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hmbench-icdar-fr-hmbyt5-bs8-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.1697148312.de2e83fddbee.1952.9 +3 -0
  6. test.tsv +0 -0
  7. training.log +263 -0
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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 22:13:55 0.0002 1.0021 0.1648 0.4595 0.5204 0.4881 0.3425
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+ 2 22:22:20 0.0001 0.1354 0.0871 0.7112 0.7410 0.7258 0.5933
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+ 3 22:31:03 0.0001 0.0765 0.0886 0.7192 0.7534 0.7359 0.6066
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+ 4 22:39:49 0.0001 0.0519 0.1074 0.7168 0.7760 0.7452 0.6175
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+ 5 22:48:32 0.0001 0.0385 0.1225 0.7533 0.7771 0.7650 0.6367
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+ 6 22:57:31 0.0001 0.0302 0.1594 0.7522 0.7760 0.7639 0.6370
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+ 7 23:06:35 0.0001 0.0224 0.1707 0.7344 0.7851 0.7589 0.6321
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+ 8 23:15:33 0.0000 0.0181 0.1880 0.7462 0.7749 0.7603 0.6337
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+ 9 23:24:14 0.0000 0.0148 0.1875 0.7492 0.7873 0.7678 0.6415
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+ 10 23:32:55 0.0000 0.0111 0.1963 0.7447 0.7885 0.7659 0.6383
runs/events.out.tfevents.1697148312.de2e83fddbee.1952.9 ADDED
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test.tsv ADDED
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training.log ADDED
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+ 2023-10-12 22:05:12,690 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 22:05:12,692 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)
43
+ )
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+ (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
45
+ (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)
63
+ )
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+ )
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+ )
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+ (locked_dropout): LockedDropout(p=0.5)
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+ (linear): Linear(in_features=1472, out_features=13, bias=True)
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+ (loss_function): CrossEntropyLoss()
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+ )"
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+ 2023-10-12 22:05:12,692 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 22:05:12,692 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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+ 2023-10-12 22:05:12,692 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 22:05:12,693 Train: 7936 sentences
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+ 2023-10-12 22:05:12,693 (train_with_dev=False, train_with_test=False)
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+ 2023-10-12 22:05:12,693 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 22:05:12,693 Training Params:
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+ 2023-10-12 22:05:12,693 - learning_rate: "0.00016"
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+ 2023-10-12 22:05:12,693 - mini_batch_size: "8"
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+ 2023-10-12 22:05:12,693 - max_epochs: "10"
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+ 2023-10-12 22:05:12,693 - shuffle: "True"
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+ 2023-10-12 22:05:12,693 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 22:05:12,693 Plugins:
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+ 2023-10-12 22:05:12,693 - TensorboardLogger
