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hmbench-letemps-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-1

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.1697160375.c8b2203b18a8.2923.1 +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 01:42:38 0.0002 0.7603 0.1450 0.4875 0.3570 0.4122 0.2664
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+ 2 01:58:32 0.0001 0.0971 0.1063 0.5322 0.7368 0.6180 0.4554
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+ 3 02:14:17 0.0001 0.0600 0.1486 0.5445 0.7414 0.6279 0.4679
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+ 4 02:30:42 0.0001 0.0440 0.1784 0.5140 0.6945 0.5908 0.4251
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+ 5 02:47:26 0.0001 0.0320 0.2254 0.5479 0.7323 0.6268 0.4638
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+ 6 03:04:12 0.0001 0.0241 0.2681 0.5622 0.7757 0.6519 0.4917
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+ 7 03:20:35 0.0001 0.0189 0.2960 0.5595 0.7906 0.6553 0.4961
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+ 8 03:36:57 0.0000 0.0128 0.3349 0.5543 0.7769 0.6470 0.4878
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+ 9 03:53:31 0.0000 0.0106 0.3527 0.5525 0.7883 0.6497 0.4904
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+ 10 04:10:27 0.0000 0.0084 0.3520 0.5591 0.7632 0.6454 0.4862
runs/events.out.tfevents.1697160375.c8b2203b18a8.2923.1 ADDED
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test.tsv ADDED
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training.log ADDED
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+ 2023-10-13 01:26:15,069 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 01:26:15,072 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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+ 2023-10-13 01:26:15,072 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 01:26:15,072 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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+ 2023-10-13 01:26:15,072 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 01:26:15,072 Train: 14465 sentences
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+ 2023-10-13 01:26:15,072 (train_with_dev=False, train_with_test=False)
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+ 2023-10-13 01:26:15,072 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 01:26:15,072 Training Params:
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+ 2023-10-13 01:26:15,072 - learning_rate: "0.00016"
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+ 2023-10-13 01:26:15,072 - mini_batch_size: "8"
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+ 2023-10-13 01:26:15,073 - max_epochs: "10"
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+ 2023-10-13 01:26:15,073 - shuffle: "True"
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+ 2023-10-13 01:26:15,073 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 01:26:15,073 Plugins:
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+ 2023-10-13 01:26:15,073 - TensorboardLogger
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+ 2023-10-13 01:26:15,073 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-13 01:26:15,073 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 01:26:15,073 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-13 01:26:15,073 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-13 01:26:15,073 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 01:26:15,073 Computation:
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+ 2023-10-13 01:26:15,073 - compute on device: cuda:0
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+ 2023-10-13 01:26:15,073 - embedding storage: none
