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hmbench-letemps-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-3

Token Classification
Flair
PyTorch
TensorBoard
French
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.1697263202.c8b2203b18a8.2923.11 +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 06:18:02 0.0002 0.5965 0.1265 0.5214 0.7254 0.6067 0.4437
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+ 2 06:36:03 0.0001 0.0894 0.1183 0.5359 0.7517 0.6257 0.4620
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+ 3 06:54:40 0.0001 0.0634 0.1800 0.5787 0.6773 0.6241 0.4603
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+ 4 07:12:48 0.0001 0.0463 0.2200 0.5421 0.7883 0.6424 0.4815
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+ 5 07:30:54 0.0001 0.0327 0.2478 0.5751 0.7757 0.6605 0.5011
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+ 6 07:48:45 0.0001 0.0232 0.2600 0.5701 0.7906 0.6625 0.5062
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+ 7 08:06:46 0.0001 0.0145 0.3031 0.5657 0.7929 0.6603 0.5029
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+ 8 08:24:40 0.0000 0.0092 0.3336 0.5663 0.7723 0.6534 0.4938
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+ 9 08:42:29 0.0000 0.0059 0.3782 0.5600 0.7792 0.6517 0.4921
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+ 10 09:00:08 0.0000 0.0042 0.3907 0.5579 0.7998 0.6573 0.4989
runs/events.out.tfevents.1697263202.c8b2203b18a8.2923.11 ADDED
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test.tsv ADDED
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training.log ADDED
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+ 2023-10-14 06:00:02,910 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 06:00:02,912 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)
42
+ (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)
50
+ (wi_1): Linear(in_features=1472, out_features=3584, bias=False)
51
+ (wo): Linear(in_features=3584, out_features=1472, bias=False)
52
+ (dropout): Dropout(p=0.1, inplace=False)
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+ (act): NewGELUActivation()
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+ )
55
+ (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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+ 2023-10-14 06:00:02,912 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 06:00:02,912 MultiCorpus: 14465 train + 1392 dev + 2432 test sentences
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+ - NER_HIPE_2022 Corpus: 14465 train + 1392 dev + 2432 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/letemps/fr/with_doc_seperator
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+ 2023-10-14 06:00:02,913 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 06:00:02,913 Train: 14465 sentences
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+ 2023-10-14 06:00:02,913 (train_with_dev=False, train_with_test=False)
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+ 2023-10-14 06:00:02,913 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 06:00:02,913 Training Params:
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+ 2023-10-14 06:00:02,913 - learning_rate: "0.00016"
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+ 2023-10-14 06:00:02,913 - mini_batch_size: "4"
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+ 2023-10-14 06:00:02,913 - max_epochs: "10"
