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

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.1697180959.c8b2203b18a8.2923.3 +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 07:26:58 0.0002 0.6290 0.1230 0.5237 0.5572 0.5399 0.3811
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+ 2 07:44:18 0.0001 0.0940 0.1127 0.5617 0.6773 0.6141 0.4502
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+ 3 08:01:36 0.0001 0.0659 0.1726 0.5433 0.7185 0.6187 0.4574
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+ 4 08:19:25 0.0001 0.0493 0.2102 0.5539 0.7643 0.6423 0.4834
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+ 5 08:37:22 0.0001 0.0360 0.2428 0.5603 0.7334 0.6353 0.4745
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+ 6 08:55:13 0.0001 0.0241 0.3024 0.5470 0.7254 0.6237 0.4638
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+ 7 09:13:29 0.0001 0.0180 0.3239 0.5445 0.7071 0.6152 0.4541
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+ 8 09:30:58 0.0000 0.0114 0.3562 0.5601 0.7357 0.6360 0.4766
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+ 9 09:48:36 0.0000 0.0083 0.3850 0.5610 0.7471 0.6408 0.4816
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+ 10 10:06:48 0.0000 0.0042 0.3937 0.5543 0.7597 0.6409 0.4826
runs/events.out.tfevents.1697180959.c8b2203b18a8.2923.3 ADDED
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test.tsv ADDED
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training.log ADDED
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+ 2023-10-13 07:09:19,789 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 07:09:19,791 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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+ 2023-10-13 07:09:19,791 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 07:09:19,791 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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+ 2023-10-13 07:09:19,791 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 07:09:19,791 Train: 14465 sentences
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+ 2023-10-13 07:09:19,791 (train_with_dev=False, train_with_test=False)
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+ 2023-10-13 07:09:19,792 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 07:09:19,792 Training Params:
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+ 2023-10-13 07:09:19,792 - learning_rate: "0.00016"
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+ 2023-10-13 07:09:19,792 - mini_batch_size: "4"
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+ 2023-10-13 07:09:19,792 - max_epochs: "10"
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+ 2023-10-13 07:09:19,792 - shuffle: "True"
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+ 2023-10-13 07:09:19,792 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 07:09:19,792 Plugins:
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+ 2023-10-13 07:09:19,792 - TensorboardLogger
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+ 2023-10-13 07:09:19,792 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-13 07:09:19,792 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 07:09:19,792 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-13 07:09:19,792 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-13 07:09:19,792 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 07:09:19,792 Computation:
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+ 2023-10-13 07:09:19,793 - compute on device: cuda:0
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+ 2023-10-13 07:09:19,793 - embedding storage: none
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+ 2023-10-13 07:09:19,793 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 07:09:19,793 Model training base path: "hmbench-letemps/fr-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-1"
