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hmbench-newseye-de-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-3

Token Classification
Flair
PyTorch
TensorBoard
German
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.1696991760.6d4c7681f95b.1253.8 +3 -0
  6. test.tsv +0 -0
  7. training.log +263 -0
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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 02:59:16 0.0001 0.9578 0.1386 0.0000 0.0000 0.0000 0.0000
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+ 2 03:22:49 0.0001 0.1859 0.1448 0.2483 0.4129 0.3101 0.1841
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+ 3 03:46:38 0.0001 0.1044 0.1813 0.2527 0.5777 0.3516 0.2142
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+ 4 04:09:59 0.0001 0.0701 0.2580 0.2657 0.5208 0.3519 0.2148
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+ 5 04:33:52 0.0001 0.0531 0.3203 0.2580 0.5814 0.3574 0.2191
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+ 6 04:57:23 0.0001 0.0378 0.4134 0.2425 0.5966 0.3448 0.2100
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+ 7 05:21:05 0.0001 0.0310 0.3856 0.2546 0.5549 0.3490 0.2129
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+ 8 05:44:21 0.0000 0.0219 0.4008 0.2941 0.5909 0.3927 0.2457
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+ 9 06:07:57 0.0000 0.0156 0.4159 0.2887 0.5928 0.3883 0.2426
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+ 10 06:31:28 0.0000 0.0112 0.4330 0.2936 0.5833 0.3906 0.2443
runs/events.out.tfevents.1696991760.6d4c7681f95b.1253.8 ADDED
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test.tsv ADDED
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training.log ADDED
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+ 2023-10-11 02:36:00,775 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 02:36:00,778 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=17, bias=True)
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+ (loss_function): CrossEntropyLoss()
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+ )"
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+ 2023-10-11 02:36:00,778 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 02:36:00,778 MultiCorpus: 20847 train + 1123 dev + 3350 test sentences
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+ - NER_HIPE_2022 Corpus: 20847 train + 1123 dev + 3350 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/de/with_doc_seperator
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+ 2023-10-11 02:36:00,778 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 02:36:00,778 Train: 20847 sentences
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+ 2023-10-11 02:36:00,779 (train_with_dev=False, train_with_test=False)
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+ 2023-10-11 02:36:00,779 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 02:36:00,779 Training Params:
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+ 2023-10-11 02:36:00,779 - learning_rate: "0.00015"
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+ 2023-10-11 02:36:00,779 - mini_batch_size: "8"
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+ 2023-10-11 02:36:00,779 - max_epochs: "10"
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+ 2023-10-11 02:36:00,779 - shuffle: "True"
