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hmbench-newseye-fr-hmbyt5-bs8-wsFalse-e10-lr0.00015-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.1697057555.de2e83fddbee.1120.16 +3 -0
  6. test.tsv +0 -0
  7. training.log +264 -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 21:01:17 0.0001 1.3620 0.2529 0.4132 0.3823 0.3972 0.2821
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+ 2 21:09:49 0.0001 0.1797 0.1036 0.7250 0.7782 0.7507 0.6272
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+ 3 21:18:26 0.0001 0.0744 0.1087 0.7570 0.8095 0.7824 0.6618
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+ 4 21:26:52 0.0001 0.0502 0.1237 0.7575 0.8204 0.7877 0.6685
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+ 5 21:35:19 0.0001 0.0370 0.1354 0.7846 0.8177 0.8008 0.6853
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+ 6 21:44:03 0.0001 0.0283 0.1587 0.7916 0.8218 0.8064 0.6911
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+ 7 21:53:07 0.0001 0.0227 0.1752 0.7863 0.8259 0.8056 0.6906
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+ 8 22:02:09 0.0000 0.0178 0.1876 0.7786 0.8231 0.8003 0.6821
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+ 9 22:10:45 0.0000 0.0151 0.1894 0.7884 0.8163 0.8021 0.6849
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+ 10 22:19:07 0.0000 0.0120 0.1914 0.7887 0.8177 0.8029 0.6861
runs/events.out.tfevents.1697057555.de2e83fddbee.1120.16 ADDED
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test.tsv ADDED
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training.log ADDED
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+ 2023-10-11 20:52:35,997 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 20:52:35,999 Model: "SequenceTagger(
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+ (embeddings): ByT5Embeddings(
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+ (model): T5EncoderModel(
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+ (shared): Embedding(384, 1472)
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+ (encoder): T5Stack(
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+ (embed_tokens): Embedding(384, 1472)
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+ (block): ModuleList(
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+ (0): T5Block(
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+ (layer): ModuleList(
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+ (0): T5LayerSelfAttention(
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+ (SelfAttention): T5Attention(
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+ (q): Linear(in_features=1472, out_features=384, bias=False)
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+ (k): Linear(in_features=1472, out_features=384, bias=False)
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+ (v): Linear(in_features=1472, out_features=384, bias=False)
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+ (o): Linear(in_features=384, out_features=1472, bias=False)
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+ (relative_attention_bias): Embedding(32, 6)
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+ )
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+ (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ (1): T5LayerFF(
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+ (DenseReluDense): T5DenseGatedActDense(
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+ (wi_0): Linear(in_features=1472, out_features=3584, bias=False)
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+ (wi_1): Linear(in_features=1472, out_features=3584, bias=False)
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+ (wo): Linear(in_features=3584, out_features=1472, bias=False)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ (act): NewGELUActivation()
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+ )
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+ (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ )
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+ )
