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hmbench-letemps-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-4

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.1697284348.c8b2203b18a8.2923.13 +3 -0
  6. test.tsv +0 -0
  7. training.log +261 -0
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final-model.pt ADDED
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loss.tsv ADDED
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+ EPOCH TIMESTAMP LEARNING_RATE TRAIN_LOSS DEV_LOSS DEV_PRECISION DEV_RECALL DEV_F1 DEV_ACCURACY
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+ 1 12:09:05 0.0002 0.7418 0.1306 0.5559 0.4382 0.4901 0.3398
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+ 2 12:25:38 0.0001 0.0905 0.0948 0.5493 0.7391 0.6302 0.4681
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+ 3 12:42:52 0.0001 0.0576 0.1368 0.5422 0.7494 0.6292 0.4689
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+ 4 12:59:12 0.0001 0.0389 0.1905 0.5369 0.7918 0.6399 0.4816
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+ 5 13:15:45 0.0001 0.0297 0.2319 0.5596 0.7414 0.6378 0.4779
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+ 6 13:32:24 0.0001 0.0216 0.2723 0.5593 0.7826 0.6524 0.4953
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+ 7 13:48:58 0.0001 0.0154 0.2977 0.5680 0.7265 0.6376 0.4767
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+ 8 14:05:34 0.0000 0.0116 0.3240 0.5584 0.7609 0.6441 0.4854
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+ 9 14:22:26 0.0000 0.0078 0.3510 0.5614 0.7632 0.6469 0.4904
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+ 10 14:39:11 0.0000 0.0062 0.3546 0.5572 0.7574 0.6421 0.4846
runs/events.out.tfevents.1697284348.c8b2203b18a8.2923.13 ADDED
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test.tsv ADDED
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training.log ADDED
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+ 2023-10-14 11:52:28,279 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 11:52:28,281 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)
51
+ (wo): Linear(in_features=3584, out_features=1472, bias=False)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ (act): NewGELUActivation()
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+ )
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+ (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ )
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+ )
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+ )
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+ (final_layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ )
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+ )
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+ (locked_dropout): LockedDropout(p=0.5)
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+ (linear): Linear(in_features=1472, out_features=13, bias=True)
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+ (loss_function): CrossEntropyLoss()
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+ )"
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+ 2023-10-14 11:52:28,281 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 11:52:28,281 MultiCorpus: 14465 train + 1392 dev + 2432 test sentences
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+ - NER_HIPE_2022 Corpus: 14465 train + 1392 dev + 2432 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/letemps/fr/with_doc_seperator
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+ 2023-10-14 11:52:28,281 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 11:52:28,282 Train: 14465 sentences
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+ 2023-10-14 11:52:28,282 (train_with_dev=False, train_with_test=False)
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+ 2023-10-14 11:52:28,282 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 11:52:28,282 Training Params:
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+ 2023-10-14 11:52:28,282 - learning_rate: "0.00016"
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+ 2023-10-14 11:52:28,282 - mini_batch_size: "8"
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+ 2023-10-14 11:52:28,282 - max_epochs: "10"
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+ 2023-10-14 11:52:28,282 - shuffle: "True"
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+ 2023-10-14 11:52:28,282 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 11:52:28,282 Plugins:
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+ 2023-10-14 11:52:28,282 - TensorboardLogger
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+ 2023-10-14 11:52:28,282 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-14 11:52:28,282 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 11:52:28,282 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-14 11:52:28,282 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-14 11:52:28,283 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 11:52:28,283 Computation:
91
+ 2023-10-14 11:52:28,283 - compute on device: cuda:0
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+ 2023-10-14 11:52:28,283 - embedding storage: none
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+ 2023-10-14 11:52:28,283 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 11:52:28,283 Model training base path: "hmbench-letemps/fr-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-4"
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+ 2023-10-14 11:52:28,283 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 11:52:28,283 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 11:52:28,283 Logging anything other than scalars to TensorBoard is currently not supported.