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+ 2023-10-14 06:00:02,913 - shuffle: "True"
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+ 2023-10-14 06:00:02,913 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 06:00:02,913 Plugins:
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+ 2023-10-14 06:00:02,913 - TensorboardLogger
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+ 2023-10-14 06:00:02,913 - LinearScheduler | warmup_fraction: '0.1'
86
+ 2023-10-14 06:00:02,913 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 06:00:02,913 Final evaluation on model from best epoch (best-model.pt)
88
+ 2023-10-14 06:00:02,914 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-14 06:00:02,914 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 06:00:02,914 Computation:
91
+ 2023-10-14 06:00:02,914 - compute on device: cuda:0
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+ 2023-10-14 06:00:02,914 - embedding storage: none
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+ 2023-10-14 06:00:02,914 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 06:00:02,914 Model training base path: "hmbench-letemps/fr-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-3"
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+ 2023-10-14 06:00:02,914 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 06:00:02,914 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 06:00:02,914 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-14 06:01:53,445 epoch 1 - iter 361/3617 - loss 2.47239461 - time (sec): 110.53 - samples/sec: 341.23 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-14 06:03:37,266 epoch 1 - iter 722/3617 - loss 2.07484525 - time (sec): 214.35 - samples/sec: 347.51 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-14 06:05:25,472 epoch 1 - iter 1083/3617 - loss 1.61456832 - time (sec): 322.56 - samples/sec: 350.12 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-14 06:07:08,611 epoch 1 - iter 1444/3617 - loss 1.27675526 - time (sec): 425.69 - samples/sec: 355.51 - lr: 0.000064 - momentum: 0.000000
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+ 2023-10-14 06:08:50,479 epoch 1 - iter 1805/3617 - loss 1.05993658 - time (sec): 527.56 - samples/sec: 358.18 - lr: 0.000080 - momentum: 0.000000
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+ 2023-10-14 06:10:29,696 epoch 1 - iter 2166/3617 - loss 0.91231426 - time (sec): 626.78 - samples/sec: 361.53 - lr: 0.000096 - momentum: 0.000000
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+ 2023-10-14 06:12:09,104 epoch 1 - iter 2527/3617 - loss 0.80617937 - time (sec): 726.19 - samples/sec: 362.44 - lr: 0.000112 - momentum: 0.000000
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+ 2023-10-14 06:13:53,682 epoch 1 - iter 2888/3617 - loss 0.72280386 - time (sec): 830.77 - samples/sec: 362.49 - lr: 0.000128 - momentum: 0.000000
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+ 2023-10-14 06:15:37,793 epoch 1 - iter 3249/3617 - loss 0.65137338 - time (sec): 934.88 - samples/sec: 364.42 - lr: 0.000144 - momentum: 0.000000
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+ 2023-10-14 06:17:19,931 epoch 1 - iter 3610/3617 - loss 0.59733239 - time (sec): 1037.01 - samples/sec: 365.74 - lr: 0.000160 - momentum: 0.000000
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+ 2023-10-14 06:17:21,610 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 06:17:21,611 EPOCH 1 done: loss 0.5965 - lr: 0.000160
