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hmbench-icdar-nl-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4

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.1697564295.bce904bcef33.2251.12 +3 -0
  5. test.tsv +0 -0
  6. training.log +238 -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 17:39:31 0.0000 0.4146 0.1012 0.7742 0.6198 0.6885 0.5348
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+ 2 17:40:48 0.0000 0.0906 0.0899 0.8959 0.7293 0.8041 0.6756
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+ 4 17:43:22 0.0000 0.0528 0.0985 0.8817 0.8388 0.8597 0.7653
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+ 5 17:44:38 0.0000 0.0381 0.1179 0.8870 0.8192 0.8518 0.7517
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+ 6 17:45:55 0.0000 0.0280 0.1215 0.8852 0.8202 0.8515 0.7526
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+ 7 17:47:10 0.0000 0.0219 0.1272 0.9002 0.8388 0.8684 0.7785
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+ 8 17:48:25 0.0000 0.0147 0.1398 0.9003 0.8306 0.8641 0.7723
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+ 9 17:49:42 0.0000 0.0100 0.1419 0.9072 0.8285 0.8661 0.7756
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+ 10 17:50:56 0.0000 0.0074 0.1447 0.9076 0.8316 0.8679 0.7770
runs/events.out.tfevents.1697564295.bce904bcef33.2251.12 ADDED
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test.tsv ADDED
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training.log ADDED
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+ 2023-10-17 17:38:15,449 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 17:38:15,450 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 17:38:15,450 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 17:38:15,450 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 17:38:15,451 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 17:38:15,451 Train: 5777 sentences
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+ 2023-10-17 17:38:15,451 (train_with_dev=False, train_with_test=False)
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+ 2023-10-17 17:38:15,451 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 17:38:15,451 Training Params:
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+ 2023-10-17 17:38:15,451 - learning_rate: "3e-05"
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+ 2023-10-17 17:38:15,451 - mini_batch_size: "4"
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+ 2023-10-17 17:38:15,451 - max_epochs: "10"
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+ 2023-10-17 17:38:15,451 - shuffle: "True"
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+ 2023-10-17 17:38:15,451 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 17:38:15,451 Plugins:
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+ 2023-10-17 17:38:15,451 - TensorboardLogger
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+ 2023-10-17 17:38:15,451 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-17 17:38:15,451 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 17:38:15,451 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-17 17:38:15,451 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-17 17:38:15,451 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 17:38:15,451 Computation:
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+ 2023-10-17 17:38:15,451 - compute on device: cuda:0
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+ 2023-10-17 17:38:15,451 - embedding storage: none
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+ 2023-10-17 17:38:15,451 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 17:38:15,451 Model training base path: "hmbench-icdar/nl-hmteams/teams-base-historic-multilingual-discriminator-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4"
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+ 2023-10-17 17:38:15,451 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 17:38:15,451 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 17:38:15,451 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-17 17:38:22,526 epoch 1 - iter 144/1445 - loss 2.78078658 - time (sec): 7.07 - samples/sec: 2289.63 - lr: 0.000003 - momentum: 0.000000
