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hmbench-icdar-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-5

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.1697204463.de2e83fddbee.1952.19 +3 -0
  6. test.tsv +0 -0
  7. training.log +262 -0
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dev.tsv ADDED
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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 13:50:48 0.0002 0.8433 0.1322 0.6689 0.6855 0.6771 0.5387
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+ 2 14:00:35 0.0001 0.1188 0.0911 0.7468 0.7206 0.7334 0.5953
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+ 3 14:10:24 0.0001 0.0753 0.0957 0.7284 0.7919 0.7588 0.6329
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+ 4 14:20:00 0.0001 0.0538 0.1287 0.7405 0.7749 0.7573 0.6325
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+ 5 14:29:25 0.0001 0.0399 0.1565 0.7481 0.7726 0.7602 0.6295
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+ 6 14:38:56 0.0001 0.0295 0.1645 0.7353 0.7636 0.7492 0.6187
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+ 7 14:48:34 0.0001 0.0203 0.1975 0.7434 0.7704 0.7567 0.6242
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+ 8 14:58:09 0.0000 0.0145 0.2158 0.7426 0.7636 0.7529 0.6210
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+ 9 15:07:45 0.0000 0.0108 0.2295 0.7647 0.7647 0.7647 0.6341
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+ 10 15:17:37 0.0000 0.0071 0.2328 0.7500 0.7704 0.7600 0.6294
runs/events.out.tfevents.1697204463.de2e83fddbee.1952.19 ADDED
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test.tsv ADDED
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training.log ADDED
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+ 2023-10-13 13:41:03,373 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 13:41:03,375 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)
50
+ (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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+ )
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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+ 2023-10-13 13:41:03,375 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 13:41:03,375 MultiCorpus: 7936 train + 992 dev + 992 test sentences
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+ - NER_ICDAR_EUROPEANA Corpus: 7936 train + 992 dev + 992 test sentences - /root/.flair/datasets/ner_icdar_europeana/fr
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+ 2023-10-13 13:41:03,375 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 13:41:03,375 Train: 7936 sentences
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+ 2023-10-13 13:41:03,375 (train_with_dev=False, train_with_test=False)
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+ 2023-10-13 13:41:03,376 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 13:41:03,376 Training Params:
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+ 2023-10-13 13:41:03,376 - learning_rate: "0.00016"
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+ 2023-10-13 13:41:03,376 - mini_batch_size: "4"
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+ 2023-10-13 13:41:03,376 - max_epochs: "10"
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+ 2023-10-13 13:41:03,376 - shuffle: "True"
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+ 2023-10-13 13:41:03,376 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 13:41:03,376 Plugins:
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+ 2023-10-13 13:41:03,376 - TensorboardLogger
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+ 2023-10-13 13:41:03,376 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-13 13:41:03,376 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 13:41:03,376 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-13 13:41:03,376 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-13 13:41:03,376 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 13:41:03,376 Computation:
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+ 2023-10-13 13:41:03,377 - compute on device: cuda:0
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+ 2023-10-13 13:41:03,377 - embedding storage: none
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+ 2023-10-13 13:41:03,377 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 13:41:03,377 Model training base path: "hmbench-icdar/fr-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-5"
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+ 2023-10-13 13:41:03,377 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 13:41:03,377 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 13:41:03,377 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-13 13:41:57,697 epoch 1 - iter 198/1984 - loss 2.53411240 - time (sec): 54.32 - samples/sec: 325.47 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-13 13:42:51,087 epoch 1 - iter 396/1984 - loss 2.34200986 - time (sec): 107.71 - samples/sec: 309.82 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-13 13:43:46,734 epoch 1 - iter 594/1984 - loss 2.00986386 - time (sec): 163.35 - samples/sec: 309.73 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-13 13:44:41,691 epoch 1 - iter 792/1984 - loss 1.71728426 - time (sec): 218.31 - samples/sec: 300.53 - lr: 0.000064 - momentum: 0.000000
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+ 2023-10-13 13:45:39,863 epoch 1 - iter 990/1984 - loss 1.47309135 - time (sec): 276.48 - samples/sec: 295.09 - lr: 0.000080 - momentum: 0.000000
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+ 2023-10-13 13:46:37,854 epoch 1 - iter 1188/1984 - loss 1.28054026 - time (sec): 334.48 - samples/sec: 291.62 - lr: 0.000096 - momentum: 0.000000
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+ 2023-10-13 13:47:32,695 epoch 1 - iter 1386/1984 - loss 1.13053154 - time (sec): 389.32 - samples/sec: 293.66 - lr: 0.000112 - momentum: 0.000000
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+ 2023-10-13 13:48:27,496 epoch 1 - iter 1584/1984 - loss 1.01770682 - time (sec): 444.12 - samples/sec: 293.40 - lr: 0.000128 - momentum: 0.000000
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+ 2023-10-13 13:49:25,748 epoch 1 - iter 1782/1984 - loss 0.91618266 - time (sec): 502.37 - samples/sec: 294.61 - lr: 0.000144 - momentum: 0.000000
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+ 2023-10-13 13:50:22,623 epoch 1 - iter 1980/1984 - loss 0.84428444 - time (sec): 559.24 - samples/sec: 292.81 - lr: 0.000160 - momentum: 0.000000
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+ 2023-10-13 13:50:23,697 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 13:50:23,697 EPOCH 1 done: loss 0.8433 - lr: 0.000160
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+ 2023-10-13 13:50:48,714 DEV : loss 0.13220053911209106 - f1-score (micro avg) 0.6771
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+ 2023-10-13 13:50:48,754 saving best model
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+ 2023-10-13 13:50:49,635 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 13:51:44,743 epoch 2 - iter 198/1984 - loss 0.15716909 - time (sec): 55.11 - samples/sec: 300.33 - lr: 0.000158 - momentum: 0.000000
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+ 2023-10-13 13:52:39,667 epoch 2 - iter 396/1984 - loss 0.14341696 - time (sec): 110.03 - samples/sec: 302.03 - lr: 0.000156 - momentum: 0.000000
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+ 2023-10-13 13:53:39,458 epoch 2 - iter 594/1984 - loss 0.13622103 - time (sec): 169.82 - samples/sec: 295.61 - lr: 0.000155 - momentum: 0.000000
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+ 2023-10-13 13:54:35,548 epoch 2 - iter 792/1984 - loss 0.13486939 - time (sec): 225.91 - samples/sec: 291.39 - lr: 0.000153 - momentum: 0.000000
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+ 2023-10-13 13:55:31,683 epoch 2 - iter 990/1984 - loss 0.13032807 - time (sec): 282.05 - samples/sec: 291.80 - lr: 0.000151 - momentum: 0.000000
