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hmbench-icdar-nl-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-5

Token Classification
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Dutch
sequence-tagger-model
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  1. best-model.pt +3 -0
  2. dev.tsv +0 -0
  3. final-model.pt +3 -0
  4. loss.tsv +11 -0
  5. runs/events.out.tfevents.1697132859.c8b2203b18a8.2408.8 +3 -0
  6. test.tsv +0 -0
  7. training.log +262 -0
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final-model.pt 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 17:54:34 0.0001 1.1029 0.2338 0.0000 0.0000 0.0000 0.0000
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+ 2 18:01:25 0.0001 0.1300 0.1119 0.7732 0.7572 0.7651 0.6319
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+ 3 18:08:14 0.0001 0.0781 0.0785 0.8551 0.8110 0.8324 0.7235
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+ 4 18:15:15 0.0001 0.0506 0.0786 0.9048 0.8048 0.8518 0.7541
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+ 5 18:22:12 0.0001 0.0353 0.0762 0.8824 0.8295 0.8552 0.7554
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+ 6 18:29:18 0.0001 0.0252 0.0881 0.8741 0.8605 0.8673 0.7800
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+ 7 18:36:15 0.0001 0.0207 0.0964 0.8782 0.8419 0.8597 0.7689
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+ 8 18:43:18 0.0000 0.0167 0.1077 0.8764 0.8347 0.8550 0.7601
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+ 9 18:50:13 0.0000 0.0136 0.1127 0.8781 0.8337 0.8553 0.7620
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+ 10 18:57:10 0.0000 0.0116 0.1159 0.8811 0.8347 0.8573 0.7644
runs/events.out.tfevents.1697132859.c8b2203b18a8.2408.8 ADDED
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test.tsv ADDED
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training.log ADDED
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+ 2023-10-12 17:47:39,484 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 17:47:39,486 Model: "SequenceTagger(
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+ (embeddings): ByT5Embeddings(
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+ (model): T5EncoderModel(
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+ (shared): Embedding(384, 1472)
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+ (encoder): T5Stack(
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+ (embed_tokens): Embedding(384, 1472)
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+ (block): ModuleList(
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+ (0): T5Block(
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+ (layer): ModuleList(
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+ (0): T5LayerSelfAttention(
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+ (SelfAttention): T5Attention(
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+ (q): Linear(in_features=1472, out_features=384, bias=False)
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+ (k): Linear(in_features=1472, out_features=384, bias=False)
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+ (v): Linear(in_features=1472, out_features=384, bias=False)
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+ (o): Linear(in_features=384, out_features=1472, bias=False)
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+ (relative_attention_bias): Embedding(32, 6)
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+ )
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+ (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ (1): T5LayerFF(
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+ (DenseReluDense): T5DenseGatedActDense(
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+ (wi_0): Linear(in_features=1472, out_features=3584, bias=False)
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+ (wi_1): Linear(in_features=1472, out_features=3584, bias=False)