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+ 2023-10-17 17:38:29,934 epoch 1 - iter 288/1445 - loss 1.51861606 - time (sec): 14.48 - samples/sec: 2361.02 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-17 17:38:37,086 epoch 1 - iter 432/1445 - loss 1.07913069 - time (sec): 21.63 - samples/sec: 2389.34 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-17 17:38:44,314 epoch 1 - iter 576/1445 - loss 0.85096615 - time (sec): 28.86 - samples/sec: 2396.22 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-17 17:38:51,463 epoch 1 - iter 720/1445 - loss 0.70608937 - time (sec): 36.01 - samples/sec: 2419.98 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 17:38:58,764 epoch 1 - iter 864/1445 - loss 0.60898489 - time (sec): 43.31 - samples/sec: 2437.90 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-17 17:39:06,108 epoch 1 - iter 1008/1445 - loss 0.53851173 - time (sec): 50.66 - samples/sec: 2434.66 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 17:39:13,470 epoch 1 - iter 1152/1445 - loss 0.48695757 - time (sec): 58.02 - samples/sec: 2434.01 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 17:39:20,969 epoch 1 - iter 1296/1445 - loss 0.44708419 - time (sec): 65.52 - samples/sec: 2427.59 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 17:39:27,888 epoch 1 - iter 1440/1445 - loss 0.41563598 - time (sec): 72.44 - samples/sec: 2424.95 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-17 17:39:28,119 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 17:39:28,120 EPOCH 1 done: loss 0.4146 - lr: 0.000030
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+ 2023-10-17 17:39:31,097 DEV : loss 0.10123064368963242 - f1-score (micro avg) 0.6885
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+ 2023-10-17 17:39:31,119 saving best model
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+ 2023-10-17 17:39:31,520 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 17:39:38,950 epoch 2 - iter 144/1445 - loss 0.09208746 - time (sec): 7.43 - samples/sec: 2505.79 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-17 17:39:46,043 epoch 2 - iter 288/1445 - loss 0.09395078 - time (sec): 14.52 - samples/sec: 2443.88 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 17:39:53,806 epoch 2 - iter 432/1445 - loss 0.09675004 - time (sec): 22.28 - samples/sec: 2402.53 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 17:40:01,000 epoch 2 - iter 576/1445 - loss 0.09339561 - time (sec): 29.48 - samples/sec: 2413.55 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 17:40:08,104 epoch 2 - iter 720/1445 - loss 0.09667081 - time (sec): 36.58 - samples/sec: 2401.10 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 17:40:15,323 epoch 2 - iter 864/1445 - loss 0.09827203 - time (sec): 43.80 - samples/sec: 2384.73 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 17:40:23,213 epoch 2 - iter 1008/1445 - loss 0.09597707 - time (sec): 51.69 - samples/sec: 2384.96 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 17:40:30,320 epoch 2 - iter 1152/1445 - loss 0.09430318 - time (sec): 58.80 - samples/sec: 2373.58 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 17:40:37,393 epoch 2 - iter 1296/1445 - loss 0.09280542 - time (sec): 65.87 - samples/sec: 2384.11 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 17:40:44,895 epoch 2 - iter 1440/1445 - loss 0.09043549 - time (sec): 73.37 - samples/sec: 2395.99 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 17:40:45,128 ----------------------------------------------------------------------------------------------------
100
+ 2023-10-17 17:40:45,129 EPOCH 2 done: loss 0.0906 - lr: 0.000027
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+ 2023-10-17 17:40:48,972 DEV : loss 0.08994048088788986 - f1-score (micro avg) 0.8041
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+ 2023-10-17 17:40:48,997 saving best model