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+ (1-11): 11 x T5Block(
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+ (layer): ModuleList(
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+ (0): T5LayerSelfAttention(
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+ (SelfAttention): T5Attention(
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+ (q): Linear(in_features=1472, out_features=384, bias=False)
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+ (k): Linear(in_features=1472, out_features=384, bias=False)
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+ (v): Linear(in_features=1472, out_features=384, bias=False)
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+ (o): Linear(in_features=384, out_features=1472, bias=False)
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+ )
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+ (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ (1): T5LayerFF(
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+ (DenseReluDense): T5DenseGatedActDense(
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+ (wi_0): Linear(in_features=1472, out_features=3584, bias=False)
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+ (wi_1): Linear(in_features=1472, out_features=3584, bias=False)
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+ (wo): Linear(in_features=3584, out_features=1472, bias=False)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ (act): NewGELUActivation()
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+ )
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+ (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ )
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+ )
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+ )
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+ (final_layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ )
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+ )
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+ (locked_dropout): LockedDropout(p=0.5)
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+ (linear): Linear(in_features=1472, out_features=17, bias=True)
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+ (loss_function): CrossEntropyLoss()
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+ )"
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+ 2023-10-11 20:52:36,000 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 20:52:36,000 MultiCorpus: 7142 train + 698 dev + 2570 test sentences
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+ - NER_HIPE_2022 Corpus: 7142 train + 698 dev + 2570 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/fr/with_doc_seperator
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+ 2023-10-11 20:52:36,000 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 20:52:36,000 Train: 7142 sentences
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+ 2023-10-11 20:52:36,000 (train_with_dev=False, train_with_test=False)
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+ 2023-10-11 20:52:36,000 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 20:52:36,000 Training Params:
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+ 2023-10-11 20:52:36,000 - learning_rate: "0.00015"
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+ 2023-10-11 20:52:36,000 - mini_batch_size: "8"
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+ 2023-10-11 20:52:36,000 - max_epochs: "10"
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+ 2023-10-11 20:52:36,000 - shuffle: "True"
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+ 2023-10-11 20:52:36,000 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 20:52:36,001 Plugins:
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+ 2023-10-11 20:52:36,001 - TensorboardLogger
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+ 2023-10-11 20:52:36,001 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-11 20:52:36,001 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 20:52:36,001 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-11 20:52:36,001 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-11 20:52:36,001 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 20:52:36,001 Computation:
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+ 2023-10-11 20:52:36,001 - compute on device: cuda:0
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+ 2023-10-11 20:52:36,001 - embedding storage: none
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+ 2023-10-11 20:52:36,001 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 20:52:36,001 Model training base path: "hmbench-newseye/fr-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-5"
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+ 2023-10-11 20:52:36,001 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 20:52:36,001 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 20:52:36,002 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-11 20:53:30,393 epoch 1 - iter 89/893 - loss 2.81613488 - time (sec): 54.39 - samples/sec: 498.00 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-11 20:54:22,518 epoch 1 - iter 178/893 - loss 2.73845748 - time (sec): 106.51 - samples/sec: 496.29 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-11 20:55:13,898 epoch 1 - iter 267/893 - loss 2.54046487 - time (sec): 157.89 - samples/sec: 504.70 - lr: 0.000045 - momentum: 0.000000
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+ 2023-10-11 20:56:03,634 epoch 1 - iter 356/893 - loss 2.33753858 - time (sec): 207.63 - samples/sec: 504.86 - lr: 0.000060 - momentum: 0.000000
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+ 2023-10-11 20:56:52,756 epoch 1 - iter 445/893 - loss 2.11803692 - time (sec): 256.75 - samples/sec: 503.90 - lr: 0.000075 - momentum: 0.000000
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+ 2023-10-11 20:57:41,859 epoch 1 - iter 534/893 - loss 1.90825297 - time (sec): 305.86 - samples/sec: 500.33 - lr: 0.000090 - momentum: 0.000000
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+ 2023-10-11 20:58:30,865 epoch 1 - iter 623/893 - loss 1.73583373 - time (sec): 354.86 - samples/sec: 498.90 - lr: 0.000104 - momentum: 0.000000
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+ 2023-10-11 20:59:18,284 epoch 1 - iter 712/893 - loss 1.60212408 - time (sec): 402.28 - samples/sec: 495.84 - lr: 0.000119 - momentum: 0.000000
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+ 2023-10-11 21:00:07,780 epoch 1 - iter 801/893 - loss 1.47431684 - time (sec): 451.78 - samples/sec: 494.97 - lr: 0.000134 - momentum: 0.000000
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+ 2023-10-11 21:00:56,330 epoch 1 - iter 890/893 - loss 1.36510703 - time (sec): 500.33 - samples/sec: 495.86 - lr: 0.000149 - momentum: 0.000000
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+ 2023-10-11 21:00:57,770 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 21:00:57,771 EPOCH 1 done: loss 1.3620 - lr: 0.000149
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+ 2023-10-11 21:01:17,012 DEV : loss 0.2528855502605438 - f1-score (micro avg) 0.3972
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+ 2023-10-11 21:01:17,041 saving best model
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+ 2023-10-11 21:01:17,905 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 21:02:06,967 epoch 2 - iter 89/893 - loss 0.31703165 - time (sec): 49.06 - samples/sec: 508.66 - lr: 0.000148 - momentum: 0.000000
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+ 2023-10-11 21:02:56,720 epoch 2 - iter 178/893 - loss 0.29370793 - time (sec): 98.81 - samples/sec: 509.36 - lr: 0.000147 - momentum: 0.000000
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+ 2023-10-11 21:03:47,381 epoch 2 - iter 267/893 - loss 0.26771875 - time (sec): 149.47 - samples/sec: 504.46 - lr: 0.000145 - momentum: 0.000000
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+ 2023-10-11 21:04:35,944 epoch 2 - iter 356/893 - loss 0.24749931 - time (sec): 198.04 - samples/sec: 504.18 - lr: 0.000143 - momentum: 0.000000
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+ 2023-10-11 21:05:25,721 epoch 2 - iter 445/893 - loss 0.22753805 - time (sec): 247.81 - samples/sec: 509.20 - lr: 0.000142 - momentum: 0.000000
