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hmbench-letemps-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-3

Token Classification
Flair
PyTorch
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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.1697252212.c8b2203b18a8.2923.10 +3 -0
  6. test.tsv +0 -0
  7. training.log +262 -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 03:14:03 0.0001 0.6115 0.1287 0.5271 0.7346 0.6138 0.4515
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+ 2 03:31:21 0.0001 0.0899 0.1199 0.5292 0.7666 0.6262 0.4621
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+ 3 03:50:10 0.0001 0.0640 0.1678 0.5869 0.7185 0.6461 0.4846
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+ 4 04:08:52 0.0001 0.0467 0.2165 0.5360 0.7838 0.6366 0.4777
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+ 5 04:27:17 0.0001 0.0326 0.2349 0.5756 0.7231 0.6410 0.4810
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+ 6 04:45:30 0.0001 0.0227 0.2885 0.5774 0.7471 0.6514 0.4921
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+ 7 05:03:46 0.0001 0.0148 0.3005 0.5816 0.7300 0.6474 0.4863
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+ 8 05:21:56 0.0000 0.0097 0.3371 0.5707 0.7529 0.6492 0.4899
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+ 9 05:39:46 0.0000 0.0066 0.3554 0.5692 0.7574 0.6500 0.4911
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+ 10 05:57:55 0.0000 0.0045 0.3686 0.5684 0.7700 0.6540 0.4959
runs/events.out.tfevents.1697252212.c8b2203b18a8.2923.10 ADDED
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test.tsv ADDED
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training.log ADDED
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+ 2023-10-14 02:56:52,966 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 02:56:52,968 Model: "SequenceTagger(
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+ (embeddings): ByT5Embeddings(
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+ (model): T5EncoderModel(
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+ (shared): Embedding(384, 1472)
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+ (encoder): T5Stack(
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+ (embed_tokens): Embedding(384, 1472)
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+ (block): ModuleList(
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+ (0): T5Block(
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+ (layer): ModuleList(
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+ (0): T5LayerSelfAttention(
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+ (SelfAttention): T5Attention(
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+ (q): Linear(in_features=1472, out_features=384, bias=False)
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+ (k): Linear(in_features=1472, out_features=384, bias=False)
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+ (v): Linear(in_features=1472, out_features=384, bias=False)
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+ (o): Linear(in_features=384, out_features=1472, bias=False)
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+ (relative_attention_bias): Embedding(32, 6)
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+ )
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+ (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ (1): T5LayerFF(
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+ (DenseReluDense): T5DenseGatedActDense(
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+ (wi_0): Linear(in_features=1472, out_features=3584, bias=False)
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+ (wi_1): Linear(in_features=1472, out_features=3584, bias=False)
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+ (wo): Linear(in_features=3584, out_features=1472, bias=False)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ (act): NewGELUActivation()