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+ (wo): Linear(in_features=3584, out_features=1472, bias=False)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ (act): NewGELUActivation()
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+ )
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+ (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, 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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+ (1-11): 11 x T5Block(
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+ (layer): ModuleList(
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+ (0): T5LayerSelfAttention(
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+ (SelfAttention): T5Attention(
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+ (q): Linear(in_features=1472, out_features=384, bias=False)
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+ (k): Linear(in_features=1472, out_features=384, bias=False)
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+ (v): Linear(in_features=1472, out_features=384, bias=False)
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+ (o): Linear(in_features=384, out_features=1472, bias=False)
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+ )
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+ (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ (1): T5LayerFF(
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+ (DenseReluDense): T5DenseGatedActDense(
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+ (wi_0): Linear(in_features=1472, out_features=3584, bias=False)
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+ (wi_1): Linear(in_features=1472, out_features=3584, bias=False)
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+ (wo): Linear(in_features=3584, out_features=1472, bias=False)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ (act): NewGELUActivation()
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+ )
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+ (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, 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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+ (final_layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, 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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+ (locked_dropout): LockedDropout(p=0.5)
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+ (linear): Linear(in_features=1472, out_features=13, bias=True)
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+ (loss_function): CrossEntropyLoss()
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+ )"
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+ 2023-10-12 17:47:39,486 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 17:47:39,486 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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+ 2023-10-12 17:47:39,487 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 17:47:39,487 Train: 5777 sentences
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+ 2023-10-12 17:47:39,487 (train_with_dev=False, train_with_test=False)
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+ 2023-10-12 17:47:39,487 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 17:47:39,487 Training Params:
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+ 2023-10-12 17:47:39,487 - learning_rate: "0.00015"
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+ 2023-10-12 17:47:39,487 - mini_batch_size: "8"
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+ 2023-10-12 17:47:39,487 - max_epochs: "10"
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+ 2023-10-12 17:47:39,487 - shuffle: "True"
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+ 2023-10-12 17:47:39,487 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 17:47:39,487 Plugins:
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+ 2023-10-12 17:47:39,487 - TensorboardLogger