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+ 2023-10-14 06:18:02,444 DEV : loss 0.12650439143180847 - f1-score (micro avg) 0.6067
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+ 2023-10-14 06:18:02,513 saving best model
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+ 2023-10-14 06:18:03,590 ----------------------------------------------------------------------------------------------------
113
+ 2023-10-14 06:19:58,085 epoch 2 - iter 361/3617 - loss 0.09250931 - time (sec): 114.49 - samples/sec: 331.01 - lr: 0.000158 - momentum: 0.000000
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+ 2023-10-14 06:21:42,643 epoch 2 - iter 722/3617 - loss 0.09407297 - time (sec): 219.05 - samples/sec: 342.51 - lr: 0.000156 - momentum: 0.000000
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+ 2023-10-14 06:23:32,345 epoch 2 - iter 1083/3617 - loss 0.09368204 - time (sec): 328.75 - samples/sec: 353.59 - lr: 0.000155 - momentum: 0.000000
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+ 2023-10-14 06:25:14,764 epoch 2 - iter 1444/3617 - loss 0.09240527 - time (sec): 431.17 - samples/sec: 359.43 - lr: 0.000153 - momentum: 0.000000
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+ 2023-10-14 06:26:54,551 epoch 2 - iter 1805/3617 - loss 0.09322341 - time (sec): 530.96 - samples/sec: 363.88 - lr: 0.000151 - momentum: 0.000000
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+ 2023-10-14 06:28:32,658 epoch 2 - iter 2166/3617 - loss 0.09164435 - time (sec): 629.06 - samples/sec: 365.00 - lr: 0.000149 - momentum: 0.000000
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+ 2023-10-14 06:30:13,945 epoch 2 - iter 2527/3617 - loss 0.09049817 - time (sec): 730.35 - samples/sec: 365.65 - lr: 0.000148 - momentum: 0.000000
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+ 2023-10-14 06:31:53,651 epoch 2 - iter 2888/3617 - loss 0.09055334 - time (sec): 830.06 - samples/sec: 366.60 - lr: 0.000146 - momentum: 0.000000
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+ 2023-10-14 06:33:36,995 epoch 2 - iter 3249/3617 - loss 0.09003575 - time (sec): 933.40 - samples/sec: 366.95 - lr: 0.000144 - momentum: 0.000000
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+ 2023-10-14 06:35:21,055 epoch 2 - iter 3610/3617 - loss 0.08946027 - time (sec): 1037.46 - samples/sec: 365.54 - lr: 0.000142 - momentum: 0.000000
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+ 2023-10-14 06:35:22,984 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 06:35:22,984 EPOCH 2 done: loss 0.0894 - lr: 0.000142
125
+ 2023-10-14 06:36:03,645 DEV : loss 0.11830706894397736 - f1-score (micro avg) 0.6257
126
+ 2023-10-14 06:36:03,706 saving best model
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+ 2023-10-14 06:36:09,241 ----------------------------------------------------------------------------------------------------
128
+ 2023-10-14 06:37:52,453 epoch 3 - iter 361/3617 - loss 0.06677874 - time (sec): 103.21 - samples/sec: 362.87 - lr: 0.000140 - momentum: 0.000000
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+ 2023-10-14 06:39:35,134 epoch 3 - iter 722/3617 - loss 0.06628824 - time (sec): 205.89 - samples/sec: 374.82 - lr: 0.000139 - momentum: 0.000000
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+ 2023-10-14 06:41:18,032 epoch 3 - iter 1083/3617 - loss 0.06459732 - time (sec): 308.79 - samples/sec: 372.39 - lr: 0.000137 - momentum: 0.000000