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+ 2023-10-11 21:06:13,587 epoch 2 - iter 534/893 - loss 0.21653155 - time (sec): 295.68 - samples/sec: 505.52 - lr: 0.000140 - momentum: 0.000000
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+ 2023-10-11 21:07:01,206 epoch 2 - iter 623/893 - loss 0.20530533 - time (sec): 343.30 - samples/sec: 504.85 - lr: 0.000138 - momentum: 0.000000
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+ 2023-10-11 21:07:49,818 epoch 2 - iter 712/893 - loss 0.19565496 - time (sec): 391.91 - samples/sec: 507.23 - lr: 0.000137 - momentum: 0.000000
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+ 2023-10-11 21:08:37,917 epoch 2 - iter 801/893 - loss 0.18851018 - time (sec): 440.01 - samples/sec: 506.57 - lr: 0.000135 - momentum: 0.000000
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+ 2023-10-11 21:09:26,153 epoch 2 - iter 890/893 - loss 0.17990919 - time (sec): 488.25 - samples/sec: 507.13 - lr: 0.000133 - momentum: 0.000000
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+ 2023-10-11 21:09:27,932 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 21:09:27,933 EPOCH 2 done: loss 0.1797 - lr: 0.000133
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+ 2023-10-11 21:09:49,130 DEV : loss 0.10362720489501953 - f1-score (micro avg) 0.7507
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+ 2023-10-11 21:09:49,162 saving best model
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+ 2023-10-11 21:09:51,732 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 21:10:41,847 epoch 3 - iter 89/893 - loss 0.08229146 - time (sec): 50.11 - samples/sec: 489.78 - lr: 0.000132 - momentum: 0.000000
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+ 2023-10-11 21:11:31,776 epoch 3 - iter 178/893 - loss 0.08114947 - time (sec): 100.04 - samples/sec: 500.73 - lr: 0.000130 - momentum: 0.000000
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+ 2023-10-11 21:12:20,073 epoch 3 - iter 267/893 - loss 0.08051125 - time (sec): 148.34 - samples/sec: 497.04 - lr: 0.000128 - momentum: 0.000000
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+ 2023-10-11 21:13:08,650 epoch 3 - iter 356/893 - loss 0.07991414 - time (sec): 196.91 - samples/sec: 497.08 - lr: 0.000127 - momentum: 0.000000
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+ 2023-10-11 21:13:59,030 epoch 3 - iter 445/893 - loss 0.07654832 - time (sec): 247.29 - samples/sec: 499.98 - lr: 0.000125 - momentum: 0.000000
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+ 2023-10-11 21:14:49,928 epoch 3 - iter 534/893 - loss 0.07698540 - time (sec): 298.19 - samples/sec: 500.71 - lr: 0.000123 - momentum: 0.000000
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+ 2023-10-11 21:15:39,596 epoch 3 - iter 623/893 - loss 0.07568197 - time (sec): 347.86 - samples/sec: 500.44 - lr: 0.000122 - momentum: 0.000000
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+ 2023-10-11 21:16:26,772 epoch 3 - iter 712/893 - loss 0.07552582 - time (sec): 395.04 - samples/sec: 499.42 - lr: 0.000120 - momentum: 0.000000
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+ 2023-10-11 21:17:14,772 epoch 3 - iter 801/893 - loss 0.07615903 - time (sec): 443.04 - samples/sec: 500.35 - lr: 0.000118 - momentum: 0.000000
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+ 2023-10-11 21:18:04,158 epoch 3 - iter 890/893 - loss 0.07422971 - time (sec): 492.42 - samples/sec: 503.52 - lr: 0.000117 - momentum: 0.000000
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+ 2023-10-11 21:18:05,654 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 21:18:05,654 EPOCH 3 done: loss 0.0744 - lr: 0.000117
140
+ 2023-10-11 21:18:26,816 DEV : loss 0.10866602510213852 - f1-score (micro avg) 0.7824
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+ 2023-10-11 21:18:26,845 saving best model
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+ 2023-10-11 21:18:29,409 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 21:19:18,462 epoch 4 - iter 89/893 - loss 0.05155868 - time (sec): 49.05 - samples/sec: 546.85 - lr: 0.000115 - momentum: 0.000000