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+ )
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+ (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ )
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+ )
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+ (1-11): 11 x T5Block(
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+ (layer): ModuleList(
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+ (0): T5LayerSelfAttention(
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+ (SelfAttention): T5Attention(
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+ (q): Linear(in_features=1472, out_features=384, bias=False)
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+ (k): Linear(in_features=1472, out_features=384, bias=False)
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+ (v): Linear(in_features=1472, out_features=384, bias=False)
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+ (o): Linear(in_features=384, out_features=1472, bias=False)
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+ )
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+ (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ (1): T5LayerFF(
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+ (DenseReluDense): T5DenseGatedActDense(
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+ (wi_0): Linear(in_features=1472, out_features=3584, bias=False)
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+ (wi_1): Linear(in_features=1472, out_features=3584, bias=False)
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+ (wo): Linear(in_features=3584, out_features=1472, bias=False)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ (act): NewGELUActivation()
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+ )
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+ (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ )
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+ )
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+ )
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+ (final_layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ )
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+ )
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+ (locked_dropout): LockedDropout(p=0.5)
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+ (linear): Linear(in_features=1472, out_features=13, bias=True)
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+ (loss_function): CrossEntropyLoss()
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+ )"
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+ 2023-10-14 02:56:52,968 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 02:56:52,969 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 02:56:52,969 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 02:56:52,969 Train: 14465 sentences
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+ 2023-10-14 02:56:52,969 (train_with_dev=False, train_with_test=False)
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+ 2023-10-14 02:56:52,969 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 02:56:52,969 Training Params:
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+ 2023-10-14 02:56:52,969 - learning_rate: "0.00015"
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+ 2023-10-14 02:56:52,969 - mini_batch_size: "4"
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+ 2023-10-14 02:56:52,969 - max_epochs: "10"
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+ 2023-10-14 02:56:52,969 - shuffle: "True"
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+ 2023-10-14 02:56:52,969 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 02:56:52,970 Plugins:
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+ 2023-10-14 02:56:52,970 - TensorboardLogger
