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hmbench-icdar-nl-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1

Token Classification
Flair
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Dutch
sequence-tagger-model
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  1. best-model.pt +3 -0
  2. dev.tsv +0 -0
  3. loss.tsv +11 -0
  4. runs/events.out.tfevents.1697558152.bce904bcef33.2251.3 +3 -0
  5. test.tsv +0 -0
  6. training.log +239 -0
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dev.tsv ADDED
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loss.tsv ADDED
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+ EPOCH TIMESTAMP LEARNING_RATE TRAIN_LOSS DEV_LOSS DEV_PRECISION DEV_RECALL DEV_F1 DEV_ACCURACY
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+ 1 15:56:47 0.0000 0.3940 0.0830 0.8520 0.6839 0.7587 0.6170
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+ 2 15:57:45 0.0000 0.0831 0.0802 0.8783 0.7231 0.7932 0.6641
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+ 7 16:02:30 0.0000 0.0159 0.1268 0.8980 0.8182 0.8562 0.7593
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+ 8 16:03:26 0.0000 0.0101 0.1283 0.9057 0.8337 0.8682 0.7782
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+ 9 16:04:23 0.0000 0.0072 0.1468 0.9036 0.8326 0.8667 0.7772
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+ 10 16:05:19 0.0000 0.0052 0.1494 0.9021 0.8378 0.8688 0.7791
runs/events.out.tfevents.1697558152.bce904bcef33.2251.3 ADDED
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test.tsv ADDED
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training.log ADDED
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+ 2023-10-17 15:55:52,625 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:55:52,626 Model: "SequenceTagger(
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+ (embeddings): TransformerWordEmbeddings(
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+ (model): ElectraModel(
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+ (embeddings): ElectraEmbeddings(
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+ (word_embeddings): Embedding(32001, 768)
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+ (position_embeddings): Embedding(512, 768)
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+ (token_type_embeddings): Embedding(2, 768)
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+ (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ (encoder): ElectraEncoder(
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+ (layer): ModuleList(
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+ (0-11): 12 x ElectraLayer(
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+ (attention): ElectraAttention(
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+ (self): ElectraSelfAttention(
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+ (query): Linear(in_features=768, out_features=768, bias=True)
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+ (key): Linear(in_features=768, out_features=768, bias=True)
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+ (value): Linear(in_features=768, out_features=768, bias=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ (output): ElectraSelfOutput(
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+ (dense): Linear(in_features=768, out_features=768, bias=True)
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+ (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ )
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+ (intermediate): ElectraIntermediate(
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+ (dense): Linear(in_features=768, out_features=3072, bias=True)
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+ (intermediate_act_fn): GELUActivation()
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+ )
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+ (output): ElectraOutput(
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+ (dense): Linear(in_features=3072, out_features=768, bias=True)
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+ (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ )
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+ )