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+ 2023-10-17 17:40:49,509 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 17:40:56,927 epoch 3 - iter 144/1445 - loss 0.05872201 - time (sec): 7.41 - samples/sec: 2360.16 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 17:41:03,913 epoch 3 - iter 288/1445 - loss 0.05857562 - time (sec): 14.40 - samples/sec: 2420.88 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 17:41:11,051 epoch 3 - iter 432/1445 - loss 0.06614001 - time (sec): 21.53 - samples/sec: 2394.73 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 17:41:18,016 epoch 3 - iter 576/1445 - loss 0.06718519 - time (sec): 28.50 - samples/sec: 2399.07 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 17:41:25,276 epoch 3 - iter 720/1445 - loss 0.06571201 - time (sec): 35.76 - samples/sec: 2412.78 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 17:41:32,934 epoch 3 - iter 864/1445 - loss 0.06765137 - time (sec): 43.42 - samples/sec: 2442.62 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 17:41:40,390 epoch 3 - iter 1008/1445 - loss 0.06716439 - time (sec): 50.87 - samples/sec: 2446.41 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 17:41:47,590 epoch 3 - iter 1152/1445 - loss 0.06595038 - time (sec): 58.07 - samples/sec: 2439.72 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 17:41:54,737 epoch 3 - iter 1296/1445 - loss 0.06557252 - time (sec): 65.22 - samples/sec: 2434.72 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 17:42:01,793 epoch 3 - iter 1440/1445 - loss 0.06558181 - time (sec): 72.28 - samples/sec: 2428.06 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 17:42:02,065 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 17:42:02,065 EPOCH 3 done: loss 0.0655 - lr: 0.000023
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+ 2023-10-17 17:42:05,446 DEV : loss 0.08329488337039948 - f1-score (micro avg) 0.8576
117
+ 2023-10-17 17:42:05,463 saving best model
118
+ 2023-10-17 17:42:05,984 ----------------------------------------------------------------------------------------------------
119
+ 2023-10-17 17:42:13,079 epoch 4 - iter 144/1445 - loss 0.03908482 - time (sec): 7.09 - samples/sec: 2434.08 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 17:42:20,247 epoch 4 - iter 288/1445 - loss 0.04314761 - time (sec): 14.26 - samples/sec: 2426.27 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 17:42:27,325 epoch 4 - iter 432/1445 - loss 0.04479377 - time (sec): 21.34 - samples/sec: 2452.70 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 17:42:34,473 epoch 4 - iter 576/1445 - loss 0.04639701 - time (sec): 28.49 - samples/sec: 2443.28 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 17:42:41,304 epoch 4 - iter 720/1445 - loss 0.04663083 - time (sec): 35.32 - samples/sec: 2432.99 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 17:42:48,590 epoch 4 - iter 864/1445 - loss 0.04877294 - time (sec): 42.60 - samples/sec: 2446.39 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 17:42:56,157 epoch 4 - iter 1008/1445 - loss 0.05120701 - time (sec): 50.17 - samples/sec: 2442.48 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 17:43:03,410 epoch 4 - iter 1152/1445 - loss 0.05143852 - time (sec): 57.42 - samples/sec: 2428.03 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 17:43:11,041 epoch 4 - iter 1296/1445 - loss 0.05077155 - time (sec): 65.05 - samples/sec: 2416.48 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 17:43:18,170 epoch 4 - iter 1440/1445 - loss 0.05272378 - time (sec): 72.18 - samples/sec: 2432.26 - lr: 0.000020 - momentum: 0.000000
129
+ 2023-10-17 17:43:18,411 ----------------------------------------------------------------------------------------------------
130
+ 2023-10-17 17:43:18,411 EPOCH 4 done: loss 0.0528 - lr: 0.000020
131
+ 2023-10-17 17:43:22,251 DEV : loss 0.09850851446390152 - f1-score (micro avg) 0.8597
132
+ 2023-10-17 17:43:22,267 saving best model
133
+ 2023-10-17 17:43:22,711 ----------------------------------------------------------------------------------------------------
134
+ 2023-10-17 17:43:29,952 epoch 5 - iter 144/1445 - loss 0.02913580 - time (sec): 7.24 - samples/sec: 2455.71 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 17:43:37,014 epoch 5 - iter 288/1445 - loss 0.03254544 - time (sec): 14.30 - samples/sec: 2472.75 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-17 17:43:44,071 epoch 5 - iter 432/1445 - loss 0.03570376 - time (sec): 21.36 - samples/sec: 2430.96 - lr: 0.000019 - momentum: 0.000000
