4 - iter 504/723 - loss 0.04277641 - time (sec): 42.11 - samples/sec: 2914.76 - lr: 0.000035 - momentum: 0.000000
2023-10-14 11:12:39,122 epoch 4 - iter 576/723 - loss 0.04268064 - time (sec): 48.12 - samples/sec: 2919.51 - lr: 0.000034 - momentum: 0.000000
2023-10-14 11:12:45,234 epoch 4 - iter 648/723 - loss 0.04288486 - time (sec): 54.24 - samples/sec: 2923.74 - lr: 0.000034 - momentum: 0.000000
2023-10-14 11:12:51,242 epoch 4 - iter 720/723 - loss 0.04212052 - time (sec): 60.24 - samples/sec: 2914.87 - lr: 0.000033 - momentum: 0.000000
2023-10-14 11:12:51,437 ----------------------------------------------------------------------------------------------------
2023-10-14 11:12:51,437 EPOCH 4 done: loss 0.0422 - lr: 0.000033
2023-10-14 11:12:55,395 DEV : loss 0.08765760809183121 - f1-score (micro avg)  0.8267
2023-10-14 11:12:55,411 saving best model
2023-10-14 11:12:55,905 ----------------------------------------------------------------------------------------------------
2023-10-14 11:13:02,279 epoch 5 - iter 72/723 - loss 0.03255457 - time (sec): 6.37 - samples/sec: 2890.41 - lr: 0.000033 - momentum: 0.000000
2023-10-14 11:13:07,749 epoch 5 - iter 144/723 - loss 0.02936104 - time (sec): 11.84 - samples/sec: 2992.68 - lr: 0.000032 - momentum: 0.000000
2023-10-14 11:13:14,058 epoch 5 - iter 216/723 - loss 0.02869964 - time (sec): 18.15 - samples/sec: 2982.64 - lr: 0.000032 - momentum: 0.000000
2023-10-14 11:13:19,868 epoch 5 - iter 288/723 - loss 0.03183997 - time (sec): 23.96 - samples/sec: 2956.14 - lr: 0.000031 - momentum: 0.000000
2023-10-14 11:13:25,531 epoch 5 - iter 360/723 - loss 0.03090078 - time (sec): 29.62 - samples/sec: 2958.85 - lr: 0.000031 - momentum: 0.000000
2023-10-14 11:13:31,005 epoch 5 - iter 432/723 - loss 0.03133124 - time (sec): 35.10 - samples/sec: 2956.71 - lr: 0.000030 - momentum: 0.000000
2023-10-14 11:13:37,056 epoch 5 - iter 504/723 - loss 0.03078959 - time (sec): 41.15 - samples/sec: 2963.95 - lr: 0.000029 - momentum: 0.000000
2023-10-14 11:13:43,185 epoch 5 - iter 576/723 - loss 0.03127458 - time (sec): 47.28 - samples/sec: 2955.69 - lr: 0.000029 - momentum: 0.000000
2023-10-14 11:13:49,383 epoch 5 - iter 648/723 - loss 0.03267352 - time (sec): 53.48 - samples/sec: 2959.58 - lr: 0.000028 - momentum: 0.000000
2023-10-14 11:13:55,255 epoch 5 - iter 720/723 - loss 0.03166451 - time (sec): 59.35 - samples/sec: 2960.54 - lr: 0.000028 - momentum: 0.000000
2023-10-14 11:13:55,425 ----------------------------------------------------------------------------------------------------
2023-10-14 11:13:55,425 EPOCH 5 done: loss 0.0316 - lr: 0.000028
2023-10-14 11:13:58,953 DEV : loss 0.13083083927631378 - f1-score (micro avg)  0.802
2023-10-14 11:13:58,970 ----------------------------------------------------------------------------------------------------
2023-10-14 11:14:05,067 epoch 6 - iter 72/723 - loss 0.01949715 - time (sec): 6.10 - samples/sec: 2846.45 - lr: 0.000027 - momentum: 0.000000
2023-10-14 11:14:11,288 epoch 6 - iter 144/723 - loss 0.02577060 - time (sec): 12.32 - samples/sec: 2847.72 - lr: 0.000027 - momentum: 0.000000
2023-10-14 11:14:17,115 epoch 6 - iter 216/723 - loss 0.02325824 - time (sec): 18.14 - samples/sec: 2893.21 - lr: 0.000026 - momentum: 0.000000