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+ 2023-10-12 22:05:12,693 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-12 22:05:12,693 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 22:05:12,693 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-12 22:05:12,694 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-12 22:05:12,694 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 22:05:12,694 Computation:
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+ 2023-10-12 22:05:12,694 - compute on device: cuda:0
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+ 2023-10-12 22:05:12,694 - embedding storage: none
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+ 2023-10-12 22:05:12,694 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 22:05:12,694 Model training base path: "hmbench-icdar/fr-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-3"
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+ 2023-10-12 22:05:12,694 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 22:05:12,694 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 22:05:12,694 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-12 22:06:02,586 epoch 1 - iter 99/992 - loss 2.53866750 - time (sec): 49.89 - samples/sec: 321.99 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-12 22:06:51,649 epoch 1 - iter 198/992 - loss 2.44203287 - time (sec): 98.95 - samples/sec: 321.09 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-12 22:07:42,422 epoch 1 - iter 297/992 - loss 2.21386372 - time (sec): 149.73 - samples/sec: 325.56 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-12 22:08:33,193 epoch 1 - iter 396/992 - loss 1.97014077 - time (sec): 200.50 - samples/sec: 325.47 - lr: 0.000064 - momentum: 0.000000
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+ 2023-10-12 22:09:25,877 epoch 1 - iter 495/992 - loss 1.71310585 - time (sec): 253.18 - samples/sec: 324.30 - lr: 0.000080 - momentum: 0.000000
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+ 2023-10-12 22:10:17,664 epoch 1 - iter 594/992 - loss 1.49579279 - time (sec): 304.97 - samples/sec: 322.49 - lr: 0.000096 - momentum: 0.000000
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+ 2023-10-12 22:11:05,041 epoch 1 - iter 693/992 - loss 1.33675130 - time (sec): 352.35 - samples/sec: 323.61 - lr: 0.000112 - momentum: 0.000000
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+ 2023-10-12 22:11:53,190 epoch 1 - iter 792/992 - loss 1.20207715 - time (sec): 400.49 - samples/sec: 325.89 - lr: 0.000128 - momentum: 0.000000
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+ 2023-10-12 22:12:41,655 epoch 1 - iter 891/992 - loss 1.09115549 - time (sec): 448.96 - samples/sec: 328.46 - lr: 0.000144 - momentum: 0.000000
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+ 2023-10-12 22:13:30,799 epoch 1 - iter 990/992 - loss 1.00336945 - time (sec): 498.10 - samples/sec: 328.65 - lr: 0.000160 - momentum: 0.000000
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+ 2023-10-12 22:13:31,769 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 22:13:31,769 EPOCH 1 done: loss 1.0021 - lr: 0.000160
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+ 2023-10-12 22:13:55,906 DEV : loss 0.16479156911373138 - f1-score (micro avg) 0.4881
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+ 2023-10-12 22:13:55,944 saving best model
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+ 2023-10-12 22:13:56,845 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 22:14:45,524 epoch 2 - iter 99/992 - loss 0.16749492 - time (sec): 48.68 - samples/sec: 345.97 - lr: 0.000158 - momentum: 0.000000
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+ 2023-10-12 22:15:33,867 epoch 2 - iter 198/992 - loss 0.16513327 - time (sec): 97.02 - samples/sec: 340.72 - lr: 0.000156 - momentum: 0.000000
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+ 2023-10-12 22:16:21,989 epoch 2 - iter 297/992 - loss 0.16266570 - time (sec): 145.14 - samples/sec: 340.03 - lr: 0.000155 - momentum: 0.000000
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+ 2023-10-12 22:17:09,262 epoch 2 - iter 396/992 - loss 0.15468695 - time (sec): 192.42 - samples/sec: 343.81 - lr: 0.000153 - momentum: 0.000000