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+ 2023-10-12 17:47:39,487 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-12 17:47:39,487 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 17:47:39,488 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-12 17:47:39,488 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-12 17:47:39,488 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 17:47:39,488 Computation:
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+ 2023-10-12 17:47:39,488 - compute on device: cuda:0
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+ 2023-10-12 17:47:39,488 - embedding storage: none
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+ 2023-10-12 17:47:39,488 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 17:47:39,488 Model training base path: "hmbench-icdar/nl-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-5"
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+ 2023-10-12 17:47:39,488 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 17:47:39,488 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 17:47:39,488 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-12 17:48:18,952 epoch 1 - iter 72/723 - loss 2.54426887 - time (sec): 39.46 - samples/sec: 457.63 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-12 17:48:58,124 epoch 1 - iter 144/723 - loss 2.48027812 - time (sec): 78.63 - samples/sec: 459.08 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-12 17:49:36,310 epoch 1 - iter 216/723 - loss 2.32386957 - time (sec): 116.82 - samples/sec: 448.35 - lr: 0.000045 - momentum: 0.000000
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+ 2023-10-12 17:50:14,644 epoch 1 - iter 288/723 - loss 2.11630297 - time (sec): 155.15 - samples/sec: 451.57 - lr: 0.000060 - momentum: 0.000000
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+ 2023-10-12 17:50:53,387 epoch 1 - iter 360/723 - loss 1.89885572 - time (sec): 193.90 - samples/sec: 452.96 - lr: 0.000074 - momentum: 0.000000
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+ 2023-10-12 17:51:31,686 epoch 1 - iter 432/723 - loss 1.68800148 - time (sec): 232.20 - samples/sec: 450.91 - lr: 0.000089 - momentum: 0.000000
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+ 2023-10-12 17:52:11,717 epoch 1 - iter 504/723 - loss 1.48803245 - time (sec): 272.23 - samples/sec: 449.72 - lr: 0.000104 - momentum: 0.000000
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+ 2023-10-12 17:52:51,394 epoch 1 - iter 576/723 - loss 1.34139358 - time (sec): 311.90 - samples/sec: 446.33 - lr: 0.000119 - momentum: 0.000000
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+ 2023-10-12 17:53:31,923 epoch 1 - iter 648/723 - loss 1.21633560 - time (sec): 352.43 - samples/sec: 445.27 - lr: 0.000134 - momentum: 0.000000
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+ 2023-10-12 17:54:12,600 epoch 1 - iter 720/723 - loss 1.10698770 - time (sec): 393.11 - samples/sec: 446.34 - lr: 0.000149 - momentum: 0.000000
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+ 2023-10-12 17:54:13,966 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 17:54:13,967 EPOCH 1 done: loss 1.1029 - lr: 0.000149
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+ 2023-10-12 17:54:34,128 DEV : loss 0.23375172913074493 - f1-score (micro avg) 0.0
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+ 2023-10-12 17:54:34,158 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 17:55:12,547 epoch 2 - iter 72/723 - loss 0.16731668 - time (sec): 38.39 - samples/sec: 454.44 - lr: 0.000148 - momentum: 0.000000
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+ 2023-10-12 17:55:51,979 epoch 2 - iter 144/723 - loss 0.15482755 - time (sec): 77.82 - samples/sec: 455.74 - lr: 0.000147 - momentum: 0.000000
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+ 2023-10-12 17:56:29,875 epoch 2 - iter 216/723 - loss 0.15195012 - time (sec): 115.71 - samples/sec: 449.25 - lr: 0.000145 - momentum: 0.000000