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+ 2023-10-13 07:09:19,793 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 07:09:19,793 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 07:09:19,793 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-13 07:11:03,127 epoch 1 - iter 361/3617 - loss 2.54672490 - time (sec): 103.33 - samples/sec: 372.78 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-13 07:12:44,471 epoch 1 - iter 722/3617 - loss 2.14481700 - time (sec): 204.68 - samples/sec: 372.30 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-13 07:14:23,374 epoch 1 - iter 1083/3617 - loss 1.68863182 - time (sec): 303.58 - samples/sec: 372.84 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-13 07:16:07,432 epoch 1 - iter 1444/3617 - loss 1.33089974 - time (sec): 407.64 - samples/sec: 373.61 - lr: 0.000064 - momentum: 0.000000
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+ 2023-10-13 07:17:50,485 epoch 1 - iter 1805/3617 - loss 1.10545660 - time (sec): 510.69 - samples/sec: 371.79 - lr: 0.000080 - momentum: 0.000000
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+ 2023-10-13 07:19:32,500 epoch 1 - iter 2166/3617 - loss 0.95603286 - time (sec): 612.71 - samples/sec: 370.31 - lr: 0.000096 - momentum: 0.000000
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+ 2023-10-13 07:21:14,877 epoch 1 - iter 2527/3617 - loss 0.84785682 - time (sec): 715.08 - samples/sec: 368.44 - lr: 0.000112 - momentum: 0.000000
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+ 2023-10-13 07:22:59,295 epoch 1 - iter 2888/3617 - loss 0.75882497 - time (sec): 819.50 - samples/sec: 368.18 - lr: 0.000128 - momentum: 0.000000
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+ 2023-10-13 07:24:39,030 epoch 1 - iter 3249/3617 - loss 0.69053264 - time (sec): 919.23 - samples/sec: 369.76 - lr: 0.000144 - momentum: 0.000000
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+ 2023-10-13 07:26:17,579 epoch 1 - iter 3610/3617 - loss 0.62979015 - time (sec): 1017.78 - samples/sec: 372.72 - lr: 0.000160 - momentum: 0.000000
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+ 2023-10-13 07:26:19,360 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 07:26:19,361 EPOCH 1 done: loss 0.6290 - lr: 0.000160
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+ 2023-10-13 07:26:58,014 DEV : loss 0.12302889674901962 - f1-score (micro avg) 0.5399
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+ 2023-10-13 07:26:58,075 saving best model
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+ 2023-10-13 07:26:58,931 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 07:28:41,476 epoch 2 - iter 361/3617 - loss 0.09184357 - time (sec): 102.54 - samples/sec: 379.17 - lr: 0.000158 - momentum: 0.000000
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+ 2023-10-13 07:30:19,808 epoch 2 - iter 722/3617 - loss 0.09724116 - time (sec): 200.88 - samples/sec: 385.81 - lr: 0.000156 - momentum: 0.000000
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+ 2023-10-13 07:31:58,762 epoch 2 - iter 1083/3617 - loss 0.09717697 - time (sec): 299.83 - samples/sec: 385.45 - lr: 0.000155 - momentum: 0.000000
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+ 2023-10-13 07:33:40,743 epoch 2 - iter 1444/3617 - loss 0.09928124 - time (sec): 401.81 - samples/sec: 379.33 - lr: 0.000153 - momentum: 0.000000
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+ 2023-10-13 07:35:19,332 epoch 2 - iter 1805/3617 - loss 0.09824636 - time (sec): 500.40 - samples/sec: 378.12 - lr: 0.000151 - momentum: 0.000000
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+ 2023-10-13 07:36:59,984 epoch 2 - iter 2166/3617 - loss 0.09948203 - time (sec): 601.05 - samples/sec: 378.45 - lr: 0.000149 - momentum: 0.000000