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+ )
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+ )
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+ )
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+ (locked_dropout): LockedDropout(p=0.5)
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+ (linear): Linear(in_features=768, out_features=13, bias=True)
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+ (loss_function): CrossEntropyLoss()
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+ )"
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+ 2023-10-17 15:55:52,626 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:55:52,626 MultiCorpus: 5777 train + 722 dev + 723 test sentences
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+ - NER_ICDAR_EUROPEANA Corpus: 5777 train + 722 dev + 723 test sentences - /root/.flair/datasets/ner_icdar_europeana/nl
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+ 2023-10-17 15:55:52,627 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:55:52,627 Train: 5777 sentences
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+ 2023-10-17 15:55:52,627 (train_with_dev=False, train_with_test=False)
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+ 2023-10-17 15:55:52,627 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:55:52,627 Training Params:
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+ 2023-10-17 15:55:52,627 - learning_rate: "5e-05"
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+ 2023-10-17 15:55:52,627 - mini_batch_size: "8"
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+ 2023-10-17 15:55:52,627 - max_epochs: "10"
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+ 2023-10-17 15:55:52,627 - shuffle: "True"
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+ 2023-10-17 15:55:52,627 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:55:52,627 Plugins:
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+ 2023-10-17 15:55:52,627 - TensorboardLogger
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+ 2023-10-17 15:55:52,627 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-17 15:55:52,627 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:55:52,627 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-17 15:55:52,627 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-17 15:55:52,627 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:55:52,627 Computation:
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+ 2023-10-17 15:55:52,627 - compute on device: cuda:0
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+ 2023-10-17 15:55:52,627 - embedding storage: none
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+ 2023-10-17 15:55:52,627 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:55:52,627 Model training base path: "hmbench-icdar/nl-hmteams/teams-base-historic-multilingual-discriminator-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1"
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+ 2023-10-17 15:55:52,628 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:55:52,628 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:55:52,628 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-17 15:55:58,075 epoch 1 - iter 72/723 - loss 2.37947798 - time (sec): 5.45 - samples/sec: 3407.16 - lr: 0.000005 - momentum: 0.000000
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+ 2023-10-17 15:56:03,002 epoch 1 - iter 144/723 - loss 1.44089682 - time (sec): 10.37 - samples/sec: 3320.20 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-17 15:56:08,099 epoch 1 - iter 216/723 - loss 1.01364540 - time (sec): 15.47 - samples/sec: 3367.82 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 15:56:13,081 epoch 1 - iter 288/723 - loss 0.80827500 - time (sec): 20.45 - samples/sec: 3338.59 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 15:56:18,269 epoch 1 - iter 360/723 - loss 0.67177544 - time (sec): 25.64 - samples/sec: 3373.62 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 15:56:23,923 epoch 1 - iter 432/723 - loss 0.57566837 - time (sec): 31.29 - samples/sec: 3371.60 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-17 15:56:29,072 epoch 1 - iter 504/723 - loss 0.51023937 - time (sec): 36.44 - samples/sec: 3378.90 - lr: 0.000035 - momentum: 0.000000
