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hmbench-topres19th-en-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5

Token Classification
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English
sequence-tagger-model
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  1. best-model.pt +3 -0
  2. dev.tsv +0 -0
  3. loss.tsv +11 -0
  4. runs/events.out.tfevents.1697553220.4c6324b99746.1159.19 +3 -0
  5. test.tsv +0 -0
  6. training.log +236 -0
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dev.tsv 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 14:34:53 0.0000 0.3936 0.0564 0.7306 0.7553 0.7427 0.6047
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+ 2 14:36:09 0.0000 0.0762 0.0562 0.7676 0.7806 0.7741 0.6401
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+ 3 14:37:27 0.0000 0.0507 0.0668 0.7365 0.8608 0.7938 0.6645
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+ 4 14:38:42 0.0000 0.0357 0.0921 0.7451 0.8017 0.7724 0.6507
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+ 5 14:39:54 0.0000 0.0281 0.0908 0.7727 0.7890 0.7808 0.6516
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+ 6 14:41:08 0.0000 0.0163 0.1028 0.7742 0.8101 0.7918 0.6667
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+ 7 14:42:23 0.0000 0.0118 0.1268 0.7003 0.8481 0.7672 0.6422
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+ 8 14:43:38 0.0000 0.0081 0.1236 0.7590 0.7975 0.7778 0.6540
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+ 9 14:44:52 0.0000 0.0051 0.1239 0.7441 0.7975 0.7699 0.6429
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+ 10 14:46:07 0.0000 0.0036 0.1246 0.7661 0.8017 0.7835 0.6620
runs/events.out.tfevents.1697553220.4c6324b99746.1159.19 ADDED
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test.tsv ADDED
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training.log ADDED
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+ 2023-10-17 14:33:40,027 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:33:40,029 Model: "SequenceTagger(
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+ (embeddings): TransformerWordEmbeddings(
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+ (model): ElectraModel(
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+ (embeddings): ElectraEmbeddings(
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+ (word_embeddings): Embedding(32001, 768)
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+ (position_embeddings): Embedding(512, 768)
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+ (token_type_embeddings): Embedding(2, 768)
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+ (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ (encoder): ElectraEncoder(
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+ (layer): ModuleList(
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+ (0-11): 12 x ElectraLayer(
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+ (attention): ElectraAttention(
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+ (self): ElectraSelfAttention(
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+ (query): Linear(in_features=768, out_features=768, bias=True)
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+ (key): Linear(in_features=768, out_features=768, bias=True)
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+ (value): Linear(in_features=768, out_features=768, bias=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ (output): ElectraSelfOutput(
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+ (dense): Linear(in_features=768, out_features=768, bias=True)
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+ (LayerNorm): LayerNorm((768,), eps=1e-12, 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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+ (intermediate): ElectraIntermediate(
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+ (dense): Linear(in_features=768, out_features=3072, bias=True)
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+ (intermediate_act_fn): GELUActivation()
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+ )
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+ (output): ElectraOutput(