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+ 2023-10-13 13:56:24,784 epoch 2 - iter 1188/1984 - loss 0.12849675 - time (sec): 335.15 - samples/sec: 294.12 - lr: 0.000149 - momentum: 0.000000
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+ 2023-10-13 13:57:19,699 epoch 2 - iter 1386/1984 - loss 0.12609233 - time (sec): 390.06 - samples/sec: 294.46 - lr: 0.000148 - momentum: 0.000000
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+ 2023-10-13 13:58:16,450 epoch 2 - iter 1584/1984 - loss 0.12288083 - time (sec): 446.81 - samples/sec: 292.77 - lr: 0.000146 - momentum: 0.000000
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+ 2023-10-13 13:59:10,814 epoch 2 - iter 1782/1984 - loss 0.12152366 - time (sec): 501.18 - samples/sec: 291.77 - lr: 0.000144 - momentum: 0.000000
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+ 2023-10-13 14:00:08,264 epoch 2 - iter 1980/1984 - loss 0.11885738 - time (sec): 558.63 - samples/sec: 293.08 - lr: 0.000142 - momentum: 0.000000
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+ 2023-10-13 14:00:09,548 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 14:00:09,549 EPOCH 2 done: loss 0.1188 - lr: 0.000142
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+ 2023-10-13 14:00:35,360 DEV : loss 0.09111367911100388 - f1-score (micro avg) 0.7334
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+ 2023-10-13 14:00:35,406 saving best model
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+ 2023-10-13 14:00:37,985 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 14:01:35,507 epoch 3 - iter 198/1984 - loss 0.06992910 - time (sec): 57.52 - samples/sec: 279.98 - lr: 0.000140 - momentum: 0.000000
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+ 2023-10-13 14:02:30,138 epoch 3 - iter 396/1984 - loss 0.07739811 - time (sec): 112.15 - samples/sec: 289.02 - lr: 0.000139 - momentum: 0.000000
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+ 2023-10-13 14:03:23,678 epoch 3 - iter 594/1984 - loss 0.07861216 - time (sec): 165.69 - samples/sec: 292.77 - lr: 0.000137 - momentum: 0.000000
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+ 2023-10-13 14:04:18,698 epoch 3 - iter 792/1984 - loss 0.07894264 - time (sec): 220.71 - samples/sec: 294.08 - lr: 0.000135 - momentum: 0.000000
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+ 2023-10-13 14:05:11,588 epoch 3 - iter 990/1984 - loss 0.07810640 - time (sec): 273.60 - samples/sec: 295.90 - lr: 0.000133 - momentum: 0.000000
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+ 2023-10-13 14:06:07,163 epoch 3 - iter 1188/1984 - loss 0.07738219 - time (sec): 329.17 - samples/sec: 296.93 - lr: 0.000132 - momentum: 0.000000
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+ 2023-10-13 14:07:04,929 epoch 3 - iter 1386/1984 - loss 0.07780412 - time (sec): 386.94 - samples/sec: 295.25 - lr: 0.000130 - momentum: 0.000000
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+ 2023-10-13 14:08:00,504 epoch 3 - iter 1584/1984 - loss 0.07607150 - time (sec): 442.51 - samples/sec: 295.09 - lr: 0.000128 - momentum: 0.000000
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+ 2023-10-13 14:08:57,596 epoch 3 - iter 1782/1984 - loss 0.07488198 - time (sec): 499.61 - samples/sec: 294.57 - lr: 0.000126 - momentum: 0.000000
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+ 2023-10-13 14:09:56,502 epoch 3 - iter 1980/1984 - loss 0.07537555 - time (sec): 558.51 - samples/sec: 293.01 - lr: 0.000125 - momentum: 0.000000
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+ 2023-10-13 14:09:57,672 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 14:09:57,673 EPOCH 3 done: loss 0.0753 - lr: 0.000125
140
+ 2023-10-13 14:10:24,639 DEV : loss 0.09566155821084976 - f1-score (micro avg) 0.7588
141
+ 2023-10-13 14:10:24,680 saving best model