98
+ 2023-10-14 11:54:01,906 epoch 1 - iter 180/1809 - loss 2.54702501 - time (sec): 93.62 - samples/sec: 389.75 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-14 11:55:34,893 epoch 1 - iter 360/1809 - loss 2.27602894 - time (sec): 186.61 - samples/sec: 400.06 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-14 11:57:10,309 epoch 1 - iter 540/1809 - loss 1.92434741 - time (sec): 282.02 - samples/sec: 402.87 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-14 11:58:44,161 epoch 1 - iter 720/1809 - loss 1.57584407 - time (sec): 375.88 - samples/sec: 404.57 - lr: 0.000064 - momentum: 0.000000
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+ 2023-10-14 12:00:18,267 epoch 1 - iter 900/1809 - loss 1.32127986 - time (sec): 469.98 - samples/sec: 403.03 - lr: 0.000080 - momentum: 0.000000
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+ 2023-10-14 12:01:56,606 epoch 1 - iter 1080/1809 - loss 1.13001630 - time (sec): 568.32 - samples/sec: 402.37 - lr: 0.000095 - momentum: 0.000000
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+ 2023-10-14 12:03:33,121 epoch 1 - iter 1260/1809 - loss 0.99420573 - time (sec): 664.84 - samples/sec: 401.11 - lr: 0.000111 - momentum: 0.000000
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+ 2023-10-14 12:05:10,045 epoch 1 - iter 1440/1809 - loss 0.89109878 - time (sec): 761.76 - samples/sec: 400.07 - lr: 0.000127 - momentum: 0.000000
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+ 2023-10-14 12:06:47,130 epoch 1 - iter 1620/1809 - loss 0.81100176 - time (sec): 858.85 - samples/sec: 397.02 - lr: 0.000143 - momentum: 0.000000
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+ 2023-10-14 12:08:23,566 epoch 1 - iter 1800/1809 - loss 0.74361325 - time (sec): 955.28 - samples/sec: 396.06 - lr: 0.000159 - momentum: 0.000000
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+ 2023-10-14 12:08:27,689 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 12:08:27,690 EPOCH 1 done: loss 0.7418 - lr: 0.000159
110
+ 2023-10-14 12:09:05,444 DEV : loss 0.13058921694755554 - f1-score (micro avg) 0.4901
111
+ 2023-10-14 12:09:05,501 saving best model
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+ 2023-10-14 12:09:06,504 ----------------------------------------------------------------------------------------------------
113
+ 2023-10-14 12:10:41,018 epoch 2 - iter 180/1809 - loss 0.11763819 - time (sec): 94.51 - samples/sec: 398.13 - lr: 0.000158 - momentum: 0.000000
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+ 2023-10-14 12:12:15,894 epoch 2 - iter 360/1809 - loss 0.11288606 - time (sec): 189.39 - samples/sec: 401.69 - lr: 0.000156 - momentum: 0.000000
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+ 2023-10-14 12:13:53,929 epoch 2 - iter 540/1809 - loss 0.11069431 - time (sec): 287.42 - samples/sec: 392.41 - lr: 0.000155 - momentum: 0.000000
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+ 2023-10-14 12:15:25,447 epoch 2 - iter 720/1809 - loss 0.10746830 - time (sec): 378.94 - samples/sec: 395.96 - lr: 0.000153 - momentum: 0.000000
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+ 2023-10-14 12:16:57,242 epoch 2 - iter 900/1809 - loss 0.10386008 - time (sec): 470.74 - samples/sec: 398.06 - lr: 0.000151 - momentum: 0.000000