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+ (dense): Linear(in_features=3072, out_features=768, bias=True)
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+ (LayerNorm): LayerNorm((768,), eps=1e-12, 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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+ )
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+ )
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+ (locked_dropout): LockedDropout(p=0.5)
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+ (linear): Linear(in_features=768, out_features=13, bias=True)
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+ (loss_function): CrossEntropyLoss()
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+ )"
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+ 2023-10-17 14:33:40,029 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:33:40,030 MultiCorpus: 6183 train + 680 dev + 2113 test sentences
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+ - NER_HIPE_2022 Corpus: 6183 train + 680 dev + 2113 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/topres19th/en/with_doc_seperator
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+ 2023-10-17 14:33:40,030 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:33:40,030 Train: 6183 sentences
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+ 2023-10-17 14:33:40,030 (train_with_dev=False, train_with_test=False)
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+ 2023-10-17 14:33:40,030 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:33:40,030 Training Params:
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+ 2023-10-17 14:33:40,030 - learning_rate: "5e-05"
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+ 2023-10-17 14:33:40,030 - mini_batch_size: "8"
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+ 2023-10-17 14:33:40,030 - max_epochs: "10"
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+ 2023-10-17 14:33:40,030 - shuffle: "True"
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+ 2023-10-17 14:33:40,030 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:33:40,031 Plugins:
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+ 2023-10-17 14:33:40,031 - TensorboardLogger
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+ 2023-10-17 14:33:40,031 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-17 14:33:40,031 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:33:40,031 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-17 14:33:40,031 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-17 14:33:40,031 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:33:40,031 Computation:
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+ 2023-10-17 14:33:40,031 - compute on device: cuda:0
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+ 2023-10-17 14:33:40,031 - embedding storage: none
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+ 2023-10-17 14:33:40,031 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:33:40,031 Model training base path: "hmbench-topres19th/en-hmteams/teams-base-historic-multilingual-discriminator-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5"
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+ 2023-10-17 14:33:40,031 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:33:40,031 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:33:40,032 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-17 14:33:47,292 epoch 1 - iter 77/773 - loss 2.50923587 - time (sec): 7.26 - samples/sec: 1831.85 - lr: 0.000005 - momentum: 0.000000
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+ 2023-10-17 14:33:54,411 epoch 1 - iter 154/773 - loss 1.42130164 - time (sec): 14.38 - samples/sec: 1820.77 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-17 14:34:01,440 epoch 1 - iter 231/773 - loss 1.00481350 - time (sec): 21.41 - samples/sec: 1826.16 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 14:34:08,979 epoch 1 - iter 308/773 - loss 0.80200716 - time (sec): 28.95 - samples/sec: 1752.52 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 14:34:16,018 epoch 1 - iter 385/773 - loss 0.68374915 - time (sec): 35.98 - samples/sec: 1715.41 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 14:34:22,978 epoch 1 - iter 462/773 - loss 0.58561188 - time (sec): 42.95 - samples/sec: 1737.12 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-17 14:34:29,816 epoch 1 - iter 539/773 - loss 0.52024904 - time (sec): 49.78 - samples/sec: 1741.94 - lr: 0.000035 - momentum: 0.000000