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+ 2023-10-11 21:20:06,688 epoch 4 - iter 178/893 - loss 0.04973704 - time (sec): 97.27 - samples/sec: 522.35 - lr: 0.000113 - momentum: 0.000000
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+ 2023-10-11 21:20:54,538 epoch 4 - iter 267/893 - loss 0.04803400 - time (sec): 145.12 - samples/sec: 523.03 - lr: 0.000112 - momentum: 0.000000
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+ 2023-10-11 21:21:42,141 epoch 4 - iter 356/893 - loss 0.04813800 - time (sec): 192.73 - samples/sec: 521.30 - lr: 0.000110 - momentum: 0.000000
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+ 2023-10-11 21:22:29,528 epoch 4 - iter 445/893 - loss 0.05071141 - time (sec): 240.11 - samples/sec: 516.67 - lr: 0.000108 - momentum: 0.000000
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+ 2023-10-11 21:23:17,733 epoch 4 - iter 534/893 - loss 0.05008976 - time (sec): 288.32 - samples/sec: 518.01 - lr: 0.000107 - momentum: 0.000000
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+ 2023-10-11 21:24:04,878 epoch 4 - iter 623/893 - loss 0.05021815 - time (sec): 335.46 - samples/sec: 516.12 - lr: 0.000105 - momentum: 0.000000
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+ 2023-10-11 21:24:52,076 epoch 4 - iter 712/893 - loss 0.05021718 - time (sec): 382.66 - samples/sec: 515.30 - lr: 0.000103 - momentum: 0.000000
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+ 2023-10-11 21:25:41,335 epoch 4 - iter 801/893 - loss 0.05019721 - time (sec): 431.92 - samples/sec: 518.39 - lr: 0.000102 - momentum: 0.000000
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+ 2023-10-11 21:26:29,456 epoch 4 - iter 890/893 - loss 0.05021542 - time (sec): 480.04 - samples/sec: 516.82 - lr: 0.000100 - momentum: 0.000000
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+ 2023-10-11 21:26:30,870 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 21:26:30,870 EPOCH 4 done: loss 0.0502 - lr: 0.000100
155
+ 2023-10-11 21:26:52,142 DEV : loss 0.12372089177370071 - f1-score (micro avg) 0.7877
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+ 2023-10-11 21:26:52,174 saving best model
157
+ 2023-10-11 21:26:54,776 ----------------------------------------------------------------------------------------------------
158
+ 2023-10-11 21:27:42,615 epoch 5 - iter 89/893 - loss 0.03046838 - time (sec): 47.83 - samples/sec: 512.85 - lr: 0.000098 - momentum: 0.000000
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+ 2023-10-11 21:28:30,404 epoch 5 - iter 178/893 - loss 0.03085776 - time (sec): 95.62 - samples/sec: 512.12 - lr: 0.000097 - momentum: 0.000000
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+ 2023-10-11 21:29:19,488 epoch 5 - iter 267/893 - loss 0.03370752 - time (sec): 144.71 - samples/sec: 513.15 - lr: 0.000095 - momentum: 0.000000
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+ 2023-10-11 21:30:07,132 epoch 5 - iter 356/893 - loss 0.03329933 - time (sec): 192.35 - samples/sec: 508.19 - lr: 0.000093 - momentum: 0.000000
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+ 2023-10-11 21:30:54,518 epoch 5 - iter 445/893 - loss 0.03377639 - time (sec): 239.74 - samples/sec: 507.69 - lr: 0.000092 - momentum: 0.000000
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+ 2023-10-11 21:31:42,115 epoch 5 - iter 534/893 - loss 0.03379913 - time (sec): 287.33 - samples/sec: 509.40 - lr: 0.000090 - momentum: 0.000000
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+ 2023-10-11 21:32:31,921 epoch 5 - iter 623/893 - loss 0.03448817 - time (sec): 337.14 - samples/sec: 515.02 - lr: 0.000088 - momentum: 0.000000
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+ 2023-10-11 21:33:19,817 epoch 5 - iter 712/893 - loss 0.03635184 - time (sec): 385.04 - samples/sec: 515.41 - lr: 0.000087 - momentum: 0.000000
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+ 2023-10-11 21:34:08,207 epoch 5 - iter 801/893 - loss 0.03720106 - time (sec): 433.43 - samples/sec: 515.07 - lr: 0.000085 - momentum: 0.000000