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+ 2023-10-14 02:56:52,970 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-14 02:56:52,970 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 02:56:52,970 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-14 02:56:52,970 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-14 02:56:52,970 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 02:56:52,970 Computation:
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+ 2023-10-14 02:56:52,970 - compute on device: cuda:0
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+ 2023-10-14 02:56:52,970 - embedding storage: none
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+ 2023-10-14 02:56:52,970 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 02:56:52,970 Model training base path: "hmbench-letemps/fr-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-3"
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+ 2023-10-14 02:56:52,970 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 02:56:52,970 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 02:56:52,971 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-14 02:58:32,206 epoch 1 - iter 361/3617 - loss 2.48047885 - time (sec): 99.23 - samples/sec: 380.07 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-14 03:00:10,564 epoch 1 - iter 722/3617 - loss 2.10182133 - time (sec): 197.59 - samples/sec: 376.99 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-14 03:01:51,336 epoch 1 - iter 1083/3617 - loss 1.65225929 - time (sec): 298.36 - samples/sec: 378.51 - lr: 0.000045 - momentum: 0.000000
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+ 2023-10-14 03:03:32,329 epoch 1 - iter 1444/3617 - loss 1.30928949 - time (sec): 399.36 - samples/sec: 378.96 - lr: 0.000060 - momentum: 0.000000
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+ 2023-10-14 03:05:11,749 epoch 1 - iter 1805/3617 - loss 1.08702017 - time (sec): 498.78 - samples/sec: 378.85 - lr: 0.000075 - momentum: 0.000000
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+ 2023-10-14 03:06:53,731 epoch 1 - iter 2166/3617 - loss 0.93505989 - time (sec): 600.76 - samples/sec: 377.19 - lr: 0.000090 - momentum: 0.000000
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+ 2023-10-14 03:08:31,230 epoch 1 - iter 2527/3617 - loss 0.82602626 - time (sec): 698.26 - samples/sec: 376.94 - lr: 0.000105 - momentum: 0.000000
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+ 2023-10-14 03:10:07,444 epoch 1 - iter 2888/3617 - loss 0.74069780 - time (sec): 794.47 - samples/sec: 379.05 - lr: 0.000120 - momentum: 0.000000
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+ 2023-10-14 03:11:45,292 epoch 1 - iter 3249/3617 - loss 0.66755548 - time (sec): 892.32 - samples/sec: 381.80 - lr: 0.000135 - momentum: 0.000000
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+ 2023-10-14 03:13:23,530 epoch 1 - iter 3610/3617 - loss 0.61237021 - time (sec): 990.56 - samples/sec: 382.89 - lr: 0.000150 - momentum: 0.000000
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+ 2023-10-14 03:13:25,225 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 03:13:25,225 EPOCH 1 done: loss 0.6115 - lr: 0.000150
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+ 2023-10-14 03:14:03,125 DEV : loss 0.12874433398246765 - f1-score (micro avg) 0.6138
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+ 2023-10-14 03:14:03,190 saving best model
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+ 2023-10-14 03:14:04,104 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 03:15:40,905 epoch 2 - iter 361/3617 - loss 0.09418970 - time (sec): 96.80 - samples/sec: 391.51 - lr: 0.000148 - momentum: 0.000000
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+ 2023-10-14 03:17:19,253 epoch 2 - iter 722/3617 - loss 0.09519731 - time (sec): 195.15 - samples/sec: 384.46 - lr: 0.000147 - momentum: 0.000000
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+ 2023-10-14 03:19:03,584 epoch 2 - iter 1083/3617 - loss 0.09418116 - time (sec): 299.48 - samples/sec: 388.15 - lr: 0.000145 - momentum: 0.000000