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+ 2023-10-13 14:10:27,792 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 14:11:22,947 epoch 4 - iter 198/1984 - loss 0.05573856 - time (sec): 55.15 - samples/sec: 298.81 - lr: 0.000123 - momentum: 0.000000
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+ 2023-10-13 14:12:18,581 epoch 4 - iter 396/1984 - loss 0.05399853 - time (sec): 110.78 - samples/sec: 295.09 - lr: 0.000121 - momentum: 0.000000
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+ 2023-10-13 14:13:12,197 epoch 4 - iter 594/1984 - loss 0.05122298 - time (sec): 164.40 - samples/sec: 297.54 - lr: 0.000119 - momentum: 0.000000
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+ 2023-10-13 14:14:05,824 epoch 4 - iter 792/1984 - loss 0.05330777 - time (sec): 218.03 - samples/sec: 299.43 - lr: 0.000117 - momentum: 0.000000
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+ 2023-10-13 14:14:59,557 epoch 4 - iter 990/1984 - loss 0.05347162 - time (sec): 271.76 - samples/sec: 303.22 - lr: 0.000116 - momentum: 0.000000
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+ 2023-10-13 14:15:53,694 epoch 4 - iter 1188/1984 - loss 0.05216234 - time (sec): 325.90 - samples/sec: 304.32 - lr: 0.000114 - momentum: 0.000000
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+ 2023-10-13 14:16:49,035 epoch 4 - iter 1386/1984 - loss 0.05143628 - time (sec): 381.24 - samples/sec: 301.49 - lr: 0.000112 - momentum: 0.000000
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+ 2023-10-13 14:17:43,745 epoch 4 - iter 1584/1984 - loss 0.05184746 - time (sec): 435.95 - samples/sec: 300.80 - lr: 0.000110 - momentum: 0.000000
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+ 2023-10-13 14:18:38,926 epoch 4 - iter 1782/1984 - loss 0.05298005 - time (sec): 491.13 - samples/sec: 300.19 - lr: 0.000109 - momentum: 0.000000
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+ 2023-10-13 14:19:32,075 epoch 4 - iter 1980/1984 - loss 0.05388844 - time (sec): 544.28 - samples/sec: 300.75 - lr: 0.000107 - momentum: 0.000000
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+ 2023-10-13 14:19:33,222 ----------------------------------------------------------------------------------------------------
154
+ 2023-10-13 14:19:33,223 EPOCH 4 done: loss 0.0538 - lr: 0.000107
155
+ 2023-10-13 14:20:00,317 DEV : loss 0.12870270013809204 - f1-score (micro avg) 0.7573
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+ 2023-10-13 14:20:00,358 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 14:20:53,615 epoch 5 - iter 198/1984 - loss 0.03435675 - time (sec): 53.25 - samples/sec: 315.28 - lr: 0.000105 - momentum: 0.000000
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+ 2023-10-13 14:21:44,798 epoch 5 - iter 396/1984 - loss 0.03418837 - time (sec): 104.44 - samples/sec: 319.18 - lr: 0.000103 - momentum: 0.000000
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+ 2023-10-13 14:22:37,689 epoch 5 - iter 594/1984 - loss 0.03735641 - time (sec): 157.33 - samples/sec: 318.10 - lr: 0.000101 - momentum: 0.000000
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+ 2023-10-13 14:23:31,434 epoch 5 - iter 792/1984 - loss 0.03848171 - time (sec): 211.07 - samples/sec: 314.78 - lr: 0.000100 - momentum: 0.000000
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+ 2023-10-13 14:24:27,121 epoch 5 - iter 990/1984 - loss 0.03772221 - time (sec): 266.76 - samples/sec: 309.25 - lr: 0.000098 - momentum: 0.000000
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+ 2023-10-13 14:25:20,412 epoch 5 - iter 1188/1984 - loss 0.03984799 - time (sec): 320.05 - samples/sec: 307.07 - lr: 0.000096 - momentum: 0.000000
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+ 2023-10-13 14:26:15,156 epoch 5 - iter 1386/1984 - loss 0.03966161 - time (sec): 374.80 - samples/sec: 304.75 - lr: 0.000094 - momentum: 0.000000
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+ 2023-10-13 14:27:08,199 epoch 5 - iter 1584/1984 - loss 0.03987275 - time (sec): 427.84 - samples/sec: 306.78 - lr: 0.000093 - momentum: 0.000000