2023-10-14 11:14:23,395 epoch 6 - iter 288/723 - loss 0.02320719 - time (sec): 24.42 - samples/sec: 2887.98 - lr: 0.000026 - momentum: 0.000000
2023-10-14 11:14:29,178 epoch 6 - iter 360/723 - loss 0.02320219 - time (sec): 30.21 - samples/sec: 2894.14 - lr: 0.000025 - momentum: 0.000000
2023-10-14 11:14:35,636 epoch 6 - iter 432/723 - loss 0.02204302 - time (sec): 36.66 - samples/sec: 2864.92 - lr: 0.000024 - momentum: 0.000000
2023-10-14 11:14:41,853 epoch 6 - iter 504/723 - loss 0.02223350 - time (sec): 42.88 - samples/sec: 2865.35 - lr: 0.000024 - momentum: 0.000000
2023-10-14 11:14:48,295 epoch 6 - iter 576/723 - loss 0.02115934 - time (sec): 49.32 - samples/sec: 2883.97 - lr: 0.000023 - momentum: 0.000000
2023-10-14 11:14:54,075 epoch 6 - iter 648/723 - loss 0.02227967 - time (sec): 55.10 - samples/sec: 2887.62 - lr: 0.000023 - momentum: 0.000000
2023-10-14 11:14:59,656 epoch 6 - iter 720/723 - loss 0.02225091 - time (sec): 60.68 - samples/sec: 2896.19 - lr: 0.000022 - momentum: 0.000000
2023-10-14 11:14:59,822 ----------------------------------------------------------------------------------------------------
2023-10-14 11:14:59,822 EPOCH 6 done: loss 0.0222 - lr: 0.000022
2023-10-14 11:15:03,409 DEV : loss 0.14740866422653198 - f1-score (micro avg)  0.8142
2023-10-14 11:15:03,424 ----------------------------------------------------------------------------------------------------
2023-10-14 11:15:09,622 epoch 7 - iter 72/723 - loss 0.00957452 - time (sec): 6.20 - samples/sec: 2831.54 - lr: 0.000022 - momentum: 0.000000
2023-10-14 11:15:16,166 epoch 7 - iter 144/723 - loss 0.01188012 - time (sec): 12.74 - samples/sec: 2876.66 - lr: 0.000021 - momentum: 0.000000
2023-10-14 11:15:21,813 epoch 7 - iter 216/723 - loss 0.01261768 - time (sec): 18.39 - samples/sec: 2910.92 - lr: 0.000021 - momentum: 0.000000
2023-10-14 11:15:27,957 epoch 7 - iter 288/723 - loss 0.01369299 - time (sec): 24.53 - samples/sec: 2931.95 - lr: 0.000020 - momentum: 0.000000
2023-10-14 11:15:33,566 epoch 7 - iter 360/723 - loss 0.01532146 - time (sec): 30.14 - samples/sec: 2935.86 - lr: 0.000019 - momentum: 0.000000
2023-10-14 11:15:39,009 epoch 7 - iter 432/723 - loss 0.01513085 - time (sec): 35.58 - samples/sec: 2953.20 - lr: 0.000019 - momentum: 0.000000
2023-10-14 11:15:45,475 epoch 7 - iter 504/723 - loss 0.01624201 - time (sec): 42.05 - samples/sec: 2945.23 - lr: 0.000018 - momentum: 0.000000
2023-10-14 11:15:51,426 epoch 7 - iter 576/723 - loss 0.01671837 - time (sec): 48.00 - samples/sec: 2955.70 - lr: 0.000018 - momentum: 0.000000
2023-10-14 11:15:56,996 epoch 7 - iter 648/723 - loss 0.01672154 - time (sec): 53.57 - samples/sec: 2970.48 - lr: 0.000017 - momentum: 0.000000
2023-10-14 11:16:03,131 epoch 7 - iter 720/723 - loss 0.01649884 - time (sec): 59.71 - samples/sec: 2944.54 - lr: 0.000017 - momentum: 0.000000
2023-10-14 11:16:03,334 ----------------------------------------------------------------------------------------------------
2023-10-14 11:16:03,334 EPOCH 7 done: loss 0.0170 - lr: 0.000017
2023-10-14 11:16:07,244 DEV : loss 0.16563206911087036 - f1-score (micro avg)  0.8131