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+ 2023-10-11 02:36:00,779 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 02:36:00,779 Plugins:
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+ 2023-10-11 02:36:00,779 - TensorboardLogger
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+ 2023-10-11 02:36:00,779 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-11 02:36:00,779 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 02:36:00,779 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-11 02:36:00,780 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-11 02:36:00,780 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 02:36:00,780 Computation:
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+ 2023-10-11 02:36:00,780 - compute on device: cuda:0
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+ 2023-10-11 02:36:00,780 - embedding storage: none
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+ 2023-10-11 02:36:00,780 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 02:36:00,780 Model training base path: "hmbench-newseye/de-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-3"
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+ 2023-10-11 02:36:00,780 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 02:36:00,780 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 02:36:00,780 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-11 02:38:18,607 epoch 1 - iter 260/2606 - loss 2.79875312 - time (sec): 137.82 - samples/sec: 270.82 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-11 02:40:38,845 epoch 1 - iter 520/2606 - loss 2.53797710 - time (sec): 278.06 - samples/sec: 275.79 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-11 02:42:54,895 epoch 1 - iter 780/2606 - loss 2.17089096 - time (sec): 414.11 - samples/sec: 273.19 - lr: 0.000045 - momentum: 0.000000
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+ 2023-10-11 02:45:09,429 epoch 1 - iter 1040/2606 - loss 1.79817367 - time (sec): 548.65 - samples/sec: 272.16 - lr: 0.000060 - momentum: 0.000000
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+ 2023-10-11 02:47:25,050 epoch 1 - iter 1300/2606 - loss 1.53189687 - time (sec): 684.27 - samples/sec: 273.25 - lr: 0.000075 - momentum: 0.000000
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+ 2023-10-11 02:49:38,076 epoch 1 - iter 1560/2606 - loss 1.35818094 - time (sec): 817.29 - samples/sec: 272.98 - lr: 0.000090 - momentum: 0.000000
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+ 2023-10-11 02:51:51,661 epoch 1 - iter 1820/2606 - loss 1.22736080 - time (sec): 950.88 - samples/sec: 271.45 - lr: 0.000105 - momentum: 0.000000
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+ 2023-10-11 02:54:05,050 epoch 1 - iter 2080/2606 - loss 1.12807500 - time (sec): 1084.27 - samples/sec: 269.35 - lr: 0.000120 - momentum: 0.000000
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+ 2023-10-11 02:56:20,813 epoch 1 - iter 2340/2606 - loss 1.03625081 - time (sec): 1220.03 - samples/sec: 270.57 - lr: 0.000135 - momentum: 0.000000
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+ 2023-10-11 02:58:36,030 epoch 1 - iter 2600/2606 - loss 0.95912226 - time (sec): 1355.25 - samples/sec: 270.56 - lr: 0.000150 - momentum: 0.000000
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+ 2023-10-11 02:58:39,080 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 02:58:39,080 EPOCH 1 done: loss 0.9578 - lr: 0.000150