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+ 2023-10-13 01:26:15,073 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 01:26:15,073 Model training base path: "hmbench-letemps/fr-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-1"
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+ 2023-10-13 01:26:15,074 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 01:26:15,074 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 01:26:15,074 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-13 01:27:50,591 epoch 1 - iter 180/1809 - loss 2.57027178 - time (sec): 95.52 - samples/sec: 402.43 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-13 01:29:25,564 epoch 1 - iter 360/1809 - loss 2.33739383 - time (sec): 190.49 - samples/sec: 398.49 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-13 01:30:59,649 epoch 1 - iter 540/1809 - loss 1.98337869 - time (sec): 284.57 - samples/sec: 396.72 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-13 01:32:35,319 epoch 1 - iter 720/1809 - loss 1.61896865 - time (sec): 380.24 - samples/sec: 398.89 - lr: 0.000064 - momentum: 0.000000
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+ 2023-10-13 01:34:08,395 epoch 1 - iter 900/1809 - loss 1.35128031 - time (sec): 473.32 - samples/sec: 400.08 - lr: 0.000080 - momentum: 0.000000
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+ 2023-10-13 01:35:39,939 epoch 1 - iter 1080/1809 - loss 1.16606342 - time (sec): 564.86 - samples/sec: 400.35 - lr: 0.000095 - momentum: 0.000000
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+ 2023-10-13 01:37:11,149 epoch 1 - iter 1260/1809 - loss 1.02972052 - time (sec): 656.07 - samples/sec: 400.53 - lr: 0.000111 - momentum: 0.000000
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+ 2023-10-13 01:38:42,954 epoch 1 - iter 1440/1809 - loss 0.92088707 - time (sec): 747.88 - samples/sec: 402.22 - lr: 0.000127 - momentum: 0.000000
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+ 2023-10-13 01:40:18,744 epoch 1 - iter 1620/1809 - loss 0.83665554 - time (sec): 843.67 - samples/sec: 401.67 - lr: 0.000143 - momentum: 0.000000
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+ 2023-10-13 01:41:56,057 epoch 1 - iter 1800/1809 - loss 0.76361147 - time (sec): 940.98 - samples/sec: 401.57 - lr: 0.000159 - momentum: 0.000000
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+ 2023-10-13 01:42:00,723 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 01:42:00,723 EPOCH 1 done: loss 0.7603 - lr: 0.000159
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+ 2023-10-13 01:42:37,965 DEV : loss 0.14501185715198517 - f1-score (micro avg) 0.4122
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+ 2023-10-13 01:42:38,027 saving best model
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+ 2023-10-13 01:42:38,900 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 01:44:12,141 epoch 2 - iter 180/1809 - loss 0.11022017 - time (sec): 93.24 - samples/sec: 415.46 - lr: 0.000158 - momentum: 0.000000
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+ 2023-10-13 01:45:45,611 epoch 2 - iter 360/1809 - loss 0.11221991 - time (sec): 186.71 - samples/sec: 414.87 - lr: 0.000156 - momentum: 0.000000
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+ 2023-10-13 01:47:16,997 epoch 2 - iter 540/1809 - loss 0.10902795 - time (sec): 278.09 - samples/sec: 414.67 - lr: 0.000155 - momentum: 0.000000
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+ 2023-10-13 01:48:48,299 epoch 2 - iter 720/1809 - loss 0.10834577 - time (sec): 369.40 - samples/sec: 411.87 - lr: 0.000153 - momentum: 0.000000
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+ 2023-10-13 01:50:21,166 epoch 2 - iter 900/1809 - loss 0.10540217 - time (sec): 462.26 - samples/sec: 407.82 - lr: 0.000151 - momentum: 0.000000