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+ 2023-10-14 12:18:32,290 epoch 2 - iter 1080/1809 - loss 0.10038808 - time (sec): 565.78 - samples/sec: 399.27 - lr: 0.000149 - momentum: 0.000000
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+ 2023-10-14 12:20:11,902 epoch 2 - iter 1260/1809 - loss 0.09720591 - time (sec): 665.40 - samples/sec: 398.88 - lr: 0.000148 - momentum: 0.000000
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+ 2023-10-14 12:21:47,580 epoch 2 - iter 1440/1809 - loss 0.09543514 - time (sec): 761.07 - samples/sec: 398.87 - lr: 0.000146 - momentum: 0.000000
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+ 2023-10-14 12:23:21,787 epoch 2 - iter 1620/1809 - loss 0.09204174 - time (sec): 855.28 - samples/sec: 399.25 - lr: 0.000144 - momentum: 0.000000
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+ 2023-10-14 12:24:54,948 epoch 2 - iter 1800/1809 - loss 0.09031627 - time (sec): 948.44 - samples/sec: 399.02 - lr: 0.000142 - momentum: 0.000000
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+ 2023-10-14 12:24:59,031 ----------------------------------------------------------------------------------------------------
124
+ 2023-10-14 12:24:59,031 EPOCH 2 done: loss 0.0905 - lr: 0.000142
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+ 2023-10-14 12:25:38,013 DEV : loss 0.09481607377529144 - f1-score (micro avg) 0.6302
126
+ 2023-10-14 12:25:38,069 saving best model
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+ 2023-10-14 12:25:41,560 ----------------------------------------------------------------------------------------------------
128
+ 2023-10-14 12:27:25,068 epoch 3 - iter 180/1809 - loss 0.05732527 - time (sec): 103.50 - samples/sec: 369.83 - lr: 0.000140 - momentum: 0.000000
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+ 2023-10-14 12:28:58,543 epoch 3 - iter 360/1809 - loss 0.05785601 - time (sec): 196.98 - samples/sec: 379.54 - lr: 0.000139 - momentum: 0.000000
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+ 2023-10-14 12:30:28,432 epoch 3 - iter 540/1809 - loss 0.05962819 - time (sec): 286.87 - samples/sec: 391.90 - lr: 0.000137 - momentum: 0.000000
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+ 2023-10-14 12:31:58,859 epoch 3 - iter 720/1809 - loss 0.05938527 - time (sec): 377.29 - samples/sec: 402.14 - lr: 0.000135 - momentum: 0.000000
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+ 2023-10-14 12:33:36,210 epoch 3 - iter 900/1809 - loss 0.05901793 - time (sec): 474.64 - samples/sec: 400.35 - lr: 0.000133 - momentum: 0.000000
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+ 2023-10-14 12:35:21,233 epoch 3 - iter 1080/1809 - loss 0.05760677 - time (sec): 579.67 - samples/sec: 394.24 - lr: 0.000132 - momentum: 0.000000
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+ 2023-10-14 12:37:02,151 epoch 3 - iter 1260/1809 - loss 0.05767185 - time (sec): 680.59 - samples/sec: 391.76 - lr: 0.000130 - momentum: 0.000000
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+ 2023-10-14 12:38:38,632 epoch 3 - iter 1440/1809 - loss 0.05754962 - time (sec): 777.07 - samples/sec: 390.27 - lr: 0.000128 - momentum: 0.000000