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+ 2023-10-11 02:59:16,006 DEV : loss 0.1385965347290039 - f1-score (micro avg) 0.0
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+ 2023-10-11 02:59:16,068 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 03:01:33,372 epoch 2 - iter 260/2606 - loss 0.23906488 - time (sec): 137.30 - samples/sec: 268.82 - lr: 0.000148 - momentum: 0.000000
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+ 2023-10-11 03:03:48,996 epoch 2 - iter 520/2606 - loss 0.22848489 - time (sec): 272.92 - samples/sec: 268.13 - lr: 0.000147 - momentum: 0.000000
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+ 2023-10-11 03:06:09,371 epoch 2 - iter 780/2606 - loss 0.23177752 - time (sec): 413.30 - samples/sec: 272.39 - lr: 0.000145 - momentum: 0.000000
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+ 2023-10-11 03:08:29,617 epoch 2 - iter 1040/2606 - loss 0.22529060 - time (sec): 553.55 - samples/sec: 269.39 - lr: 0.000143 - momentum: 0.000000
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+ 2023-10-11 03:10:46,687 epoch 2 - iter 1300/2606 - loss 0.21839832 - time (sec): 690.62 - samples/sec: 266.76 - lr: 0.000142 - momentum: 0.000000
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+ 2023-10-11 03:13:03,201 epoch 2 - iter 1560/2606 - loss 0.20884696 - time (sec): 827.13 - samples/sec: 267.47 - lr: 0.000140 - momentum: 0.000000
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+ 2023-10-11 03:15:16,019 epoch 2 - iter 1820/2606 - loss 0.20568470 - time (sec): 959.95 - samples/sec: 265.71 - lr: 0.000138 - momentum: 0.000000
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+ 2023-10-11 03:17:32,731 epoch 2 - iter 2080/2606 - loss 0.19850457 - time (sec): 1096.66 - samples/sec: 265.70 - lr: 0.000137 - momentum: 0.000000
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+ 2023-10-11 03:19:49,480 epoch 2 - iter 2340/2606 - loss 0.19252617 - time (sec): 1233.41 - samples/sec: 267.36 - lr: 0.000135 - momentum: 0.000000
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+ 2023-10-11 03:22:04,628 epoch 2 - iter 2600/2606 - loss 0.18624742 - time (sec): 1368.56 - samples/sec: 267.88 - lr: 0.000133 - momentum: 0.000000
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+ 2023-10-11 03:22:07,610 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 03:22:07,610 EPOCH 2 done: loss 0.1859 - lr: 0.000133
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+ 2023-10-11 03:22:49,339 DEV : loss 0.14479756355285645 - f1-score (micro avg) 0.3101
125
+ 2023-10-11 03:22:49,395 saving best model
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+ 2023-10-11 03:22:50,414 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 03:25:06,167 epoch 3 - iter 260/2606 - loss 0.10408270 - time (sec): 135.75 - samples/sec: 257.11 - lr: 0.000132 - momentum: 0.000000
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+ 2023-10-11 03:27:23,435 epoch 3 - iter 520/2606 - loss 0.10809063 - time (sec): 273.02 - samples/sec: 260.19 - lr: 0.000130 - momentum: 0.000000
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+ 2023-10-11 03:29:40,284 epoch 3 - iter 780/2606 - loss 0.10276919 - time (sec): 409.87 - samples/sec: 260.29 - lr: 0.000128 - momentum: 0.000000
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+ 2023-10-11 03:32:00,756 epoch 3 - iter 1040/2606 - loss 0.10650096 - time (sec): 550.34 - samples/sec: 264.48 - lr: 0.000127 - momentum: 0.000000