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+ 2023-10-13 01:51:51,954 epoch 2 - iter 1080/1809 - loss 0.10504549 - time (sec): 553.05 - samples/sec: 410.17 - lr: 0.000149 - momentum: 0.000000
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+ 2023-10-13 01:53:21,268 epoch 2 - iter 1260/1809 - loss 0.10280905 - time (sec): 642.37 - samples/sec: 411.75 - lr: 0.000148 - momentum: 0.000000
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+ 2023-10-13 01:54:50,585 epoch 2 - iter 1440/1809 - loss 0.10065484 - time (sec): 731.68 - samples/sec: 413.77 - lr: 0.000146 - momentum: 0.000000
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+ 2023-10-13 01:56:20,686 epoch 2 - iter 1620/1809 - loss 0.09800215 - time (sec): 821.78 - samples/sec: 415.31 - lr: 0.000144 - momentum: 0.000000
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+ 2023-10-13 01:57:49,286 epoch 2 - iter 1800/1809 - loss 0.09732421 - time (sec): 910.38 - samples/sec: 415.28 - lr: 0.000142 - momentum: 0.000000
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+ 2023-10-13 01:57:53,403 ----------------------------------------------------------------------------------------------------
124
+ 2023-10-13 01:57:53,404 EPOCH 2 done: loss 0.0971 - lr: 0.000142
125
+ 2023-10-13 01:58:32,248 DEV : loss 0.10631529986858368 - f1-score (micro avg) 0.618
126
+ 2023-10-13 01:58:32,304 saving best model
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+ 2023-10-13 01:58:34,913 ----------------------------------------------------------------------------------------------------
128
+ 2023-10-13 02:00:04,640 epoch 3 - iter 180/1809 - loss 0.06167102 - time (sec): 89.72 - samples/sec: 425.26 - lr: 0.000140 - momentum: 0.000000
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+ 2023-10-13 02:01:36,034 epoch 3 - iter 360/1809 - loss 0.06052679 - time (sec): 181.12 - samples/sec: 422.80 - lr: 0.000139 - momentum: 0.000000
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+ 2023-10-13 02:03:04,768 epoch 3 - iter 540/1809 - loss 0.06164459 - time (sec): 269.85 - samples/sec: 419.93 - lr: 0.000137 - momentum: 0.000000
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+ 2023-10-13 02:04:33,141 epoch 3 - iter 720/1809 - loss 0.06028257 - time (sec): 358.22 - samples/sec: 420.01 - lr: 0.000135 - momentum: 0.000000
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+ 2023-10-13 02:06:04,511 epoch 3 - iter 900/1809 - loss 0.06093924 - time (sec): 449.59 - samples/sec: 418.97 - lr: 0.000133 - momentum: 0.000000
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+ 2023-10-13 02:07:33,194 epoch 3 - iter 1080/1809 - loss 0.06123599 - time (sec): 538.28 - samples/sec: 420.59 - lr: 0.000132 - momentum: 0.000000
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+ 2023-10-13 02:09:05,494 epoch 3 - iter 1260/1809 - loss 0.06096843 - time (sec): 630.58 - samples/sec: 420.26 - lr: 0.000130 - momentum: 0.000000
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+ 2023-10-13 02:10:36,079 epoch 3 - iter 1440/1809 - loss 0.06042058 - time (sec): 721.16 - samples/sec: 419.13 - lr: 0.000128 - momentum: 0.000000
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+ 2023-10-13 02:12:05,652 epoch 3 - iter 1620/1809 - loss 0.06085687 - time (sec): 810.73 - samples/sec: 419.46 - lr: 0.000126 - momentum: 0.000000
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+ 2023-10-13 02:13:34,541 epoch 3 - iter 1800/1809 - loss 0.06010337 - time (sec): 899.62 - samples/sec: 420.31 - lr: 0.000125 - momentum: 0.000000
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+ 2023-10-13 02:13:38,557 ----------------------------------------------------------------------------------------------------
139
+ 2023-10-13 02:13:38,557 EPOCH 3 done: loss 0.0600 - lr: 0.000125
140