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+ 2023-10-14 12:40:18,411 epoch 3 - iter 1620/1809 - loss 0.05726013 - time (sec): 876.85 - samples/sec: 388.54 - lr: 0.000126 - momentum: 0.000000
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+ 2023-10-14 12:41:58,428 epoch 3 - iter 1800/1809 - loss 0.05737904 - time (sec): 976.86 - samples/sec: 387.15 - lr: 0.000125 - momentum: 0.000000
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+ 2023-10-14 12:42:03,646 ----------------------------------------------------------------------------------------------------
139
+ 2023-10-14 12:42:03,646 EPOCH 3 done: loss 0.0576 - lr: 0.000125
140
+ 2023-10-14 12:42:52,619 DEV : loss 0.1367848813533783 - f1-score (micro avg) 0.6292
141
+ 2023-10-14 12:42:52,685 ----------------------------------------------------------------------------------------------------
142
+ 2023-10-14 12:44:28,197 epoch 4 - iter 180/1809 - loss 0.03871364 - time (sec): 95.51 - samples/sec: 381.69 - lr: 0.000123 - momentum: 0.000000
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+ 2023-10-14 12:46:00,663 epoch 4 - iter 360/1809 - loss 0.03856325 - time (sec): 187.98 - samples/sec: 395.23 - lr: 0.000121 - momentum: 0.000000
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+ 2023-10-14 12:47:33,114 epoch 4 - iter 540/1809 - loss 0.04060601 - time (sec): 280.43 - samples/sec: 403.91 - lr: 0.000119 - momentum: 0.000000
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+ 2023-10-14 12:49:02,712 epoch 4 - iter 720/1809 - loss 0.03906737 - time (sec): 370.02 - samples/sec: 404.57 - lr: 0.000117 - momentum: 0.000000
146
+ 2023-10-14 12:50:32,763 epoch 4 - iter 900/1809 - loss 0.03994477 - time (sec): 460.08 - samples/sec: 407.04 - lr: 0.000116 - momentum: 0.000000
147
+ 2023-10-14 12:52:04,771 epoch 4 - iter 1080/1809 - loss 0.03951109 - time (sec): 552.08 - samples/sec: 408.84 - lr: 0.000114 - momentum: 0.000000
148
+ 2023-10-14 12:53:36,911 epoch 4 - iter 1260/1809 - loss 0.04014654 - time (sec): 644.22 - samples/sec: 409.76 - lr: 0.000112 - momentum: 0.000000
149
+ 2023-10-14 12:55:12,371 epoch 4 - iter 1440/1809 - loss 0.03986687 - time (sec): 739.68 - samples/sec: 409.89 - lr: 0.000110 - momentum: 0.000000
150
+ 2023-10-14 12:56:48,117 epoch 4 - iter 1620/1809 - loss 0.03943713 - time (sec): 835.43 - samples/sec: 407.22 - lr: 0.000109 - momentum: 0.000000
151
+ 2023-10-14 12:58:28,134 epoch 4 - iter 1800/1809 - loss 0.03893369 - time (sec): 935.45 - samples/sec: 404.27 - lr: 0.000107 - momentum: 0.000000
152
+ 2023-10-14 12:58:32,852 ----------------------------------------------------------------------------------------------------
153
+ 2023-10-14 12:58:32,853 EPOCH 4 done: loss 0.0389 - lr: 0.000107
154
+ 2023-10-14 12:59:11,971 DEV : loss 0.19053448736667633 - f1-score (micro avg) 0.6399
155
+ 2023-10-14 12:59:12,035 saving best model
156
+ 2023-10-14 12:59:13,027 ----------------------------------------------------------------------------------------------------
157
+ 2023-10-14 13:00:43,768 epoch 5 - iter 180/1809 - loss 0.02307068 - time (sec): 90.74 - samples/sec: 413.03 - lr: 0.000105 - momentum: 0.000000