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+ 2023-10-17 15:56:34,679 epoch 1 - iter 576/723 - loss 0.46099667 - time (sec): 42.05 - samples/sec: 3356.09 - lr: 0.000040 - momentum: 0.000000
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+ 2023-10-17 15:56:39,840 epoch 1 - iter 648/723 - loss 0.42468783 - time (sec): 47.21 - samples/sec: 3357.68 - lr: 0.000045 - momentum: 0.000000
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+ 2023-10-17 15:56:44,665 epoch 1 - iter 720/723 - loss 0.39489656 - time (sec): 52.04 - samples/sec: 3375.89 - lr: 0.000050 - momentum: 0.000000
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+ 2023-10-17 15:56:44,835 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:56:44,836 EPOCH 1 done: loss 0.3940 - lr: 0.000050
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+ 2023-10-17 15:56:47,691 DEV : loss 0.08297927677631378 - f1-score (micro avg) 0.7587
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+ 2023-10-17 15:56:47,707 saving best model
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+ 2023-10-17 15:56:48,068 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:56:52,901 epoch 2 - iter 72/723 - loss 0.10260098 - time (sec): 4.83 - samples/sec: 3443.65 - lr: 0.000049 - momentum: 0.000000
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+ 2023-10-17 15:56:58,055 epoch 2 - iter 144/723 - loss 0.09803331 - time (sec): 9.99 - samples/sec: 3398.30 - lr: 0.000049 - momentum: 0.000000
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+ 2023-10-17 15:57:03,377 epoch 2 - iter 216/723 - loss 0.09312094 - time (sec): 15.31 - samples/sec: 3353.09 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-17 15:57:08,520 epoch 2 - iter 288/723 - loss 0.08851617 - time (sec): 20.45 - samples/sec: 3347.48 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-17 15:57:13,975 epoch 2 - iter 360/723 - loss 0.08633661 - time (sec): 25.91 - samples/sec: 3349.76 - lr: 0.000047 - momentum: 0.000000
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+ 2023-10-17 15:57:19,512 epoch 2 - iter 432/723 - loss 0.08348871 - time (sec): 31.44 - samples/sec: 3367.52 - lr: 0.000047 - momentum: 0.000000
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+ 2023-10-17 15:57:24,687 epoch 2 - iter 504/723 - loss 0.08371297 - time (sec): 36.62 - samples/sec: 3351.57 - lr: 0.000046 - momentum: 0.000000
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+ 2023-10-17 15:57:30,095 epoch 2 - iter 576/723 - loss 0.08416410 - time (sec): 42.03 - samples/sec: 3342.54 - lr: 0.000046 - momentum: 0.000000
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+ 2023-10-17 15:57:35,291 epoch 2 - iter 648/723 - loss 0.08466015 - time (sec): 47.22 - samples/sec: 3334.28 - lr: 0.000045 - momentum: 0.000000
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+ 2023-10-17 15:57:40,848 epoch 2 - iter 720/723 - loss 0.08309979 - time (sec): 52.78 - samples/sec: 3329.87 - lr: 0.000044 - momentum: 0.000000
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+ 2023-10-17 15:57:40,989 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:57:40,990 EPOCH 2 done: loss 0.0831 - lr: 0.000044
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+ 2023-10-17 15:57:45,313 DEV : loss 0.08019406348466873 - f1-score (micro avg) 0.7932
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+ 2023-10-17 15:57:45,342 saving best model
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+ 2023-10-17 15:57:45,877 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:57:51,210 epoch 3 - iter 72/723 - loss 0.06351360 - time (sec): 5.33 - samples/sec: 3259.26 - lr: 0.000044 - momentum: 0.000000
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+ 2023-10-17 15:57:56,293 epoch 3 - iter 144/723 - loss 0.06222447 - time (sec): 10.41 - samples/sec: 3325.69 - lr: 0.000043 - momentum: 0.000000
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+ 2023-10-17 15:58:01,784 epoch 3 - iter 216/723 - loss 0.05814085 - time (sec): 15.90 - samples/sec: 3370.01 - lr: 0.000043 - momentum: 0.000000
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+ 2023-10-17 15:58:06,838 epoch 3 - iter 288/723 - loss 0.05953396 - time (sec): 20.96 - samples/sec: 3374.65 - lr: 0.000042 - momentum: 0.000000