+ 2023-10-13 02:14:17,081 DEV : loss 0.1486276537179947 - f1-score (micro avg) 0.6279
141
+ 2023-10-13 02:14:17,138 saving best model
142
+ 2023-10-13 02:14:19,719 ----------------------------------------------------------------------------------------------------
143
+ 2023-10-13 02:15:49,206 epoch 4 - iter 180/1809 - loss 0.04438082 - time (sec): 89.48 - samples/sec: 412.12 - lr: 0.000123 - momentum: 0.000000
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+ 2023-10-13 02:17:20,752 epoch 4 - iter 360/1809 - loss 0.04634535 - time (sec): 181.03 - samples/sec: 421.41 - lr: 0.000121 - momentum: 0.000000
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+ 2023-10-13 02:18:53,652 epoch 4 - iter 540/1809 - loss 0.04429804 - time (sec): 273.93 - samples/sec: 414.29 - lr: 0.000119 - momentum: 0.000000
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+ 2023-10-13 02:20:26,564 epoch 4 - iter 720/1809 - loss 0.04282892 - time (sec): 366.84 - samples/sec: 410.30 - lr: 0.000117 - momentum: 0.000000
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+ 2023-10-13 02:21:58,754 epoch 4 - iter 900/1809 - loss 0.04357623 - time (sec): 459.03 - samples/sec: 408.03 - lr: 0.000116 - momentum: 0.000000
148
+ 2023-10-13 02:23:32,299 epoch 4 - iter 1080/1809 - loss 0.04492686 - time (sec): 552.57 - samples/sec: 407.98 - lr: 0.000114 - momentum: 0.000000
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+ 2023-10-13 02:25:06,137 epoch 4 - iter 1260/1809 - loss 0.04500505 - time (sec): 646.41 - samples/sec: 406.96 - lr: 0.000112 - momentum: 0.000000
150
+ 2023-10-13 02:26:42,107 epoch 4 - iter 1440/1809 - loss 0.04465500 - time (sec): 742.38 - samples/sec: 405.46 - lr: 0.000110 - momentum: 0.000000
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+ 2023-10-13 02:28:20,025 epoch 4 - iter 1620/1809 - loss 0.04378505 - time (sec): 840.30 - samples/sec: 405.00 - lr: 0.000109 - momentum: 0.000000
152
+ 2023-10-13 02:29:56,157 epoch 4 - iter 1800/1809 - loss 0.04381280 - time (sec): 936.43 - samples/sec: 403.84 - lr: 0.000107 - momentum: 0.000000
153
+ 2023-10-13 02:30:00,550 ----------------------------------------------------------------------------------------------------
154
+ 2023-10-13 02:30:00,551 EPOCH 4 done: loss 0.0440 - lr: 0.000107
155
+ 2023-10-13 02:30:42,177 DEV : loss 0.1783849447965622 - f1-score (micro avg) 0.5908
156
+ 2023-10-13 02:30:42,244 ----------------------------------------------------------------------------------------------------
157
+ 2023-10-13 02:32:18,971 epoch 5 - iter 180/1809 - loss 0.02710856 - time (sec): 96.72 - samples/sec: 396.17 - lr: 0.000105 - momentum: 0.000000
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+ 2023-10-13 02:33:56,021 epoch 5 - iter 360/1809 - loss 0.02681704 - time (sec): 193.77 - samples/sec: 395.65 - lr: 0.000103 - momentum: 0.000000
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+ 2023-10-13 02:35:30,935 epoch 5 - iter 540/1809 - loss 0.02671099 - time (sec): 288.69 - samples/sec: 392.24 - lr: 0.000101 - momentum: 0.000000
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+ 2023-10-13 02:37:05,596 epoch 5 - iter 720/1809 - loss 0.02957950 - time (sec): 383.35 - samples/sec: 390.35 - lr: 0.000100 - momentum: 0.000000
161
+ 2023-10-13 02:38:39,902 epoch 5 - iter 900/1809 - loss 0.03107445 - time (sec): 477.66 - samples/sec: 391.89 - lr: 0.000098 - momentum: 0.000000
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+ 2023-10-13 02:40:15,203 epoch 5 - iter 1080/1809 - loss 0.03053907 - time (sec): 572.96 - samples/sec: 393.23 - lr: 0.000096 - momentum: 0.000000
163
+ 2023-10-13 02:41:51,148 epoch 5 - iter 1260/1809 - loss 0.03099549 - time (sec): 668.90 - samples/sec: 392.67 - lr: 0.000094 - momentum: 0.000000
164
+ 2023-10-13 02:43:24,310 epoch 5 - iter 1440/1809 - loss 0.03239734 - time (sec): 762.06 - samples/sec: 393.07 - lr: 0.000093 - momentum: 0.000000