2023-10-14 11:16:07,261 ----------------------------------------------------------------------------------------------------
2023-10-14 11:16:13,097 epoch 8 - iter 72/723 - loss 0.01815901 - time (sec): 5.83 - samples/sec: 2864.79 - lr: 0.000016 - momentum: 0.000000
2023-10-14 11:16:19,181 epoch 8 - iter 144/723 - loss 0.01454238 - time (sec): 11.92 - samples/sec: 2902.43 - lr: 0.000016 - momentum: 0.000000
2023-10-14 11:16:25,206 epoch 8 - iter 216/723 - loss 0.01396639 - time (sec): 17.94 - samples/sec: 2916.65 - lr: 0.000015 - momentum: 0.000000
2023-10-14 11:16:31,319 epoch 8 - iter 288/723 - loss 0.01233712 - time (sec): 24.06 - samples/sec: 2896.51 - lr: 0.000014 - momentum: 0.000000
2023-10-14 11:16:37,272 epoch 8 - iter 360/723 - loss 0.01149011 - time (sec): 30.01 - samples/sec: 2930.63 - lr: 0.000014 - momentum: 0.000000
2023-10-14 11:16:42,721 epoch 8 - iter 432/723 - loss 0.01140242 - time (sec): 35.46 - samples/sec: 2948.80 - lr: 0.000013 - momentum: 0.000000
2023-10-14 11:16:49,014 epoch 8 - iter 504/723 - loss 0.01267287 - time (sec): 41.75 - samples/sec: 2939.73 - lr: 0.000013 - momentum: 0.000000
2023-10-14 11:16:54,938 epoch 8 - iter 576/723 - loss 0.01271844 - time (sec): 47.68 - samples/sec: 2944.93 - lr: 0.000012 - momentum: 0.000000
2023-10-14 11:17:00,516 epoch 8 - iter 648/723 - loss 0.01192778 - time (sec): 53.25 - samples/sec: 2958.87 - lr: 0.000012 - momentum: 0.000000
2023-10-14 11:17:06,796 epoch 8 - iter 720/723 - loss 0.01221300 - time (sec): 59.53 - samples/sec: 2946.63 - lr: 0.000011 - momentum: 0.000000
2023-10-14 11:17:07,022 ----------------------------------------------------------------------------------------------------
2023-10-14 11:17:07,022 EPOCH 8 done: loss 0.0122 - lr: 0.000011
2023-10-14 11:17:10,588 DEV : loss 0.1542436182498932 - f1-score (micro avg)  0.8318
2023-10-14 11:17:10,605 saving best model
2023-10-14 11:17:11,147 ----------------------------------------------------------------------------------------------------
2023-10-14 11:17:17,372 epoch 9 - iter 72/723 - loss 0.00632595 - time (sec): 6.22 - samples/sec: 2919.02 - lr: 0.000011 - momentum: 0.000000
2023-10-14 11:17:23,181 epoch 9 - iter 144/723 - loss 0.00583846 - time (sec): 12.03 - samples/sec: 2936.80 - lr: 0.000010 - momentum: 0.000000
2023-10-14 11:17:29,380 epoch 9 - iter 216/723 - loss 0.00746569 - time (sec): 18.23 - samples/sec: 2858.87 - lr: 0.000009 - momentum: 0.000000
2023-10-14 11:17:36,031 epoch 9 - iter 288/723 - loss 0.00781052 - time (sec): 24.88 - samples/sec: 2868.30 - lr: 0.000009 - momentum: 0.000000
2023-10-14 11:17:41,837 epoch 9 - iter 360/723 - loss 0.00707142 - time (sec): 30.69 - samples/sec: 2892.67 - lr: 0.000008 - momentum: 0.000000
2023-10-14 11:17:47,948 epoch 9 - iter 432/723 - loss 0.00709441 - time (sec): 36.80 - samples/sec: 2898.81 - lr: 0.000008 - momentum: 0.000000
2023-10-14 11:17:53,401 epoch 9 - iter 504/723 - loss 0.00711054 - time (sec): 42.25 - samples/sec: 2923.32 - lr: 0.000007 - momentum: 0.000000
2023-10-14 11:17:59,535 epoch 9 - iter 576/723 - loss 0.00729666 - time (sec): 48.39 - samples/sec: 2923.90 - lr: 0.000007 - momentum: 0.000000
2023-10-14 11:18:05,332 epoch 9 - iter 648/723 - loss 0.00763742 - time (sec): 54.18 - samples/sec: 2924.87 - lr: 0.000006 - momentum: 0.000000