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+ 2023-10-17 15:58:11,731 epoch 3 - iter 360/723 - loss 0.05864225 - time (sec): 25.85 - samples/sec: 3384.29 - lr: 0.000042 - momentum: 0.000000
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+ 2023-10-17 15:58:16,914 epoch 3 - iter 432/723 - loss 0.05836093 - time (sec): 31.03 - samples/sec: 3393.36 - lr: 0.000041 - momentum: 0.000000
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+ 2023-10-17 15:58:22,123 epoch 3 - iter 504/723 - loss 0.05729257 - time (sec): 36.24 - samples/sec: 3367.08 - lr: 0.000041 - momentum: 0.000000
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+ 2023-10-17 15:58:27,144 epoch 3 - iter 576/723 - loss 0.05782325 - time (sec): 41.26 - samples/sec: 3380.00 - lr: 0.000040 - momentum: 0.000000
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+ 2023-10-17 15:58:32,502 epoch 3 - iter 648/723 - loss 0.05829220 - time (sec): 46.62 - samples/sec: 3381.03 - lr: 0.000039 - momentum: 0.000000
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+ 2023-10-17 15:58:37,805 epoch 3 - iter 720/723 - loss 0.05769844 - time (sec): 51.93 - samples/sec: 3385.52 - lr: 0.000039 - momentum: 0.000000
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+ 2023-10-17 15:58:37,963 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:58:37,963 EPOCH 3 done: loss 0.0579 - lr: 0.000039
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+ 2023-10-17 15:58:41,429 DEV : loss 0.07650606334209442 - f1-score (micro avg) 0.861
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+ 2023-10-17 15:58:41,449 saving best model
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+ 2023-10-17 15:58:42,093 ----------------------------------------------------------------------------------------------------
119
+ 2023-10-17 15:58:47,673 epoch 4 - iter 72/723 - loss 0.03823239 - time (sec): 5.57 - samples/sec: 3273.99 - lr: 0.000038 - momentum: 0.000000
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+ 2023-10-17 15:58:53,016 epoch 4 - iter 144/723 - loss 0.04683747 - time (sec): 10.92 - samples/sec: 3257.37 - lr: 0.000038 - momentum: 0.000000
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+ 2023-10-17 15:58:58,126 epoch 4 - iter 216/723 - loss 0.04128104 - time (sec): 16.03 - samples/sec: 3301.87 - lr: 0.000037 - momentum: 0.000000
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+ 2023-10-17 15:59:03,292 epoch 4 - iter 288/723 - loss 0.04079176 - time (sec): 21.19 - samples/sec: 3317.40 - lr: 0.000037 - momentum: 0.000000
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+ 2023-10-17 15:59:08,209 epoch 4 - iter 360/723 - loss 0.04062909 - time (sec): 26.11 - samples/sec: 3328.56 - lr: 0.000036 - momentum: 0.000000
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+ 2023-10-17 15:59:13,592 epoch 4 - iter 432/723 - loss 0.04066133 - time (sec): 31.49 - samples/sec: 3318.86 - lr: 0.000036 - momentum: 0.000000
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+ 2023-10-17 15:59:19,610 epoch 4 - iter 504/723 - loss 0.04003049 - time (sec): 37.51 - samples/sec: 3260.74 - lr: 0.000035 - momentum: 0.000000
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+ 2023-10-17 15:59:24,910 epoch 4 - iter 576/723 - loss 0.03955197 - time (sec): 42.81 - samples/sec: 3267.84 - lr: 0.000034 - momentum: 0.000000
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+ 2023-10-17 15:59:30,268 epoch 4 - iter 648/723 - loss 0.03999115 - time (sec): 48.17 - samples/sec: 3272.18 - lr: 0.000034 - momentum: 0.000000
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+ 2023-10-17 15:59:35,731 epoch 4 - iter 720/723 - loss 0.04250353 - time (sec): 53.63 - samples/sec: 3277.71 - lr: 0.000033 - momentum: 0.000000
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+ 2023-10-17 15:59:35,904 ----------------------------------------------------------------------------------------------------
130
+ 2023-10-17 15:59:35,904 EPOCH 4 done: loss 0.0424 - lr: 0.000033
131
+ 2023-10-17 15:59:39,265 DEV : loss 0.07505105435848236 - f1-score (micro avg) 0.8508
132