137
+ 2023-10-17 17:43:51,427 epoch 5 - iter 576/1445 - loss 0.03709659 - time (sec): 28.71 - samples/sec: 2451.97 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-17 17:43:58,280 epoch 5 - iter 720/1445 - loss 0.03495719 - time (sec): 35.57 - samples/sec: 2450.94 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-17 17:44:05,351 epoch 5 - iter 864/1445 - loss 0.03454321 - time (sec): 42.64 - samples/sec: 2449.50 - lr: 0.000018 - momentum: 0.000000
140
+ 2023-10-17 17:44:12,501 epoch 5 - iter 1008/1445 - loss 0.03603597 - time (sec): 49.79 - samples/sec: 2433.62 - lr: 0.000018 - momentum: 0.000000
141
+ 2023-10-17 17:44:19,757 epoch 5 - iter 1152/1445 - loss 0.03646270 - time (sec): 57.04 - samples/sec: 2435.72 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-17 17:44:27,159 epoch 5 - iter 1296/1445 - loss 0.03650110 - time (sec): 64.45 - samples/sec: 2431.31 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-17 17:44:34,782 epoch 5 - iter 1440/1445 - loss 0.03803697 - time (sec): 72.07 - samples/sec: 2437.25 - lr: 0.000017 - momentum: 0.000000
144
+ 2023-10-17 17:44:35,026 ----------------------------------------------------------------------------------------------------
145
+ 2023-10-17 17:44:35,027 EPOCH 5 done: loss 0.0381 - lr: 0.000017
146
+ 2023-10-17 17:44:38,550 DEV : loss 0.1178504228591919 - f1-score (micro avg) 0.8518
147
+ 2023-10-17 17:44:38,569 ----------------------------------------------------------------------------------------------------
148
+ 2023-10-17 17:44:46,150 epoch 6 - iter 144/1445 - loss 0.02532486 - time (sec): 7.58 - samples/sec: 2270.25 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-17 17:44:53,390 epoch 6 - iter 288/1445 - loss 0.02904802 - time (sec): 14.82 - samples/sec: 2313.74 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-17 17:45:00,617 epoch 6 - iter 432/1445 - loss 0.02918864 - time (sec): 22.05 - samples/sec: 2378.19 - lr: 0.000016 - momentum: 0.000000
151
+ 2023-10-17 17:45:07,572 epoch 6 - iter 576/1445 - loss 0.02552071 - time (sec): 29.00 - samples/sec: 2348.34 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 17:45:15,347 epoch 6 - iter 720/1445 - loss 0.02625914 - time (sec): 36.78 - samples/sec: 2380.60 - lr: 0.000015 - momentum: 0.000000
153
+ 2023-10-17 17:45:22,880 epoch 6 - iter 864/1445 - loss 0.02735437 - time (sec): 44.31 - samples/sec: 2377.18 - lr: 0.000015 - momentum: 0.000000
154
+ 2023-10-17 17:45:30,013 epoch 6 - iter 1008/1445 - loss 0.02621190 - time (sec): 51.44 - samples/sec: 2380.53 - lr: 0.000014 - momentum: 0.000000
155
+ 2023-10-17 17:45:37,375 epoch 6 - iter 1152/1445 - loss 0.02598037 - time (sec): 58.80 - samples/sec: 2407.78 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-17 17:45:44,521 epoch 6 - iter 1296/1445 - loss 0.02707724 - time (sec): 65.95 - samples/sec: 2399.92 - lr: 0.000014 - momentum: 0.000000
157
+ 2023-10-17 17:45:51,591 epoch 6 - iter 1440/1445 - loss 0.02796616 - time (sec): 73.02 - samples/sec: 2407.42 - lr: 0.000013 - momentum: 0.000000
158
+ 2023-10-17 17:45:51,807 ----------------------------------------------------------------------------------------------------
159
+ 2023-10-17 17:45:51,807 EPOCH 6 done: loss 0.0280 - lr: 0.000013
160
+ 2023-10-17 17:45:55,246 DEV : loss 0.12150729447603226 - f1-score (micro avg) 0.8515
161
+ 2023-10-17 17:45:55,264 ----------------------------------------------------------------------------------------------------
162
+ 2023-10-17 17:46:02,158 epoch 7 - iter 144/1445 - loss 0.01685010 - time (sec): 6.89 - samples/sec: 2430.06 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-17 17:46:09,209 epoch 7 - iter 288/1445 - loss 0.01264635 - time (sec): 13.94 - samples/sec: 2462.29 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-17 17:46:16,421 epoch 7 - iter 432/1445 - loss 0.01653909 - time (sec): 21.16 - samples/sec: 2502.88 - lr: 0.000012 - momentum: 0.000000
165