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+ 2023-10-14 06:43:02,845 epoch 3 - iter 1444/3617 - loss 0.06301256 - time (sec): 413.60 - samples/sec: 369.24 - lr: 0.000135 - momentum: 0.000000
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+ 2023-10-14 06:44:48,454 epoch 3 - iter 1805/3617 - loss 0.06356181 - time (sec): 519.21 - samples/sec: 370.00 - lr: 0.000133 - momentum: 0.000000
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+ 2023-10-14 06:46:34,140 epoch 3 - iter 2166/3617 - loss 0.06323142 - time (sec): 624.90 - samples/sec: 368.82 - lr: 0.000132 - momentum: 0.000000
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+ 2023-10-14 06:48:23,285 epoch 3 - iter 2527/3617 - loss 0.06443387 - time (sec): 734.04 - samples/sec: 363.27 - lr: 0.000130 - momentum: 0.000000
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+ 2023-10-14 06:50:15,844 epoch 3 - iter 2888/3617 - loss 0.06348206 - time (sec): 846.60 - samples/sec: 358.62 - lr: 0.000128 - momentum: 0.000000
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+ 2023-10-14 06:52:05,894 epoch 3 - iter 3249/3617 - loss 0.06319253 - time (sec): 956.65 - samples/sec: 355.86 - lr: 0.000126 - momentum: 0.000000
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+ 2023-10-14 06:53:56,573 epoch 3 - iter 3610/3617 - loss 0.06352064 - time (sec): 1067.33 - samples/sec: 355.17 - lr: 0.000124 - momentum: 0.000000
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+ 2023-10-14 06:53:58,615 ----------------------------------------------------------------------------------------------------
139
+ 2023-10-14 06:53:58,615 EPOCH 3 done: loss 0.0634 - lr: 0.000124
140
+ 2023-10-14 06:54:40,585 DEV : loss 0.18002015352249146 - f1-score (micro avg) 0.6241
141
+ 2023-10-14 06:54:40,652 ----------------------------------------------------------------------------------------------------
142
+ 2023-10-14 06:56:25,424 epoch 4 - iter 361/3617 - loss 0.04589010 - time (sec): 104.77 - samples/sec: 349.69 - lr: 0.000123 - momentum: 0.000000
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+ 2023-10-14 06:58:11,340 epoch 4 - iter 722/3617 - loss 0.04524784 - time (sec): 210.69 - samples/sec: 357.08 - lr: 0.000121 - momentum: 0.000000
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+ 2023-10-14 07:00:01,015 epoch 4 - iter 1083/3617 - loss 0.04459280 - time (sec): 320.36 - samples/sec: 352.15 - lr: 0.000119 - momentum: 0.000000
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+ 2023-10-14 07:01:47,053 epoch 4 - iter 1444/3617 - loss 0.04241941 - time (sec): 426.40 - samples/sec: 351.10 - lr: 0.000117 - momentum: 0.000000
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+ 2023-10-14 07:03:29,823 epoch 4 - iter 1805/3617 - loss 0.04232486 - time (sec): 529.17 - samples/sec: 354.82 - lr: 0.000116 - momentum: 0.000000
147
+ 2023-10-14 07:05:12,160 epoch 4 - iter 2166/3617 - loss 0.04384413 - time (sec): 631.51 - samples/sec: 359.79 - lr: 0.000114 - momentum: 0.000000
148
+ 2023-10-14 07:06:59,230 epoch 4 - iter 2527/3617 - loss 0.04460396 - time (sec): 738.58 - samples/sec: 360.08 - lr: 0.000112 - momentum: 0.000000
149
+ 2023-10-14 07:08:41,529 epoch 4 - iter 2888/3617 - loss 0.04544588 - time (sec): 840.88 - samples/sec: 360.44 - lr: 0.000110 - momentum: 0.000000
150
+ 2023-10-14 07:10:25,972 epoch 4 - iter 3249/3617 - loss 0.04605584 - time (sec): 945.32 - samples/sec: 361.87 - lr: 0.000108 - momentum: 0.000000