165
+ 2023-10-13 02:45:01,678 epoch 5 - iter 1620/1809 - loss 0.03173686 - time (sec): 859.43 - samples/sec: 395.54 - lr: 0.000091 - momentum: 0.000000
166
+ 2023-10-13 02:46:39,758 epoch 5 - iter 1800/1809 - loss 0.03205991 - time (sec): 957.51 - samples/sec: 394.91 - lr: 0.000089 - momentum: 0.000000
167
+ 2023-10-13 02:46:44,304 ----------------------------------------------------------------------------------------------------
168
+ 2023-10-13 02:46:44,305 EPOCH 5 done: loss 0.0320 - lr: 0.000089
169
+ 2023-10-13 02:47:26,896 DEV : loss 0.2254001647233963 - f1-score (micro avg) 0.6268
170
+ 2023-10-13 02:47:26,979 ----------------------------------------------------------------------------------------------------
171
+ 2023-10-13 02:49:02,907 epoch 6 - iter 180/1809 - loss 0.02309495 - time (sec): 95.92 - samples/sec: 392.21 - lr: 0.000087 - momentum: 0.000000
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+ 2023-10-13 02:50:42,922 epoch 6 - iter 360/1809 - loss 0.02270880 - time (sec): 195.94 - samples/sec: 386.67 - lr: 0.000085 - momentum: 0.000000
173
+ 2023-10-13 02:52:19,953 epoch 6 - iter 540/1809 - loss 0.02278981 - time (sec): 292.97 - samples/sec: 386.21 - lr: 0.000084 - momentum: 0.000000
174
+ 2023-10-13 02:53:57,447 epoch 6 - iter 720/1809 - loss 0.02331295 - time (sec): 390.47 - samples/sec: 387.70 - lr: 0.000082 - momentum: 0.000000
175
+ 2023-10-13 02:55:33,309 epoch 6 - iter 900/1809 - loss 0.02407055 - time (sec): 486.33 - samples/sec: 389.86 - lr: 0.000080 - momentum: 0.000000
176
+ 2023-10-13 02:57:09,646 epoch 6 - iter 1080/1809 - loss 0.02408103 - time (sec): 582.66 - samples/sec: 389.96 - lr: 0.000078 - momentum: 0.000000
177
+ 2023-10-13 02:58:45,520 epoch 6 - iter 1260/1809 - loss 0.02505139 - time (sec): 678.54 - samples/sec: 390.43 - lr: 0.000077 - momentum: 0.000000
178
+ 2023-10-13 03:00:20,100 epoch 6 - iter 1440/1809 - loss 0.02498905 - time (sec): 773.12 - samples/sec: 389.95 - lr: 0.000075 - momentum: 0.000000
179
+ 2023-10-13 03:01:52,543 epoch 6 - iter 1620/1809 - loss 0.02440205 - time (sec): 865.56 - samples/sec: 391.85 - lr: 0.000073 - momentum: 0.000000
180
+ 2023-10-13 03:03:26,329 epoch 6 - iter 1800/1809 - loss 0.02416447 - time (sec): 959.35 - samples/sec: 394.14 - lr: 0.000071 - momentum: 0.000000
181
+ 2023-10-13 03:03:30,703 ----------------------------------------------------------------------------------------------------
182
+ 2023-10-13 03:03:30,704 EPOCH 6 done: loss 0.0241 - lr: 0.000071
183
+ 2023-10-13 03:04:12,643 DEV : loss 0.26813653111457825 - f1-score (micro avg) 0.6519
184
+ 2023-10-13 03:04:12,704 saving best model
185
+ 2023-10-13 03:04:15,437 ----------------------------------------------------------------------------------------------------
186
+ 2023-10-13 03:05:49,546 epoch 7 - iter 180/1809 - loss 0.01558310 - time (sec): 94.11 - samples/sec: 392.76 - lr: 0.000069 - momentum: 0.000000
187
+ 2023-10-13 03:07:23,547 epoch 7 - iter 360/1809 - loss 0.01547849 - time (sec): 188.11 - samples/sec: 402.58 - lr: 0.000068 - momentum: 0.000000
188
+ 2023-10-13 03:08:56,013 epoch 7 - iter 540/1809 - loss 0.01669214 - time (sec): 280.57 - samples/sec: 404.44 - lr: 0.000066 - momentum: 0.000000
189
+ 2023-10-13 03:10:28,801 epoch 7 - iter 720/1809 - loss 0.01856464 - time (sec): 373.36 - samples/sec: 404.84 - lr: 0.000064 - momentum: 0.000000
190
+ 2023-10-13 03:12:00,985 epoch 7 - iter 900/1809 - loss 0.01851849 - time (sec): 465.54 - samples/sec: 405.68 - lr: 0.000062 - momentum: 0.000000
191
+ 2023-10-13 03:13:32,908 epoch 7 - iter 1080/1809 - loss 0.01798567 - time (sec): 557.47 - samples/sec: 405.34 - lr: 0.000061 - momentum: 0.000000
192