+ 2023-10-17 17:46:23,521 epoch 7 - iter 576/1445 - loss 0.01929903 - time (sec): 28.26 - samples/sec: 2487.16 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-17 17:46:30,784 epoch 7 - iter 720/1445 - loss 0.01985335 - time (sec): 35.52 - samples/sec: 2493.20 - lr: 0.000012 - momentum: 0.000000
167
+ 2023-10-17 17:46:38,378 epoch 7 - iter 864/1445 - loss 0.02389990 - time (sec): 43.11 - samples/sec: 2459.10 - lr: 0.000011 - momentum: 0.000000
168
+ 2023-10-17 17:46:45,985 epoch 7 - iter 1008/1445 - loss 0.02333411 - time (sec): 50.72 - samples/sec: 2462.85 - lr: 0.000011 - momentum: 0.000000
169
+ 2023-10-17 17:46:53,205 epoch 7 - iter 1152/1445 - loss 0.02230595 - time (sec): 57.94 - samples/sec: 2440.83 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-17 17:47:00,241 epoch 7 - iter 1296/1445 - loss 0.02208829 - time (sec): 64.98 - samples/sec: 2444.66 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-17 17:47:07,359 epoch 7 - iter 1440/1445 - loss 0.02176833 - time (sec): 72.09 - samples/sec: 2437.16 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-17 17:47:07,585 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 17:47:07,586 EPOCH 7 done: loss 0.0219 - lr: 0.000010
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+ 2023-10-17 17:47:10,966 DEV : loss 0.12723445892333984 - f1-score (micro avg) 0.8684
175
+ 2023-10-17 17:47:10,985 saving best model
176
+ 2023-10-17 17:47:11,514 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 17:47:18,708 epoch 8 - iter 144/1445 - loss 0.01150973 - time (sec): 7.19 - samples/sec: 2544.39 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-17 17:47:25,677 epoch 8 - iter 288/1445 - loss 0.01301164 - time (sec): 14.15 - samples/sec: 2555.66 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-17 17:47:32,800 epoch 8 - iter 432/1445 - loss 0.01277090 - time (sec): 21.28 - samples/sec: 2514.23 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-17 17:47:39,780 epoch 8 - iter 576/1445 - loss 0.01323955 - time (sec): 28.26 - samples/sec: 2479.48 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-17 17:47:46,861 epoch 8 - iter 720/1445 - loss 0.01367934 - time (sec): 35.34 - samples/sec: 2505.38 - lr: 0.000008 - momentum: 0.000000
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+ 2023-10-17 17:47:53,573 epoch 8 - iter 864/1445 - loss 0.01302397 - time (sec): 42.05 - samples/sec: 2521.56 - lr: 0.000008 - momentum: 0.000000
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+ 2023-10-17 17:48:00,449 epoch 8 - iter 1008/1445 - loss 0.01325247 - time (sec): 48.93 - samples/sec: 2499.40 - lr: 0.000008 - momentum: 0.000000
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+ 2023-10-17 17:48:07,708 epoch 8 - iter 1152/1445 - loss 0.01320999 - time (sec): 56.19 - samples/sec: 2489.07 - lr: 0.000007 - momentum: 0.000000
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+ 2023-10-17 17:48:15,148 epoch 8 - iter 1296/1445 - loss 0.01416894 - time (sec): 63.63 - samples/sec: 2487.61 - lr: 0.000007 - momentum: 0.000000
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+ 2023-10-17 17:48:22,372 epoch 8 - iter 1440/1445 - loss 0.01469288 - time (sec): 70.85 - samples/sec: 2481.73 - lr: 0.000007 - momentum: 0.000000
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+ 2023-10-17 17:48:22,622 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 17:48:22,623 EPOCH 8 done: loss 0.0147 - lr: 0.000007
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+ 2023-10-17 17:48:25,928 DEV : loss 0.13977086544036865 - f1-score (micro avg) 0.8641
190
+ 2023-10-17 17:48:25,946 ----------------------------------------------------------------------------------------------------
191
+ 2023-10-17 17:48:33,142 epoch 9 - iter 144/1445 - loss 0.00822659 - time (sec): 7.19 - samples/sec: 2436.78 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-17 17:48:40,157 epoch 9 - iter 288/1445 - loss 0.00559292 - time (sec): 14.21 - samples/sec: 2471.90 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-17 17:48:47,291 epoch 9 - iter 432/1445 - loss 0.00857791 - time (sec): 21.34 - samples/sec: 2500.95 - lr: 0.000006 - momentum: 0.000000
194