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+ 2023-10-17 14:34:36,668 epoch 1 - iter 616/773 - loss 0.46975073 - time (sec): 56.63 - samples/sec: 1747.71 - lr: 0.000040 - momentum: 0.000000
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+ 2023-10-17 14:34:43,744 epoch 1 - iter 693/773 - loss 0.42724592 - time (sec): 63.71 - samples/sec: 1754.87 - lr: 0.000045 - momentum: 0.000000
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+ 2023-10-17 14:34:50,680 epoch 1 - iter 770/773 - loss 0.39511649 - time (sec): 70.65 - samples/sec: 1750.92 - lr: 0.000050 - momentum: 0.000000
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+ 2023-10-17 14:34:50,952 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:34:50,952 EPOCH 1 done: loss 0.3936 - lr: 0.000050
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+ 2023-10-17 14:34:53,277 DEV : loss 0.05638180673122406 - f1-score (micro avg) 0.7427
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+ 2023-10-17 14:34:53,307 saving best model
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+ 2023-10-17 14:34:53,948 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:35:01,084 epoch 2 - iter 77/773 - loss 0.08450516 - time (sec): 7.13 - samples/sec: 1724.49 - lr: 0.000049 - momentum: 0.000000
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+ 2023-10-17 14:35:08,104 epoch 2 - iter 154/773 - loss 0.07623715 - time (sec): 14.15 - samples/sec: 1741.82 - lr: 0.000049 - momentum: 0.000000
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+ 2023-10-17 14:35:16,129 epoch 2 - iter 231/773 - loss 0.07797626 - time (sec): 22.18 - samples/sec: 1685.46 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-17 14:35:23,452 epoch 2 - iter 308/773 - loss 0.07829088 - time (sec): 29.50 - samples/sec: 1683.48 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-17 14:35:30,778 epoch 2 - iter 385/773 - loss 0.07642468 - time (sec): 36.83 - samples/sec: 1694.33 - lr: 0.000047 - momentum: 0.000000
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+ 2023-10-17 14:35:38,282 epoch 2 - iter 462/773 - loss 0.07853226 - time (sec): 44.33 - samples/sec: 1712.25 - lr: 0.000047 - momentum: 0.000000
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+ 2023-10-17 14:35:45,196 epoch 2 - iter 539/773 - loss 0.07797723 - time (sec): 51.25 - samples/sec: 1708.62 - lr: 0.000046 - momentum: 0.000000
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+ 2023-10-17 14:35:52,242 epoch 2 - iter 616/773 - loss 0.07775728 - time (sec): 58.29 - samples/sec: 1713.08 - lr: 0.000046 - momentum: 0.000000
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+ 2023-10-17 14:35:59,251 epoch 2 - iter 693/773 - loss 0.07783386 - time (sec): 65.30 - samples/sec: 1700.02 - lr: 0.000045 - momentum: 0.000000
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+ 2023-10-17 14:36:06,466 epoch 2 - iter 770/773 - loss 0.07655030 - time (sec): 72.52 - samples/sec: 1707.08 - lr: 0.000044 - momentum: 0.000000
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+ 2023-10-17 14:36:06,742 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:36:06,742 EPOCH 2 done: loss 0.0762 - lr: 0.000044
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+ 2023-10-17 14:36:09,756 DEV : loss 0.05624998360872269 - f1-score (micro avg) 0.7741
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+ 2023-10-17 14:36:09,787 saving best model
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+ 2023-10-17 14:36:11,251 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:36:18,549 epoch 3 - iter 77/773 - loss 0.04747565 - time (sec): 7.29 - samples/sec: 1682.24 - lr: 0.000044 - momentum: 0.000000
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+ 2023-10-17 14:36:26,038 epoch 3 - iter 154/773 - loss 0.05208446 - time (sec): 14.78 - samples/sec: 1713.20 - lr: 0.000043 - momentum: 0.000000
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+ 2023-10-17 14:36:33,264 epoch 3 - iter 231/773 - loss 0.05199616 - time (sec): 22.00 - samples/sec: 1689.36 - lr: 0.000043 - momentum: 0.000000