+ 2023-10-13 03:15:06,435 epoch 7 - iter 1260/1809 - loss 0.01767135 - time (sec): 650.99 - samples/sec: 405.31 - lr: 0.000059 - momentum: 0.000000
193
+ 2023-10-13 03:16:41,207 epoch 7 - iter 1440/1809 - loss 0.01898520 - time (sec): 745.77 - samples/sec: 404.12 - lr: 0.000057 - momentum: 0.000000
194
+ 2023-10-13 03:18:16,201 epoch 7 - iter 1620/1809 - loss 0.01916789 - time (sec): 840.76 - samples/sec: 404.08 - lr: 0.000055 - momentum: 0.000000
195
+ 2023-10-13 03:19:50,732 epoch 7 - iter 1800/1809 - loss 0.01895459 - time (sec): 935.29 - samples/sec: 404.42 - lr: 0.000053 - momentum: 0.000000
196
+ 2023-10-13 03:19:55,054 ----------------------------------------------------------------------------------------------------
197
+ 2023-10-13 03:19:55,054 EPOCH 7 done: loss 0.0189 - lr: 0.000053
198
+ 2023-10-13 03:20:35,004 DEV : loss 0.29598313570022583 - f1-score (micro avg) 0.6553
199
+ 2023-10-13 03:20:35,066 saving best model
200
+ 2023-10-13 03:20:37,700 ----------------------------------------------------------------------------------------------------
201
+ 2023-10-13 03:22:10,318 epoch 8 - iter 180/1809 - loss 0.01284778 - time (sec): 92.61 - samples/sec: 405.30 - lr: 0.000052 - momentum: 0.000000
202
+ 2023-10-13 03:23:42,134 epoch 8 - iter 360/1809 - loss 0.01152205 - time (sec): 184.43 - samples/sec: 411.62 - lr: 0.000050 - momentum: 0.000000
203
+ 2023-10-13 03:25:17,893 epoch 8 - iter 540/1809 - loss 0.01144334 - time (sec): 280.19 - samples/sec: 406.68 - lr: 0.000048 - momentum: 0.000000
204
+ 2023-10-13 03:26:55,544 epoch 8 - iter 720/1809 - loss 0.01247695 - time (sec): 377.84 - samples/sec: 404.95 - lr: 0.000046 - momentum: 0.000000
205
+ 2023-10-13 03:28:29,861 epoch 8 - iter 900/1809 - loss 0.01242235 - time (sec): 472.16 - samples/sec: 405.85 - lr: 0.000044 - momentum: 0.000000
206
+ 2023-10-13 03:30:00,572 epoch 8 - iter 1080/1809 - loss 0.01249440 - time (sec): 562.87 - samples/sec: 404.47 - lr: 0.000043 - momentum: 0.000000
207
+ 2023-10-13 03:31:32,676 epoch 8 - iter 1260/1809 - loss 0.01284750 - time (sec): 654.97 - samples/sec: 404.21 - lr: 0.000041 - momentum: 0.000000
208
+ 2023-10-13 03:33:05,856 epoch 8 - iter 1440/1809 - loss 0.01286960 - time (sec): 748.15 - samples/sec: 405.03 - lr: 0.000039 - momentum: 0.000000
209
+ 2023-10-13 03:34:39,321 epoch 8 - iter 1620/1809 - loss 0.01296803 - time (sec): 841.62 - samples/sec: 405.50 - lr: 0.000037 - momentum: 0.000000
210
+ 2023-10-13 03:36:14,046 epoch 8 - iter 1800/1809 - loss 0.01287811 - time (sec): 936.34 - samples/sec: 404.13 - lr: 0.000036 - momentum: 0.000000
211
+ 2023-10-13 03:36:18,143 ----------------------------------------------------------------------------------------------------
212
+ 2023-10-13 03:36:18,143 EPOCH 8 done: loss 0.0128 - lr: 0.000036
213
+ 2023-10-13 03:36:57,138 DEV : loss 0.33492255210876465 - f1-score (micro avg) 0.647
214
+ 2023-10-13 03:36:57,201 ----------------------------------------------------------------------------------------------------
215
+ 2023-10-13 03:38:34,200 epoch 9 - iter 180/1809 - loss 0.00784411 - time (sec): 97.00 - samples/sec: 383.03 - lr: 0.000034 - momentum: 0.000000
216
+ 2023-10-13 03:40:11,602 epoch 9 - iter 360/1809 - loss 0.01133300 - time (sec): 194.40 - samples/sec: 384.49 - lr: 0.000032 - momentum: 0.000000
217
+ 2023-10-13 03:41:47,232 epoch 9 - iter 540/1809 - loss 0.01015941 - time (sec): 290.03 - samples/sec: 387.16 - lr: 0.000030 - momentum: 0.000000
218
+ 2023-10-13 03:43:23,081 epoch 9 - iter 720/1809 - loss 0.01025025 - time (sec): 385.88 - samples/sec: 394.75 - lr: 0.000028 - momentum: 0.000000
219
+ 2023-10-13 03:44:57,677 epoch 9 - iter 900/1809 - loss 0.01070525 - time (sec): 480.47 - samples/sec: 394.93 - lr: 0.000027 - momentum: 0.000000
220