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+ 2023-10-13 07:38:39,721 epoch 2 - iter 2527/3617 - loss 0.09758575 - time (sec): 700.79 - samples/sec: 378.47 - lr: 0.000148 - momentum: 0.000000
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+ 2023-10-13 07:40:20,216 epoch 2 - iter 2888/3617 - loss 0.09631999 - time (sec): 801.28 - samples/sec: 378.70 - lr: 0.000146 - momentum: 0.000000
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+ 2023-10-13 07:42:00,600 epoch 2 - iter 3249/3617 - loss 0.09404083 - time (sec): 901.67 - samples/sec: 379.47 - lr: 0.000144 - momentum: 0.000000
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+ 2023-10-13 07:43:37,048 epoch 2 - iter 3610/3617 - loss 0.09413353 - time (sec): 998.11 - samples/sec: 379.70 - lr: 0.000142 - momentum: 0.000000
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+ 2023-10-13 07:43:38,928 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 07:43:38,928 EPOCH 2 done: loss 0.0940 - lr: 0.000142
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+ 2023-10-13 07:44:17,984 DEV : loss 0.11269791424274445 - f1-score (micro avg) 0.6141
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+ 2023-10-13 07:44:18,043 saving best model
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+ 2023-10-13 07:44:20,630 ----------------------------------------------------------------------------------------------------
128
+ 2023-10-13 07:45:59,384 epoch 3 - iter 361/3617 - loss 0.05810261 - time (sec): 98.75 - samples/sec: 387.16 - lr: 0.000140 - momentum: 0.000000
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+ 2023-10-13 07:47:42,320 epoch 3 - iter 722/3617 - loss 0.06040886 - time (sec): 201.69 - samples/sec: 380.77 - lr: 0.000139 - momentum: 0.000000
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+ 2023-10-13 07:49:20,365 epoch 3 - iter 1083/3617 - loss 0.06342105 - time (sec): 299.73 - samples/sec: 379.17 - lr: 0.000137 - momentum: 0.000000
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+ 2023-10-13 07:50:58,968 epoch 3 - iter 1444/3617 - loss 0.06293381 - time (sec): 398.33 - samples/sec: 378.53 - lr: 0.000135 - momentum: 0.000000
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+ 2023-10-13 07:52:35,727 epoch 3 - iter 1805/3617 - loss 0.06498947 - time (sec): 495.09 - samples/sec: 381.36 - lr: 0.000133 - momentum: 0.000000
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+ 2023-10-13 07:54:12,518 epoch 3 - iter 2166/3617 - loss 0.06553587 - time (sec): 591.88 - samples/sec: 383.44 - lr: 0.000132 - momentum: 0.000000
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+ 2023-10-13 07:55:49,481 epoch 3 - iter 2527/3617 - loss 0.06591706 - time (sec): 688.85 - samples/sec: 385.80 - lr: 0.000130 - momentum: 0.000000
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+ 2023-10-13 07:57:29,045 epoch 3 - iter 2888/3617 - loss 0.06544978 - time (sec): 788.41 - samples/sec: 384.44 - lr: 0.000128 - momentum: 0.000000
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+ 2023-10-13 07:59:12,481 epoch 3 - iter 3249/3617 - loss 0.06554704 - time (sec): 891.85 - samples/sec: 382.70 - lr: 0.000126 - momentum: 0.000000
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+ 2023-10-13 08:00:54,592 epoch 3 - iter 3610/3617 - loss 0.06594083 - time (sec): 993.96 - samples/sec: 381.46 - lr: 0.000124 - momentum: 0.000000
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+ 2023-10-13 08:00:56,337 ----------------------------------------------------------------------------------------------------
139
+ 2023-10-13 08:00:56,337 EPOCH 3 done: loss 0.0659 - lr: 0.000124
140
+ 2023-10-13 08:01:36,047 DEV : loss 0.17262127995491028 - f1-score (micro avg) 0.6187
141
+ 2023-10-13 08:01:36,109 saving best model
142
+ 2023-10-13 08:01:38,726 ----------------------------------------------------------------------------------------------------
143