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+ 2023-10-17 14:36:40,604 epoch 3 - iter 308/773 - loss 0.04917980 - time (sec): 29.35 - samples/sec: 1690.54 - lr: 0.000042 - momentum: 0.000000
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+ 2023-10-17 14:36:47,941 epoch 3 - iter 385/773 - loss 0.04758071 - time (sec): 36.68 - samples/sec: 1678.00 - lr: 0.000042 - momentum: 0.000000
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+ 2023-10-17 14:36:55,193 epoch 3 - iter 462/773 - loss 0.04862057 - time (sec): 43.93 - samples/sec: 1685.83 - lr: 0.000041 - momentum: 0.000000
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+ 2023-10-17 14:37:02,584 epoch 3 - iter 539/773 - loss 0.04856681 - time (sec): 51.33 - samples/sec: 1684.53 - lr: 0.000041 - momentum: 0.000000
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+ 2023-10-17 14:37:09,641 epoch 3 - iter 616/773 - loss 0.05066774 - time (sec): 58.38 - samples/sec: 1694.99 - lr: 0.000040 - momentum: 0.000000
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+ 2023-10-17 14:37:16,730 epoch 3 - iter 693/773 - loss 0.05007386 - time (sec): 65.47 - samples/sec: 1706.62 - lr: 0.000039 - momentum: 0.000000
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+ 2023-10-17 14:37:23,872 epoch 3 - iter 770/773 - loss 0.05081763 - time (sec): 72.61 - samples/sec: 1704.59 - lr: 0.000039 - momentum: 0.000000
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+ 2023-10-17 14:37:24,152 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:37:24,152 EPOCH 3 done: loss 0.0507 - lr: 0.000039
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+ 2023-10-17 14:37:27,074 DEV : loss 0.0668470710515976 - f1-score (micro avg) 0.7938
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+ 2023-10-17 14:37:27,103 saving best model
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+ 2023-10-17 14:37:28,513 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:37:35,670 epoch 4 - iter 77/773 - loss 0.03373869 - time (sec): 7.15 - samples/sec: 1716.25 - lr: 0.000038 - momentum: 0.000000
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+ 2023-10-17 14:37:42,914 epoch 4 - iter 154/773 - loss 0.03223990 - time (sec): 14.40 - samples/sec: 1797.82 - lr: 0.000038 - momentum: 0.000000
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+ 2023-10-17 14:37:50,041 epoch 4 - iter 231/773 - loss 0.03664264 - time (sec): 21.52 - samples/sec: 1788.19 - lr: 0.000037 - momentum: 0.000000
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+ 2023-10-17 14:37:56,925 epoch 4 - iter 308/773 - loss 0.03580473 - time (sec): 28.41 - samples/sec: 1766.49 - lr: 0.000037 - momentum: 0.000000
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+ 2023-10-17 14:38:03,783 epoch 4 - iter 385/773 - loss 0.03747929 - time (sec): 35.27 - samples/sec: 1758.49 - lr: 0.000036 - momentum: 0.000000
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+ 2023-10-17 14:38:11,144 epoch 4 - iter 462/773 - loss 0.03752798 - time (sec): 42.63 - samples/sec: 1757.21 - lr: 0.000036 - momentum: 0.000000
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+ 2023-10-17 14:38:18,355 epoch 4 - iter 539/773 - loss 0.03661439 - time (sec): 49.84 - samples/sec: 1767.40 - lr: 0.000035 - momentum: 0.000000
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+ 2023-10-17 14:38:25,461 epoch 4 - iter 616/773 - loss 0.03508167 - time (sec): 56.94 - samples/sec: 1758.48 - lr: 0.000034 - momentum: 0.000000
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+ 2023-10-17 14:38:32,556 epoch 4 - iter 693/773 - loss 0.03524376 - time (sec): 64.04 - samples/sec: 1756.85 - lr: 0.000034 - momentum: 0.000000
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+ 2023-10-17 14:38:39,444 epoch 4 - iter 770/773 - loss 0.03569860 - time (sec): 70.93 - samples/sec: 1747.91 - lr: 0.000033 - momentum: 0.000000
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+ 2023-10-17 14:38:39,704 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:38:39,705 EPOCH 4 done: loss 0.0357 - lr: 0.000033
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+ 2023-10-17 14:38:42,665 DEV : loss 0.09208610653877258 - f1-score (micro avg) 0.7724