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+ 2023-10-12 17:57:09,651 epoch 2 - iter 288/723 - loss 0.14875477 - time (sec): 155.49 - samples/sec: 448.78 - lr: 0.000143 - momentum: 0.000000
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+ 2023-10-12 17:57:48,585 epoch 2 - iter 360/723 - loss 0.14422756 - time (sec): 194.42 - samples/sec: 448.71 - lr: 0.000142 - momentum: 0.000000
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+ 2023-10-12 17:58:27,126 epoch 2 - iter 432/723 - loss 0.14054029 - time (sec): 232.97 - samples/sec: 449.87 - lr: 0.000140 - momentum: 0.000000
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+ 2023-10-12 17:59:05,553 epoch 2 - iter 504/723 - loss 0.14009191 - time (sec): 271.39 - samples/sec: 449.88 - lr: 0.000138 - momentum: 0.000000
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+ 2023-10-12 17:59:44,287 epoch 2 - iter 576/723 - loss 0.13710606 - time (sec): 310.13 - samples/sec: 451.15 - lr: 0.000137 - momentum: 0.000000
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+ 2023-10-12 18:00:23,907 epoch 2 - iter 648/723 - loss 0.13415472 - time (sec): 349.75 - samples/sec: 452.30 - lr: 0.000135 - momentum: 0.000000
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+ 2023-10-12 18:01:03,365 epoch 2 - iter 720/723 - loss 0.13018803 - time (sec): 389.20 - samples/sec: 451.19 - lr: 0.000133 - momentum: 0.000000
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+ 2023-10-12 18:01:04,560 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 18:01:04,560 EPOCH 2 done: loss 0.1300 - lr: 0.000133
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+ 2023-10-12 18:01:25,127 DEV : loss 0.11187870055437088 - f1-score (micro avg) 0.7651
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+ 2023-10-12 18:01:25,159 saving best model
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+ 2023-10-12 18:01:26,051 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 18:02:05,492 epoch 3 - iter 72/723 - loss 0.08592561 - time (sec): 39.44 - samples/sec: 453.97 - lr: 0.000132 - momentum: 0.000000
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+ 2023-10-12 18:02:44,351 epoch 3 - iter 144/723 - loss 0.08297721 - time (sec): 78.30 - samples/sec: 459.33 - lr: 0.000130 - momentum: 0.000000
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+ 2023-10-12 18:03:22,483 epoch 3 - iter 216/723 - loss 0.08326043 - time (sec): 116.43 - samples/sec: 459.71 - lr: 0.000128 - momentum: 0.000000
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+ 2023-10-12 18:04:00,111 epoch 3 - iter 288/723 - loss 0.08082376 - time (sec): 154.06 - samples/sec: 461.41 - lr: 0.000127 - momentum: 0.000000
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+ 2023-10-12 18:04:38,650 epoch 3 - iter 360/723 - loss 0.08052324 - time (sec): 192.60 - samples/sec: 462.31 - lr: 0.000125 - momentum: 0.000000
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+ 2023-10-12 18:05:17,244 epoch 3 - iter 432/723 - loss 0.08037304 - time (sec): 231.19 - samples/sec: 466.15 - lr: 0.000123 - momentum: 0.000000
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+ 2023-10-12 18:05:56,024 epoch 3 - iter 504/723 - loss 0.07975153 - time (sec): 269.97 - samples/sec: 463.63 - lr: 0.000122 - momentum: 0.000000
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+ 2023-10-12 18:06:34,552 epoch 3 - iter 576/723 - loss 0.07988037 - time (sec): 308.50 - samples/sec: 460.86 - lr: 0.000120 - momentum: 0.000000
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+ 2023-10-12 18:07:12,746 epoch 3 - iter 648/723 - loss 0.07895931 - time (sec): 346.69 - samples/sec: 457.82 - lr: 0.000118 - momentum: 0.000000
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+ 2023-10-12 18:07:50,890 epoch 3 - iter 720/723 - loss 0.07794918 - time (sec): 384.84 - samples/sec: 456.08 - lr: 0.000117 - momentum: 0.000000
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+ 2023-10-12 18:07:52,195 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 18:07:52,195 EPOCH 3 done: loss 0.0781 - lr: 0.000117