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+ 2023-10-11 03:34:19,482 epoch 3 - iter 1300/2606 - loss 0.10951685 - time (sec): 689.07 - samples/sec: 266.61 - lr: 0.000125 - momentum: 0.000000
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+ 2023-10-11 03:36:39,185 epoch 3 - iter 1560/2606 - loss 0.10633073 - time (sec): 828.77 - samples/sec: 264.78 - lr: 0.000123 - momentum: 0.000000
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+ 2023-10-11 03:38:59,124 epoch 3 - iter 1820/2606 - loss 0.10501204 - time (sec): 968.71 - samples/sec: 263.31 - lr: 0.000122 - momentum: 0.000000
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+ 2023-10-11 03:41:17,126 epoch 3 - iter 2080/2606 - loss 0.10470219 - time (sec): 1106.71 - samples/sec: 264.03 - lr: 0.000120 - momentum: 0.000000
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+ 2023-10-11 03:43:33,651 epoch 3 - iter 2340/2606 - loss 0.10526270 - time (sec): 1243.23 - samples/sec: 263.79 - lr: 0.000118 - momentum: 0.000000
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+ 2023-10-11 03:45:53,893 epoch 3 - iter 2600/2606 - loss 0.10410180 - time (sec): 1383.48 - samples/sec: 265.09 - lr: 0.000117 - momentum: 0.000000
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+ 2023-10-11 03:45:56,835 ----------------------------------------------------------------------------------------------------
138
+ 2023-10-11 03:45:56,835 EPOCH 3 done: loss 0.1044 - lr: 0.000117
139
+ 2023-10-11 03:46:38,205 DEV : loss 0.18133316934108734 - f1-score (micro avg) 0.3516
140
+ 2023-10-11 03:46:38,261 saving best model
141
+ 2023-10-11 03:46:44,488 ----------------------------------------------------------------------------------------------------
142
+ 2023-10-11 03:48:57,610 epoch 4 - iter 260/2606 - loss 0.06859431 - time (sec): 133.12 - samples/sec: 264.04 - lr: 0.000115 - momentum: 0.000000
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+ 2023-10-11 03:51:11,126 epoch 4 - iter 520/2606 - loss 0.07027485 - time (sec): 266.63 - samples/sec: 269.61 - lr: 0.000113 - momentum: 0.000000
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+ 2023-10-11 03:53:25,561 epoch 4 - iter 780/2606 - loss 0.07085992 - time (sec): 401.07 - samples/sec: 271.87 - lr: 0.000112 - momentum: 0.000000
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+ 2023-10-11 03:55:39,197 epoch 4 - iter 1040/2606 - loss 0.07328355 - time (sec): 534.71 - samples/sec: 270.36 - lr: 0.000110 - momentum: 0.000000
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+ 2023-10-11 03:57:58,458 epoch 4 - iter 1300/2606 - loss 0.07139563 - time (sec): 673.97 - samples/sec: 274.04 - lr: 0.000108 - momentum: 0.000000
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+ 2023-10-11 04:00:15,073 epoch 4 - iter 1560/2606 - loss 0.07188588 - time (sec): 810.58 - samples/sec: 271.09 - lr: 0.000107 - momentum: 0.000000
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+ 2023-10-11 04:02:32,271 epoch 4 - iter 1820/2606 - loss 0.07168252 - time (sec): 947.78 - samples/sec: 271.34 - lr: 0.000105 - momentum: 0.000000
149
+ 2023-10-11 04:04:51,046 epoch 4 - iter 2080/2606 - loss 0.07050739 - time (sec): 1086.55 - samples/sec: 273.65 - lr: 0.000103 - momentum: 0.000000
150
+ 2023-10-11 04:07:02,394 epoch 4 - iter 2340/2606 - loss 0.07032051 - time (sec): 1217.90 - samples/sec: 272.18 - lr: 0.000102 - momentum: 0.000000