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+ 2023-10-14 13:02:22,128 epoch 5 - iter 360/1809 - loss 0.02459229 - time (sec): 189.10 - samples/sec: 413.96 - lr: 0.000103 - momentum: 0.000000
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+ 2023-10-14 13:03:56,196 epoch 5 - iter 540/1809 - loss 0.02726105 - time (sec): 283.17 - samples/sec: 412.87 - lr: 0.000101 - momentum: 0.000000
160
+ 2023-10-14 13:05:29,203 epoch 5 - iter 720/1809 - loss 0.02847084 - time (sec): 376.17 - samples/sec: 407.73 - lr: 0.000100 - momentum: 0.000000
161
+ 2023-10-14 13:07:00,178 epoch 5 - iter 900/1809 - loss 0.02858724 - time (sec): 467.15 - samples/sec: 407.20 - lr: 0.000098 - momentum: 0.000000
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+ 2023-10-14 13:08:32,646 epoch 5 - iter 1080/1809 - loss 0.02877847 - time (sec): 559.62 - samples/sec: 407.69 - lr: 0.000096 - momentum: 0.000000
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+ 2023-10-14 13:10:07,347 epoch 5 - iter 1260/1809 - loss 0.02796208 - time (sec): 654.32 - samples/sec: 406.44 - lr: 0.000094 - momentum: 0.000000
164
+ 2023-10-14 13:11:43,274 epoch 5 - iter 1440/1809 - loss 0.02825755 - time (sec): 750.24 - samples/sec: 404.27 - lr: 0.000093 - momentum: 0.000000
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+ 2023-10-14 13:13:26,339 epoch 5 - iter 1620/1809 - loss 0.02890447 - time (sec): 853.31 - samples/sec: 398.64 - lr: 0.000091 - momentum: 0.000000
166
+ 2023-10-14 13:14:59,584 epoch 5 - iter 1800/1809 - loss 0.02958885 - time (sec): 946.55 - samples/sec: 399.42 - lr: 0.000089 - momentum: 0.000000
167
+ 2023-10-14 13:15:03,940 ----------------------------------------------------------------------------------------------------
168
+ 2023-10-14 13:15:03,940 EPOCH 5 done: loss 0.0297 - lr: 0.000089
169
+ 2023-10-14 13:15:45,600 DEV : loss 0.23190708458423615 - f1-score (micro avg) 0.6378
170
+ 2023-10-14 13:15:45,666 ----------------------------------------------------------------------------------------------------
171
+ 2023-10-14 13:17:21,707 epoch 6 - iter 180/1809 - loss 0.01812127 - time (sec): 96.04 - samples/sec: 408.69 - lr: 0.000087 - momentum: 0.000000
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+ 2023-10-14 13:18:53,389 epoch 6 - iter 360/1809 - loss 0.02039867 - time (sec): 187.72 - samples/sec: 405.76 - lr: 0.000085 - momentum: 0.000000
173
+ 2023-10-14 13:20:29,251 epoch 6 - iter 540/1809 - loss 0.01933017 - time (sec): 283.58 - samples/sec: 401.10 - lr: 0.000084 - momentum: 0.000000
174
+ 2023-10-14 13:22:02,753 epoch 6 - iter 720/1809 - loss 0.02027772 - time (sec): 377.08 - samples/sec: 399.93 - lr: 0.000082 - momentum: 0.000000
175
+ 2023-10-14 13:23:37,511 epoch 6 - iter 900/1809 - loss 0.02122834 - time (sec): 471.84 - samples/sec: 398.25 - lr: 0.000080 - momentum: 0.000000
176
+ 2023-10-14 13:25:13,342 epoch 6 - iter 1080/1809 - loss 0.02222813 - time (sec): 567.67 - samples/sec: 398.86 - lr: 0.000078 - momentum: 0.000000
177
+ 2023-10-14 13:26:46,706 epoch 6 - iter 1260/1809 - loss 0.02238050 - time (sec): 661.04 - samples/sec: 399.34 - lr: 0.000077 - momentum: 0.000000