+ 2023-10-17 17:48:54,475 epoch 9 - iter 576/1445 - loss 0.01013173 - time (sec): 28.53 - samples/sec: 2500.07 - lr: 0.000005 - momentum: 0.000000
195
+ 2023-10-17 17:49:01,473 epoch 9 - iter 720/1445 - loss 0.00984675 - time (sec): 35.53 - samples/sec: 2469.71 - lr: 0.000005 - momentum: 0.000000
196
+ 2023-10-17 17:49:08,496 epoch 9 - iter 864/1445 - loss 0.00969007 - time (sec): 42.55 - samples/sec: 2481.24 - lr: 0.000005 - momentum: 0.000000
197
+ 2023-10-17 17:49:15,662 epoch 9 - iter 1008/1445 - loss 0.00977286 - time (sec): 49.71 - samples/sec: 2478.89 - lr: 0.000004 - momentum: 0.000000
198
+ 2023-10-17 17:49:24,103 epoch 9 - iter 1152/1445 - loss 0.01047676 - time (sec): 58.16 - samples/sec: 2431.23 - lr: 0.000004 - momentum: 0.000000
199
+ 2023-10-17 17:49:31,147 epoch 9 - iter 1296/1445 - loss 0.00999327 - time (sec): 65.20 - samples/sec: 2423.11 - lr: 0.000004 - momentum: 0.000000
200
+ 2023-10-17 17:49:38,579 epoch 9 - iter 1440/1445 - loss 0.00998262 - time (sec): 72.63 - samples/sec: 2418.14 - lr: 0.000003 - momentum: 0.000000
201
+ 2023-10-17 17:49:38,821 ----------------------------------------------------------------------------------------------------
202
+ 2023-10-17 17:49:38,821 EPOCH 9 done: loss 0.0100 - lr: 0.000003
203
+ 2023-10-17 17:49:42,238 DEV : loss 0.14191032946109772 - f1-score (micro avg) 0.8661
204
+ 2023-10-17 17:49:42,256 ----------------------------------------------------------------------------------------------------
205
+ 2023-10-17 17:49:49,582 epoch 10 - iter 144/1445 - loss 0.00635513 - time (sec): 7.33 - samples/sec: 2531.30 - lr: 0.000003 - momentum: 0.000000
206
+ 2023-10-17 17:49:56,970 epoch 10 - iter 288/1445 - loss 0.00533428 - time (sec): 14.71 - samples/sec: 2412.38 - lr: 0.000003 - momentum: 0.000000
207
+ 2023-10-17 17:50:04,167 epoch 10 - iter 432/1445 - loss 0.00667599 - time (sec): 21.91 - samples/sec: 2408.36 - lr: 0.000002 - momentum: 0.000000
208
+ 2023-10-17 17:50:11,207 epoch 10 - iter 576/1445 - loss 0.00622294 - time (sec): 28.95 - samples/sec: 2412.67 - lr: 0.000002 - momentum: 0.000000
209
+ 2023-10-17 17:50:18,623 epoch 10 - iter 720/1445 - loss 0.00658126 - time (sec): 36.37 - samples/sec: 2430.04 - lr: 0.000002 - momentum: 0.000000
210
+ 2023-10-17 17:50:25,770 epoch 10 - iter 864/1445 - loss 0.00769149 - time (sec): 43.51 - samples/sec: 2457.20 - lr: 0.000001 - momentum: 0.000000
211
+ 2023-10-17 17:50:32,601 epoch 10 - iter 1008/1445 - loss 0.00793393 - time (sec): 50.34 - samples/sec: 2466.53 - lr: 0.000001 - momentum: 0.000000
212
+ 2023-10-17 17:50:39,315 epoch 10 - iter 1152/1445 - loss 0.00724425 - time (sec): 57.06 - samples/sec: 2475.80 - lr: 0.000001 - momentum: 0.000000
213
+ 2023-10-17 17:50:46,386 epoch 10 - iter 1296/1445 - loss 0.00766193 - time (sec): 64.13 - samples/sec: 2487.64 - lr: 0.000000 - momentum: 0.000000
214
+ 2023-10-17 17:50:53,224 epoch 10 - iter 1440/1445 - loss 0.00738923 - time (sec): 70.97 - samples/sec: 2472.81 - lr: 0.000000 - momentum: 0.000000
215
+ 2023-10-17 17:50:53,477 ----------------------------------------------------------------------------------------------------
216
+ 2023-10-17 17:50:53,477 EPOCH 10 done: loss 0.0074 - lr: 0.000000
217
+ 2023-10-17 17:50:56,778 DEV : loss 0.14468367397785187 - f1-score (micro avg) 0.8679
218
+ 2023-10-17 17:50:57,176 ----------------------------------------------------------------------------------------------------
219
+ 2023-10-17 17:50:57,177 Loading model from best epoch ...
220
+ 2023-10-17 17:50:58,557 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
221
+ 2023-10-17 17:51:01,444
222
+ Results:
223
+ - F-score (micro) 0.8493
224
+ - F-score (macro) 0.7512
225
+ - Accuracy 0.7473
226
+
227
+ By class:
228
+ precision recall f1-score support
229
+
230
+ PER 0.8596 0.8382 0.8487 482
231
+ LOC 0.9236 0.8712 0.8966 458
232
+ ORG 0.5849 0.4493 0.5082 69
233
+
234
+ micro avg 0.8733 0.8266 0.8493 1009
235
+ macro avg 0.7894 0.7195 0.7512 1009
236
+ weighted avg 0.8699 0.8266 0.8472 1009
237
+
238
+ 2023-10-17 17:51:01,445 ----------------------------------------------------------------------------------------------------