151
+ 2023-10-11 04:09:15,575 epoch 4 - iter 2600/2606 - loss 0.07012799 - time (sec): 1351.08 - samples/sec: 271.60 - lr: 0.000100 - momentum: 0.000000
152
+ 2023-10-11 04:09:18,332 ----------------------------------------------------------------------------------------------------
153
+ 2023-10-11 04:09:18,332 EPOCH 4 done: loss 0.0701 - lr: 0.000100
154
+ 2023-10-11 04:09:59,619 DEV : loss 0.25803107023239136 - f1-score (micro avg) 0.3519
155
+ 2023-10-11 04:09:59,675 saving best model
156
+ 2023-10-11 04:10:05,959 ----------------------------------------------------------------------------------------------------
157
+ 2023-10-11 04:12:22,298 epoch 5 - iter 260/2606 - loss 0.05443710 - time (sec): 136.33 - samples/sec: 262.96 - lr: 0.000098 - momentum: 0.000000
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+ 2023-10-11 04:14:42,695 epoch 5 - iter 520/2606 - loss 0.05908808 - time (sec): 276.73 - samples/sec: 265.82 - lr: 0.000097 - momentum: 0.000000
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+ 2023-10-11 04:17:01,783 epoch 5 - iter 780/2606 - loss 0.05752056 - time (sec): 415.82 - samples/sec: 261.97 - lr: 0.000095 - momentum: 0.000000
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+ 2023-10-11 04:19:19,820 epoch 5 - iter 1040/2606 - loss 0.05497372 - time (sec): 553.86 - samples/sec: 262.52 - lr: 0.000093 - momentum: 0.000000
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+ 2023-10-11 04:21:39,929 epoch 5 - iter 1300/2606 - loss 0.05305092 - time (sec): 693.97 - samples/sec: 264.86 - lr: 0.000092 - momentum: 0.000000
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+ 2023-10-11 04:23:54,456 epoch 5 - iter 1560/2606 - loss 0.05305439 - time (sec): 828.49 - samples/sec: 264.58 - lr: 0.000090 - momentum: 0.000000
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+ 2023-10-11 04:26:10,801 epoch 5 - iter 1820/2606 - loss 0.05315558 - time (sec): 964.84 - samples/sec: 265.27 - lr: 0.000088 - momentum: 0.000000
164
+ 2023-10-11 04:28:25,838 epoch 5 - iter 2080/2606 - loss 0.05281160 - time (sec): 1099.87 - samples/sec: 264.63 - lr: 0.000087 - momentum: 0.000000
165
+ 2023-10-11 04:30:43,752 epoch 5 - iter 2340/2606 - loss 0.05256430 - time (sec): 1237.79 - samples/sec: 265.27 - lr: 0.000085 - momentum: 0.000000
166
+ 2023-10-11 04:33:07,208 epoch 5 - iter 2600/2606 - loss 0.05326034 - time (sec): 1381.24 - samples/sec: 264.92 - lr: 0.000083 - momentum: 0.000000
167
+ 2023-10-11 04:33:11,087 ----------------------------------------------------------------------------------------------------
168
+ 2023-10-11 04:33:11,087 EPOCH 5 done: loss 0.0531 - lr: 0.000083
169
+ 2023-10-11 04:33:52,049 DEV : loss 0.3203273415565491 - f1-score (micro avg) 0.3574
170
+ 2023-10-11 04:33:52,111 saving best model
171
+ 2023-10-11 04:33:55,540 ----------------------------------------------------------------------------------------------------
172
+ 2023-10-11 04:36:12,513 epoch 6 - iter 260/2606 - loss 0.03371390 - time (sec): 136.97 - samples/sec: 245.66 - lr: 0.000082 - momentum: 0.000000
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+ 2023-10-11 04:38:27,463 epoch 6 - iter 520/2606 - loss 0.03309046 - time (sec): 271.92 - samples/sec: 250.81 - lr: 0.000080 - momentum: 0.000000
174
+ 2023-10-11 04:40:45,216 epoch 6 - iter 780/2606 - loss 0.03599853 - time (sec): 409.67 - samples/sec: 255.19 - lr: 0.000078 - momentum: 0.000000