+ 2023-10-17 15:59:39,285 ----------------------------------------------------------------------------------------------------
133
+ 2023-10-17 15:59:44,496 epoch 5 - iter 72/723 - loss 0.02322825 - time (sec): 5.21 - samples/sec: 3397.18 - lr: 0.000033 - momentum: 0.000000
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+ 2023-10-17 15:59:49,984 epoch 5 - iter 144/723 - loss 0.02522339 - time (sec): 10.70 - samples/sec: 3359.84 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-17 15:59:55,357 epoch 5 - iter 216/723 - loss 0.02748650 - time (sec): 16.07 - samples/sec: 3344.27 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-17 16:00:00,150 epoch 5 - iter 288/723 - loss 0.02774826 - time (sec): 20.86 - samples/sec: 3377.69 - lr: 0.000031 - momentum: 0.000000
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+ 2023-10-17 16:00:05,489 epoch 5 - iter 360/723 - loss 0.02809467 - time (sec): 26.20 - samples/sec: 3351.38 - lr: 0.000031 - momentum: 0.000000
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+ 2023-10-17 16:00:10,895 epoch 5 - iter 432/723 - loss 0.03187831 - time (sec): 31.61 - samples/sec: 3318.68 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-17 16:00:16,149 epoch 5 - iter 504/723 - loss 0.03247153 - time (sec): 36.86 - samples/sec: 3311.27 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 16:00:21,641 epoch 5 - iter 576/723 - loss 0.03216390 - time (sec): 42.35 - samples/sec: 3319.88 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 16:00:26,598 epoch 5 - iter 648/723 - loss 0.03247873 - time (sec): 47.31 - samples/sec: 3336.04 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 16:00:32,135 epoch 5 - iter 720/723 - loss 0.03226933 - time (sec): 52.85 - samples/sec: 3323.80 - lr: 0.000028 - momentum: 0.000000
143
+ 2023-10-17 16:00:32,330 ----------------------------------------------------------------------------------------------------
144
+ 2023-10-17 16:00:32,330 EPOCH 5 done: loss 0.0322 - lr: 0.000028
145
+ 2023-10-17 16:00:36,192 DEV : loss 0.10498460382223129 - f1-score (micro avg) 0.8403
146
+ 2023-10-17 16:00:36,211 ----------------------------------------------------------------------------------------------------
147
+ 2023-10-17 16:00:41,618 epoch 6 - iter 72/723 - loss 0.02253230 - time (sec): 5.41 - samples/sec: 3237.77 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 16:00:46,772 epoch 6 - iter 144/723 - loss 0.02540519 - time (sec): 10.56 - samples/sec: 3223.17 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 16:00:52,011 epoch 6 - iter 216/723 - loss 0.02564947 - time (sec): 15.80 - samples/sec: 3265.77 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 16:00:57,844 epoch 6 - iter 288/723 - loss 0.02408256 - time (sec): 21.63 - samples/sec: 3226.31 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 16:01:03,415 epoch 6 - iter 360/723 - loss 0.02433276 - time (sec): 27.20 - samples/sec: 3234.82 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 16:01:08,535 epoch 6 - iter 432/723 - loss 0.02382939 - time (sec): 32.32 - samples/sec: 3230.90 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 16:01:13,688 epoch 6 - iter 504/723 - loss 0.02331952 - time (sec): 37.48 - samples/sec: 3270.35 - lr: 0.000024 - momentum: 0.000000
154
+ 2023-10-17 16:01:18,912 epoch 6 - iter 576/723 - loss 0.02289724 - time (sec): 42.70 - samples/sec: 3256.96 - lr: 0.000023 - momentum: 0.000000
155
+ 2023-10-17 16:01:24,284 epoch 6 - iter 648/723 - loss 0.02282930 - time (sec): 48.07 - samples/sec: 3260.65 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 16:01:29,834 epoch 6 - iter 720/723 - loss 0.02229213 - time (sec): 53.62 - samples/sec: 3272.85 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 16:01:30,026 ----------------------------------------------------------------------------------------------------
158
+ 2023-10-17 16:01:30,027 EPOCH 6 done: loss 0.0222 - lr: 0.000022
159
+ 2023-10-17 16:01:33,283 DEV : loss 0.10264434665441513 - f1-score (micro avg) 0.8651
160
+ 2023-10-17 16:01:33,308 saving best model
161
+ 2023-10-17 16:01:33,847 ----------------------------------------------------------------------------------------------------
162