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+ 2023-10-17 14:38:42,696 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:38:49,903 epoch 5 - iter 77/773 - loss 0.03208987 - time (sec): 7.20 - samples/sec: 1819.01 - lr: 0.000033 - momentum: 0.000000
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+ 2023-10-17 14:38:57,068 epoch 5 - iter 154/773 - loss 0.02963635 - time (sec): 14.37 - samples/sec: 1790.88 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-17 14:39:04,021 epoch 5 - iter 231/773 - loss 0.03156884 - time (sec): 21.32 - samples/sec: 1782.65 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-17 14:39:10,992 epoch 5 - iter 308/773 - loss 0.02957247 - time (sec): 28.29 - samples/sec: 1779.80 - lr: 0.000031 - momentum: 0.000000
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+ 2023-10-17 14:39:18,021 epoch 5 - iter 385/773 - loss 0.03043304 - time (sec): 35.32 - samples/sec: 1777.02 - lr: 0.000031 - momentum: 0.000000
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+ 2023-10-17 14:39:25,200 epoch 5 - iter 462/773 - loss 0.03088270 - time (sec): 42.50 - samples/sec: 1764.76 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-17 14:39:31,906 epoch 5 - iter 539/773 - loss 0.02923464 - time (sec): 49.21 - samples/sec: 1779.74 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 14:39:38,445 epoch 5 - iter 616/773 - loss 0.02915810 - time (sec): 55.75 - samples/sec: 1797.13 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 14:39:44,773 epoch 5 - iter 693/773 - loss 0.02815671 - time (sec): 62.07 - samples/sec: 1802.51 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 14:39:51,103 epoch 5 - iter 770/773 - loss 0.02806962 - time (sec): 68.41 - samples/sec: 1810.07 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 14:39:51,338 ----------------------------------------------------------------------------------------------------
144
+ 2023-10-17 14:39:51,339 EPOCH 5 done: loss 0.0281 - lr: 0.000028
145
+ 2023-10-17 14:39:54,281 DEV : loss 0.09081842750310898 - f1-score (micro avg) 0.7808
146
+ 2023-10-17 14:39:54,311 ----------------------------------------------------------------------------------------------------
147
+ 2023-10-17 14:40:01,302 epoch 6 - iter 77/773 - loss 0.01833530 - time (sec): 6.99 - samples/sec: 1755.27 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 14:40:08,326 epoch 6 - iter 154/773 - loss 0.01654716 - time (sec): 14.01 - samples/sec: 1770.07 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 14:40:15,751 epoch 6 - iter 231/773 - loss 0.01693263 - time (sec): 21.44 - samples/sec: 1749.34 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 14:40:22,848 epoch 6 - iter 308/773 - loss 0.01631071 - time (sec): 28.54 - samples/sec: 1762.95 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 14:40:29,756 epoch 6 - iter 385/773 - loss 0.01560773 - time (sec): 35.44 - samples/sec: 1745.74 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 14:40:37,016 epoch 6 - iter 462/773 - loss 0.01553854 - time (sec): 42.70 - samples/sec: 1752.52 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 14:40:43,841 epoch 6 - iter 539/773 - loss 0.01650694 - time (sec): 49.53 - samples/sec: 1737.47 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 14:40:50,797 epoch 6 - iter 616/773 - loss 0.01698956 - time (sec): 56.48 - samples/sec: 1733.26 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 14:40:57,982 epoch 6 - iter 693/773 - loss 0.01685377 - time (sec): 63.67 - samples/sec: 1744.16 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 14:41:05,215 epoch 6 - iter 770/773 - loss 0.01627500 - time (sec): 70.90 - samples/sec: 1746.88 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 14:41:05,480 ----------------------------------------------------------------------------------------------------
158
+ 2023-10-17 14:41:05,481 EPOCH 6 done: loss 0.0163 - lr: 0.000022
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+ 2023-10-17 14:41:08,499 DEV : loss 0.10279172658920288 - f1-score (micro avg) 0.7918
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+ 2023-10-17 14:41:08,530 ----------------------------------------------------------------------------------------------------
161