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+ 2023-10-12 22:17:57,533 epoch 2 - iter 495/992 - loss 0.15190678 - time (sec): 240.69 - samples/sec: 341.26 - lr: 0.000151 - momentum: 0.000000
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+ 2023-10-12 22:18:44,949 epoch 2 - iter 594/992 - loss 0.14770062 - time (sec): 288.10 - samples/sec: 343.88 - lr: 0.000149 - momentum: 0.000000
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+ 2023-10-12 22:19:32,810 epoch 2 - iter 693/992 - loss 0.14365046 - time (sec): 335.96 - samples/sec: 344.85 - lr: 0.000148 - momentum: 0.000000
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+ 2023-10-12 22:20:20,690 epoch 2 - iter 792/992 - loss 0.14111146 - time (sec): 383.84 - samples/sec: 342.67 - lr: 0.000146 - momentum: 0.000000
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+ 2023-10-12 22:21:07,617 epoch 2 - iter 891/992 - loss 0.13769670 - time (sec): 430.77 - samples/sec: 342.53 - lr: 0.000144 - momentum: 0.000000
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+ 2023-10-12 22:21:54,279 epoch 2 - iter 990/992 - loss 0.13551472 - time (sec): 477.43 - samples/sec: 342.97 - lr: 0.000142 - momentum: 0.000000
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+ 2023-10-12 22:21:55,197 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 22:21:55,197 EPOCH 2 done: loss 0.1354 - lr: 0.000142
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+ 2023-10-12 22:22:20,768 DEV : loss 0.08708374202251434 - f1-score (micro avg) 0.7258
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+ 2023-10-12 22:22:20,817 saving best model
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+ 2023-10-12 22:22:23,472 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 22:23:12,687 epoch 3 - iter 99/992 - loss 0.07926069 - time (sec): 49.21 - samples/sec: 348.70 - lr: 0.000140 - momentum: 0.000000
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+ 2023-10-12 22:24:00,548 epoch 3 - iter 198/992 - loss 0.08319851 - time (sec): 97.07 - samples/sec: 341.76 - lr: 0.000139 - momentum: 0.000000
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+ 2023-10-12 22:24:48,441 epoch 3 - iter 297/992 - loss 0.07989106 - time (sec): 144.96 - samples/sec: 337.38 - lr: 0.000137 - momentum: 0.000000
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+ 2023-10-12 22:25:35,848 epoch 3 - iter 396/992 - loss 0.07688124 - time (sec): 192.37 - samples/sec: 336.27 - lr: 0.000135 - momentum: 0.000000
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+ 2023-10-12 22:26:24,723 epoch 3 - iter 495/992 - loss 0.07665381 - time (sec): 241.24 - samples/sec: 337.07 - lr: 0.000133 - momentum: 0.000000
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+ 2023-10-12 22:27:13,187 epoch 3 - iter 594/992 - loss 0.07779634 - time (sec): 289.71 - samples/sec: 336.66 - lr: 0.000132 - momentum: 0.000000
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+ 2023-10-12 22:28:07,961 epoch 3 - iter 693/992 - loss 0.07743573 - time (sec): 344.48 - samples/sec: 329.27 - lr: 0.000130 - momentum: 0.000000
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+ 2023-10-12 22:28:59,877 epoch 3 - iter 792/992 - loss 0.07691112 - time (sec): 396.40 - samples/sec: 327.63 - lr: 0.000128 - momentum: 0.000000
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+ 2023-10-12 22:29:48,612 epoch 3 - iter 891/992 - loss 0.07602786 - time (sec): 445.13 - samples/sec: 329.00 - lr: 0.000126 - momentum: 0.000000
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+ 2023-10-12 22:30:37,961 epoch 3 - iter 990/992 - loss 0.07645169 - time (sec): 494.48 - samples/sec: 331.20 - lr: 0.000125 - momentum: 0.000000
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+ 2023-10-12 22:30:38,911 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 22:30:38,912 EPOCH 3 done: loss 0.0765 - lr: 0.000125
140
+ 2023-10-12 22:31:03,320 DEV : loss 0.08860880136489868 - f1-score (micro avg) 0.7359