175
+ 2023-10-11 04:42:57,982 epoch 6 - iter 1040/2606 - loss 0.03691222 - time (sec): 542.44 - samples/sec: 258.71 - lr: 0.000077 - momentum: 0.000000
176
+ 2023-10-11 04:45:12,781 epoch 6 - iter 1300/2606 - loss 0.03826054 - time (sec): 677.24 - samples/sec: 261.80 - lr: 0.000075 - momentum: 0.000000
177
+ 2023-10-11 04:47:24,869 epoch 6 - iter 1560/2606 - loss 0.03728935 - time (sec): 809.32 - samples/sec: 262.45 - lr: 0.000073 - momentum: 0.000000
178
+ 2023-10-11 04:49:41,097 epoch 6 - iter 1820/2606 - loss 0.03682751 - time (sec): 945.55 - samples/sec: 266.43 - lr: 0.000072 - momentum: 0.000000
179
+ 2023-10-11 04:51:58,399 epoch 6 - iter 2080/2606 - loss 0.03749443 - time (sec): 1082.85 - samples/sec: 267.50 - lr: 0.000070 - momentum: 0.000000
180
+ 2023-10-11 04:54:20,332 epoch 6 - iter 2340/2606 - loss 0.03754226 - time (sec): 1224.79 - samples/sec: 268.67 - lr: 0.000068 - momentum: 0.000000
181
+ 2023-10-11 04:56:39,733 epoch 6 - iter 2600/2606 - loss 0.03778431 - time (sec): 1364.19 - samples/sec: 268.75 - lr: 0.000067 - momentum: 0.000000
182
+ 2023-10-11 04:56:42,821 ----------------------------------------------------------------------------------------------------
183
+ 2023-10-11 04:56:42,821 EPOCH 6 done: loss 0.0378 - lr: 0.000067
184
+ 2023-10-11 04:57:23,462 DEV : loss 0.4133872985839844 - f1-score (micro avg) 0.3448
185
+ 2023-10-11 04:57:23,515 ----------------------------------------------------------------------------------------------------
186
+ 2023-10-11 04:59:43,378 epoch 7 - iter 260/2606 - loss 0.02500855 - time (sec): 139.86 - samples/sec: 286.63 - lr: 0.000065 - momentum: 0.000000
187
+ 2023-10-11 05:01:57,357 epoch 7 - iter 520/2606 - loss 0.02465180 - time (sec): 273.84 - samples/sec: 277.16 - lr: 0.000063 - momentum: 0.000000
188
+ 2023-10-11 05:04:15,775 epoch 7 - iter 780/2606 - loss 0.02468536 - time (sec): 412.26 - samples/sec: 274.92 - lr: 0.000062 - momentum: 0.000000
189
+ 2023-10-11 05:06:37,944 epoch 7 - iter 1040/2606 - loss 0.02691277 - time (sec): 554.43 - samples/sec: 273.88 - lr: 0.000060 - momentum: 0.000000
190
+ 2023-10-11 05:08:55,685 epoch 7 - iter 1300/2606 - loss 0.03046514 - time (sec): 692.17 - samples/sec: 268.08 - lr: 0.000058 - momentum: 0.000000
191
+ 2023-10-11 05:11:14,370 epoch 7 - iter 1560/2606 - loss 0.03146291 - time (sec): 830.85 - samples/sec: 268.60 - lr: 0.000057 - momentum: 0.000000
192
+ 2023-10-11 05:13:33,566 epoch 7 - iter 1820/2606 - loss 0.03250450 - time (sec): 970.05 - samples/sec: 266.48 - lr: 0.000055 - momentum: 0.000000
193
+ 2023-10-11 05:15:49,540 epoch 7 - iter 2080/2606 - loss 0.03159278 - time (sec): 1106.02 - samples/sec: 265.63 - lr: 0.000053 - momentum: 0.000000
194
+ 2023-10-11 05:18:07,114 epoch 7 - iter 2340/2606 - loss 0.03124657 - time (sec): 1243.60 - samples/sec: 265.89 - lr: 0.000052 - momentum: 0.000000
195
+ 2023-10-11 05:20:21,597 epoch 7 - iter 2600/2606 - loss 0.03094040 - time (sec): 1378.08 - samples/sec: 266.18 - lr: 0.000050 - momentum: 0.000000
196
+ 2023-10-11 05:20:24,427 ----------------------------------------------------------------------------------------------------
197
+ 2023-10-11 05:20:24,428 EPOCH 7 done: loss 0.0310 - lr: 0.000050
198
+ 2023-10-11 05:21:05,201 DEV : loss 0.3855676054954529 - f1-score (micro avg) 0.349
199
+ 2023-10-11 05:21:05,255 ----------------------------------------------------------------------------------------------------