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+ 2023-10-13 14:28:03,847 epoch 5 - iter 1782/1984 - loss 0.04040681 - time (sec): 483.49 - samples/sec: 305.38 - lr: 0.000091 - momentum: 0.000000
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+ 2023-10-13 14:28:57,477 epoch 5 - iter 1980/1984 - loss 0.03995813 - time (sec): 537.12 - samples/sec: 304.92 - lr: 0.000089 - momentum: 0.000000
167
+ 2023-10-13 14:28:58,562 ----------------------------------------------------------------------------------------------------
168
+ 2023-10-13 14:28:58,563 EPOCH 5 done: loss 0.0399 - lr: 0.000089
169
+ 2023-10-13 14:29:25,637 DEV : loss 0.15645428001880646 - f1-score (micro avg) 0.7602
170
+ 2023-10-13 14:29:25,679 saving best model
171
+ 2023-10-13 14:29:28,321 ----------------------------------------------------------------------------------------------------
172
+ 2023-10-13 14:30:21,475 epoch 6 - iter 198/1984 - loss 0.02467542 - time (sec): 53.15 - samples/sec: 321.45 - lr: 0.000087 - momentum: 0.000000
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+ 2023-10-13 14:31:13,093 epoch 6 - iter 396/1984 - loss 0.02376319 - time (sec): 104.77 - samples/sec: 320.03 - lr: 0.000085 - momentum: 0.000000
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+ 2023-10-13 14:32:04,586 epoch 6 - iter 594/1984 - loss 0.02474508 - time (sec): 156.26 - samples/sec: 315.70 - lr: 0.000084 - momentum: 0.000000
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+ 2023-10-13 14:32:57,169 epoch 6 - iter 792/1984 - loss 0.02868125 - time (sec): 208.84 - samples/sec: 315.69 - lr: 0.000082 - momentum: 0.000000
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+ 2023-10-13 14:33:51,982 epoch 6 - iter 990/1984 - loss 0.02813004 - time (sec): 263.66 - samples/sec: 311.99 - lr: 0.000080 - momentum: 0.000000
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+ 2023-10-13 14:34:49,635 epoch 6 - iter 1188/1984 - loss 0.02767063 - time (sec): 321.31 - samples/sec: 306.76 - lr: 0.000078 - momentum: 0.000000
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+ 2023-10-13 14:35:44,096 epoch 6 - iter 1386/1984 - loss 0.02786452 - time (sec): 375.77 - samples/sec: 305.99 - lr: 0.000077 - momentum: 0.000000
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+ 2023-10-13 14:36:36,019 epoch 6 - iter 1584/1984 - loss 0.02885341 - time (sec): 427.69 - samples/sec: 306.12 - lr: 0.000075 - momentum: 0.000000
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+ 2023-10-13 14:37:29,223 epoch 6 - iter 1782/1984 - loss 0.02817717 - time (sec): 480.90 - samples/sec: 306.03 - lr: 0.000073 - momentum: 0.000000
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+ 2023-10-13 14:38:27,626 epoch 6 - iter 1980/1984 - loss 0.02939511 - time (sec): 539.30 - samples/sec: 303.56 - lr: 0.000071 - momentum: 0.000000
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+ 2023-10-13 14:38:28,789 ----------------------------------------------------------------------------------------------------
183
+ 2023-10-13 14:38:28,789 EPOCH 6 done: loss 0.0295 - lr: 0.000071
184
+ 2023-10-13 14:38:56,206 DEV : loss 0.16446241736412048 - f1-score (micro avg) 0.7492
185
+ 2023-10-13 14:38:56,257 ----------------------------------------------------------------------------------------------------
186
+ 2023-10-13 14:39:51,637 epoch 7 - iter 198/1984 - loss 0.01809156 - time (sec): 55.38 - samples/sec: 297.72 - lr: 0.000069 - momentum: 0.000000
187
+ 2023-10-13 14:40:46,224 epoch 7 - iter 396/1984 - loss 0.01613317 - time (sec): 109.96 - samples/sec: 293.23 - lr: 0.000068 - momentum: 0.000000
188
+ 2023-10-13 14:41:41,377 epoch 7 - iter 594/1984 - loss 0.01702457 - time (sec): 165.12 - samples/sec: 297.91 - lr: 0.000066 - momentum: 0.000000
189
+ 2023-10-13 14:42:36,232 epoch 7 - iter 792/1984 - loss 0.01784643 - time (sec): 219.97 - samples/sec: 296.04 - lr: 0.000064 - momentum: 0.000000
190