+ 2023-10-13 08:03:17,667 epoch 4 - iter 361/3617 - loss 0.04571932 - time (sec): 98.94 - samples/sec: 372.96 - lr: 0.000123 - momentum: 0.000000
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+ 2023-10-13 08:04:59,641 epoch 4 - iter 722/3617 - loss 0.04768170 - time (sec): 200.91 - samples/sec: 380.44 - lr: 0.000121 - momentum: 0.000000
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+ 2023-10-13 08:06:45,305 epoch 4 - iter 1083/3617 - loss 0.04760406 - time (sec): 306.57 - samples/sec: 371.61 - lr: 0.000119 - momentum: 0.000000
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+ 2023-10-13 08:08:29,628 epoch 4 - iter 1444/3617 - loss 0.04660555 - time (sec): 410.90 - samples/sec: 367.32 - lr: 0.000117 - momentum: 0.000000
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+ 2023-10-13 08:10:11,625 epoch 4 - iter 1805/3617 - loss 0.04686239 - time (sec): 512.90 - samples/sec: 366.00 - lr: 0.000116 - momentum: 0.000000
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+ 2023-10-13 08:11:51,533 epoch 4 - iter 2166/3617 - loss 0.04882203 - time (sec): 612.80 - samples/sec: 369.00 - lr: 0.000114 - momentum: 0.000000
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+ 2023-10-13 08:13:32,397 epoch 4 - iter 2527/3617 - loss 0.04981878 - time (sec): 713.67 - samples/sec: 369.31 - lr: 0.000112 - momentum: 0.000000
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+ 2023-10-13 08:15:15,443 epoch 4 - iter 2888/3617 - loss 0.04947922 - time (sec): 816.71 - samples/sec: 369.59 - lr: 0.000110 - momentum: 0.000000
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+ 2023-10-13 08:16:59,452 epoch 4 - iter 3249/3617 - loss 0.04870494 - time (sec): 920.72 - samples/sec: 370.52 - lr: 0.000108 - momentum: 0.000000
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+ 2023-10-13 08:18:43,800 epoch 4 - iter 3610/3617 - loss 0.04931029 - time (sec): 1025.07 - samples/sec: 369.92 - lr: 0.000107 - momentum: 0.000000
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+ 2023-10-13 08:18:45,673 ----------------------------------------------------------------------------------------------------
154
+ 2023-10-13 08:18:45,673 EPOCH 4 done: loss 0.0493 - lr: 0.000107
155
+ 2023-10-13 08:19:25,861 DEV : loss 0.21019545197486877 - f1-score (micro avg) 0.6423
156
+ 2023-10-13 08:19:25,919 saving best model
157
+ 2023-10-13 08:19:28,524 ----------------------------------------------------------------------------------------------------
158
+ 2023-10-13 08:21:10,444 epoch 5 - iter 361/3617 - loss 0.03375571 - time (sec): 101.91 - samples/sec: 377.94 - lr: 0.000105 - momentum: 0.000000
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+ 2023-10-13 08:22:55,141 epoch 5 - iter 722/3617 - loss 0.03056571 - time (sec): 206.61 - samples/sec: 372.05 - lr: 0.000103 - momentum: 0.000000
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+ 2023-10-13 08:24:42,670 epoch 5 - iter 1083/3617 - loss 0.03134028 - time (sec): 314.14 - samples/sec: 361.24 - lr: 0.000101 - momentum: 0.000000
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+ 2023-10-13 08:26:25,328 epoch 5 - iter 1444/3617 - loss 0.03200139 - time (sec): 416.80 - samples/sec: 360.14 - lr: 0.000100 - momentum: 0.000000
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+ 2023-10-13 08:28:05,490 epoch 5 - iter 1805/3617 - loss 0.03332061 - time (sec): 516.96 - samples/sec: 363.22 - lr: 0.000098 - momentum: 0.000000
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+ 2023-10-13 08:29:47,849 epoch 5 - iter 2166/3617 - loss 0.03328642 - time (sec): 619.32 - samples/sec: 364.98 - lr: 0.000096 - momentum: 0.000000
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+ 2023-10-13 08:31:31,345 epoch 5 - iter 2527/3617 - loss 0.03444733 - time (sec): 722.81 - samples/sec: 364.33 - lr: 0.000094 - momentum: 0.000000