200
+ 2023-10-11 05:23:17,454 epoch 8 - iter 260/2606 - loss 0.01823084 - time (sec): 132.20 - samples/sec: 278.11 - lr: 0.000048 - momentum: 0.000000
201
+ 2023-10-11 05:25:30,878 epoch 8 - iter 520/2606 - loss 0.02290933 - time (sec): 265.62 - samples/sec: 278.67 - lr: 0.000047 - momentum: 0.000000
202
+ 2023-10-11 05:27:43,132 epoch 8 - iter 780/2606 - loss 0.02219691 - time (sec): 397.88 - samples/sec: 276.89 - lr: 0.000045 - momentum: 0.000000
203
+ 2023-10-11 05:29:55,683 epoch 8 - iter 1040/2606 - loss 0.02069056 - time (sec): 530.43 - samples/sec: 276.21 - lr: 0.000043 - momentum: 0.000000
204
+ 2023-10-11 05:32:15,605 epoch 8 - iter 1300/2606 - loss 0.02116472 - time (sec): 670.35 - samples/sec: 274.99 - lr: 0.000042 - momentum: 0.000000
205
+ 2023-10-11 05:34:31,899 epoch 8 - iter 1560/2606 - loss 0.02187514 - time (sec): 806.64 - samples/sec: 273.92 - lr: 0.000040 - momentum: 0.000000
206
+ 2023-10-11 05:36:46,629 epoch 8 - iter 1820/2606 - loss 0.02138843 - time (sec): 941.37 - samples/sec: 272.65 - lr: 0.000038 - momentum: 0.000000
207
+ 2023-10-11 05:39:04,653 epoch 8 - iter 2080/2606 - loss 0.02142026 - time (sec): 1079.40 - samples/sec: 271.95 - lr: 0.000037 - momentum: 0.000000
208
+ 2023-10-11 05:41:23,550 epoch 8 - iter 2340/2606 - loss 0.02137287 - time (sec): 1218.29 - samples/sec: 271.99 - lr: 0.000035 - momentum: 0.000000
209
+ 2023-10-11 05:43:36,354 epoch 8 - iter 2600/2606 - loss 0.02183994 - time (sec): 1351.10 - samples/sec: 271.12 - lr: 0.000033 - momentum: 0.000000
210
+ 2023-10-11 05:43:39,801 ----------------------------------------------------------------------------------------------------
211
+ 2023-10-11 05:43:39,801 EPOCH 8 done: loss 0.0219 - lr: 0.000033
212
+ 2023-10-11 05:44:21,249 DEV : loss 0.40076038241386414 - f1-score (micro avg) 0.3927
213
+ 2023-10-11 05:44:21,303 saving best model
214
+ 2023-10-11 05:44:27,529 ----------------------------------------------------------------------------------------------------
215
+ 2023-10-11 05:46:47,143 epoch 9 - iter 260/2606 - loss 0.01797232 - time (sec): 139.61 - samples/sec: 274.39 - lr: 0.000032 - momentum: 0.000000
216
+ 2023-10-11 05:49:06,372 epoch 9 - iter 520/2606 - loss 0.01701648 - time (sec): 278.84 - samples/sec: 271.57 - lr: 0.000030 - momentum: 0.000000
217
+ 2023-10-11 05:51:24,008 epoch 9 - iter 780/2606 - loss 0.01514496 - time (sec): 416.47 - samples/sec: 267.41 - lr: 0.000028 - momentum: 0.000000
218
+ 2023-10-11 05:53:38,087 epoch 9 - iter 1040/2606 - loss 0.01510022 - time (sec): 550.55 - samples/sec: 265.10 - lr: 0.000027 - momentum: 0.000000
219
+ 2023-10-11 05:55:53,133 epoch 9 - iter 1300/2606 - loss 0.01530686 - time (sec): 685.60 - samples/sec: 267.48 - lr: 0.000025 - momentum: 0.000000
220
+ 2023-10-11 05:58:07,417 epoch 9 - iter 1560/2606 - loss 0.01503083 - time (sec): 819.88 - samples/sec: 266.57 - lr: 0.000023 - momentum: 0.000000
221
+ 2023-10-11 06:00:23,208 epoch 9 - iter 1820/2606 - loss 0.01507330 - time (sec): 955.67 - samples/sec: 267.36 - lr: 0.000022 - momentum: 0.000000
222
+ 2023-10-11 06:02:38,780 epoch 9 - iter 2080/2606 - loss 0.01452415 - time (sec): 1091.25 - samples/sec: 268.44 - lr: 0.000020 - momentum: 0.000000
223
+ 2023-10-11 06:04:56,244 epoch 9 - iter 2340/2606 - loss 0.01529258 - time (sec): 1228.71 - samples/sec: 268.86 - lr: 0.000018 - momentum: 0.000000
224