139
+ 2023-10-12 18:08:14,169 DEV : loss 0.0785035490989685 - f1-score (micro avg) 0.8324
140
+ 2023-10-12 18:08:14,200 saving best model
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+ 2023-10-12 18:08:16,769 ----------------------------------------------------------------------------------------------------
142
+ 2023-10-12 18:08:58,498 epoch 4 - iter 72/723 - loss 0.05759562 - time (sec): 41.72 - samples/sec: 429.84 - lr: 0.000115 - momentum: 0.000000
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+ 2023-10-12 18:09:39,681 epoch 4 - iter 144/723 - loss 0.05741867 - time (sec): 82.91 - samples/sec: 419.13 - lr: 0.000113 - momentum: 0.000000
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+ 2023-10-12 18:10:20,375 epoch 4 - iter 216/723 - loss 0.05415096 - time (sec): 123.60 - samples/sec: 424.18 - lr: 0.000112 - momentum: 0.000000
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+ 2023-10-12 18:11:00,926 epoch 4 - iter 288/723 - loss 0.05445295 - time (sec): 164.15 - samples/sec: 435.34 - lr: 0.000110 - momentum: 0.000000
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+ 2023-10-12 18:11:39,771 epoch 4 - iter 360/723 - loss 0.05261383 - time (sec): 203.00 - samples/sec: 439.52 - lr: 0.000108 - momentum: 0.000000
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+ 2023-10-12 18:12:18,198 epoch 4 - iter 432/723 - loss 0.05135342 - time (sec): 241.42 - samples/sec: 439.86 - lr: 0.000107 - momentum: 0.000000
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+ 2023-10-12 18:12:57,140 epoch 4 - iter 504/723 - loss 0.05062735 - time (sec): 280.37 - samples/sec: 439.37 - lr: 0.000105 - momentum: 0.000000
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+ 2023-10-12 18:13:36,398 epoch 4 - iter 576/723 - loss 0.04994851 - time (sec): 319.62 - samples/sec: 442.45 - lr: 0.000103 - momentum: 0.000000
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+ 2023-10-12 18:14:15,270 epoch 4 - iter 648/723 - loss 0.05141664 - time (sec): 358.50 - samples/sec: 443.12 - lr: 0.000102 - momentum: 0.000000
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+ 2023-10-12 18:14:53,270 epoch 4 - iter 720/723 - loss 0.05052236 - time (sec): 396.50 - samples/sec: 443.46 - lr: 0.000100 - momentum: 0.000000
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+ 2023-10-12 18:14:54,369 ----------------------------------------------------------------------------------------------------
153
+ 2023-10-12 18:14:54,369 EPOCH 4 done: loss 0.0506 - lr: 0.000100
154
+ 2023-10-12 18:15:15,356 DEV : loss 0.07858217507600784 - f1-score (micro avg) 0.8518
155
+ 2023-10-12 18:15:15,388 saving best model
156
+ 2023-10-12 18:15:17,971 ----------------------------------------------------------------------------------------------------
157
+ 2023-10-12 18:15:59,176 epoch 5 - iter 72/723 - loss 0.03874655 - time (sec): 41.20 - samples/sec: 458.15 - lr: 0.000098 - momentum: 0.000000
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+ 2023-10-12 18:16:39,098 epoch 5 - iter 144/723 - loss 0.03347685 - time (sec): 81.12 - samples/sec: 447.35 - lr: 0.000097 - momentum: 0.000000
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+ 2023-10-12 18:17:16,677 epoch 5 - iter 216/723 - loss 0.03299884 - time (sec): 118.70 - samples/sec: 436.84 - lr: 0.000095 - momentum: 0.000000
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+ 2023-10-12 18:17:54,188 epoch 5 - iter 288/723 - loss 0.03250430 - time (sec): 156.21 - samples/sec: 435.65 - lr: 0.000093 - momentum: 0.000000
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+ 2023-10-12 18:18:33,812 epoch 5 - iter 360/723 - loss 0.03475104 - time (sec): 195.84 - samples/sec: 442.33 - lr: 0.000092 - momentum: 0.000000
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+ 2023-10-12 18:19:12,566 epoch 5 - iter 432/723 - loss 0.03403611 - time (sec): 234.59 - samples/sec: 443.37 - lr: 0.000090 - momentum: 0.000000