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+ 2023-10-14 03:20:44,465 epoch 2 - iter 1444/3617 - loss 0.09280081 - time (sec): 400.36 - samples/sec: 387.09 - lr: 0.000143 - momentum: 0.000000
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+ 2023-10-14 03:22:24,435 epoch 2 - iter 1805/3617 - loss 0.09367715 - time (sec): 500.33 - samples/sec: 386.16 - lr: 0.000142 - momentum: 0.000000
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+ 2023-10-14 03:24:00,186 epoch 2 - iter 2166/3617 - loss 0.09233796 - time (sec): 596.08 - samples/sec: 385.19 - lr: 0.000140 - momentum: 0.000000
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+ 2023-10-14 03:25:37,405 epoch 2 - iter 2527/3617 - loss 0.09099030 - time (sec): 693.30 - samples/sec: 385.19 - lr: 0.000138 - momentum: 0.000000
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+ 2023-10-14 03:27:18,849 epoch 2 - iter 2888/3617 - loss 0.09098331 - time (sec): 794.74 - samples/sec: 382.89 - lr: 0.000137 - momentum: 0.000000
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+ 2023-10-14 03:28:58,925 epoch 2 - iter 3249/3617 - loss 0.09046497 - time (sec): 894.82 - samples/sec: 382.77 - lr: 0.000135 - momentum: 0.000000
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+ 2023-10-14 03:30:35,687 epoch 2 - iter 3610/3617 - loss 0.08998216 - time (sec): 991.58 - samples/sec: 382.45 - lr: 0.000133 - momentum: 0.000000
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+ 2023-10-14 03:30:37,445 ----------------------------------------------------------------------------------------------------
124
+ 2023-10-14 03:30:37,445 EPOCH 2 done: loss 0.0899 - lr: 0.000133
125
+ 2023-10-14 03:31:21,578 DEV : loss 0.11994253098964691 - f1-score (micro avg) 0.6262
126
+ 2023-10-14 03:31:21,672 saving best model
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+ 2023-10-14 03:31:24,476 ----------------------------------------------------------------------------------------------------
128
+ 2023-10-14 03:33:17,847 epoch 3 - iter 361/3617 - loss 0.06787226 - time (sec): 113.36 - samples/sec: 330.36 - lr: 0.000132 - momentum: 0.000000
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+ 2023-10-14 03:35:08,246 epoch 3 - iter 722/3617 - loss 0.06752508 - time (sec): 223.76 - samples/sec: 344.88 - lr: 0.000130 - momentum: 0.000000
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+ 2023-10-14 03:36:56,698 epoch 3 - iter 1083/3617 - loss 0.06518068 - time (sec): 332.22 - samples/sec: 346.12 - lr: 0.000128 - momentum: 0.000000
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+ 2023-10-14 03:38:44,830 epoch 3 - iter 1444/3617 - loss 0.06371927 - time (sec): 440.35 - samples/sec: 346.81 - lr: 0.000127 - momentum: 0.000000
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+ 2023-10-14 03:40:32,739 epoch 3 - iter 1805/3617 - loss 0.06387658 - time (sec): 548.26 - samples/sec: 350.40 - lr: 0.000125 - momentum: 0.000000
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+ 2023-10-14 03:42:18,558 epoch 3 - iter 2166/3617 - loss 0.06346408 - time (sec): 654.08 - samples/sec: 352.37 - lr: 0.000123 - momentum: 0.000000
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+ 2023-10-14 03:44:05,888 epoch 3 - iter 2527/3617 - loss 0.06459000 - time (sec): 761.41 - samples/sec: 350.21 - lr: 0.000122 - momentum: 0.000000
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+ 2023-10-14 03:45:55,184 epoch 3 - iter 2888/3617 - loss 0.06401459 - time (sec): 870.70 - samples/sec: 348.70 - lr: 0.000120 - momentum: 0.000000
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+ 2023-10-14 03:47:39,172 epoch 3 - iter 3249/3617 - loss 0.06394856 - time (sec): 974.69 - samples/sec: 349.27 - lr: 0.000118 - momentum: 0.000000
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+ 2023-10-14 03:49:25,723 epoch 3 - iter 3610/3617 - loss 0.06409756 - time (sec): 1081.24 - samples/sec: 350.60 - lr: 0.000117 - momentum: 0.000000