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+ 2023-10-13 08:33:13,022 epoch 5 - iter 2888/3617 - loss 0.03503322 - time (sec): 824.49 - samples/sec: 364.33 - lr: 0.000092 - momentum: 0.000000
166
+ 2023-10-13 08:34:58,383 epoch 5 - iter 3249/3617 - loss 0.03543944 - time (sec): 929.85 - samples/sec: 366.65 - lr: 0.000091 - momentum: 0.000000
167
+ 2023-10-13 08:36:40,423 epoch 5 - iter 3610/3617 - loss 0.03600676 - time (sec): 1031.89 - samples/sec: 367.67 - lr: 0.000089 - momentum: 0.000000
168
+ 2023-10-13 08:36:42,044 ----------------------------------------------------------------------------------------------------
169
+ 2023-10-13 08:36:42,045 EPOCH 5 done: loss 0.0360 - lr: 0.000089
170
+ 2023-10-13 08:37:22,806 DEV : loss 0.2428499311208725 - f1-score (micro avg) 0.6353
171
+ 2023-10-13 08:37:22,864 ----------------------------------------------------------------------------------------------------
172
+ 2023-10-13 08:39:03,567 epoch 6 - iter 361/3617 - loss 0.02475435 - time (sec): 100.70 - samples/sec: 374.38 - lr: 0.000087 - momentum: 0.000000
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+ 2023-10-13 08:40:46,107 epoch 6 - iter 722/3617 - loss 0.02240235 - time (sec): 203.24 - samples/sec: 373.84 - lr: 0.000085 - momentum: 0.000000
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+ 2023-10-13 08:42:26,885 epoch 6 - iter 1083/3617 - loss 0.02242435 - time (sec): 304.02 - samples/sec: 373.28 - lr: 0.000084 - momentum: 0.000000
175
+ 2023-10-13 08:44:07,737 epoch 6 - iter 1444/3617 - loss 0.02215591 - time (sec): 404.87 - samples/sec: 374.59 - lr: 0.000082 - momentum: 0.000000
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+ 2023-10-13 08:45:50,341 epoch 6 - iter 1805/3617 - loss 0.02397631 - time (sec): 507.47 - samples/sec: 374.23 - lr: 0.000080 - momentum: 0.000000
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+ 2023-10-13 08:47:34,746 epoch 6 - iter 2166/3617 - loss 0.02419269 - time (sec): 611.88 - samples/sec: 372.36 - lr: 0.000078 - momentum: 0.000000
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+ 2023-10-13 08:49:19,195 epoch 6 - iter 2527/3617 - loss 0.02490330 - time (sec): 716.33 - samples/sec: 370.90 - lr: 0.000076 - momentum: 0.000000
179
+ 2023-10-13 08:51:05,354 epoch 6 - iter 2888/3617 - loss 0.02459970 - time (sec): 822.49 - samples/sec: 367.63 - lr: 0.000075 - momentum: 0.000000
180
+ 2023-10-13 08:52:48,651 epoch 6 - iter 3249/3617 - loss 0.02441037 - time (sec): 925.78 - samples/sec: 367.52 - lr: 0.000073 - momentum: 0.000000
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+ 2023-10-13 08:54:31,614 epoch 6 - iter 3610/3617 - loss 0.02410353 - time (sec): 1028.75 - samples/sec: 368.80 - lr: 0.000071 - momentum: 0.000000
182
+ 2023-10-13 08:54:33,301 ----------------------------------------------------------------------------------------------------
183
+ 2023-10-13 08:54:33,302 EPOCH 6 done: loss 0.0241 - lr: 0.000071
184
+ 2023-10-13 08:55:13,801 DEV : loss 0.30240556597709656 - f1-score (micro avg) 0.6237
185
+ 2023-10-13 08:55:13,880 ----------------------------------------------------------------------------------------------------
186
+ 2023-10-13 08:56:59,065 epoch 7 - iter 361/3617 - loss 0.01445564 - time (sec): 105.18 - samples/sec: 353.40 - lr: 0.000069 - momentum: 0.000000
187
+ 2023-10-13 08:58:43,231 epoch 7 - iter 722/3617 - loss 0.01467506 - time (sec): 209.35 - samples/sec: 362.74 - lr: 0.000068 - momentum: 0.000000
188
+ 2023-10-13 09:00:28,382 epoch 7 - iter 1083/3617 - loss 0.01578205 - time (sec): 314.50 - samples/sec: 361.82 - lr: 0.000066 - momentum: 0.000000
189
+ 2023-10-13 09:02:13,449 epoch 7 - iter 1444/3617 - loss 0.01703422 - time (sec): 419.57 - samples/sec: 360.77 - lr: 0.000064 - momentum: 0.000000
190
+ 2023-10-13 09:03:56,020 epoch 7 - iter 1805/3617 - loss 0.01763882 - time (sec): 522.14 - samples/sec: 362.88 - lr: 0.000062 - momentum: 0.000000
191