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+ 2023-10-12 18:19:52,453 epoch 5 - iter 504/723 - loss 0.03407641 - time (sec): 274.48 - samples/sec: 446.40 - lr: 0.000088 - momentum: 0.000000
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+ 2023-10-12 18:20:31,367 epoch 5 - iter 576/723 - loss 0.03399255 - time (sec): 313.39 - samples/sec: 447.69 - lr: 0.000087 - momentum: 0.000000
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+ 2023-10-12 18:21:10,192 epoch 5 - iter 648/723 - loss 0.03424309 - time (sec): 352.22 - samples/sec: 448.30 - lr: 0.000085 - momentum: 0.000000
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+ 2023-10-12 18:21:48,915 epoch 5 - iter 720/723 - loss 0.03511790 - time (sec): 390.94 - samples/sec: 448.58 - lr: 0.000083 - momentum: 0.000000
167
+ 2023-10-12 18:21:50,307 ----------------------------------------------------------------------------------------------------
168
+ 2023-10-12 18:21:50,308 EPOCH 5 done: loss 0.0353 - lr: 0.000083
169
+ 2023-10-12 18:22:12,540 DEV : loss 0.0762009546160698 - f1-score (micro avg) 0.8552
170
+ 2023-10-12 18:22:12,572 saving best model
171
+ 2023-10-12 18:22:15,194 ----------------------------------------------------------------------------------------------------
172
+ 2023-10-12 18:22:54,690 epoch 6 - iter 72/723 - loss 0.02292044 - time (sec): 39.49 - samples/sec: 435.70 - lr: 0.000082 - momentum: 0.000000
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+ 2023-10-12 18:23:33,953 epoch 6 - iter 144/723 - loss 0.02437413 - time (sec): 78.75 - samples/sec: 438.41 - lr: 0.000080 - momentum: 0.000000
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+ 2023-10-12 18:24:13,910 epoch 6 - iter 216/723 - loss 0.02690559 - time (sec): 118.71 - samples/sec: 441.44 - lr: 0.000078 - momentum: 0.000000
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+ 2023-10-12 18:24:54,865 epoch 6 - iter 288/723 - loss 0.02616528 - time (sec): 159.67 - samples/sec: 440.99 - lr: 0.000077 - momentum: 0.000000
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+ 2023-10-12 18:25:34,380 epoch 6 - iter 360/723 - loss 0.02600927 - time (sec): 199.18 - samples/sec: 442.46 - lr: 0.000075 - momentum: 0.000000
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+ 2023-10-12 18:26:14,694 epoch 6 - iter 432/723 - loss 0.02342678 - time (sec): 239.50 - samples/sec: 444.95 - lr: 0.000073 - momentum: 0.000000
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+ 2023-10-12 18:26:55,637 epoch 6 - iter 504/723 - loss 0.02526975 - time (sec): 280.44 - samples/sec: 443.58 - lr: 0.000072 - momentum: 0.000000
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+ 2023-10-12 18:27:35,655 epoch 6 - iter 576/723 - loss 0.02479113 - time (sec): 320.46 - samples/sec: 439.85 - lr: 0.000070 - momentum: 0.000000
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+ 2023-10-12 18:28:15,107 epoch 6 - iter 648/723 - loss 0.02441460 - time (sec): 359.91 - samples/sec: 438.05 - lr: 0.000068 - momentum: 0.000000
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+ 2023-10-12 18:28:56,224 epoch 6 - iter 720/723 - loss 0.02520439 - time (sec): 401.03 - samples/sec: 438.06 - lr: 0.000067 - momentum: 0.000000
182
+ 2023-10-12 18:28:57,424 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 18:28:57,425 EPOCH 6 done: loss 0.0252 - lr: 0.000067
184
+ 2023-10-12 18:29:18,375 DEV : loss 0.08811366558074951 - f1-score (micro avg) 0.8673
185
+ 2023-10-12 18:29:18,405 saving best model
186
+ 2023-10-12 18:29:20,978 ----------------------------------------------------------------------------------------------------
187
+ 2023-10-12 18:30:00,020 epoch 7 - iter 72/723 - loss 0.02375220 - time (sec): 39.04 - samples/sec: 451.86 - lr: 0.000065 - momentum: 0.000000
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+ 2023-10-12 18:30:39,390 epoch 7 - iter 144/723 - loss 0.02317751 - time (sec): 78.41 - samples/sec: 453.74 - lr: 0.000063 - momentum: 0.000000
189
+ 2023-10-12 18:31:17,750 epoch 7 - iter 216/723 - loss 0.02191170 - time (sec): 116.77 - samples/sec: 446.14 - lr: 0.000062 - momentum: 0.000000