+ 2023-10-13 14:43:31,504 epoch 7 - iter 990/1984 - loss 0.01766601 - time (sec): 275.24 - samples/sec: 296.10 - lr: 0.000062 - momentum: 0.000000
191
+ 2023-10-13 14:44:29,053 epoch 7 - iter 1188/1984 - loss 0.01798869 - time (sec): 332.79 - samples/sec: 293.84 - lr: 0.000061 - momentum: 0.000000
192
+ 2023-10-13 14:45:23,368 epoch 7 - iter 1386/1984 - loss 0.01843451 - time (sec): 387.11 - samples/sec: 294.70 - lr: 0.000059 - momentum: 0.000000
193
+ 2023-10-13 14:46:15,280 epoch 7 - iter 1584/1984 - loss 0.01841142 - time (sec): 439.02 - samples/sec: 296.13 - lr: 0.000057 - momentum: 0.000000
194
+ 2023-10-13 14:47:10,847 epoch 7 - iter 1782/1984 - loss 0.01957244 - time (sec): 494.59 - samples/sec: 297.35 - lr: 0.000055 - momentum: 0.000000
195
+ 2023-10-13 14:48:05,500 epoch 7 - iter 1980/1984 - loss 0.02031150 - time (sec): 549.24 - samples/sec: 298.17 - lr: 0.000053 - momentum: 0.000000
196
+ 2023-10-13 14:48:06,548 ----------------------------------------------------------------------------------------------------
197
+ 2023-10-13 14:48:06,548 EPOCH 7 done: loss 0.0203 - lr: 0.000053
198
+ 2023-10-13 14:48:34,612 DEV : loss 0.19750244915485382 - f1-score (micro avg) 0.7567
199
+ 2023-10-13 14:48:34,664 ----------------------------------------------------------------------------------------------------
200
+ 2023-10-13 14:49:28,793 epoch 8 - iter 198/1984 - loss 0.01418068 - time (sec): 54.13 - samples/sec: 304.44 - lr: 0.000052 - momentum: 0.000000
201
+ 2023-10-13 14:50:22,561 epoch 8 - iter 396/1984 - loss 0.01608296 - time (sec): 107.90 - samples/sec: 307.64 - lr: 0.000050 - momentum: 0.000000
202
+ 2023-10-13 14:51:15,361 epoch 8 - iter 594/1984 - loss 0.01364728 - time (sec): 160.69 - samples/sec: 306.85 - lr: 0.000048 - momentum: 0.000000
203
+ 2023-10-13 14:52:08,131 epoch 8 - iter 792/1984 - loss 0.01376922 - time (sec): 213.46 - samples/sec: 309.15 - lr: 0.000046 - momentum: 0.000000
204
+ 2023-10-13 14:53:02,352 epoch 8 - iter 990/1984 - loss 0.01311662 - time (sec): 267.69 - samples/sec: 307.25 - lr: 0.000045 - momentum: 0.000000
205
+ 2023-10-13 14:53:57,556 epoch 8 - iter 1188/1984 - loss 0.01375357 - time (sec): 322.89 - samples/sec: 305.33 - lr: 0.000043 - momentum: 0.000000
206
+ 2023-10-13 14:54:52,392 epoch 8 - iter 1386/1984 - loss 0.01337745 - time (sec): 377.73 - samples/sec: 304.21 - lr: 0.000041 - momentum: 0.000000
207
+ 2023-10-13 14:55:49,698 epoch 8 - iter 1584/1984 - loss 0.01404364 - time (sec): 435.03 - samples/sec: 300.09 - lr: 0.000039 - momentum: 0.000000
208
+ 2023-10-13 14:56:44,413 epoch 8 - iter 1782/1984 - loss 0.01463217 - time (sec): 489.75 - samples/sec: 300.14 - lr: 0.000037 - momentum: 0.000000
209
+ 2023-10-13 14:57:39,656 epoch 8 - iter 1980/1984 - loss 0.01450393 - time (sec): 544.99 - samples/sec: 300.46 - lr: 0.000036 - momentum: 0.000000
210
+ 2023-10-13 14:57:40,732 ----------------------------------------------------------------------------------------------------
211
+ 2023-10-13 14:57:40,733 EPOCH 8 done: loss 0.0145 - lr: 0.000036
212
+ 2023-10-13 14:58:09,479 DEV : loss 0.21577665209770203 - f1-score (micro avg) 0.7529
213
+ 2023-10-13 14:58:09,519 ----------------------------------------------------------------------------------------------------
214
+ 2023-10-13 14:59:03,230 epoch 9 - iter 198/1984 - loss 0.00877228 - time (sec): 53.71 - samples/sec: 293.64 - lr: 0.000034 - momentum: 0.000000
215
+ 2023-10-13 15:00:00,025 epoch 9 - iter 396/1984 - loss 0.00937105 - time (sec): 110.50 - samples/sec: 287.85 - lr: 0.000032 - momentum: 0.000000
216
+ 2023-10-13 15:00:54,419 epoch 9 - iter 594/1984 - loss 0.00856868 - time (sec): 164.90 - samples/sec: 293.52 - lr: 0.000030 - momentum: 0.000000
217
+ 2023-10-13 15:01:49,560 epoch 9 - iter 792/1984 - loss 0.00989234 - time (sec): 220.04 - samples/sec: 295.67 - lr: 0.000029 - momentum: 0.000000
218
+ 2023-10-13 15:02:47,168 epoch 9 - iter 990/1984 - loss 0.01106797 - time (sec): 277.65 - samples/sec: 292.62 - lr: 0.000027 - momentum: 0.000000
219