2023-10-14 11:18:11,175 epoch 9 - iter 720/723 - loss 0.00760018 - time (sec): 60.03 - samples/sec: 2927.43 - lr: 0.000006 - momentum: 0.000000
2023-10-14 11:18:11,415 ----------------------------------------------------------------------------------------------------
2023-10-14 11:18:11,415 EPOCH 9 done: loss 0.0077 - lr: 0.000006
2023-10-14 11:18:14,900 DEV : loss 0.16931197047233582 - f1-score (micro avg)  0.826
2023-10-14 11:18:14,916 ----------------------------------------------------------------------------------------------------
2023-10-14 11:18:20,563 epoch 10 - iter 72/723 - loss 0.00226973 - time (sec): 5.65 - samples/sec: 2956.98 - lr: 0.000005 - momentum: 0.000000
2023-10-14 11:18:26,764 epoch 10 - iter 144/723 - loss 0.00486341 - time (sec): 11.85 - samples/sec: 2931.85 - lr: 0.000004 - momentum: 0.000000
2023-10-14 11:18:33,052 epoch 10 - iter 216/723 - loss 0.00637801 - time (sec): 18.13 - samples/sec: 2889.19 - lr: 0.000004 - momentum: 0.000000
2023-10-14 11:18:39,200 epoch 10 - iter 288/723 - loss 0.00616245 - time (sec): 24.28 - samples/sec: 2929.82 - lr: 0.000003 - momentum: 0.000000
2023-10-14 11:18:45,320 epoch 10 - iter 360/723 - loss 0.00540335 - time (sec): 30.40 - samples/sec: 2947.65 - lr: 0.000003 - momentum: 0.000000
2023-10-14 11:18:51,198 epoch 10 - iter 432/723 - loss 0.00563038 - time (sec): 36.28 - samples/sec: 2945.76 - lr: 0.000002 - momentum: 0.000000
2023-10-14 11:18:56,544 epoch 10 - iter 504/723 - loss 0.00536321 - time (sec): 41.63 - samples/sec: 2944.91 - lr: 0.000002 - momentum: 0.000000
2023-10-14 11:19:02,296 epoch 10 - iter 576/723 - loss 0.00493682 - time (sec): 47.38 - samples/sec: 2940.31 - lr: 0.000001 - momentum: 0.000000
2023-10-14 11:19:08,584 epoch 10 - iter 648/723 - loss 0.00541293 - time (sec): 53.67 - samples/sec: 2939.73 - lr: 0.000001 - momentum: 0.000000
2023-10-14 11:19:14,511 epoch 10 - iter 720/723 - loss 0.00514294 - time (sec): 59.59 - samples/sec: 2944.81 - lr: 0.000000 - momentum: 0.000000
2023-10-14 11:19:14,745 ----------------------------------------------------------------------------------------------------
2023-10-14 11:19:14,745 EPOCH 10 done: loss 0.0052 - lr: 0.000000
2023-10-14 11:19:18,797 DEV : loss 0.17554564774036407 - f1-score (micro avg)  0.8284
2023-10-14 11:19:19,239 ----------------------------------------------------------------------------------------------------
2023-10-14 11:19:19,240 Loading model from best epoch ...
2023-10-14 11:19:20,833 SequenceTagger predicts: Dictionary with 13 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
2023-10-14 11:19:24,050 
Results:
- F-score (micro) 0.8061
- F-score (macro) 0.6941
- Accuracy 0.6877

By class:
              precision    recall  f1-score   support

         PER     0.7714    0.8610    0.8137       482
         LOC     0.8868    0.8210    0.8526       458
         ORG     0.4643    0.3768    0.4160        69

   micro avg     0.8026    0.8097    0.8061      1009
   macro avg     0.7075    0.6863    0.6941      1009
weighted avg     0.8028    0.8097    0.8042      1009

2023-10-14 11:19:24,050 ----------------------------------------------------------------------------------------------------