+ 2023-10-17 14:41:15,513 epoch 7 - iter 77/773 - loss 0.00452528 - time (sec): 6.98 - samples/sec: 1685.35 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 14:41:22,501 epoch 7 - iter 154/773 - loss 0.00876548 - time (sec): 13.97 - samples/sec: 1722.63 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 14:41:30,027 epoch 7 - iter 231/773 - loss 0.01243303 - time (sec): 21.49 - samples/sec: 1687.99 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 14:41:37,202 epoch 7 - iter 308/773 - loss 0.01319413 - time (sec): 28.67 - samples/sec: 1701.70 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 14:41:44,698 epoch 7 - iter 385/773 - loss 0.01232171 - time (sec): 36.17 - samples/sec: 1714.09 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-17 14:41:51,565 epoch 7 - iter 462/773 - loss 0.01118046 - time (sec): 43.03 - samples/sec: 1726.55 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-17 14:41:58,599 epoch 7 - iter 539/773 - loss 0.01102460 - time (sec): 50.07 - samples/sec: 1731.55 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-17 14:42:05,563 epoch 7 - iter 616/773 - loss 0.01052311 - time (sec): 57.03 - samples/sec: 1742.46 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-17 14:42:12,767 epoch 7 - iter 693/773 - loss 0.01166359 - time (sec): 64.23 - samples/sec: 1755.31 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-17 14:42:19,919 epoch 7 - iter 770/773 - loss 0.01166227 - time (sec): 71.39 - samples/sec: 1735.49 - lr: 0.000017 - momentum: 0.000000
171
+ 2023-10-17 14:42:20,202 ----------------------------------------------------------------------------------------------------
172
+ 2023-10-17 14:42:20,203 EPOCH 7 done: loss 0.0118 - lr: 0.000017
173
+ 2023-10-17 14:42:23,314 DEV : loss 0.12683075666427612 - f1-score (micro avg) 0.7672
174
+ 2023-10-17 14:42:23,351 ----------------------------------------------------------------------------------------------------
175
+ 2023-10-17 14:42:30,519 epoch 8 - iter 77/773 - loss 0.01092306 - time (sec): 7.17 - samples/sec: 1632.15 - lr: 0.000016 - momentum: 0.000000
176
+ 2023-10-17 14:42:37,610 epoch 8 - iter 154/773 - loss 0.00834521 - time (sec): 14.26 - samples/sec: 1700.73 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-17 14:42:44,625 epoch 8 - iter 231/773 - loss 0.00763347 - time (sec): 21.27 - samples/sec: 1715.42 - lr: 0.000015 - momentum: 0.000000
178
+ 2023-10-17 14:42:51,811 epoch 8 - iter 308/773 - loss 0.00734188 - time (sec): 28.46 - samples/sec: 1728.36 - lr: 0.000014 - momentum: 0.000000
179
+ 2023-10-17 14:42:59,357 epoch 8 - iter 385/773 - loss 0.00782979 - time (sec): 36.00 - samples/sec: 1723.98 - lr: 0.000014 - momentum: 0.000000
180
+ 2023-10-17 14:43:06,454 epoch 8 - iter 462/773 - loss 0.00716809 - time (sec): 43.10 - samples/sec: 1728.60 - lr: 0.000013 - momentum: 0.000000
181
+ 2023-10-17 14:43:13,826 epoch 8 - iter 539/773 - loss 0.00773899 - time (sec): 50.47 - samples/sec: 1734.28 - lr: 0.000013 - momentum: 0.000000
182
+ 2023-10-17 14:43:20,901 epoch 8 - iter 616/773 - loss 0.00783439 - time (sec): 57.55 - samples/sec: 1726.13 - lr: 0.000012 - momentum: 0.000000
183
+ 2023-10-17 14:43:27,798 epoch 8 - iter 693/773 - loss 0.00774684 - time (sec): 64.45 - samples/sec: 1726.79 - lr: 0.000012 - momentum: 0.000000
184
+ 2023-10-17 14:43:35,007 epoch 8 - iter 770/773 - loss 0.00791622 - time (sec): 71.65 - samples/sec: 1727.63 - lr: 0.000011 - momentum: 0.000000
185
+ 2023-10-17 14:43:35,283 ----------------------------------------------------------------------------------------------------
186
+ 2023-10-17 14:43:35,284 EPOCH 8 done: loss 0.0081 - lr: 0.000011
187
+ 2023-10-17 14:43:38,140 DEV : loss 0.12362485378980637 - f1-score (micro avg) 0.7778
188
+ 2023-10-17 14:43:38,169 ----------------------------------------------------------------------------------------------------
189
+ 2023-10-17 14:43:45,185 epoch 9 - iter 77/773 - loss 0.00194071 - time (sec): 7.01 - samples/sec: 1862.43 - lr: 0.000011 - momentum: 0.000000
190
+ 2023-10-17 14:43:52,369 epoch 9 - iter 154/773 - loss 0.00273045 - time (sec): 14.20 - samples/sec: 1830.87 - lr: 0.000010 - momentum: 0.000000
191
+ 2023-10-17 14:43:59,729 epoch 9 - iter 231/773 - loss 0.00364150 - time (sec): 21.56 - samples/sec: 1778.00 - lr: 0.000009 - momentum: 0.000000