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+ 2023-10-12 22:31:03,362 saving best model
142
+ 2023-10-12 22:31:05,994 ----------------------------------------------------------------------------------------------------
143
+ 2023-10-12 22:31:55,030 epoch 4 - iter 99/992 - loss 0.05266051 - time (sec): 49.03 - samples/sec: 355.22 - lr: 0.000123 - momentum: 0.000000
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+ 2023-10-12 22:32:41,837 epoch 4 - iter 198/992 - loss 0.05108763 - time (sec): 95.84 - samples/sec: 346.53 - lr: 0.000121 - momentum: 0.000000
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+ 2023-10-12 22:33:29,635 epoch 4 - iter 297/992 - loss 0.05139942 - time (sec): 143.63 - samples/sec: 344.00 - lr: 0.000119 - momentum: 0.000000
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+ 2023-10-12 22:34:17,373 epoch 4 - iter 396/992 - loss 0.04998338 - time (sec): 191.37 - samples/sec: 343.45 - lr: 0.000117 - momentum: 0.000000
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+ 2023-10-12 22:35:05,184 epoch 4 - iter 495/992 - loss 0.05009155 - time (sec): 239.18 - samples/sec: 343.37 - lr: 0.000116 - momentum: 0.000000
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+ 2023-10-12 22:35:52,496 epoch 4 - iter 594/992 - loss 0.05093715 - time (sec): 286.49 - samples/sec: 342.57 - lr: 0.000114 - momentum: 0.000000
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+ 2023-10-12 22:36:39,570 epoch 4 - iter 693/992 - loss 0.05159004 - time (sec): 333.57 - samples/sec: 340.90 - lr: 0.000112 - momentum: 0.000000
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+ 2023-10-12 22:37:32,868 epoch 4 - iter 792/992 - loss 0.05202951 - time (sec): 386.87 - samples/sec: 337.64 - lr: 0.000110 - momentum: 0.000000
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+ 2023-10-12 22:38:26,673 epoch 4 - iter 891/992 - loss 0.05194402 - time (sec): 440.67 - samples/sec: 333.04 - lr: 0.000109 - momentum: 0.000000
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+ 2023-10-12 22:39:21,320 epoch 4 - iter 990/992 - loss 0.05169748 - time (sec): 495.32 - samples/sec: 330.46 - lr: 0.000107 - momentum: 0.000000
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+ 2023-10-12 22:39:22,402 ----------------------------------------------------------------------------------------------------
154
+ 2023-10-12 22:39:22,402 EPOCH 4 done: loss 0.0519 - lr: 0.000107
155
+ 2023-10-12 22:39:49,852 DEV : loss 0.10738497972488403 - f1-score (micro avg) 0.7452
156
+ 2023-10-12 22:39:49,898 saving best model
157
+ 2023-10-12 22:39:52,641 ----------------------------------------------------------------------------------------------------
158
+ 2023-10-12 22:40:45,393 epoch 5 - iter 99/992 - loss 0.03744860 - time (sec): 52.75 - samples/sec: 294.32 - lr: 0.000105 - momentum: 0.000000
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+ 2023-10-12 22:41:36,500 epoch 5 - iter 198/992 - loss 0.03310035 - time (sec): 103.85 - samples/sec: 307.26 - lr: 0.000103 - momentum: 0.000000
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+ 2023-10-12 22:42:23,692 epoch 5 - iter 297/992 - loss 0.03625772 - time (sec): 151.05 - samples/sec: 320.74 - lr: 0.000101 - momentum: 0.000000
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+ 2023-10-12 22:43:11,285 epoch 5 - iter 396/992 - loss 0.03810834 - time (sec): 198.64 - samples/sec: 320.58 - lr: 0.000100 - momentum: 0.000000
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+ 2023-10-12 22:43:59,504 epoch 5 - iter 495/992 - loss 0.03758287 - time (sec): 246.86 - samples/sec: 321.20 - lr: 0.000098 - momentum: 0.000000
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+ 2023-10-12 22:44:48,523 epoch 5 - iter 594/992 - loss 0.03713120 - time (sec): 295.88 - samples/sec: 324.15 - lr: 0.000096 - momentum: 0.000000
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+ 2023-10-12 22:45:37,246 epoch 5 - iter 693/992 - loss 0.03650804 - time (sec): 344.60 - samples/sec: 330.08 - lr: 0.000094 - momentum: 0.000000