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+ 2023-10-14 03:49:27,697 ----------------------------------------------------------------------------------------------------
139
+ 2023-10-14 03:49:27,697 EPOCH 3 done: loss 0.0640 - lr: 0.000117
140
+ 2023-10-14 03:50:10,253 DEV : loss 0.1677953451871872 - f1-score (micro avg) 0.6461
141
+ 2023-10-14 03:50:10,312 saving best model
142
+ 2023-10-14 03:50:13,061 ----------------------------------------------------------------------------------------------------
143
+ 2023-10-14 03:52:00,514 epoch 4 - iter 361/3617 - loss 0.04549276 - time (sec): 107.45 - samples/sec: 340.97 - lr: 0.000115 - momentum: 0.000000
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+ 2023-10-14 03:53:47,270 epoch 4 - iter 722/3617 - loss 0.04285214 - time (sec): 214.20 - samples/sec: 351.22 - lr: 0.000113 - momentum: 0.000000
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+ 2023-10-14 03:55:39,687 epoch 4 - iter 1083/3617 - loss 0.04301779 - time (sec): 326.62 - samples/sec: 345.40 - lr: 0.000112 - momentum: 0.000000
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+ 2023-10-14 03:57:21,761 epoch 4 - iter 1444/3617 - loss 0.04227254 - time (sec): 428.70 - samples/sec: 349.22 - lr: 0.000110 - momentum: 0.000000
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+ 2023-10-14 03:59:08,557 epoch 4 - iter 1805/3617 - loss 0.04271265 - time (sec): 535.49 - samples/sec: 350.63 - lr: 0.000108 - momentum: 0.000000
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+ 2023-10-14 04:00:56,545 epoch 4 - iter 2166/3617 - loss 0.04401912 - time (sec): 643.48 - samples/sec: 353.09 - lr: 0.000107 - momentum: 0.000000
149
+ 2023-10-14 04:02:49,728 epoch 4 - iter 2527/3617 - loss 0.04552488 - time (sec): 756.66 - samples/sec: 351.47 - lr: 0.000105 - momentum: 0.000000
150
+ 2023-10-14 04:04:38,740 epoch 4 - iter 2888/3617 - loss 0.04620049 - time (sec): 865.67 - samples/sec: 350.11 - lr: 0.000103 - momentum: 0.000000
151
+ 2023-10-14 04:06:26,364 epoch 4 - iter 3249/3617 - loss 0.04661299 - time (sec): 973.30 - samples/sec: 351.46 - lr: 0.000102 - momentum: 0.000000
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+ 2023-10-14 04:08:08,784 epoch 4 - iter 3610/3617 - loss 0.04677504 - time (sec): 1075.72 - samples/sec: 352.63 - lr: 0.000100 - momentum: 0.000000
153
+ 2023-10-14 04:08:10,581 ----------------------------------------------------------------------------------------------------
154
+ 2023-10-14 04:08:10,581 EPOCH 4 done: loss 0.0467 - lr: 0.000100
155
+ 2023-10-14 04:08:52,500 DEV : loss 0.2164839208126068 - f1-score (micro avg) 0.6366
156
+ 2023-10-14 04:08:52,567 ----------------------------------------------------------------------------------------------------
157
+ 2023-10-14 04:10:41,619 epoch 5 - iter 361/3617 - loss 0.02826111 - time (sec): 109.05 - samples/sec: 352.78 - lr: 0.000098 - momentum: 0.000000
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+ 2023-10-14 04:12:31,812 epoch 5 - iter 722/3617 - loss 0.02975887 - time (sec): 219.24 - samples/sec: 348.59 - lr: 0.000097 - momentum: 0.000000
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+ 2023-10-14 04:14:13,673 epoch 5 - iter 1083/3617 - loss 0.03117931 - time (sec): 321.10 - samples/sec: 353.89 - lr: 0.000095 - momentum: 0.000000
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+ 2023-10-14 04:15:59,664 epoch 5 - iter 1444/3617 - loss 0.03114051 - time (sec): 427.09 - samples/sec: 351.78 - lr: 0.000093 - momentum: 0.000000
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+ 2023-10-14 04:17:51,747 epoch 5 - iter 1805/3617 - loss 0.03157137 - time (sec): 539.18 - samples/sec: 350.38 - lr: 0.000092 - momentum: 0.000000
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+ 2023-10-14 04:19:39,699 epoch 5 - iter 2166/3617 - loss 0.03117937 - time (sec): 647.13 - samples/sec: 349.96 - lr: 0.000090 - momentum: 0.000000