+ 2023-10-13 09:05:39,265 epoch 7 - iter 2166/3617 - loss 0.01724767 - time (sec): 625.38 - samples/sec: 362.10 - lr: 0.000060 - momentum: 0.000000
192
+ 2023-10-13 09:07:26,199 epoch 7 - iter 2527/3617 - loss 0.01745495 - time (sec): 732.32 - samples/sec: 361.47 - lr: 0.000059 - momentum: 0.000000
193
+ 2023-10-13 09:09:12,338 epoch 7 - iter 2888/3617 - loss 0.01801878 - time (sec): 838.46 - samples/sec: 360.57 - lr: 0.000057 - momentum: 0.000000
194
+ 2023-10-13 09:10:58,550 epoch 7 - iter 3249/3617 - loss 0.01838554 - time (sec): 944.67 - samples/sec: 360.61 - lr: 0.000055 - momentum: 0.000000
195
+ 2023-10-13 09:12:45,236 epoch 7 - iter 3610/3617 - loss 0.01799741 - time (sec): 1051.35 - samples/sec: 360.80 - lr: 0.000053 - momentum: 0.000000
196
+ 2023-10-13 09:12:46,988 ----------------------------------------------------------------------------------------------------
197
+ 2023-10-13 09:12:46,988 EPOCH 7 done: loss 0.0180 - lr: 0.000053
198
+ 2023-10-13 09:13:29,166 DEV : loss 0.32392749190330505 - f1-score (micro avg) 0.6152
199
+ 2023-10-13 09:13:29,227 ----------------------------------------------------------------------------------------------------
200
+ 2023-10-13 09:15:11,286 epoch 8 - iter 361/3617 - loss 0.00831627 - time (sec): 102.06 - samples/sec: 368.88 - lr: 0.000052 - momentum: 0.000000
201
+ 2023-10-13 09:16:52,695 epoch 8 - iter 722/3617 - loss 0.00914200 - time (sec): 203.46 - samples/sec: 373.90 - lr: 0.000050 - momentum: 0.000000
202
+ 2023-10-13 09:18:32,396 epoch 8 - iter 1083/3617 - loss 0.00912467 - time (sec): 303.17 - samples/sec: 376.89 - lr: 0.000048 - momentum: 0.000000
203
+ 2023-10-13 09:20:14,373 epoch 8 - iter 1444/3617 - loss 0.00993485 - time (sec): 405.14 - samples/sec: 378.39 - lr: 0.000046 - momentum: 0.000000
204
+ 2023-10-13 09:21:55,557 epoch 8 - iter 1805/3617 - loss 0.01122757 - time (sec): 506.33 - samples/sec: 379.34 - lr: 0.000044 - momentum: 0.000000
205
+ 2023-10-13 09:23:35,609 epoch 8 - iter 2166/3617 - loss 0.01152886 - time (sec): 606.38 - samples/sec: 376.14 - lr: 0.000043 - momentum: 0.000000
206
+ 2023-10-13 09:25:14,099 epoch 8 - iter 2527/3617 - loss 0.01150134 - time (sec): 704.87 - samples/sec: 376.75 - lr: 0.000041 - momentum: 0.000000
207
+ 2023-10-13 09:26:54,414 epoch 8 - iter 2888/3617 - loss 0.01131531 - time (sec): 805.18 - samples/sec: 377.48 - lr: 0.000039 - momentum: 0.000000
208
+ 2023-10-13 09:28:34,512 epoch 8 - iter 3249/3617 - loss 0.01111063 - time (sec): 905.28 - samples/sec: 377.77 - lr: 0.000037 - momentum: 0.000000
209
+ 2023-10-13 09:30:14,589 epoch 8 - iter 3610/3617 - loss 0.01137858 - time (sec): 1005.36 - samples/sec: 377.30 - lr: 0.000036 - momentum: 0.000000
210
+ 2023-10-13 09:30:16,327 ----------------------------------------------------------------------------------------------------
211
+ 2023-10-13 09:30:16,328 EPOCH 8 done: loss 0.0114 - lr: 0.000036
212
+ 2023-10-13 09:30:58,519 DEV : loss 0.3561553359031677 - f1-score (micro avg) 0.636
213
+ 2023-10-13 09:30:58,579 ----------------------------------------------------------------------------------------------------
214
+ 2023-10-13 09:32:40,941 epoch 9 - iter 361/3617 - loss 0.00723439 - time (sec): 102.36 - samples/sec: 364.00 - lr: 0.000034 - momentum: 0.000000
215
+ 2023-10-13 09:34:22,522 epoch 9 - iter 722/3617 - loss 0.00867757 - time (sec): 203.94 - samples/sec: 367.26 - lr: 0.000032 - momentum: 0.000000
216
+ 2023-10-13 09:36:01,585 epoch 9 - iter 1083/3617 - loss 0.00720214 - time (sec): 303.00 - samples/sec: 371.87 - lr: 0.000030 - momentum: 0.000000
217
+ 2023-10-13 09:37:46,141 epoch 9 - iter 1444/3617 - loss 0.00884133 - time (sec): 407.56 - samples/sec: 374.63 - lr: 0.000028 - momentum: 0.000000
218
+ 2023-10-13 09:39:27,863 epoch 9 - iter 1805/3617 - loss 0.00912211 - time (sec): 509.28 - samples/sec: 373.69 - lr: 0.000027 - momentum: 0.000000