178
+ 2023-10-14 13:28:21,436 epoch 6 - iter 1440/1809 - loss 0.02161858 - time (sec): 755.77 - samples/sec: 400.35 - lr: 0.000075 - momentum: 0.000000
179
+ 2023-10-14 13:29:57,051 epoch 6 - iter 1620/1809 - loss 0.02203621 - time (sec): 851.38 - samples/sec: 399.42 - lr: 0.000073 - momentum: 0.000000
180
+ 2023-10-14 13:31:35,172 epoch 6 - iter 1800/1809 - loss 0.02164417 - time (sec): 949.50 - samples/sec: 398.33 - lr: 0.000071 - momentum: 0.000000
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+ 2023-10-14 13:31:39,297 ----------------------------------------------------------------------------------------------------
182
+ 2023-10-14 13:31:39,298 EPOCH 6 done: loss 0.0216 - lr: 0.000071
183
+ 2023-10-14 13:32:24,318 DEV : loss 0.272296279668808 - f1-score (micro avg) 0.6524
184
+ 2023-10-14 13:32:24,397 saving best model
185
+ 2023-10-14 13:32:31,032 ----------------------------------------------------------------------------------------------------
186
+ 2023-10-14 13:34:10,803 epoch 7 - iter 180/1809 - loss 0.01105902 - time (sec): 99.77 - samples/sec: 406.73 - lr: 0.000069 - momentum: 0.000000
187
+ 2023-10-14 13:35:57,197 epoch 7 - iter 360/1809 - loss 0.01259388 - time (sec): 206.16 - samples/sec: 376.08 - lr: 0.000068 - momentum: 0.000000
188
+ 2023-10-14 13:37:31,991 epoch 7 - iter 540/1809 - loss 0.01335206 - time (sec): 300.95 - samples/sec: 382.63 - lr: 0.000066 - momentum: 0.000000
189
+ 2023-10-14 13:39:05,951 epoch 7 - iter 720/1809 - loss 0.01459110 - time (sec): 394.91 - samples/sec: 385.81 - lr: 0.000064 - momentum: 0.000000
190
+ 2023-10-14 13:40:38,429 epoch 7 - iter 900/1809 - loss 0.01490005 - time (sec): 487.39 - samples/sec: 388.72 - lr: 0.000062 - momentum: 0.000000
191
+ 2023-10-14 13:42:11,096 epoch 7 - iter 1080/1809 - loss 0.01507190 - time (sec): 580.06 - samples/sec: 391.67 - lr: 0.000061 - momentum: 0.000000
192
+ 2023-10-14 13:43:41,678 epoch 7 - iter 1260/1809 - loss 0.01473060 - time (sec): 670.64 - samples/sec: 395.61 - lr: 0.000059 - momentum: 0.000000
193
+ 2023-10-14 13:45:12,938 epoch 7 - iter 1440/1809 - loss 0.01465842 - time (sec): 761.90 - samples/sec: 399.60 - lr: 0.000057 - momentum: 0.000000
194
+ 2023-10-14 13:46:44,587 epoch 7 - iter 1620/1809 - loss 0.01508552 - time (sec): 853.55 - samples/sec: 400.09 - lr: 0.000055 - momentum: 0.000000
195
+ 2023-10-14 13:48:13,609 epoch 7 - iter 1800/1809 - loss 0.01547761 - time (sec): 942.57 - samples/sec: 400.65 - lr: 0.000053 - momentum: 0.000000
196
+ 2023-10-14 13:48:18,270 ----------------------------------------------------------------------------------------------------
197
+ 2023-10-14 13:48:18,270 EPOCH 7 done: loss 0.0154 - lr: 0.000053
198
+ 2023-10-14 13:48:57,977 DEV : loss 0.29768648743629456 - f1-score (micro avg) 0.6376
199
+ 2023-10-14 13:48:58,052 ----------------------------------------------------------------------------------------------------
200
+ 2023-10-14 13:50:33,364 epoch 8 - iter 180/1809 - loss 0.01237417 - time (sec): 95.31 - samples/sec: 401.98 - lr: 0.000052 - momentum: 0.000000
201
+ 2023-10-14 13:52:16,095 epoch 8 - iter 360/1809 - loss 0.01212823 - time (sec): 198.04 - samples/sec: 390.76 - lr: 0.000050 - momentum: 0.000000