151
+ 2023-10-14 07:12:06,260 epoch 4 - iter 3610/3617 - loss 0.04631529 - time (sec): 1045.61 - samples/sec: 362.78 - lr: 0.000107 - momentum: 0.000000
152
+ 2023-10-14 07:12:07,916 ----------------------------------------------------------------------------------------------------
153
+ 2023-10-14 07:12:07,916 EPOCH 4 done: loss 0.0463 - lr: 0.000107
154
+ 2023-10-14 07:12:47,955 DEV : loss 0.2199789583683014 - f1-score (micro avg) 0.6424
155
+ 2023-10-14 07:12:48,014 saving best model
156
+ 2023-10-14 07:12:49,081 ----------------------------------------------------------------------------------------------------
157
+ 2023-10-14 07:14:30,776 epoch 5 - iter 361/3617 - loss 0.02732386 - time (sec): 101.69 - samples/sec: 378.30 - lr: 0.000105 - momentum: 0.000000
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+ 2023-10-14 07:16:19,492 epoch 5 - iter 722/3617 - loss 0.02918770 - time (sec): 210.41 - samples/sec: 363.23 - lr: 0.000103 - momentum: 0.000000
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+ 2023-10-14 07:18:11,252 epoch 5 - iter 1083/3617 - loss 0.03233304 - time (sec): 322.17 - samples/sec: 352.73 - lr: 0.000101 - momentum: 0.000000
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+ 2023-10-14 07:20:01,023 epoch 5 - iter 1444/3617 - loss 0.03284681 - time (sec): 431.94 - samples/sec: 347.83 - lr: 0.000100 - momentum: 0.000000
161
+ 2023-10-14 07:21:44,113 epoch 5 - iter 1805/3617 - loss 0.03214533 - time (sec): 535.03 - samples/sec: 353.10 - lr: 0.000098 - momentum: 0.000000
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+ 2023-10-14 07:23:24,045 epoch 5 - iter 2166/3617 - loss 0.03217787 - time (sec): 634.96 - samples/sec: 356.66 - lr: 0.000096 - momentum: 0.000000
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+ 2023-10-14 07:25:04,587 epoch 5 - iter 2527/3617 - loss 0.03195592 - time (sec): 735.50 - samples/sec: 357.47 - lr: 0.000094 - momentum: 0.000000
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+ 2023-10-14 07:26:45,009 epoch 5 - iter 2888/3617 - loss 0.03247486 - time (sec): 835.93 - samples/sec: 359.86 - lr: 0.000092 - momentum: 0.000000
165
+ 2023-10-14 07:28:30,143 epoch 5 - iter 3249/3617 - loss 0.03317830 - time (sec): 941.06 - samples/sec: 361.44 - lr: 0.000091 - momentum: 0.000000
166
+ 2023-10-14 07:30:10,900 epoch 5 - iter 3610/3617 - loss 0.03266682 - time (sec): 1041.82 - samples/sec: 364.12 - lr: 0.000089 - momentum: 0.000000
167
+ 2023-10-14 07:30:12,602 ----------------------------------------------------------------------------------------------------
168
+ 2023-10-14 07:30:12,603 EPOCH 5 done: loss 0.0327 - lr: 0.000089
169
+ 2023-10-14 07:30:54,210 DEV : loss 0.24780842661857605 - f1-score (micro avg) 0.6605
170
+ 2023-10-14 07:30:54,277 saving best model
171
+ 2023-10-14 07:30:59,067 ----------------------------------------------------------------------------------------------------
172
+ 2023-10-14 07:32:41,778 epoch 6 - iter 361/3617 - loss 0.01937433 - time (sec): 102.70 - samples/sec: 382.13 - lr: 0.000087 - momentum: 0.000000
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+ 2023-10-14 07:34:22,211 epoch 6 - iter 722/3617 - loss 0.01983213 - time (sec): 203.13 - samples/sec: 379.33 - lr: 0.000085 - momentum: 0.000000
174
+ 2023-10-14 07:36:00,973 epoch 6 - iter 1083/3617 - loss 0.02234270 - time (sec): 301.89 - samples/sec: 379.02 - lr: 0.000084 - momentum: 0.000000
175