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+ 2023-10-12 22:46:26,597 epoch 5 - iter 792/992 - loss 0.03660515 - time (sec): 393.95 - samples/sec: 330.86 - lr: 0.000093 - momentum: 0.000000
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+ 2023-10-12 22:47:16,389 epoch 5 - iter 891/992 - loss 0.03830079 - time (sec): 443.74 - samples/sec: 330.21 - lr: 0.000091 - momentum: 0.000000
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+ 2023-10-12 22:48:05,714 epoch 5 - iter 990/992 - loss 0.03848700 - time (sec): 493.07 - samples/sec: 331.93 - lr: 0.000089 - momentum: 0.000000
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+ 2023-10-12 22:48:06,780 ----------------------------------------------------------------------------------------------------
169
+ 2023-10-12 22:48:06,781 EPOCH 5 done: loss 0.0385 - lr: 0.000089
170
+ 2023-10-12 22:48:32,775 DEV : loss 0.12247787415981293 - f1-score (micro avg) 0.765
171
+ 2023-10-12 22:48:32,824 saving best model
172
+ 2023-10-12 22:48:35,498 ----------------------------------------------------------------------------------------------------
173
+ 2023-10-12 22:49:23,615 epoch 6 - iter 99/992 - loss 0.03956974 - time (sec): 48.10 - samples/sec: 337.82 - lr: 0.000087 - momentum: 0.000000
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+ 2023-10-12 22:50:13,289 epoch 6 - iter 198/992 - loss 0.03197861 - time (sec): 97.78 - samples/sec: 329.46 - lr: 0.000085 - momentum: 0.000000
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+ 2023-10-12 22:51:03,269 epoch 6 - iter 297/992 - loss 0.03209333 - time (sec): 147.76 - samples/sec: 330.30 - lr: 0.000084 - momentum: 0.000000
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+ 2023-10-12 22:51:57,716 epoch 6 - iter 396/992 - loss 0.03241736 - time (sec): 202.21 - samples/sec: 323.77 - lr: 0.000082 - momentum: 0.000000
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+ 2023-10-12 22:52:50,732 epoch 6 - iter 495/992 - loss 0.03193044 - time (sec): 255.22 - samples/sec: 321.77 - lr: 0.000080 - momentum: 0.000000
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+ 2023-10-12 22:53:41,437 epoch 6 - iter 594/992 - loss 0.03185070 - time (sec): 305.93 - samples/sec: 320.29 - lr: 0.000078 - momentum: 0.000000
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+ 2023-10-12 22:54:31,597 epoch 6 - iter 693/992 - loss 0.03141795 - time (sec): 356.09 - samples/sec: 323.41 - lr: 0.000077 - momentum: 0.000000
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+ 2023-10-12 22:55:20,841 epoch 6 - iter 792/992 - loss 0.02995177 - time (sec): 405.33 - samples/sec: 324.97 - lr: 0.000075 - momentum: 0.000000
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+ 2023-10-12 22:56:13,061 epoch 6 - iter 891/992 - loss 0.03069202 - time (sec): 457.55 - samples/sec: 324.19 - lr: 0.000073 - momentum: 0.000000
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+ 2023-10-12 22:57:04,066 epoch 6 - iter 990/992 - loss 0.03019184 - time (sec): 508.56 - samples/sec: 322.03 - lr: 0.000071 - momentum: 0.000000
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+ 2023-10-12 22:57:05,223 ----------------------------------------------------------------------------------------------------
184
+ 2023-10-12 22:57:05,223 EPOCH 6 done: loss 0.0302 - lr: 0.000071
185
+ 2023-10-12 22:57:31,123 DEV : loss 0.15935450792312622 - f1-score (micro avg) 0.7639
186
+ 2023-10-12 22:57:31,171 ----------------------------------------------------------------------------------------------------
187
+ 2023-10-12 22:58:24,920 epoch 7 - iter 99/992 - loss 0.01651467 - time (sec): 53.75 - samples/sec: 300.62 - lr: 0.000069 - momentum: 0.000000
188
+ 2023-10-12 22:59:19,604 epoch 7 - iter 198/992 - loss 0.01743649 - time (sec): 108.43 - samples/sec: 303.71 - lr: 0.000068 - momentum: 0.000000
189
+ 2023-10-12 23:00:15,752 epoch 7 - iter 297/992 - loss 0.01956424 - time (sec): 164.58 - samples/sec: 297.51 - lr: 0.000066 - momentum: 0.000000
190