190
+ 2023-10-12 18:31:56,342 epoch 7 - iter 288/723 - loss 0.02214116 - time (sec): 155.36 - samples/sec: 441.87 - lr: 0.000060 - momentum: 0.000000
191
+ 2023-10-12 18:32:35,819 epoch 7 - iter 360/723 - loss 0.02354976 - time (sec): 194.84 - samples/sec: 445.48 - lr: 0.000058 - momentum: 0.000000
192
+ 2023-10-12 18:33:15,315 epoch 7 - iter 432/723 - loss 0.02157574 - time (sec): 234.33 - samples/sec: 444.74 - lr: 0.000057 - momentum: 0.000000
193
+ 2023-10-12 18:33:54,392 epoch 7 - iter 504/723 - loss 0.02134366 - time (sec): 273.41 - samples/sec: 445.97 - lr: 0.000055 - momentum: 0.000000
194
+ 2023-10-12 18:34:33,498 epoch 7 - iter 576/723 - loss 0.02118785 - time (sec): 312.51 - samples/sec: 444.59 - lr: 0.000053 - momentum: 0.000000
195
+ 2023-10-12 18:35:12,523 epoch 7 - iter 648/723 - loss 0.02048925 - time (sec): 351.54 - samples/sec: 445.23 - lr: 0.000052 - momentum: 0.000000
196
+ 2023-10-12 18:35:52,164 epoch 7 - iter 720/723 - loss 0.02077841 - time (sec): 391.18 - samples/sec: 448.61 - lr: 0.000050 - momentum: 0.000000
197
+ 2023-10-12 18:35:53,549 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 18:35:53,550 EPOCH 7 done: loss 0.0207 - lr: 0.000050
199
+ 2023-10-12 18:36:15,769 DEV : loss 0.09639902412891388 - f1-score (micro avg) 0.8597
200
+ 2023-10-12 18:36:15,806 ----------------------------------------------------------------------------------------------------
201
+ 2023-10-12 18:36:57,015 epoch 8 - iter 72/723 - loss 0.01788093 - time (sec): 41.21 - samples/sec: 450.42 - lr: 0.000048 - momentum: 0.000000
202
+ 2023-10-12 18:37:36,612 epoch 8 - iter 144/723 - loss 0.01763665 - time (sec): 80.80 - samples/sec: 444.40 - lr: 0.000047 - momentum: 0.000000
203
+ 2023-10-12 18:38:16,755 epoch 8 - iter 216/723 - loss 0.01670632 - time (sec): 120.95 - samples/sec: 436.75 - lr: 0.000045 - momentum: 0.000000
204
+ 2023-10-12 18:38:57,813 epoch 8 - iter 288/723 - loss 0.01563682 - time (sec): 162.00 - samples/sec: 444.00 - lr: 0.000043 - momentum: 0.000000
205
+ 2023-10-12 18:39:37,387 epoch 8 - iter 360/723 - loss 0.01520499 - time (sec): 201.58 - samples/sec: 443.01 - lr: 0.000042 - momentum: 0.000000
206
+ 2023-10-12 18:40:16,403 epoch 8 - iter 432/723 - loss 0.01554144 - time (sec): 240.59 - samples/sec: 439.58 - lr: 0.000040 - momentum: 0.000000
207
+ 2023-10-12 18:40:55,597 epoch 8 - iter 504/723 - loss 0.01575127 - time (sec): 279.79 - samples/sec: 439.79 - lr: 0.000038 - momentum: 0.000000
208
+ 2023-10-12 18:41:34,231 epoch 8 - iter 576/723 - loss 0.01547145 - time (sec): 318.42 - samples/sec: 438.17 - lr: 0.000037 - momentum: 0.000000
209
+ 2023-10-12 18:42:14,603 epoch 8 - iter 648/723 - loss 0.01719346 - time (sec): 358.79 - samples/sec: 440.09 - lr: 0.000035 - momentum: 0.000000
210
+ 2023-10-12 18:42:54,038 epoch 8 - iter 720/723 - loss 0.01672576 - time (sec): 398.23 - samples/sec: 441.26 - lr: 0.000033 - momentum: 0.000000
211
+ 2023-10-12 18:42:55,161 ----------------------------------------------------------------------------------------------------
212
+ 2023-10-12 18:42:55,161 EPOCH 8 done: loss 0.0167 - lr: 0.000033
213
+ 2023-10-12 18:43:18,087 DEV : loss 0.10770395398139954 - f1-score (micro avg) 0.855
214
+ 2023-10-12 18:43:18,120 ----------------------------------------------------------------------------------------------------
215
+ 2023-10-12 18:43:58,068 epoch 9 - iter 72/723 - loss 0.00592187 - time (sec): 39.95 - samples/sec: 460.70 - lr: 0.000032 - momentum: 0.000000
216
+ 2023-10-12 18:44:37,416 epoch 9 - iter 144/723 - loss 0.01701284 - time (sec): 79.29 - samples/sec: 469.13 - lr: 0.000030 - momentum: 0.000000
217
+ 2023-10-12 18:45:16,392 epoch 9 - iter 216/723 - loss 0.01704092 - time (sec): 118.27 - samples/sec: 465.48 - lr: 0.000028 - momentum: 0.000000
218
+ 2023-10-12 18:45:54,208 epoch 9 - iter 288/723 - loss 0.01622595 - time (sec): 156.09 - samples/sec: 455.77 - lr: 0.000027 - momentum: 0.000000