219
+ 2023-10-13 09:41:06,474 epoch 9 - iter 2166/3617 - loss 0.00902114 - time (sec): 607.89 - samples/sec: 375.21 - lr: 0.000025 - momentum: 0.000000
220
+ 2023-10-13 09:42:44,579 epoch 9 - iter 2527/3617 - loss 0.00876123 - time (sec): 706.00 - samples/sec: 375.58 - lr: 0.000023 - momentum: 0.000000
221
+ 2023-10-13 09:44:26,686 epoch 9 - iter 2888/3617 - loss 0.00893095 - time (sec): 808.10 - samples/sec: 375.68 - lr: 0.000021 - momentum: 0.000000
222
+ 2023-10-13 09:46:08,982 epoch 9 - iter 3249/3617 - loss 0.00856866 - time (sec): 910.40 - samples/sec: 375.95 - lr: 0.000020 - momentum: 0.000000
223
+ 2023-10-13 09:47:53,550 epoch 9 - iter 3610/3617 - loss 0.00830378 - time (sec): 1014.97 - samples/sec: 373.82 - lr: 0.000018 - momentum: 0.000000
224
+ 2023-10-13 09:47:55,260 ----------------------------------------------------------------------------------------------------
225
+ 2023-10-13 09:47:55,260 EPOCH 9 done: loss 0.0083 - lr: 0.000018
226
+ 2023-10-13 09:48:36,184 DEV : loss 0.38502201437950134 - f1-score (micro avg) 0.6408
227
+ 2023-10-13 09:48:36,253 ----------------------------------------------------------------------------------------------------
228
+ 2023-10-13 09:50:20,200 epoch 10 - iter 361/3617 - loss 0.00438003 - time (sec): 103.94 - samples/sec: 366.40 - lr: 0.000016 - momentum: 0.000000
229
+ 2023-10-13 09:52:04,672 epoch 10 - iter 722/3617 - loss 0.00381567 - time (sec): 208.42 - samples/sec: 361.18 - lr: 0.000014 - momentum: 0.000000
230
+ 2023-10-13 09:53:50,255 epoch 10 - iter 1083/3617 - loss 0.00444144 - time (sec): 314.00 - samples/sec: 360.25 - lr: 0.000012 - momentum: 0.000000
231
+ 2023-10-13 09:55:35,823 epoch 10 - iter 1444/3617 - loss 0.00443475 - time (sec): 419.57 - samples/sec: 358.31 - lr: 0.000011 - momentum: 0.000000
232
+ 2023-10-13 09:57:23,187 epoch 10 - iter 1805/3617 - loss 0.00403389 - time (sec): 526.93 - samples/sec: 357.77 - lr: 0.000009 - momentum: 0.000000
233
+ 2023-10-13 09:59:06,440 epoch 10 - iter 2166/3617 - loss 0.00409837 - time (sec): 630.18 - samples/sec: 359.97 - lr: 0.000007 - momentum: 0.000000
234
+ 2023-10-13 10:00:50,582 epoch 10 - iter 2527/3617 - loss 0.00436368 - time (sec): 734.33 - samples/sec: 360.70 - lr: 0.000005 - momentum: 0.000000
235
+ 2023-10-13 10:02:33,802 epoch 10 - iter 2888/3617 - loss 0.00446848 - time (sec): 837.55 - samples/sec: 361.82 - lr: 0.000004 - momentum: 0.000000
236
+ 2023-10-13 10:04:19,545 epoch 10 - iter 3249/3617 - loss 0.00433966 - time (sec): 943.29 - samples/sec: 361.99 - lr: 0.000002 - momentum: 0.000000
237
+ 2023-10-13 10:06:04,901 epoch 10 - iter 3610/3617 - loss 0.00416053 - time (sec): 1048.65 - samples/sec: 361.81 - lr: 0.000000 - momentum: 0.000000
238
+ 2023-10-13 10:06:06,582 ----------------------------------------------------------------------------------------------------
239
+ 2023-10-13 10:06:06,582 EPOCH 10 done: loss 0.0042 - lr: 0.000000
240
+ 2023-10-13 10:06:48,722 DEV : loss 0.3937250077724457 - f1-score (micro avg) 0.6409
241
+ 2023-10-13 10:06:49,678 ----------------------------------------------------------------------------------------------------
242
+ 2023-10-13 10:06:49,680 Loading model from best epoch ...
243
+ 2023-10-13 10:06:53,669 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
+ 2023-10-13 10:07:56,286
245
+ Results:
246
+ - F-score (micro) 0.6366
247
+ - F-score (macro) 0.4589
248
+ - Accuracy 0.4766
249
+
250
+ By class:
251
+ precision recall f1-score support
252
+
253
+ loc 0.6312 0.8020 0.7064 591
254
+ pers 0.5514 0.6611 0.6013 357
255
+ org 0.1081 0.0506 0.0690 79
256
+
257
+ micro avg 0.5872 0.6952 0.6366 1027
258
+ macro avg 0.4302 0.5046 0.4589 1027
259
+ weighted avg 0.5632 0.6952 0.6208 1027
260
+
261
+ 2023-10-13 10:07:56,287 ----------------------------------------------------------------------------------------------------