+ 2023-10-13 03:46:31,742 epoch 9 - iter 1080/1809 - loss 0.01028318 - time (sec): 574.54 - samples/sec: 396.01 - lr: 0.000025 - momentum: 0.000000
221
+ 2023-10-13 03:48:04,906 epoch 9 - iter 1260/1809 - loss 0.01021383 - time (sec): 667.70 - samples/sec: 396.05 - lr: 0.000023 - momentum: 0.000000
222
+ 2023-10-13 03:49:39,964 epoch 9 - iter 1440/1809 - loss 0.01089912 - time (sec): 762.76 - samples/sec: 396.96 - lr: 0.000021 - momentum: 0.000000
223
+ 2023-10-13 03:51:13,537 epoch 9 - iter 1620/1809 - loss 0.01101134 - time (sec): 856.33 - samples/sec: 398.65 - lr: 0.000020 - momentum: 0.000000
224
+ 2023-10-13 03:52:47,726 epoch 9 - iter 1800/1809 - loss 0.01064439 - time (sec): 950.52 - samples/sec: 398.11 - lr: 0.000018 - momentum: 0.000000
225
+ 2023-10-13 03:52:51,949 ----------------------------------------------------------------------------------------------------
226
+ 2023-10-13 03:52:51,950 EPOCH 9 done: loss 0.0106 - lr: 0.000018
227
+ 2023-10-13 03:53:31,082 DEV : loss 0.3527080714702606 - f1-score (micro avg) 0.6497
228
+ 2023-10-13 03:53:31,150 ----------------------------------------------------------------------------------------------------
229
+ 2023-10-13 03:55:10,271 epoch 10 - iter 180/1809 - loss 0.01074091 - time (sec): 99.12 - samples/sec: 381.35 - lr: 0.000016 - momentum: 0.000000
230
+ 2023-10-13 03:56:50,888 epoch 10 - iter 360/1809 - loss 0.00827756 - time (sec): 199.74 - samples/sec: 375.73 - lr: 0.000014 - momentum: 0.000000
231
+ 2023-10-13 03:58:30,839 epoch 10 - iter 540/1809 - loss 0.00823781 - time (sec): 299.69 - samples/sec: 376.56 - lr: 0.000012 - momentum: 0.000000
232
+ 2023-10-13 04:00:10,229 epoch 10 - iter 720/1809 - loss 0.00811760 - time (sec): 399.08 - samples/sec: 375.88 - lr: 0.000011 - momentum: 0.000000
233
+ 2023-10-13 04:01:47,773 epoch 10 - iter 900/1809 - loss 0.00799705 - time (sec): 496.62 - samples/sec: 378.88 - lr: 0.000009 - momentum: 0.000000
234
+ 2023-10-13 04:03:23,198 epoch 10 - iter 1080/1809 - loss 0.00755984 - time (sec): 592.05 - samples/sec: 382.04 - lr: 0.000007 - momentum: 0.000000
235
+ 2023-10-13 04:04:58,210 epoch 10 - iter 1260/1809 - loss 0.00766798 - time (sec): 687.06 - samples/sec: 384.62 - lr: 0.000005 - momentum: 0.000000
236
+ 2023-10-13 04:06:32,716 epoch 10 - iter 1440/1809 - loss 0.00809054 - time (sec): 781.56 - samples/sec: 387.08 - lr: 0.000004 - momentum: 0.000000
237
+ 2023-10-13 04:08:08,141 epoch 10 - iter 1620/1809 - loss 0.00862815 - time (sec): 876.99 - samples/sec: 388.22 - lr: 0.000002 - momentum: 0.000000
238
+ 2023-10-13 04:09:43,867 epoch 10 - iter 1800/1809 - loss 0.00839058 - time (sec): 972.71 - samples/sec: 389.06 - lr: 0.000000 - momentum: 0.000000
239
+ 2023-10-13 04:09:48,012 ----------------------------------------------------------------------------------------------------
240
+ 2023-10-13 04:09:48,012 EPOCH 10 done: loss 0.0084 - lr: 0.000000
241
+ 2023-10-13 04:10:26,982 DEV : loss 0.3519783318042755 - f1-score (micro avg) 0.6454
242
+ 2023-10-13 04:10:27,904 ----------------------------------------------------------------------------------------------------
243
+ 2023-10-13 04:10:27,906 Loading model from best epoch ...
244
+ 2023-10-13 04:10:32,462 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
+ 2023-10-13 04:11:31,927
246
+ Results:
247
+ - F-score (micro) 0.6338
248
+ - F-score (macro) 0.4822
249
+ - Accuracy 0.4769
250
+
251
+ By class:
252
+ precision recall f1-score support
253
+
254
+ loc 0.6496 0.7530 0.6975 591
255
+ pers 0.5405 0.7479 0.6275 357
256
+ org 0.1304 0.1139 0.1216 79
257
+
258
+ micro avg 0.5777 0.7020 0.6338 1027
259
+ macro avg 0.4402 0.5383 0.4822 1027
260
+ weighted avg 0.5718 0.7020 0.6289 1027
261
+
262
+ 2023-10-13 04:11:31,927 ----------------------------------------------------------------------------------------------------