+ 2023-10-17 16:01:39,082 epoch 7 - iter 72/723 - loss 0.02479169 - time (sec): 5.23 - samples/sec: 3301.29 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 16:01:44,110 epoch 7 - iter 144/723 - loss 0.01936541 - time (sec): 10.26 - samples/sec: 3316.51 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 16:01:49,477 epoch 7 - iter 216/723 - loss 0.02032545 - time (sec): 15.63 - samples/sec: 3324.49 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 16:01:55,078 epoch 7 - iter 288/723 - loss 0.01949508 - time (sec): 21.23 - samples/sec: 3298.74 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 16:02:00,277 epoch 7 - iter 360/723 - loss 0.01828023 - time (sec): 26.43 - samples/sec: 3304.72 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-17 16:02:05,591 epoch 7 - iter 432/723 - loss 0.01757906 - time (sec): 31.74 - samples/sec: 3339.90 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-17 16:02:10,817 epoch 7 - iter 504/723 - loss 0.01601183 - time (sec): 36.97 - samples/sec: 3330.88 - lr: 0.000018 - momentum: 0.000000
169
+ 2023-10-17 16:02:16,408 epoch 7 - iter 576/723 - loss 0.01523872 - time (sec): 42.56 - samples/sec: 3294.84 - lr: 0.000018 - momentum: 0.000000
170
+ 2023-10-17 16:02:21,808 epoch 7 - iter 648/723 - loss 0.01596978 - time (sec): 47.96 - samples/sec: 3296.43 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-17 16:02:26,919 epoch 7 - iter 720/723 - loss 0.01592490 - time (sec): 53.07 - samples/sec: 3312.38 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-17 16:02:27,093 ----------------------------------------------------------------------------------------------------
173
+ 2023-10-17 16:02:27,093 EPOCH 7 done: loss 0.0159 - lr: 0.000017
174
+ 2023-10-17 16:02:30,303 DEV : loss 0.12677793204784393 - f1-score (micro avg) 0.8562
175
+ 2023-10-17 16:02:30,320 ----------------------------------------------------------------------------------------------------
176
+ 2023-10-17 16:02:35,341 epoch 8 - iter 72/723 - loss 0.00768027 - time (sec): 5.02 - samples/sec: 3216.93 - lr: 0.000016 - momentum: 0.000000
177
+ 2023-10-17 16:02:40,992 epoch 8 - iter 144/723 - loss 0.00861469 - time (sec): 10.67 - samples/sec: 3237.83 - lr: 0.000016 - momentum: 0.000000
178
+ 2023-10-17 16:02:45,889 epoch 8 - iter 216/723 - loss 0.00745035 - time (sec): 15.57 - samples/sec: 3359.60 - lr: 0.000015 - momentum: 0.000000
179
+ 2023-10-17 16:02:51,120 epoch 8 - iter 288/723 - loss 0.00916909 - time (sec): 20.80 - samples/sec: 3319.12 - lr: 0.000014 - momentum: 0.000000
180
+ 2023-10-17 16:02:56,348 epoch 8 - iter 360/723 - loss 0.00933421 - time (sec): 26.03 - samples/sec: 3307.99 - lr: 0.000014 - momentum: 0.000000
181
+ 2023-10-17 16:03:02,032 epoch 8 - iter 432/723 - loss 0.00912919 - time (sec): 31.71 - samples/sec: 3297.77 - lr: 0.000013 - momentum: 0.000000
182
+ 2023-10-17 16:03:07,157 epoch 8 - iter 504/723 - loss 0.00939804 - time (sec): 36.84 - samples/sec: 3322.55 - lr: 0.000013 - momentum: 0.000000
183
+ 2023-10-17 16:03:12,227 epoch 8 - iter 576/723 - loss 0.00986990 - time (sec): 41.91 - samples/sec: 3323.69 - lr: 0.000012 - momentum: 0.000000
184
+ 2023-10-17 16:03:17,717 epoch 8 - iter 648/723 - loss 0.00970328 - time (sec): 47.40 - samples/sec: 3330.36 - lr: 0.000012 - momentum: 0.000000
185
+ 2023-10-17 16:03:22,900 epoch 8 - iter 720/723 - loss 0.01012613 - time (sec): 52.58 - samples/sec: 3337.38 - lr: 0.000011 - momentum: 0.000000
186
+ 2023-10-17 16:03:23,135 ----------------------------------------------------------------------------------------------------
187
+ 2023-10-17 16:03:23,136 EPOCH 8 done: loss 0.0101 - lr: 0.000011
188
+ 2023-10-17 16:03:26,332 DEV : loss 0.1282780021429062 - f1-score (micro avg) 0.8682
189
+ 2023-10-17 16:03:26,349 saving best model
190
+ 2023-10-17 16:03:26,916 ----------------------------------------------------------------------------------------------------
191
+ 2023-10-17 16:03:32,954 epoch 9 - iter 72/723 - loss 0.00707734 - time (sec): 6.03 - samples/sec: 3186.11 - lr: 0.000011 - momentum: 0.000000
192
+ 2023-10-17 16:03:37,845 epoch 9 - iter 144/723 - loss 0.00625959 - time (sec): 10.92 - samples/sec: 3214.57 - lr: 0.000010 - momentum: 0.000000
193
+ 2023-10-17 16:03:43,549 epoch 9 - iter 216/723 - loss 0.00647191 - time (sec): 16.62 - samples/sec: 3258.48 - lr: 0.000009 - momentum: 0.000000