202
+ 2023-10-14 13:53:49,344 epoch 8 - iter 540/1809 - loss 0.01054509 - time (sec): 291.29 - samples/sec: 395.46 - lr: 0.000048 - momentum: 0.000000
203
+ 2023-10-14 13:55:23,207 epoch 8 - iter 720/1809 - loss 0.01148564 - time (sec): 385.15 - samples/sec: 393.79 - lr: 0.000046 - momentum: 0.000000
204
+ 2023-10-14 13:56:59,828 epoch 8 - iter 900/1809 - loss 0.01104383 - time (sec): 481.77 - samples/sec: 394.42 - lr: 0.000044 - momentum: 0.000000
205
+ 2023-10-14 13:58:32,234 epoch 8 - iter 1080/1809 - loss 0.01171246 - time (sec): 574.18 - samples/sec: 394.37 - lr: 0.000043 - momentum: 0.000000
206
+ 2023-10-14 14:00:06,597 epoch 8 - iter 1260/1809 - loss 0.01136383 - time (sec): 668.54 - samples/sec: 396.67 - lr: 0.000041 - momentum: 0.000000
207
+ 2023-10-14 14:01:39,016 epoch 8 - iter 1440/1809 - loss 0.01180198 - time (sec): 760.96 - samples/sec: 397.42 - lr: 0.000039 - momentum: 0.000000
208
+ 2023-10-14 14:03:17,681 epoch 8 - iter 1620/1809 - loss 0.01178500 - time (sec): 859.63 - samples/sec: 395.94 - lr: 0.000037 - momentum: 0.000000
209
+ 2023-10-14 14:04:51,444 epoch 8 - iter 1800/1809 - loss 0.01159822 - time (sec): 953.39 - samples/sec: 396.91 - lr: 0.000036 - momentum: 0.000000
210
+ 2023-10-14 14:04:55,478 ----------------------------------------------------------------------------------------------------
211
+ 2023-10-14 14:04:55,479 EPOCH 8 done: loss 0.0116 - lr: 0.000036
212
+ 2023-10-14 14:05:34,550 DEV : loss 0.32400456070899963 - f1-score (micro avg) 0.6441
213
+ 2023-10-14 14:05:34,614 ----------------------------------------------------------------------------------------------------
214
+ 2023-10-14 14:07:04,671 epoch 9 - iter 180/1809 - loss 0.00428776 - time (sec): 90.06 - samples/sec: 403.01 - lr: 0.000034 - momentum: 0.000000
215
+ 2023-10-14 14:08:39,160 epoch 9 - iter 360/1809 - loss 0.00657256 - time (sec): 184.54 - samples/sec: 394.04 - lr: 0.000032 - momentum: 0.000000
216
+ 2023-10-14 14:10:22,284 epoch 9 - iter 540/1809 - loss 0.00795673 - time (sec): 287.67 - samples/sec: 385.39 - lr: 0.000030 - momentum: 0.000000
217
+ 2023-10-14 14:11:58,636 epoch 9 - iter 720/1809 - loss 0.00803158 - time (sec): 384.02 - samples/sec: 389.03 - lr: 0.000028 - momentum: 0.000000
218
+ 2023-10-14 14:13:34,188 epoch 9 - iter 900/1809 - loss 0.00763463 - time (sec): 479.57 - samples/sec: 392.43 - lr: 0.000027 - momentum: 0.000000
219
+ 2023-10-14 14:15:10,464 epoch 9 - iter 1080/1809 - loss 0.00728638 - time (sec): 575.85 - samples/sec: 391.55 - lr: 0.000025 - momentum: 0.000000
220
+ 2023-10-14 14:16:47,265 epoch 9 - iter 1260/1809 - loss 0.00750028 - time (sec): 672.65 - samples/sec: 391.08 - lr: 0.000023 - momentum: 0.000000
221
+ 2023-10-14 14:18:20,876 epoch 9 - iter 1440/1809 - loss 0.00787103 - time (sec): 766.26 - samples/sec: 394.45 - lr: 0.000021 - momentum: 0.000000
222
+ 2023-10-14 14:19:54,396 epoch 9 - iter 1620/1809 - loss 0.00770686 - time (sec): 859.78 - samples/sec: 396.21 - lr: 0.000020 - momentum: 0.000000
223
+ 2023-10-14 14:21:40,785 epoch 9 - iter 1800/1809 - loss 0.00782706 - time (sec): 966.17 - samples/sec: 391.34 - lr: 0.000018 - momentum: 0.000000