+ 2023-10-13 15:03:41,878 epoch 9 - iter 1188/1984 - loss 0.01081355 - time (sec): 332.36 - samples/sec: 288.46 - lr: 0.000025 - momentum: 0.000000
220
+ 2023-10-13 15:04:36,367 epoch 9 - iter 1386/1984 - loss 0.01050288 - time (sec): 386.85 - samples/sec: 292.92 - lr: 0.000023 - momentum: 0.000000
221
+ 2023-10-13 15:05:30,076 epoch 9 - iter 1584/1984 - loss 0.01056348 - time (sec): 440.55 - samples/sec: 294.63 - lr: 0.000021 - momentum: 0.000000
222
+ 2023-10-13 15:06:27,189 epoch 9 - iter 1782/1984 - loss 0.01102912 - time (sec): 497.67 - samples/sec: 295.61 - lr: 0.000020 - momentum: 0.000000
223
+ 2023-10-13 15:07:18,598 epoch 9 - iter 1980/1984 - loss 0.01087141 - time (sec): 549.08 - samples/sec: 297.93 - lr: 0.000018 - momentum: 0.000000
224
+ 2023-10-13 15:07:19,699 ----------------------------------------------------------------------------------------------------
225
+ 2023-10-13 15:07:19,699 EPOCH 9 done: loss 0.0108 - lr: 0.000018
226
+ 2023-10-13 15:07:45,626 DEV : loss 0.22953416407108307 - f1-score (micro avg) 0.7647
227
+ 2023-10-13 15:07:45,671 saving best model
228
+ 2023-10-13 15:07:48,309 ----------------------------------------------------------------------------------------------------
229
+ 2023-10-13 15:08:44,790 epoch 10 - iter 198/1984 - loss 0.00464760 - time (sec): 56.48 - samples/sec: 299.72 - lr: 0.000016 - momentum: 0.000000
230
+ 2023-10-13 15:09:43,385 epoch 10 - iter 396/1984 - loss 0.00822453 - time (sec): 115.07 - samples/sec: 283.39 - lr: 0.000014 - momentum: 0.000000
231
+ 2023-10-13 15:10:37,532 epoch 10 - iter 594/1984 - loss 0.00828961 - time (sec): 169.22 - samples/sec: 286.73 - lr: 0.000013 - momentum: 0.000000
232
+ 2023-10-13 15:11:31,359 epoch 10 - iter 792/1984 - loss 0.00709603 - time (sec): 223.04 - samples/sec: 288.73 - lr: 0.000011 - momentum: 0.000000
233
+ 2023-10-13 15:12:28,943 epoch 10 - iter 990/1984 - loss 0.00651970 - time (sec): 280.63 - samples/sec: 289.30 - lr: 0.000009 - momentum: 0.000000
234
+ 2023-10-13 15:13:24,258 epoch 10 - iter 1188/1984 - loss 0.00667521 - time (sec): 335.94 - samples/sec: 291.68 - lr: 0.000007 - momentum: 0.000000
235
+ 2023-10-13 15:14:23,470 epoch 10 - iter 1386/1984 - loss 0.00650330 - time (sec): 395.16 - samples/sec: 290.20 - lr: 0.000005 - momentum: 0.000000
236
+ 2023-10-13 15:15:17,651 epoch 10 - iter 1584/1984 - loss 0.00684118 - time (sec): 449.34 - samples/sec: 292.52 - lr: 0.000004 - momentum: 0.000000
237
+ 2023-10-13 15:16:11,772 epoch 10 - iter 1782/1984 - loss 0.00684599 - time (sec): 503.46 - samples/sec: 293.68 - lr: 0.000002 - momentum: 0.000000
238
+ 2023-10-13 15:17:08,238 epoch 10 - iter 1980/1984 - loss 0.00707841 - time (sec): 559.92 - samples/sec: 292.19 - lr: 0.000000 - momentum: 0.000000
239
+ 2023-10-13 15:17:09,533 ----------------------------------------------------------------------------------------------------
240
+ 2023-10-13 15:17:09,534 EPOCH 10 done: loss 0.0071 - lr: 0.000000
241
+ 2023-10-13 15:17:37,000 DEV : loss 0.23275645077228546 - f1-score (micro avg) 0.76
242
+ 2023-10-13 15:17:38,020 ----------------------------------------------------------------------------------------------------
243
+ 2023-10-13 15:17:38,023 Loading model from best epoch ...
244
+ 2023-10-13 15:17:42,458 SequenceTagger predicts: Dictionary with 13 tags: O, S-PER, B-PER, E-PER, I-PER, S-LOC, B-LOC, E-LOC, I-LOC, S-ORG, B-ORG, E-ORG, I-ORG
245
+ 2023-10-13 15:18:07,926
246
+ Results:
247
+ - F-score (micro) 0.7594
248
+ - F-score (macro) 0.6623
249
+ - Accuracy 0.6372
250
+
251
+ By class:
252
+ precision recall f1-score support
253
+
254
+ LOC 0.8053 0.8397 0.8221 655
255
+ PER 0.7076 0.7489 0.7277 223
256
+ ORG 0.5341 0.3701 0.4372 127
257
+
258
+ micro avg 0.7587 0.7602 0.7594 1005
259
+ macro avg 0.6823 0.6529 0.6623 1005
260
+ weighted avg 0.7493 0.7602 0.7525 1005
261
+
262
+ 2023-10-13 15:18:07,927 ----------------------------------------------------------------------------------------------------