+ 2023-10-14 07:37:43,093 epoch 6 - iter 1444/3617 - loss 0.02403649 - time (sec): 404.01 - samples/sec: 373.77 - lr: 0.000082 - momentum: 0.000000
176
+ 2023-10-14 07:39:32,914 epoch 6 - iter 1805/3617 - loss 0.02399559 - time (sec): 513.83 - samples/sec: 365.51 - lr: 0.000080 - momentum: 0.000000
177
+ 2023-10-14 07:41:13,200 epoch 6 - iter 2166/3617 - loss 0.02304717 - time (sec): 614.12 - samples/sec: 366.92 - lr: 0.000078 - momentum: 0.000000
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+ 2023-10-14 07:42:57,050 epoch 6 - iter 2527/3617 - loss 0.02261731 - time (sec): 717.97 - samples/sec: 369.09 - lr: 0.000076 - momentum: 0.000000
179
+ 2023-10-14 07:44:39,865 epoch 6 - iter 2888/3617 - loss 0.02304199 - time (sec): 820.78 - samples/sec: 370.40 - lr: 0.000075 - momentum: 0.000000
180
+ 2023-10-14 07:46:20,394 epoch 6 - iter 3249/3617 - loss 0.02338086 - time (sec): 921.31 - samples/sec: 369.43 - lr: 0.000073 - momentum: 0.000000
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+ 2023-10-14 07:48:04,126 epoch 6 - iter 3610/3617 - loss 0.02327186 - time (sec): 1025.04 - samples/sec: 369.81 - lr: 0.000071 - momentum: 0.000000
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+ 2023-10-14 07:48:06,205 ----------------------------------------------------------------------------------------------------
183
+ 2023-10-14 07:48:06,205 EPOCH 6 done: loss 0.0232 - lr: 0.000071
184
+ 2023-10-14 07:48:44,950 DEV : loss 0.2599741816520691 - f1-score (micro avg) 0.6625
185
+ 2023-10-14 07:48:45,008 saving best model
186
+ 2023-10-14 07:48:46,022 ----------------------------------------------------------------------------------------------------
187
+ 2023-10-14 07:50:26,907 epoch 7 - iter 361/3617 - loss 0.01112762 - time (sec): 100.88 - samples/sec: 380.64 - lr: 0.000069 - momentum: 0.000000
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+ 2023-10-14 07:52:10,004 epoch 7 - iter 722/3617 - loss 0.01292842 - time (sec): 203.98 - samples/sec: 373.47 - lr: 0.000068 - momentum: 0.000000
189
+ 2023-10-14 07:53:56,320 epoch 7 - iter 1083/3617 - loss 0.01286273 - time (sec): 310.30 - samples/sec: 365.83 - lr: 0.000066 - momentum: 0.000000
190
+ 2023-10-14 07:55:39,361 epoch 7 - iter 1444/3617 - loss 0.01371638 - time (sec): 413.34 - samples/sec: 370.42 - lr: 0.000064 - momentum: 0.000000
191
+ 2023-10-14 07:57:19,068 epoch 7 - iter 1805/3617 - loss 0.01339942 - time (sec): 513.04 - samples/sec: 371.39 - lr: 0.000062 - momentum: 0.000000
192
+ 2023-10-14 07:59:01,846 epoch 7 - iter 2166/3617 - loss 0.01421913 - time (sec): 615.82 - samples/sec: 370.62 - lr: 0.000060 - momentum: 0.000000
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+ 2023-10-14 08:00:47,048 epoch 7 - iter 2527/3617 - loss 0.01439729 - time (sec): 721.02 - samples/sec: 370.52 - lr: 0.000059 - momentum: 0.000000
194
+ 2023-10-14 08:02:27,477 epoch 7 - iter 2888/3617 - loss 0.01432233 - time (sec): 821.45 - samples/sec: 370.63 - lr: 0.000057 - momentum: 0.000000
195
+ 2023-10-14 08:04:08,239 epoch 7 - iter 3249/3617 - loss 0.01466633 - time (sec): 922.21 - samples/sec: 371.36 - lr: 0.000055 - momentum: 0.000000
196
+ 2023-10-14 08:05:56,328 epoch 7 - iter 3610/3617 - loss 0.01454880 - time (sec): 1030.30 - samples/sec: 368.20 - lr: 0.000053 - momentum: 0.000000
197
+ 2023-10-14 08:05:58,204 ----------------------------------------------------------------------------------------------------
198
+ 2023-10-14 08:05:58,204 EPOCH 7 done: loss 0.0145 - lr: 0.000053