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+ 2023-10-11 21:34:56,748 epoch 5 - iter 890/893 - loss 0.03706033 - time (sec): 481.97 - samples/sec: 514.77 - lr: 0.000083 - momentum: 0.000000
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+ 2023-10-11 21:34:58,157 ----------------------------------------------------------------------------------------------------
169
+ 2023-10-11 21:34:58,158 EPOCH 5 done: loss 0.0370 - lr: 0.000083
170
+ 2023-10-11 21:35:19,477 DEV : loss 0.1354159116744995 - f1-score (micro avg) 0.8008
171
+ 2023-10-11 21:35:19,508 saving best model
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+ 2023-10-11 21:35:22,255 ----------------------------------------------------------------------------------------------------
173
+ 2023-10-11 21:36:12,944 epoch 6 - iter 89/893 - loss 0.02532107 - time (sec): 50.69 - samples/sec: 506.97 - lr: 0.000082 - momentum: 0.000000
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+ 2023-10-11 21:37:02,222 epoch 6 - iter 178/893 - loss 0.02623938 - time (sec): 99.96 - samples/sec: 498.02 - lr: 0.000080 - momentum: 0.000000
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+ 2023-10-11 21:37:54,722 epoch 6 - iter 267/893 - loss 0.02667748 - time (sec): 152.46 - samples/sec: 505.98 - lr: 0.000078 - momentum: 0.000000
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+ 2023-10-11 21:38:44,103 epoch 6 - iter 356/893 - loss 0.02740607 - time (sec): 201.84 - samples/sec: 501.98 - lr: 0.000077 - momentum: 0.000000
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+ 2023-10-11 21:39:34,876 epoch 6 - iter 445/893 - loss 0.02812179 - time (sec): 252.62 - samples/sec: 504.13 - lr: 0.000075 - momentum: 0.000000
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+ 2023-10-11 21:40:23,868 epoch 6 - iter 534/893 - loss 0.02732688 - time (sec): 301.61 - samples/sec: 502.95 - lr: 0.000073 - momentum: 0.000000
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+ 2023-10-11 21:41:12,773 epoch 6 - iter 623/893 - loss 0.02745474 - time (sec): 350.51 - samples/sec: 501.52 - lr: 0.000072 - momentum: 0.000000
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+ 2023-10-11 21:42:02,849 epoch 6 - iter 712/893 - loss 0.02741235 - time (sec): 400.59 - samples/sec: 501.62 - lr: 0.000070 - momentum: 0.000000
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+ 2023-10-11 21:42:51,619 epoch 6 - iter 801/893 - loss 0.02707534 - time (sec): 449.36 - samples/sec: 499.01 - lr: 0.000068 - momentum: 0.000000
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+ 2023-10-11 21:43:40,632 epoch 6 - iter 890/893 - loss 0.02836216 - time (sec): 498.37 - samples/sec: 496.97 - lr: 0.000067 - momentum: 0.000000
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+ 2023-10-11 21:43:42,374 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 21:43:42,375 EPOCH 6 done: loss 0.0283 - lr: 0.000067
185
+ 2023-10-11 21:44:03,672 DEV : loss 0.15866339206695557 - f1-score (micro avg) 0.8064
186
+ 2023-10-11 21:44:03,703 saving best model
187
+ 2023-10-11 21:44:06,269 ----------------------------------------------------------------------------------------------------
188
+ 2023-10-11 21:44:55,120 epoch 7 - iter 89/893 - loss 0.02508843 - time (sec): 48.85 - samples/sec: 492.72 - lr: 0.000065 - momentum: 0.000000
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+ 2023-10-11 21:45:46,535 epoch 7 - iter 178/893 - loss 0.02218286 - time (sec): 100.26 - samples/sec: 496.71 - lr: 0.000063 - momentum: 0.000000
190
+ 2023-10-11 21:46:37,443 epoch 7 - iter 267/893 - loss 0.02238652 - time (sec): 151.17 - samples/sec: 487.09 - lr: 0.000062 - momentum: 0.000000
191
+ 2023-10-11 21:47:30,667 epoch 7 - iter 356/893 - loss 0.02043595 - time (sec): 204.39 - samples/sec: 486.68 - lr: 0.000060 - momentum: 0.000000
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+ 2023-10-11 21:48:22,320 epoch 7 - iter 445/893 - loss 0.02102355 - time (sec): 256.05 - samples/sec: 486.24 - lr: 0.000058 - momentum: 0.000000