192
+ 2023-10-17 14:44:06,692 epoch 9 - iter 308/773 - loss 0.00314935 - time (sec): 28.52 - samples/sec: 1747.14 - lr: 0.000009 - momentum: 0.000000
193
+ 2023-10-17 14:44:13,912 epoch 9 - iter 385/773 - loss 0.00381506 - time (sec): 35.74 - samples/sec: 1759.68 - lr: 0.000008 - momentum: 0.000000
194
+ 2023-10-17 14:44:21,090 epoch 9 - iter 462/773 - loss 0.00476997 - time (sec): 42.92 - samples/sec: 1763.78 - lr: 0.000008 - momentum: 0.000000
195
+ 2023-10-17 14:44:28,137 epoch 9 - iter 539/773 - loss 0.00427351 - time (sec): 49.97 - samples/sec: 1762.97 - lr: 0.000007 - momentum: 0.000000
196
+ 2023-10-17 14:44:35,251 epoch 9 - iter 616/773 - loss 0.00437576 - time (sec): 57.08 - samples/sec: 1741.96 - lr: 0.000007 - momentum: 0.000000
197
+ 2023-10-17 14:44:42,591 epoch 9 - iter 693/773 - loss 0.00480221 - time (sec): 64.42 - samples/sec: 1737.41 - lr: 0.000006 - momentum: 0.000000
198
+ 2023-10-17 14:44:49,550 epoch 9 - iter 770/773 - loss 0.00516866 - time (sec): 71.38 - samples/sec: 1732.98 - lr: 0.000006 - momentum: 0.000000
199
+ 2023-10-17 14:44:49,828 ----------------------------------------------------------------------------------------------------
200
+ 2023-10-17 14:44:49,828 EPOCH 9 done: loss 0.0051 - lr: 0.000006
201
+ 2023-10-17 14:44:52,751 DEV : loss 0.12392386794090271 - f1-score (micro avg) 0.7699
202
+ 2023-10-17 14:44:52,780 ----------------------------------------------------------------------------------------------------
203
+ 2023-10-17 14:45:00,250 epoch 10 - iter 77/773 - loss 0.00532611 - time (sec): 7.47 - samples/sec: 1608.63 - lr: 0.000005 - momentum: 0.000000
204
+ 2023-10-17 14:45:07,239 epoch 10 - iter 154/773 - loss 0.00378488 - time (sec): 14.46 - samples/sec: 1647.56 - lr: 0.000005 - momentum: 0.000000
205
+ 2023-10-17 14:45:14,478 epoch 10 - iter 231/773 - loss 0.00382497 - time (sec): 21.70 - samples/sec: 1688.02 - lr: 0.000004 - momentum: 0.000000
206
+ 2023-10-17 14:45:21,681 epoch 10 - iter 308/773 - loss 0.00350380 - time (sec): 28.90 - samples/sec: 1711.86 - lr: 0.000003 - momentum: 0.000000
207
+ 2023-10-17 14:45:28,814 epoch 10 - iter 385/773 - loss 0.00387383 - time (sec): 36.03 - samples/sec: 1709.60 - lr: 0.000003 - momentum: 0.000000
208
+ 2023-10-17 14:45:35,914 epoch 10 - iter 462/773 - loss 0.00390395 - time (sec): 43.13 - samples/sec: 1713.52 - lr: 0.000002 - momentum: 0.000000
209
+ 2023-10-17 14:45:43,094 epoch 10 - iter 539/773 - loss 0.00380626 - time (sec): 50.31 - samples/sec: 1717.59 - lr: 0.000002 - momentum: 0.000000
210
+ 2023-10-17 14:45:50,077 epoch 10 - iter 616/773 - loss 0.00372703 - time (sec): 57.29 - samples/sec: 1724.36 - lr: 0.000001 - momentum: 0.000000
211
+ 2023-10-17 14:45:56,991 epoch 10 - iter 693/773 - loss 0.00367782 - time (sec): 64.21 - samples/sec: 1740.68 - lr: 0.000001 - momentum: 0.000000
212
+ 2023-10-17 14:46:03,998 epoch 10 - iter 770/773 - loss 0.00357798 - time (sec): 71.22 - samples/sec: 1737.64 - lr: 0.000000 - momentum: 0.000000
213
+ 2023-10-17 14:46:04,264 ----------------------------------------------------------------------------------------------------
214
+ 2023-10-17 14:46:04,264 EPOCH 10 done: loss 0.0036 - lr: 0.000000
215
+ 2023-10-17 14:46:07,181 DEV : loss 0.12464166432619095 - f1-score (micro avg) 0.7835
216
+ 2023-10-17 14:46:07,879 ----------------------------------------------------------------------------------------------------
217
+ 2023-10-17 14:46:07,881 Loading model from best epoch ...
218
+ 2023-10-17 14:46:10,198 SequenceTagger predicts: Dictionary with 13 tags: O, S-LOC, B-LOC, E-LOC, I-LOC, S-BUILDING, B-BUILDING, E-BUILDING, I-BUILDING, S-STREET, B-STREET, E-STREET, I-STREET
219
+ 2023-10-17 14:46:18,809
220
+ Results:
221
+ - F-score (micro) 0.7858
222
+ - F-score (macro) 0.6784
223
+ - Accuracy 0.6705
224
+
225
+ By class:
226
+ precision recall f1-score support
227
+
228
+ LOC 0.8015 0.8795 0.8387 946
229
+ BUILDING 0.5374 0.6216 0.5764 185
230
+ STREET 0.5479 0.7143 0.6202 56
231
+
232
+ micro avg 0.7449 0.8315 0.7858 1187
233
+ macro avg 0.6290 0.7385 0.6784 1187
234
+ weighted avg 0.7484 0.8315 0.7875 1187
235
+
236
+ 2023-10-17 14:46:18,809 ----------------------------------------------------------------------------------------------------