163
+ 2023-10-14 04:21:21,870 epoch 5 - iter 2527/3617 - loss 0.03194759 - time (sec): 749.30 - samples/sec: 350.89 - lr: 0.000088 - momentum: 0.000000
164
+ 2023-10-14 04:23:09,331 epoch 5 - iter 2888/3617 - loss 0.03227297 - time (sec): 856.76 - samples/sec: 351.11 - lr: 0.000087 - momentum: 0.000000
165
+ 2023-10-14 04:24:50,326 epoch 5 - iter 3249/3617 - loss 0.03237174 - time (sec): 957.76 - samples/sec: 355.14 - lr: 0.000085 - momentum: 0.000000
166
+ 2023-10-14 04:26:33,987 epoch 5 - iter 3610/3617 - loss 0.03258639 - time (sec): 1061.42 - samples/sec: 357.40 - lr: 0.000083 - momentum: 0.000000
167
+ 2023-10-14 04:26:35,865 ----------------------------------------------------------------------------------------------------
168
+ 2023-10-14 04:26:35,865 EPOCH 5 done: loss 0.0326 - lr: 0.000083
169
+ 2023-10-14 04:27:17,687 DEV : loss 0.23494853079319 - f1-score (micro avg) 0.641
170
+ 2023-10-14 04:27:17,752 ----------------------------------------------------------------------------------------------------
171
+ 2023-10-14 04:29:14,894 epoch 6 - iter 361/3617 - loss 0.01939447 - time (sec): 117.14 - samples/sec: 335.01 - lr: 0.000082 - momentum: 0.000000
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+ 2023-10-14 04:30:58,369 epoch 6 - iter 722/3617 - loss 0.01924277 - time (sec): 220.61 - samples/sec: 349.27 - lr: 0.000080 - momentum: 0.000000
173
+ 2023-10-14 04:32:36,989 epoch 6 - iter 1083/3617 - loss 0.02053705 - time (sec): 319.23 - samples/sec: 358.43 - lr: 0.000078 - momentum: 0.000000
174
+ 2023-10-14 04:34:18,846 epoch 6 - iter 1444/3617 - loss 0.02164758 - time (sec): 421.09 - samples/sec: 358.61 - lr: 0.000077 - momentum: 0.000000
175
+ 2023-10-14 04:36:07,097 epoch 6 - iter 1805/3617 - loss 0.02255132 - time (sec): 529.34 - samples/sec: 354.80 - lr: 0.000075 - momentum: 0.000000
176
+ 2023-10-14 04:37:50,944 epoch 6 - iter 2166/3617 - loss 0.02251730 - time (sec): 633.19 - samples/sec: 355.87 - lr: 0.000073 - momentum: 0.000000
177
+ 2023-10-14 04:39:38,861 epoch 6 - iter 2527/3617 - loss 0.02245883 - time (sec): 741.11 - samples/sec: 357.56 - lr: 0.000072 - momentum: 0.000000
178
+ 2023-10-14 04:41:21,087 epoch 6 - iter 2888/3617 - loss 0.02197406 - time (sec): 843.33 - samples/sec: 360.49 - lr: 0.000070 - momentum: 0.000000
179
+ 2023-10-14 04:43:02,150 epoch 6 - iter 3249/3617 - loss 0.02290527 - time (sec): 944.40 - samples/sec: 360.40 - lr: 0.000068 - momentum: 0.000000
180
+ 2023-10-14 04:44:46,979 epoch 6 - iter 3610/3617 - loss 0.02272655 - time (sec): 1049.22 - samples/sec: 361.29 - lr: 0.000067 - momentum: 0.000000
181
+ 2023-10-14 04:44:48,993 ----------------------------------------------------------------------------------------------------
182
+ 2023-10-14 04:44:48,994 EPOCH 6 done: loss 0.0227 - lr: 0.000067
183
+ 2023-10-14 04:45:30,501 DEV : loss 0.28848496079444885 - f1-score (micro avg) 0.6514
184
+ 2023-10-14 04:45:30,570 saving best model
185
+ 2023-10-14 04:45:35,631 ----------------------------------------------------------------------------------------------------
186
+ 2023-10-14 04:47:22,333 epoch 7 - iter 361/3617 - loss 0.01165935 - time (sec): 106.69 - samples/sec: 359.92 - lr: 0.000065 - momentum: 0.000000
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+ 2023-10-14 04:49:03,875 epoch 7 - iter 722/3617 - loss 0.01137913 - time (sec): 208.23 - samples/sec: 365.84 - lr: 0.000063 - momentum: 0.000000
188
+ 2023-10-14 04:50:48,226 epoch 7 - iter 1083/3617 - loss 0.01305213 - time (sec): 312.58 - samples/sec: 363.16 - lr: 0.000062 - momentum: 0.000000
189
+ 2023-10-14 04:52:34,504 epoch 7 - iter 1444/3617 - loss 0.01287060 - time (sec): 418.86 - samples/sec: 365.53 - lr: 0.000060 - momentum: 0.000000
190
+ 2023-10-14 04:54:21,015 epoch 7 - iter 1805/3617 - loss 0.01312161 - time (sec): 525.37 - samples/sec: 362.68 - lr: 0.000058 - momentum: 0.000000
191