+ 2023-10-12 23:01:10,441 epoch 7 - iter 396/992 - loss 0.02209278 - time (sec): 219.27 - samples/sec: 298.43 - lr: 0.000064 - momentum: 0.000000
191
+ 2023-10-12 23:02:00,796 epoch 7 - iter 495/992 - loss 0.02209311 - time (sec): 269.62 - samples/sec: 302.36 - lr: 0.000062 - momentum: 0.000000
192
+ 2023-10-12 23:02:53,389 epoch 7 - iter 594/992 - loss 0.02242793 - time (sec): 322.22 - samples/sec: 305.28 - lr: 0.000061 - momentum: 0.000000
193
+ 2023-10-12 23:03:41,135 epoch 7 - iter 693/992 - loss 0.02205529 - time (sec): 369.96 - samples/sec: 310.06 - lr: 0.000059 - momentum: 0.000000
194
+ 2023-10-12 23:04:29,920 epoch 7 - iter 792/992 - loss 0.02172005 - time (sec): 418.75 - samples/sec: 313.47 - lr: 0.000057 - momentum: 0.000000
195
+ 2023-10-12 23:05:18,911 epoch 7 - iter 891/992 - loss 0.02166572 - time (sec): 467.74 - samples/sec: 314.91 - lr: 0.000055 - momentum: 0.000000
196
+ 2023-10-12 23:06:07,894 epoch 7 - iter 990/992 - loss 0.02245169 - time (sec): 516.72 - samples/sec: 316.80 - lr: 0.000053 - momentum: 0.000000
197
+ 2023-10-12 23:06:08,942 ----------------------------------------------------------------------------------------------------
198
+ 2023-10-12 23:06:08,943 EPOCH 7 done: loss 0.0224 - lr: 0.000053
199
+ 2023-10-12 23:06:35,694 DEV : loss 0.1707056164741516 - f1-score (micro avg) 0.7589
200
+ 2023-10-12 23:06:35,736 ----------------------------------------------------------------------------------------------------
201
+ 2023-10-12 23:07:27,949 epoch 8 - iter 99/992 - loss 0.01962247 - time (sec): 52.21 - samples/sec: 316.83 - lr: 0.000052 - momentum: 0.000000
202
+ 2023-10-12 23:08:19,108 epoch 8 - iter 198/992 - loss 0.01859288 - time (sec): 103.37 - samples/sec: 308.87 - lr: 0.000050 - momentum: 0.000000
203
+ 2023-10-12 23:09:10,193 epoch 8 - iter 297/992 - loss 0.01679401 - time (sec): 154.46 - samples/sec: 315.72 - lr: 0.000048 - momentum: 0.000000
204
+ 2023-10-12 23:09:59,592 epoch 8 - iter 396/992 - loss 0.01831256 - time (sec): 203.85 - samples/sec: 321.87 - lr: 0.000046 - momentum: 0.000000
205
+ 2023-10-12 23:10:50,216 epoch 8 - iter 495/992 - loss 0.01858703 - time (sec): 254.48 - samples/sec: 322.29 - lr: 0.000045 - momentum: 0.000000
206
+ 2023-10-12 23:11:39,881 epoch 8 - iter 594/992 - loss 0.01869986 - time (sec): 304.14 - samples/sec: 322.59 - lr: 0.000043 - momentum: 0.000000
207
+ 2023-10-12 23:12:32,934 epoch 8 - iter 693/992 - loss 0.01788096 - time (sec): 357.20 - samples/sec: 319.54 - lr: 0.000041 - momentum: 0.000000
208
+ 2023-10-12 23:13:25,150 epoch 8 - iter 792/992 - loss 0.01720215 - time (sec): 409.41 - samples/sec: 320.00 - lr: 0.000039 - momentum: 0.000000
209
+ 2023-10-12 23:14:15,414 epoch 8 - iter 891/992 - loss 0.01721473 - time (sec): 459.68 - samples/sec: 319.64 - lr: 0.000037 - momentum: 0.000000
210
+ 2023-10-12 23:15:07,027 epoch 8 - iter 990/992 - loss 0.01805657 - time (sec): 511.29 - samples/sec: 320.02 - lr: 0.000036 - momentum: 0.000000
211
+ 2023-10-12 23:15:07,988 ----------------------------------------------------------------------------------------------------
212
+ 2023-10-12 23:15:07,988 EPOCH 8 done: loss 0.0181 - lr: 0.000036
213
+ 2023-10-12 23:15:33,788 DEV : loss 0.1880449652671814 - f1-score (micro avg) 0.7603
214
+ 2023-10-12 23:15:33,833 ----------------------------------------------------------------------------------------------------
215
+ 2023-10-12 23:16:24,732 epoch 9 - iter 99/992 - loss 0.01209024 - time (sec): 50.90 - samples/sec: 303.76 - lr: 0.000034 - momentum: 0.000000
216
+ 2023-10-12 23:17:12,430 epoch 9 - iter 198/992 - loss 0.01145554 - time (sec): 98.59 - samples/sec: 313.15 - lr: 0.000032 - momentum: 0.000000
217
+ 2023-10-12 23:18:00,671 epoch 9 - iter 297/992 - loss 0.01364701 - time (sec): 146.83 - samples/sec: 321.91 - lr: 0.000030 - momentum: 0.000000
218
+ 2023-10-12 23:18:51,788 epoch 9 - iter 396/992 - loss 0.01407126 - time (sec): 197.95 - samples/sec: 324.82 - lr: 0.000029 - momentum: 0.000000
219