224
+ 2023-10-14 14:21:45,616 ----------------------------------------------------------------------------------------------------
225
+ 2023-10-14 14:21:45,616 EPOCH 9 done: loss 0.0078 - lr: 0.000018
226
+ 2023-10-14 14:22:26,271 DEV : loss 0.3510294556617737 - f1-score (micro avg) 0.6469
227
+ 2023-10-14 14:22:26,332 ----------------------------------------------------------------------------------------------------
228
+ 2023-10-14 14:24:08,212 epoch 10 - iter 180/1809 - loss 0.00465200 - time (sec): 101.88 - samples/sec: 366.12 - lr: 0.000016 - momentum: 0.000000
229
+ 2023-10-14 14:25:42,942 epoch 10 - iter 360/1809 - loss 0.00473108 - time (sec): 196.61 - samples/sec: 384.51 - lr: 0.000014 - momentum: 0.000000
230
+ 2023-10-14 14:27:21,238 epoch 10 - iter 540/1809 - loss 0.00620538 - time (sec): 294.90 - samples/sec: 387.50 - lr: 0.000012 - momentum: 0.000000
231
+ 2023-10-14 14:28:57,390 epoch 10 - iter 720/1809 - loss 0.00594168 - time (sec): 391.06 - samples/sec: 387.91 - lr: 0.000011 - momentum: 0.000000
232
+ 2023-10-14 14:30:32,580 epoch 10 - iter 900/1809 - loss 0.00587265 - time (sec): 486.25 - samples/sec: 389.34 - lr: 0.000009 - momentum: 0.000000
233
+ 2023-10-14 14:32:07,017 epoch 10 - iter 1080/1809 - loss 0.00574894 - time (sec): 580.68 - samples/sec: 391.80 - lr: 0.000007 - momentum: 0.000000
234
+ 2023-10-14 14:33:40,067 epoch 10 - iter 1260/1809 - loss 0.00587125 - time (sec): 673.73 - samples/sec: 393.69 - lr: 0.000005 - momentum: 0.000000
235
+ 2023-10-14 14:35:15,347 epoch 10 - iter 1440/1809 - loss 0.00584501 - time (sec): 769.01 - samples/sec: 395.67 - lr: 0.000004 - momentum: 0.000000
236
+ 2023-10-14 14:36:49,543 epoch 10 - iter 1620/1809 - loss 0.00598522 - time (sec): 863.21 - samples/sec: 395.43 - lr: 0.000002 - momentum: 0.000000
237
+ 2023-10-14 14:38:23,873 epoch 10 - iter 1800/1809 - loss 0.00617272 - time (sec): 957.54 - samples/sec: 394.90 - lr: 0.000000 - momentum: 0.000000
238
+ 2023-10-14 14:38:28,207 ----------------------------------------------------------------------------------------------------
239
+ 2023-10-14 14:38:28,208 EPOCH 10 done: loss 0.0062 - lr: 0.000000
240
+ 2023-10-14 14:39:11,475 DEV : loss 0.35460981726646423 - f1-score (micro avg) 0.6421
241
+ 2023-10-14 14:39:13,412 ----------------------------------------------------------------------------------------------------
242
+ 2023-10-14 14:39:13,414 Loading model from best epoch ...
243
+ 2023-10-14 14:39:17,156 SequenceTagger predicts: Dictionary with 13 tags: O, S-loc, B-loc, E-loc, I-loc, S-pers, B-pers, E-pers, I-pers, S-org, B-org, E-org, I-org
244
+ 2023-10-14 14:40:16,219
245
+ Results:
246
+ - F-score (micro) 0.6364
247
+ - F-score (macro) 0.4866
248
+ - Accuracy 0.4794
249
+
250
+ By class:
251
+ precision recall f1-score support
252
+
253
+ loc 0.6326 0.7547 0.6883 591
254
+ pers 0.5737 0.7087 0.6341 357
255
+ org 0.1731 0.1139 0.1374 79
256
+
257
+ micro avg 0.5910 0.6894 0.6364 1027
258
+ macro avg 0.4598 0.5258 0.4866 1027
259
+ weighted avg 0.5768 0.6894 0.6271 1027
260
+
261
+ 2023-10-14 14:40:16,219 ----------------------------------------------------------------------------------------------------