199
+ 2023-10-14 08:06:46,371 DEV : loss 0.303059846162796 - f1-score (micro avg) 0.6603
200
+ 2023-10-14 08:06:46,454 ----------------------------------------------------------------------------------------------------
201
+ 2023-10-14 08:08:30,534 epoch 8 - iter 361/3617 - loss 0.00660663 - time (sec): 104.08 - samples/sec: 356.89 - lr: 0.000052 - momentum: 0.000000
202
+ 2023-10-14 08:10:12,010 epoch 8 - iter 722/3617 - loss 0.00864086 - time (sec): 205.55 - samples/sec: 366.15 - lr: 0.000050 - momentum: 0.000000
203
+ 2023-10-14 08:11:54,676 epoch 8 - iter 1083/3617 - loss 0.00759540 - time (sec): 308.22 - samples/sec: 372.41 - lr: 0.000048 - momentum: 0.000000
204
+ 2023-10-14 08:13:35,504 epoch 8 - iter 1444/3617 - loss 0.00839154 - time (sec): 409.05 - samples/sec: 372.79 - lr: 0.000046 - momentum: 0.000000
205
+ 2023-10-14 08:15:20,638 epoch 8 - iter 1805/3617 - loss 0.00820435 - time (sec): 514.18 - samples/sec: 372.20 - lr: 0.000044 - momentum: 0.000000
206
+ 2023-10-14 08:17:02,003 epoch 8 - iter 2166/3617 - loss 0.00843406 - time (sec): 615.55 - samples/sec: 371.47 - lr: 0.000043 - momentum: 0.000000
207
+ 2023-10-14 08:18:41,684 epoch 8 - iter 2527/3617 - loss 0.00913255 - time (sec): 715.23 - samples/sec: 372.84 - lr: 0.000041 - momentum: 0.000000
208
+ 2023-10-14 08:20:30,352 epoch 8 - iter 2888/3617 - loss 0.00931845 - time (sec): 823.90 - samples/sec: 369.40 - lr: 0.000039 - momentum: 0.000000
209
+ 2023-10-14 08:22:10,317 epoch 8 - iter 3249/3617 - loss 0.00922798 - time (sec): 923.86 - samples/sec: 369.93 - lr: 0.000037 - momentum: 0.000000
210
+ 2023-10-14 08:23:58,744 epoch 8 - iter 3610/3617 - loss 0.00921932 - time (sec): 1032.29 - samples/sec: 367.62 - lr: 0.000036 - momentum: 0.000000
211
+ 2023-10-14 08:24:00,299 ----------------------------------------------------------------------------------------------------
212
+ 2023-10-14 08:24:00,299 EPOCH 8 done: loss 0.0092 - lr: 0.000036
213
+ 2023-10-14 08:24:39,988 DEV : loss 0.3336223363876343 - f1-score (micro avg) 0.6534
214
+ 2023-10-14 08:24:40,055 ----------------------------------------------------------------------------------------------------
215
+ 2023-10-14 08:26:22,702 epoch 9 - iter 361/3617 - loss 0.00528020 - time (sec): 102.65 - samples/sec: 377.08 - lr: 0.000034 - momentum: 0.000000
216
+ 2023-10-14 08:28:04,835 epoch 9 - iter 722/3617 - loss 0.00615366 - time (sec): 204.78 - samples/sec: 381.57 - lr: 0.000032 - momentum: 0.000000
217
+ 2023-10-14 08:29:42,817 epoch 9 - iter 1083/3617 - loss 0.00574831 - time (sec): 302.76 - samples/sec: 381.88 - lr: 0.000030 - momentum: 0.000000
218
+ 2023-10-14 08:31:21,510 epoch 9 - iter 1444/3617 - loss 0.00593530 - time (sec): 401.45 - samples/sec: 383.40 - lr: 0.000028 - momentum: 0.000000
219
+ 2023-10-14 08:33:07,164 epoch 9 - iter 1805/3617 - loss 0.00576089 - time (sec): 507.11 - samples/sec: 377.05 - lr: 0.000027 - momentum: 0.000000
220
+ 2023-10-14 08:34:51,885 epoch 9 - iter 2166/3617 - loss 0.00642936 - time (sec): 611.83 - samples/sec: 375.27 - lr: 0.000025 - momentum: 0.000000
221
+ 2023-10-14 08:36:35,388 epoch 9 - iter 2527/3617 - loss 0.00589172 - time (sec): 715.33 - samples/sec: 373.83 - lr: 0.000023 - momentum: 0.000000
222
+ 2023-10-14 08:38:16,323 epoch 9 - iter 2888/3617 - loss 0.00596664 - time (sec): 816.27 - samples/sec: 372.98 - lr: 0.000021 - momentum: 0.000000
223