194
+ 2023-10-17 16:03:49,022 epoch 9 - iter 288/723 - loss 0.00700090 - time (sec): 22.10 - samples/sec: 3278.32 - lr: 0.000009 - momentum: 0.000000
195
+ 2023-10-17 16:03:54,257 epoch 9 - iter 360/723 - loss 0.00711976 - time (sec): 27.33 - samples/sec: 3277.30 - lr: 0.000008 - momentum: 0.000000
196
+ 2023-10-17 16:03:59,095 epoch 9 - iter 432/723 - loss 0.00675222 - time (sec): 32.17 - samples/sec: 3281.09 - lr: 0.000008 - momentum: 0.000000
197
+ 2023-10-17 16:04:04,324 epoch 9 - iter 504/723 - loss 0.00726827 - time (sec): 37.40 - samples/sec: 3294.08 - lr: 0.000007 - momentum: 0.000000
198
+ 2023-10-17 16:04:09,786 epoch 9 - iter 576/723 - loss 0.00751472 - time (sec): 42.86 - samples/sec: 3297.36 - lr: 0.000007 - momentum: 0.000000
199
+ 2023-10-17 16:04:15,024 epoch 9 - iter 648/723 - loss 0.00706634 - time (sec): 48.10 - samples/sec: 3304.88 - lr: 0.000006 - momentum: 0.000000
200
+ 2023-10-17 16:04:19,751 epoch 9 - iter 720/723 - loss 0.00724864 - time (sec): 52.82 - samples/sec: 3321.30 - lr: 0.000006 - momentum: 0.000000
201
+ 2023-10-17 16:04:20,017 ----------------------------------------------------------------------------------------------------
202
+ 2023-10-17 16:04:20,017 EPOCH 9 done: loss 0.0072 - lr: 0.000006
203
+ 2023-10-17 16:04:23,192 DEV : loss 0.14683492481708527 - f1-score (micro avg) 0.8667
204
+ 2023-10-17 16:04:23,209 ----------------------------------------------------------------------------------------------------
205
+ 2023-10-17 16:04:28,333 epoch 10 - iter 72/723 - loss 0.00847938 - time (sec): 5.12 - samples/sec: 3406.42 - lr: 0.000005 - momentum: 0.000000
206
+ 2023-10-17 16:04:33,664 epoch 10 - iter 144/723 - loss 0.00565771 - time (sec): 10.45 - samples/sec: 3353.37 - lr: 0.000004 - momentum: 0.000000
207
+ 2023-10-17 16:04:38,677 epoch 10 - iter 216/723 - loss 0.00518803 - time (sec): 15.47 - samples/sec: 3342.28 - lr: 0.000004 - momentum: 0.000000
208
+ 2023-10-17 16:04:43,557 epoch 10 - iter 288/723 - loss 0.00507785 - time (sec): 20.35 - samples/sec: 3339.28 - lr: 0.000003 - momentum: 0.000000
209
+ 2023-10-17 16:04:49,150 epoch 10 - iter 360/723 - loss 0.00510449 - time (sec): 25.94 - samples/sec: 3341.65 - lr: 0.000003 - momentum: 0.000000
210
+ 2023-10-17 16:04:54,672 epoch 10 - iter 432/723 - loss 0.00546394 - time (sec): 31.46 - samples/sec: 3349.75 - lr: 0.000002 - momentum: 0.000000
211
+ 2023-10-17 16:04:59,864 epoch 10 - iter 504/723 - loss 0.00482428 - time (sec): 36.65 - samples/sec: 3329.56 - lr: 0.000002 - momentum: 0.000000
212
+ 2023-10-17 16:05:05,555 epoch 10 - iter 576/723 - loss 0.00506029 - time (sec): 42.34 - samples/sec: 3308.61 - lr: 0.000001 - momentum: 0.000000
213
+ 2023-10-17 16:05:10,771 epoch 10 - iter 648/723 - loss 0.00511610 - time (sec): 47.56 - samples/sec: 3324.46 - lr: 0.000001 - momentum: 0.000000
214
+ 2023-10-17 16:05:16,035 epoch 10 - iter 720/723 - loss 0.00523258 - time (sec): 52.82 - samples/sec: 3326.99 - lr: 0.000000 - momentum: 0.000000
215
+ 2023-10-17 16:05:16,193 ----------------------------------------------------------------------------------------------------
216
+ 2023-10-17 16:05:16,193 EPOCH 10 done: loss 0.0052 - lr: 0.000000
217
+ 2023-10-17 16:05:19,916 DEV : loss 0.14944744110107422 - f1-score (micro avg) 0.8688
218
+ 2023-10-17 16:05:19,934 saving best model
219
+ 2023-10-17 16:05:20,741 ----------------------------------------------------------------------------------------------------
220
+ 2023-10-17 16:05:20,742 Loading model from best epoch ...
221
+ 2023-10-17 16:05:22,172 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
222
+ 2023-10-17 16:05:25,188
223
+ Results:
224
+ - F-score (micro) 0.8476
225
+ - F-score (macro) 0.7371
226
+ - Accuracy 0.7448
227
+
228
+ By class:
229
+ precision recall f1-score support
230
+
231
+ PER 0.8633 0.8257 0.8441 482
232
+ LOC 0.9458 0.8755 0.9093 458
233
+ ORG 0.4839 0.4348 0.4580 69
234
+
235
+ micro avg 0.8754 0.8216 0.8476 1009
236
+ macro avg 0.7643 0.7120 0.7371 1009
237
+ weighted avg 0.8748 0.8216 0.8473 1009
238
+
239
+ 2023-10-17 16:05:25,188 ----------------------------------------------------------------------------------------------------