+ 2023-10-14 04:56:05,373 epoch 7 - iter 2166/3617 - loss 0.01362263 - time (sec): 629.73 - samples/sec: 362.43 - lr: 0.000057 - momentum: 0.000000
192
+ 2023-10-14 04:57:53,808 epoch 7 - iter 2527/3617 - loss 0.01454342 - time (sec): 738.16 - samples/sec: 361.92 - lr: 0.000055 - momentum: 0.000000
193
+ 2023-10-14 04:59:36,320 epoch 7 - iter 2888/3617 - loss 0.01455589 - time (sec): 840.68 - samples/sec: 362.15 - lr: 0.000053 - momentum: 0.000000
194
+ 2023-10-14 05:01:16,947 epoch 7 - iter 3249/3617 - loss 0.01486078 - time (sec): 941.30 - samples/sec: 363.83 - lr: 0.000052 - momentum: 0.000000
195
+ 2023-10-14 05:03:01,921 epoch 7 - iter 3610/3617 - loss 0.01483858 - time (sec): 1046.28 - samples/sec: 362.57 - lr: 0.000050 - momentum: 0.000000
196
+ 2023-10-14 05:03:03,721 ----------------------------------------------------------------------------------------------------
197
+ 2023-10-14 05:03:03,721 EPOCH 7 done: loss 0.0148 - lr: 0.000050
198
+ 2023-10-14 05:03:46,800 DEV : loss 0.3004520535469055 - f1-score (micro avg) 0.6474
199
+ 2023-10-14 05:03:46,866 ----------------------------------------------------------------------------------------------------
200
+ 2023-10-14 05:05:31,450 epoch 8 - iter 361/3617 - loss 0.00618969 - time (sec): 104.58 - samples/sec: 355.17 - lr: 0.000048 - momentum: 0.000000
201
+ 2023-10-14 05:07:14,817 epoch 8 - iter 722/3617 - loss 0.00964368 - time (sec): 207.95 - samples/sec: 361.93 - lr: 0.000047 - momentum: 0.000000
202
+ 2023-10-14 05:09:01,458 epoch 8 - iter 1083/3617 - loss 0.01105306 - time (sec): 314.59 - samples/sec: 364.87 - lr: 0.000045 - momentum: 0.000000
203
+ 2023-10-14 05:10:45,447 epoch 8 - iter 1444/3617 - loss 0.01059372 - time (sec): 418.58 - samples/sec: 364.30 - lr: 0.000043 - momentum: 0.000000
204
+ 2023-10-14 05:12:30,524 epoch 8 - iter 1805/3617 - loss 0.01012078 - time (sec): 523.66 - samples/sec: 365.47 - lr: 0.000042 - momentum: 0.000000
205
+ 2023-10-14 05:14:14,804 epoch 8 - iter 2166/3617 - loss 0.00963085 - time (sec): 627.94 - samples/sec: 364.14 - lr: 0.000040 - momentum: 0.000000
206
+ 2023-10-14 05:16:02,304 epoch 8 - iter 2527/3617 - loss 0.00979590 - time (sec): 735.44 - samples/sec: 362.59 - lr: 0.000038 - momentum: 0.000000
207
+ 2023-10-14 05:17:45,665 epoch 8 - iter 2888/3617 - loss 0.00961432 - time (sec): 838.80 - samples/sec: 362.84 - lr: 0.000037 - momentum: 0.000000
208
+ 2023-10-14 05:19:28,394 epoch 8 - iter 3249/3617 - loss 0.00997350 - time (sec): 941.53 - samples/sec: 362.99 - lr: 0.000035 - momentum: 0.000000
209
+ 2023-10-14 05:21:12,120 epoch 8 - iter 3610/3617 - loss 0.00969534 - time (sec): 1045.25 - samples/sec: 363.06 - lr: 0.000033 - momentum: 0.000000
210
+ 2023-10-14 05:21:13,905 ----------------------------------------------------------------------------------------------------
211
+ 2023-10-14 05:21:13,905 EPOCH 8 done: loss 0.0097 - lr: 0.000033
212
+ 2023-10-14 05:21:55,993 DEV : loss 0.33705711364746094 - f1-score (micro avg) 0.6492
213
+ 2023-10-14 05:21:56,061 ----------------------------------------------------------------------------------------------------
214
+ 2023-10-14 05:23:41,737 epoch 9 - iter 361/3617 - loss 0.00768336 - time (sec): 105.67 - samples/sec: 366.27 - lr: 0.000032 - momentum: 0.000000
215
+ 2023-10-14 05:25:32,318 epoch 9 - iter 722/3617 - loss 0.00809368 - time (sec): 216.25 - samples/sec: 361.32 - lr: 0.000030 - momentum: 0.000000
216
+ 2023-10-14 05:27:20,923 epoch 9 - iter 1083/3617 - loss 0.00801755 - time (sec): 324.86 - samples/sec: 355.90 - lr: 0.000028 - momentum: 0.000000
217
+ 2023-10-14 05:29:02,262 epoch 9 - iter 1444/3617 - loss 0.00794549 - time (sec): 426.20 - samples/sec: 361.14 - lr: 0.000027 - momentum: 0.000000
218
+ 2023-10-14 05:30:41,183 epoch 9 - iter 1805/3617 - loss 0.00771750 - time (sec): 525.12 - samples/sec: 364.12 - lr: 0.000025 - momentum: 0.000000
219