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+ 2023-10-11 21:49:15,960 epoch 7 - iter 534/893 - loss 0.02018963 - time (sec): 309.69 - samples/sec: 482.13 - lr: 0.000057 - momentum: 0.000000
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+ 2023-10-11 21:50:07,792 epoch 7 - iter 623/893 - loss 0.02143349 - time (sec): 361.52 - samples/sec: 480.22 - lr: 0.000055 - momentum: 0.000000
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+ 2023-10-11 21:50:59,256 epoch 7 - iter 712/893 - loss 0.02184410 - time (sec): 412.98 - samples/sec: 479.36 - lr: 0.000053 - momentum: 0.000000
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+ 2023-10-11 21:51:50,666 epoch 7 - iter 801/893 - loss 0.02268425 - time (sec): 464.39 - samples/sec: 480.74 - lr: 0.000052 - momentum: 0.000000
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+ 2023-10-11 21:52:42,522 epoch 7 - iter 890/893 - loss 0.02266701 - time (sec): 516.25 - samples/sec: 480.13 - lr: 0.000050 - momentum: 0.000000
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+ 2023-10-11 21:52:44,260 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 21:52:44,260 EPOCH 7 done: loss 0.0227 - lr: 0.000050
200
+ 2023-10-11 21:53:07,308 DEV : loss 0.17522385716438293 - f1-score (micro avg) 0.8056
201
+ 2023-10-11 21:53:07,340 ----------------------------------------------------------------------------------------------------
202
+ 2023-10-11 21:53:59,208 epoch 8 - iter 89/893 - loss 0.01896678 - time (sec): 51.87 - samples/sec: 482.91 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-11 21:54:51,807 epoch 8 - iter 178/893 - loss 0.01940469 - time (sec): 104.46 - samples/sec: 481.54 - lr: 0.000047 - momentum: 0.000000
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+ 2023-10-11 21:55:42,555 epoch 8 - iter 267/893 - loss 0.01877862 - time (sec): 155.21 - samples/sec: 484.20 - lr: 0.000045 - momentum: 0.000000
205
+ 2023-10-11 21:56:32,597 epoch 8 - iter 356/893 - loss 0.01814048 - time (sec): 205.25 - samples/sec: 479.00 - lr: 0.000043 - momentum: 0.000000
206
+ 2023-10-11 21:57:23,920 epoch 8 - iter 445/893 - loss 0.01757910 - time (sec): 256.58 - samples/sec: 474.93 - lr: 0.000042 - momentum: 0.000000
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+ 2023-10-11 21:58:17,174 epoch 8 - iter 534/893 - loss 0.01741564 - time (sec): 309.83 - samples/sec: 477.97 - lr: 0.000040 - momentum: 0.000000
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+ 2023-10-11 21:59:08,333 epoch 8 - iter 623/893 - loss 0.01792758 - time (sec): 360.99 - samples/sec: 473.96 - lr: 0.000038 - momentum: 0.000000
209
+ 2023-10-11 22:00:01,125 epoch 8 - iter 712/893 - loss 0.01725608 - time (sec): 413.78 - samples/sec: 476.51 - lr: 0.000037 - momentum: 0.000000
210
+ 2023-10-11 22:00:54,116 epoch 8 - iter 801/893 - loss 0.01742091 - time (sec): 466.77 - samples/sec: 478.74 - lr: 0.000035 - momentum: 0.000000
211
+ 2023-10-11 22:01:45,063 epoch 8 - iter 890/893 - loss 0.01785493 - time (sec): 517.72 - samples/sec: 478.83 - lr: 0.000033 - momentum: 0.000000
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+ 2023-10-11 22:01:46,660 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 22:01:46,661 EPOCH 8 done: loss 0.0178 - lr: 0.000033
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+ 2023-10-11 22:02:09,359 DEV : loss 0.18762467801570892 - f1-score (micro avg) 0.8003
215
+ 2023-10-11 22:02:09,391 ----------------------------------------------------------------------------------------------------
216
+ 2023-10-11 22:02:59,516 epoch 9 - iter 89/893 - loss 0.01401632 - time (sec): 50.12 - samples/sec: 517.17 - lr: 0.000032 - momentum: 0.000000
217
+ 2023-10-11 22:03:48,274 epoch 9 - iter 178/893 - loss 0.01214473 - time (sec): 98.88 - samples/sec: 505.06 - lr: 0.000030 - momentum: 0.000000
218
+ 2023-10-11 22:04:37,160 epoch 9 - iter 267/893 - loss 0.01334299 - time (sec): 147.77 - samples/sec: 502.93 - lr: 0.000028 - momentum: 0.000000
219
+ 2023-10-11 22:05:26,043 epoch 9 - iter 356/893 - loss 0.01321979 - time (sec): 196.65 - samples/sec: 499.97 - lr: 0.000027 - momentum: 0.000000
220