+ 2023-10-12 23:19:45,370 epoch 9 - iter 495/992 - loss 0.01342117 - time (sec): 251.53 - samples/sec: 321.54 - lr: 0.000027 - momentum: 0.000000
220
+ 2023-10-12 23:20:34,936 epoch 9 - iter 594/992 - loss 0.01418408 - time (sec): 301.10 - samples/sec: 327.44 - lr: 0.000025 - momentum: 0.000000
221
+ 2023-10-12 23:21:24,448 epoch 9 - iter 693/992 - loss 0.01478215 - time (sec): 350.61 - samples/sec: 329.28 - lr: 0.000023 - momentum: 0.000000
222
+ 2023-10-12 23:22:12,712 epoch 9 - iter 792/992 - loss 0.01519139 - time (sec): 398.88 - samples/sec: 331.22 - lr: 0.000022 - momentum: 0.000000
223
+ 2023-10-12 23:23:00,954 epoch 9 - iter 891/992 - loss 0.01476678 - time (sec): 447.12 - samples/sec: 332.32 - lr: 0.000020 - momentum: 0.000000
224
+ 2023-10-12 23:23:47,907 epoch 9 - iter 990/992 - loss 0.01486460 - time (sec): 494.07 - samples/sec: 331.20 - lr: 0.000018 - momentum: 0.000000
225
+ 2023-10-12 23:23:48,866 ----------------------------------------------------------------------------------------------------
226
+ 2023-10-12 23:23:48,866 EPOCH 9 done: loss 0.0148 - lr: 0.000018
227
+ 2023-10-12 23:24:14,572 DEV : loss 0.18750455975532532 - f1-score (micro avg) 0.7678
228
+ 2023-10-12 23:24:14,613 saving best model
229
+ 2023-10-12 23:24:17,232 ----------------------------------------------------------------------------------------------------
230
+ 2023-10-12 23:25:06,168 epoch 10 - iter 99/992 - loss 0.00876544 - time (sec): 48.93 - samples/sec: 337.18 - lr: 0.000016 - momentum: 0.000000
231
+ 2023-10-12 23:25:52,988 epoch 10 - iter 198/992 - loss 0.00939953 - time (sec): 95.75 - samples/sec: 347.04 - lr: 0.000014 - momentum: 0.000000
232
+ 2023-10-12 23:26:40,053 epoch 10 - iter 297/992 - loss 0.01057757 - time (sec): 142.82 - samples/sec: 350.83 - lr: 0.000013 - momentum: 0.000000
233
+ 2023-10-12 23:27:28,303 epoch 10 - iter 396/992 - loss 0.01038951 - time (sec): 191.07 - samples/sec: 345.97 - lr: 0.000011 - momentum: 0.000000
234
+ 2023-10-12 23:28:16,166 epoch 10 - iter 495/992 - loss 0.01021717 - time (sec): 238.93 - samples/sec: 344.94 - lr: 0.000009 - momentum: 0.000000
235
+ 2023-10-12 23:29:08,104 epoch 10 - iter 594/992 - loss 0.01086773 - time (sec): 290.87 - samples/sec: 337.98 - lr: 0.000007 - momentum: 0.000000
236
+ 2023-10-12 23:29:57,166 epoch 10 - iter 693/992 - loss 0.01145449 - time (sec): 339.93 - samples/sec: 338.36 - lr: 0.000006 - momentum: 0.000000
237
+ 2023-10-12 23:30:45,548 epoch 10 - iter 792/992 - loss 0.01111800 - time (sec): 388.31 - samples/sec: 336.93 - lr: 0.000004 - momentum: 0.000000
238
+ 2023-10-12 23:31:36,159 epoch 10 - iter 891/992 - loss 0.01070743 - time (sec): 438.92 - samples/sec: 336.74 - lr: 0.000002 - momentum: 0.000000
239
+ 2023-10-12 23:32:29,104 epoch 10 - iter 990/992 - loss 0.01101518 - time (sec): 491.87 - samples/sec: 332.77 - lr: 0.000000 - momentum: 0.000000
240
+ 2023-10-12 23:32:30,054 ----------------------------------------------------------------------------------------------------
241
+ 2023-10-12 23:32:30,054 EPOCH 10 done: loss 0.0111 - lr: 0.000000
242
+ 2023-10-12 23:32:55,364 DEV : loss 0.19627229869365692 - f1-score (micro avg) 0.7659
243
+ 2023-10-12 23:32:56,332 ----------------------------------------------------------------------------------------------------
244
+ 2023-10-12 23:32:56,334 Loading model from best epoch ...
245
+ 2023-10-12 23:32:59,958 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
246
+ 2023-10-12 23:33:23,960
247
+ Results:
248
+ - F-score (micro) 0.7784
249
+ - F-score (macro) 0.6876
250
+ - Accuracy 0.6589
251
+
252
+ By class:
253
+ precision recall f1-score support
254
+
255
+ LOC 0.8260 0.8550 0.8402 655
256
+ PER 0.7236 0.7982 0.7591 223
257
+ ORG 0.5094 0.4252 0.4635 127
258
+
259
+ micro avg 0.7689 0.7881 0.7784 1005
260
+ macro avg 0.6863 0.6928 0.6876 1005
261
+ weighted avg 0.7632 0.7881 0.7746 1005
262
+
263
+ 2023-10-12 23:33:23,960 ----------------------------------------------------------------------------------------------------