+ 2023-10-11 06:07:13,677 epoch 9 - iter 2600/2606 - loss 0.01565526 - time (sec): 1366.14 - samples/sec: 268.43 - lr: 0.000017 - momentum: 0.000000
225
+ 2023-10-11 06:07:16,755 ----------------------------------------------------------------------------------------------------
226
+ 2023-10-11 06:07:16,755 EPOCH 9 done: loss 0.0156 - lr: 0.000017
227
+ 2023-10-11 06:07:57,652 DEV : loss 0.4159277677536011 - f1-score (micro avg) 0.3883
228
+ 2023-10-11 06:07:57,716 ----------------------------------------------------------------------------------------------------
229
+ 2023-10-11 06:10:16,257 epoch 10 - iter 260/2606 - loss 0.00788747 - time (sec): 138.54 - samples/sec: 263.07 - lr: 0.000015 - momentum: 0.000000
230
+ 2023-10-11 06:12:30,532 epoch 10 - iter 520/2606 - loss 0.01146169 - time (sec): 272.81 - samples/sec: 263.35 - lr: 0.000013 - momentum: 0.000000
231
+ 2023-10-11 06:14:50,774 epoch 10 - iter 780/2606 - loss 0.01052928 - time (sec): 413.06 - samples/sec: 262.47 - lr: 0.000012 - momentum: 0.000000
232
+ 2023-10-11 06:17:07,263 epoch 10 - iter 1040/2606 - loss 0.01073654 - time (sec): 549.55 - samples/sec: 261.11 - lr: 0.000010 - momentum: 0.000000
233
+ 2023-10-11 06:19:31,825 epoch 10 - iter 1300/2606 - loss 0.01015607 - time (sec): 694.11 - samples/sec: 263.35 - lr: 0.000008 - momentum: 0.000000
234
+ 2023-10-11 06:21:48,930 epoch 10 - iter 1560/2606 - loss 0.01048530 - time (sec): 831.21 - samples/sec: 263.02 - lr: 0.000007 - momentum: 0.000000
235
+ 2023-10-11 06:24:02,051 epoch 10 - iter 1820/2606 - loss 0.01120340 - time (sec): 964.33 - samples/sec: 264.27 - lr: 0.000005 - momentum: 0.000000
236
+ 2023-10-11 06:26:14,763 epoch 10 - iter 2080/2606 - loss 0.01126706 - time (sec): 1097.05 - samples/sec: 264.41 - lr: 0.000003 - momentum: 0.000000
237
+ 2023-10-11 06:28:30,333 epoch 10 - iter 2340/2606 - loss 0.01126768 - time (sec): 1232.61 - samples/sec: 267.29 - lr: 0.000002 - momentum: 0.000000
238
+ 2023-10-11 06:30:43,698 epoch 10 - iter 2600/2606 - loss 0.01126218 - time (sec): 1365.98 - samples/sec: 268.19 - lr: 0.000000 - momentum: 0.000000
239
+ 2023-10-11 06:30:46,919 ----------------------------------------------------------------------------------------------------
240
+ 2023-10-11 06:30:46,919 EPOCH 10 done: loss 0.0112 - lr: 0.000000
241
+ 2023-10-11 06:31:28,527 DEV : loss 0.4329761266708374 - f1-score (micro avg) 0.3906
242
+ 2023-10-11 06:31:29,498 ----------------------------------------------------------------------------------------------------
243
+ 2023-10-11 06:31:29,500 Loading model from best epoch ...
244
+ 2023-10-11 06:31:34,240 SequenceTagger predicts: Dictionary with 17 tags: O, S-LOC, B-LOC, E-LOC, I-LOC, S-PER, B-PER, E-PER, I-PER, S-ORG, B-ORG, E-ORG, I-ORG, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd
245
+ 2023-10-11 06:33:16,669
246
+ Results:
247
+ - F-score (micro) 0.4521
248
+ - F-score (macro) 0.3045
249
+ - Accuracy 0.2967
250
+
251
+ By class:
252
+ precision recall f1-score support
253
+
254
+ LOC 0.4853 0.5840 0.5301 1214
255
+ PER 0.3764 0.4542 0.4117 808
256
+ ORG 0.2745 0.2776 0.2761 353
257
+ HumanProd 0.0000 0.0000 0.0000 15
258
+
259
+ micro avg 0.4187 0.4912 0.4521 2390
260
+ macro avg 0.2841 0.3290 0.3045 2390
261
+ weighted avg 0.4143 0.4912 0.4492 2390
262
+
263
+ 2023-10-11 06:33:16,670 ----------------------------------------------------------------------------------------------------