+ 2023-10-14 05:32:20,956 epoch 9 - iter 2166/3617 - loss 0.00754007 - time (sec): 624.89 - samples/sec: 367.42 - lr: 0.000023 - momentum: 0.000000
220
+ 2023-10-14 05:34:00,453 epoch 9 - iter 2527/3617 - loss 0.00713992 - time (sec): 724.39 - samples/sec: 369.15 - lr: 0.000022 - momentum: 0.000000
221
+ 2023-10-14 05:35:41,205 epoch 9 - iter 2888/3617 - loss 0.00704793 - time (sec): 825.14 - samples/sec: 368.97 - lr: 0.000020 - momentum: 0.000000
222
+ 2023-10-14 05:37:23,105 epoch 9 - iter 3249/3617 - loss 0.00699019 - time (sec): 927.04 - samples/sec: 367.49 - lr: 0.000018 - momentum: 0.000000
223
+ 2023-10-14 05:39:03,150 epoch 9 - iter 3610/3617 - loss 0.00658969 - time (sec): 1027.09 - samples/sec: 369.22 - lr: 0.000017 - momentum: 0.000000
224
+ 2023-10-14 05:39:05,141 ----------------------------------------------------------------------------------------------------
225
+ 2023-10-14 05:39:05,142 EPOCH 9 done: loss 0.0066 - lr: 0.000017
226
+ 2023-10-14 05:39:46,293 DEV : loss 0.3554496467113495 - f1-score (micro avg) 0.65
227
+ 2023-10-14 05:39:46,351 ----------------------------------------------------------------------------------------------------
228
+ 2023-10-14 05:41:28,259 epoch 10 - iter 361/3617 - loss 0.00317370 - time (sec): 101.91 - samples/sec: 370.33 - lr: 0.000015 - momentum: 0.000000
229
+ 2023-10-14 05:43:12,503 epoch 10 - iter 722/3617 - loss 0.00395930 - time (sec): 206.15 - samples/sec: 358.82 - lr: 0.000013 - momentum: 0.000000
230
+ 2023-10-14 05:44:56,641 epoch 10 - iter 1083/3617 - loss 0.00484872 - time (sec): 310.29 - samples/sec: 362.49 - lr: 0.000012 - momentum: 0.000000
231
+ 2023-10-14 05:46:40,943 epoch 10 - iter 1444/3617 - loss 0.00441603 - time (sec): 414.59 - samples/sec: 360.25 - lr: 0.000010 - momentum: 0.000000
232
+ 2023-10-14 05:48:22,688 epoch 10 - iter 1805/3617 - loss 0.00440111 - time (sec): 516.33 - samples/sec: 364.19 - lr: 0.000008 - momentum: 0.000000
233
+ 2023-10-14 05:50:03,498 epoch 10 - iter 2166/3617 - loss 0.00463574 - time (sec): 617.14 - samples/sec: 365.23 - lr: 0.000007 - momentum: 0.000000
234
+ 2023-10-14 05:51:45,733 epoch 10 - iter 2527/3617 - loss 0.00475224 - time (sec): 719.38 - samples/sec: 368.15 - lr: 0.000005 - momentum: 0.000000
235
+ 2023-10-14 05:53:30,970 epoch 10 - iter 2888/3617 - loss 0.00477992 - time (sec): 824.62 - samples/sec: 366.33 - lr: 0.000003 - momentum: 0.000000
236
+ 2023-10-14 05:55:21,600 epoch 10 - iter 3249/3617 - loss 0.00469963 - time (sec): 935.25 - samples/sec: 364.85 - lr: 0.000002 - momentum: 0.000000
237
+ 2023-10-14 05:57:08,911 epoch 10 - iter 3610/3617 - loss 0.00449982 - time (sec): 1042.56 - samples/sec: 363.50 - lr: 0.000000 - momentum: 0.000000
238
+ 2023-10-14 05:57:11,042 ----------------------------------------------------------------------------------------------------
239
+ 2023-10-14 05:57:11,043 EPOCH 10 done: loss 0.0045 - lr: 0.000000
240
+ 2023-10-14 05:57:55,357 DEV : loss 0.3686419725418091 - f1-score (micro avg) 0.654
241
+ 2023-10-14 05:57:55,427 saving best model
242
+ 2023-10-14 05:58:03,839 ----------------------------------------------------------------------------------------------------
243
+ 2023-10-14 05:58:03,841 Loading model from best epoch ...
244
+ 2023-10-14 05:58:08,061 SequenceTagger predicts: Dictionary with 13 tags: O, S-loc, B-loc, E-loc, I-loc, S-pers, B-pers, E-pers, I-pers, S-org, B-org, E-org, I-org
245
+ 2023-10-14 05:59:10,367
246
+ Results:
247
+ - F-score (micro) 0.6565
248
+ - F-score (macro) 0.5195
249
+ - Accuracy 0.5017
250
+
251
+ By class:
252
+ precision recall f1-score support
253
+
254
+ loc 0.6609 0.7750 0.7134 591
255
+ pers 0.5807 0.7255 0.6451 357
256
+ org 0.2295 0.1772 0.2000 79
257
+
258
+ micro avg 0.6092 0.7118 0.6565 1027
259
+ macro avg 0.4904 0.5592 0.5195 1027
260
+ weighted avg 0.5998 0.7118 0.6502 1027
261
+
262
+ 2023-10-14 05:59:10,367 ----------------------------------------------------------------------------------------------------