+ 2023-10-14 08:40:00,323 epoch 9 - iter 3249/3617 - loss 0.00596886 - time (sec): 920.27 - samples/sec: 370.19 - lr: 0.000020 - momentum: 0.000000
224
+ 2023-10-14 08:41:46,467 epoch 9 - iter 3610/3617 - loss 0.00585104 - time (sec): 1026.41 - samples/sec: 369.47 - lr: 0.000018 - momentum: 0.000000
225
+ 2023-10-14 08:41:48,372 ----------------------------------------------------------------------------------------------------
226
+ 2023-10-14 08:41:48,373 EPOCH 9 done: loss 0.0059 - lr: 0.000018
227
+ 2023-10-14 08:42:29,867 DEV : loss 0.37816229462623596 - f1-score (micro avg) 0.6517
228
+ 2023-10-14 08:42:29,939 ----------------------------------------------------------------------------------------------------
229
+ 2023-10-14 08:44:16,008 epoch 10 - iter 361/3617 - loss 0.00562974 - time (sec): 106.07 - samples/sec: 355.80 - lr: 0.000016 - momentum: 0.000000
230
+ 2023-10-14 08:45:57,224 epoch 10 - iter 722/3617 - loss 0.00588847 - time (sec): 207.28 - samples/sec: 356.86 - lr: 0.000014 - momentum: 0.000000
231
+ 2023-10-14 08:47:37,036 epoch 10 - iter 1083/3617 - loss 0.00580326 - time (sec): 307.09 - samples/sec: 366.26 - lr: 0.000012 - momentum: 0.000000
232
+ 2023-10-14 08:49:18,323 epoch 10 - iter 1444/3617 - loss 0.00517980 - time (sec): 408.38 - samples/sec: 365.73 - lr: 0.000011 - momentum: 0.000000
233
+ 2023-10-14 08:50:59,735 epoch 10 - iter 1805/3617 - loss 0.00496664 - time (sec): 509.79 - samples/sec: 368.87 - lr: 0.000009 - momentum: 0.000000
234
+ 2023-10-14 08:52:39,423 epoch 10 - iter 2166/3617 - loss 0.00448679 - time (sec): 609.48 - samples/sec: 369.82 - lr: 0.000007 - momentum: 0.000000
235
+ 2023-10-14 08:54:21,432 epoch 10 - iter 2527/3617 - loss 0.00434716 - time (sec): 711.49 - samples/sec: 372.23 - lr: 0.000005 - momentum: 0.000000
236
+ 2023-10-14 08:56:01,306 epoch 10 - iter 2888/3617 - loss 0.00431111 - time (sec): 811.36 - samples/sec: 372.31 - lr: 0.000004 - momentum: 0.000000
237
+ 2023-10-14 08:57:43,937 epoch 10 - iter 3249/3617 - loss 0.00426079 - time (sec): 914.00 - samples/sec: 373.34 - lr: 0.000002 - momentum: 0.000000
238
+ 2023-10-14 08:59:25,667 epoch 10 - iter 3610/3617 - loss 0.00423353 - time (sec): 1015.73 - samples/sec: 373.10 - lr: 0.000000 - momentum: 0.000000
239
+ 2023-10-14 08:59:27,802 ----------------------------------------------------------------------------------------------------
240
+ 2023-10-14 08:59:27,802 EPOCH 10 done: loss 0.0042 - lr: 0.000000
241
+ 2023-10-14 09:00:08,268 DEV : loss 0.3906834125518799 - f1-score (micro avg) 0.6573
242
+ 2023-10-14 09:00:09,310 ----------------------------------------------------------------------------------------------------
243
+ 2023-10-14 09:00:09,312 Loading model from best epoch ...
244
+ 2023-10-14 09:00:13,705 SequenceTagger predicts: Dictionary with 13 tags: O, S-loc, B-loc, E-loc, I-loc, S-pers, B-pers, E-pers, I-pers, S-org, B-org, E-org, I-org
245
+ 2023-10-14 09:01:11,888
246
+ Results:
247
+ - F-score (micro) 0.6379
248
+ - F-score (macro) 0.4852
249
+ - Accuracy 0.4792
250
+
251
+ By class:
252
+ precision recall f1-score support
253
+
254
+ loc 0.6534 0.7242 0.6870 591
255
+ pers 0.5650 0.7423 0.6416 357
256
+ org 0.1702 0.1013 0.1270 79
257
+
258
+ micro avg 0.5986 0.6826 0.6379 1027
259
+ macro avg 0.4629 0.5226 0.4852 1027
260
+ weighted avg 0.5855 0.6826 0.6282 1027
261
+
262
+ 2023-10-14 09:01:11,888 ----------------------------------------------------------------------------------------------------