219
+ 2023-10-12 18:46:32,450 epoch 9 - iter 360/723 - loss 0.01556455 - time (sec): 194.33 - samples/sec: 447.69 - lr: 0.000025 - momentum: 0.000000
220
+ 2023-10-12 18:47:11,398 epoch 9 - iter 432/723 - loss 0.01494819 - time (sec): 233.28 - samples/sec: 449.75 - lr: 0.000023 - momentum: 0.000000
221
+ 2023-10-12 18:47:50,162 epoch 9 - iter 504/723 - loss 0.01504915 - time (sec): 272.04 - samples/sec: 450.43 - lr: 0.000022 - momentum: 0.000000
222
+ 2023-10-12 18:48:29,909 epoch 9 - iter 576/723 - loss 0.01463468 - time (sec): 311.79 - samples/sec: 453.71 - lr: 0.000020 - momentum: 0.000000
223
+ 2023-10-12 18:49:08,405 epoch 9 - iter 648/723 - loss 0.01388108 - time (sec): 350.28 - samples/sec: 452.53 - lr: 0.000018 - momentum: 0.000000
224
+ 2023-10-12 18:49:50,026 epoch 9 - iter 720/723 - loss 0.01368723 - time (sec): 391.90 - samples/sec: 448.25 - lr: 0.000017 - momentum: 0.000000
225
+ 2023-10-12 18:49:51,219 ----------------------------------------------------------------------------------------------------
226
+ 2023-10-12 18:49:51,220 EPOCH 9 done: loss 0.0136 - lr: 0.000017
227
+ 2023-10-12 18:50:13,188 DEV : loss 0.11267418414354324 - f1-score (micro avg) 0.8553
228
+ 2023-10-12 18:50:13,220 ----------------------------------------------------------------------------------------------------
229
+ 2023-10-12 18:50:52,654 epoch 10 - iter 72/723 - loss 0.00826221 - time (sec): 39.43 - samples/sec: 456.87 - lr: 0.000015 - momentum: 0.000000
230
+ 2023-10-12 18:51:29,673 epoch 10 - iter 144/723 - loss 0.00855540 - time (sec): 76.45 - samples/sec: 441.22 - lr: 0.000013 - momentum: 0.000000
231
+ 2023-10-12 18:52:07,710 epoch 10 - iter 216/723 - loss 0.00947687 - time (sec): 114.49 - samples/sec: 442.76 - lr: 0.000012 - momentum: 0.000000
232
+ 2023-10-12 18:52:47,939 epoch 10 - iter 288/723 - loss 0.01177807 - time (sec): 154.71 - samples/sec: 446.72 - lr: 0.000010 - momentum: 0.000000
233
+ 2023-10-12 18:53:27,103 epoch 10 - iter 360/723 - loss 0.01109256 - time (sec): 193.88 - samples/sec: 445.15 - lr: 0.000008 - momentum: 0.000000
234
+ 2023-10-12 18:54:07,126 epoch 10 - iter 432/723 - loss 0.01009327 - time (sec): 233.90 - samples/sec: 447.66 - lr: 0.000007 - momentum: 0.000000
235
+ 2023-10-12 18:54:48,429 epoch 10 - iter 504/723 - loss 0.01055010 - time (sec): 275.20 - samples/sec: 447.94 - lr: 0.000005 - momentum: 0.000000
236
+ 2023-10-12 18:55:28,401 epoch 10 - iter 576/723 - loss 0.01002322 - time (sec): 315.18 - samples/sec: 444.09 - lr: 0.000003 - momentum: 0.000000
237
+ 2023-10-12 18:56:07,902 epoch 10 - iter 648/723 - loss 0.01138945 - time (sec): 354.68 - samples/sec: 444.59 - lr: 0.000002 - momentum: 0.000000
238
+ 2023-10-12 18:56:48,588 epoch 10 - iter 720/723 - loss 0.01160600 - time (sec): 395.36 - samples/sec: 444.50 - lr: 0.000000 - momentum: 0.000000
239
+ 2023-10-12 18:56:49,706 ----------------------------------------------------------------------------------------------------
240
+ 2023-10-12 18:56:49,707 EPOCH 10 done: loss 0.0116 - lr: 0.000000
241
+ 2023-10-12 18:57:10,758 DEV : loss 0.11588139832019806 - f1-score (micro avg) 0.8573
242
+ 2023-10-12 18:57:11,656 ----------------------------------------------------------------------------------------------------
243
+ 2023-10-12 18:57:11,658 Loading model from best epoch ...
244
+ 2023-10-12 18:57:15,259 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
245
+ 2023-10-12 18:57:35,839
246
+ Results:
247
+ - F-score (micro) 0.858
248
+ - F-score (macro) 0.7492
249
+ - Accuracy 0.7592
250
+
251
+ By class:
252
+ precision recall f1-score support
253
+
254
+ PER 0.8376 0.8880 0.8620 482
255
+ LOC 0.9273 0.8908 0.9087 458
256
+ ORG 0.5082 0.4493 0.4769 69
257
+
258
+ micro avg 0.8567 0.8593 0.8580 1009
259
+ macro avg 0.7577 0.7427 0.7492 1009
260
+ weighted avg 0.8558 0.8593 0.8569 1009
261
+
262
+ 2023-10-12 18:57:35,840 ----------------------------------------------------------------------------------------------------