+ 2023-10-11 22:06:14,819 epoch 9 - iter 445/893 - loss 0.01360501 - time (sec): 245.43 - samples/sec: 500.45 - lr: 0.000025 - momentum: 0.000000
221
+ 2023-10-11 22:07:04,964 epoch 9 - iter 534/893 - loss 0.01382477 - time (sec): 295.57 - samples/sec: 504.43 - lr: 0.000023 - momentum: 0.000000
222
+ 2023-10-11 22:07:55,514 epoch 9 - iter 623/893 - loss 0.01454438 - time (sec): 346.12 - samples/sec: 506.54 - lr: 0.000022 - momentum: 0.000000
223
+ 2023-10-11 22:08:45,051 epoch 9 - iter 712/893 - loss 0.01451511 - time (sec): 395.66 - samples/sec: 506.42 - lr: 0.000020 - momentum: 0.000000
224
+ 2023-10-11 22:09:34,492 epoch 9 - iter 801/893 - loss 0.01499387 - time (sec): 445.10 - samples/sec: 504.31 - lr: 0.000019 - momentum: 0.000000
225
+ 2023-10-11 22:10:22,701 epoch 9 - iter 890/893 - loss 0.01501724 - time (sec): 493.31 - samples/sec: 502.86 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-11 22:10:24,180 ----------------------------------------------------------------------------------------------------
227
+ 2023-10-11 22:10:24,180 EPOCH 9 done: loss 0.0151 - lr: 0.000017
228
+ 2023-10-11 22:10:45,263 DEV : loss 0.18942216038703918 - f1-score (micro avg) 0.8021
229
+ 2023-10-11 22:10:45,292 ----------------------------------------------------------------------------------------------------
230
+ 2023-10-11 22:11:33,613 epoch 10 - iter 89/893 - loss 0.01255615 - time (sec): 48.32 - samples/sec: 522.34 - lr: 0.000015 - momentum: 0.000000
231
+ 2023-10-11 22:12:22,743 epoch 10 - iter 178/893 - loss 0.01274093 - time (sec): 97.45 - samples/sec: 518.18 - lr: 0.000013 - momentum: 0.000000
232
+ 2023-10-11 22:13:11,304 epoch 10 - iter 267/893 - loss 0.01151278 - time (sec): 146.01 - samples/sec: 519.59 - lr: 0.000012 - momentum: 0.000000
233
+ 2023-10-11 22:13:59,597 epoch 10 - iter 356/893 - loss 0.01202932 - time (sec): 194.30 - samples/sec: 520.48 - lr: 0.000010 - momentum: 0.000000
234
+ 2023-10-11 22:14:47,516 epoch 10 - iter 445/893 - loss 0.01209366 - time (sec): 242.22 - samples/sec: 522.14 - lr: 0.000008 - momentum: 0.000000
235
+ 2023-10-11 22:15:34,392 epoch 10 - iter 534/893 - loss 0.01129065 - time (sec): 289.10 - samples/sec: 520.44 - lr: 0.000007 - momentum: 0.000000
236
+ 2023-10-11 22:16:23,818 epoch 10 - iter 623/893 - loss 0.01226211 - time (sec): 338.52 - samples/sec: 520.53 - lr: 0.000005 - momentum: 0.000000
237
+ 2023-10-11 22:17:10,864 epoch 10 - iter 712/893 - loss 0.01183880 - time (sec): 385.57 - samples/sec: 519.33 - lr: 0.000004 - momentum: 0.000000
238
+ 2023-10-11 22:17:58,433 epoch 10 - iter 801/893 - loss 0.01204233 - time (sec): 433.14 - samples/sec: 517.95 - lr: 0.000002 - momentum: 0.000000
239
+ 2023-10-11 22:18:45,661 epoch 10 - iter 890/893 - loss 0.01204849 - time (sec): 480.37 - samples/sec: 516.66 - lr: 0.000000 - momentum: 0.000000
240
+ 2023-10-11 22:18:46,984 ----------------------------------------------------------------------------------------------------
241
+ 2023-10-11 22:18:46,984 EPOCH 10 done: loss 0.0120 - lr: 0.000000
242
+ 2023-10-11 22:19:07,928 DEV : loss 0.19141535460948944 - f1-score (micro avg) 0.8029
243
+ 2023-10-11 22:19:08,805 ----------------------------------------------------------------------------------------------------
244
+ 2023-10-11 22:19:08,807 Loading model from best epoch ...
245
+ 2023-10-11 22:19:12,543 SequenceTagger predicts: Dictionary with 17 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, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd
246
+ 2023-10-11 22:20:19,954
247
+ Results:
248
+ - F-score (micro) 0.7109
249
+ - F-score (macro) 0.6499
250
+ - Accuracy 0.5673
251
+
252
+ By class:
253
+ precision recall f1-score support
254
+
255
+ LOC 0.7284 0.7324 0.7304 1095
256
+ PER 0.7764 0.7925 0.7844 1012
257
+ ORG 0.4249 0.6022 0.4983 357
258
+ HumanProd 0.5238 0.6667 0.5867 33
259
+
260
+ micro avg 0.6864 0.7373 0.7109 2497
261
+ macro avg 0.6134 0.6985 0.6499 2497
262
+ weighted avg 0.7018 0.7373 0.7172 2497
263
+
264
+ 2023-10-11 22:20:19,954 ----------------------------------------------------------------------------------------------------