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

Token Classification
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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.1697536237.4c6324b99746.1159.3 +3 -0
  5. test.tsv +0 -0
  6. training.log +237 -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 09:51:52 0.0000 0.3216 0.0533 0.7834 0.7173 0.7489 0.6159
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+ 2 09:53:07 0.0000 0.0746 0.0613 0.7374 0.6160 0.6713 0.5141
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+ 3 09:54:23 0.0000 0.0524 0.0557 0.7608 0.8186 0.7886 0.6644
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+ 4 09:55:37 0.0000 0.0372 0.0901 0.7805 0.8101 0.7950 0.6737
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+ 5 09:56:53 0.0000 0.0266 0.1055 0.7642 0.7932 0.7785 0.6573
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+ 6 09:58:07 0.0000 0.0177 0.1158 0.7341 0.8270 0.7778 0.6533
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+ 7 09:59:22 0.0000 0.0106 0.1187 0.8304 0.8059 0.8180 0.7074
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+ 8 10:00:42 0.0000 0.0086 0.1236 0.7520 0.8059 0.7780 0.6564
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+ 9 10:01:59 0.0000 0.0055 0.1216 0.7814 0.8143 0.7975 0.6796
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+ 10 10:03:12 0.0000 0.0031 0.1230 0.7751 0.8143 0.7942 0.6748
runs/events.out.tfevents.1697536237.4c6324b99746.1159.3 ADDED
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+ size 434848
test.tsv ADDED
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training.log ADDED
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+ 2023-10-17 09:50:37,113 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:50:37,114 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 09:50:37,114 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:50:37,115 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 09:50:37,115 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:50:37,115 Train: 6183 sentences
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+ 2023-10-17 09:50:37,115 (train_with_dev=False, train_with_test=False)
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+ 2023-10-17 09:50:37,115 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:50:37,115 Training Params:
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+ 2023-10-17 09:50:37,115 - learning_rate: "5e-05"
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+ 2023-10-17 09:50:37,115 - mini_batch_size: "8"
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+ 2023-10-17 09:50:37,115 - max_epochs: "10"
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+ 2023-10-17 09:50:37,115 - shuffle: "True"
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+ 2023-10-17 09:50:37,115 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:50:37,116 Plugins:
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+ 2023-10-17 09:50:37,116 - TensorboardLogger
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+ 2023-10-17 09:50:37,116 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-17 09:50:37,116 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:50:37,116 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-17 09:50:37,116 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-17 09:50:37,116 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:50:37,116 Computation:
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+ 2023-10-17 09:50:37,116 - compute on device: cuda:0
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+ 2023-10-17 09:50:37,116 - embedding storage: none
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+ 2023-10-17 09:50:37,116 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:50:37,116 Model training base path: "hmbench-topres19th/en-hmteams/teams-base-historic-multilingual-discriminator-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1"
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+ 2023-10-17 09:50:37,116 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:50:37,116 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:50:37,117 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-17 09:50:44,240 epoch 1 - iter 77/773 - loss 1.99803994 - time (sec): 7.12 - samples/sec: 1805.03 - lr: 0.000005 - momentum: 0.000000
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+ 2023-10-17 09:50:51,561 epoch 1 - iter 154/773 - loss 1.14101515 - time (sec): 14.44 - samples/sec: 1736.92 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-17 09:50:58,592 epoch 1 - iter 231/773 - loss 0.81080166 - time (sec): 21.47 - samples/sec: 1742.53 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 09:51:05,952 epoch 1 - iter 308/773 - loss 0.63449031 - time (sec): 28.83 - samples/sec: 1748.52 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 09:51:13,243 epoch 1 - iter 385/773 - loss 0.53077408 - time (sec): 36.12 - samples/sec: 1734.04 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 09:51:20,423 epoch 1 - iter 462/773 - loss 0.46116490 - time (sec): 43.31 - samples/sec: 1730.19 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-17 09:51:28,021 epoch 1 - iter 539/773 - loss 0.41868646 - time (sec): 50.90 - samples/sec: 1703.61 - lr: 0.000035 - momentum: 0.000000
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+ 2023-10-17 09:51:35,107 epoch 1 - iter 616/773 - loss 0.38248750 - time (sec): 57.99 - samples/sec: 1703.90 - lr: 0.000040 - momentum: 0.000000
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+ 2023-10-17 09:51:42,607 epoch 1 - iter 693/773 - loss 0.34805302 - time (sec): 65.49 - samples/sec: 1703.78 - lr: 0.000045 - momentum: 0.000000
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+ 2023-10-17 09:51:49,968 epoch 1 - iter 770/773 - loss 0.32227332 - time (sec): 72.85 - samples/sec: 1702.09 - lr: 0.000050 - momentum: 0.000000
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+ 2023-10-17 09:51:50,232 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:51:50,232 EPOCH 1 done: loss 0.3216 - lr: 0.000050
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+ 2023-10-17 09:51:52,930 DEV : loss 0.05330459401011467 - f1-score (micro avg) 0.7489
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+ 2023-10-17 09:51:52,960 saving best model
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+ 2023-10-17 09:51:53,514 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:52:00,448 epoch 2 - iter 77/773 - loss 0.09677124 - time (sec): 6.93 - samples/sec: 1704.73 - lr: 0.000049 - momentum: 0.000000
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+ 2023-10-17 09:52:07,574 epoch 2 - iter 154/773 - loss 0.08008441 - time (sec): 14.06 - samples/sec: 1718.53 - lr: 0.000049 - momentum: 0.000000
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+ 2023-10-17 09:52:14,632 epoch 2 - iter 231/773 - loss 0.07559641 - time (sec): 21.12 - samples/sec: 1785.39 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-17 09:52:21,666 epoch 2 - iter 308/773 - loss 0.07646491 - time (sec): 28.15 - samples/sec: 1780.87 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-17 09:52:28,587 epoch 2 - iter 385/773 - loss 0.07753478 - time (sec): 35.07 - samples/sec: 1789.21 - lr: 0.000047 - momentum: 0.000000
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+ 2023-10-17 09:52:35,688 epoch 2 - iter 462/773 - loss 0.07853793 - time (sec): 42.17 - samples/sec: 1776.69 - lr: 0.000047 - momentum: 0.000000
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+ 2023-10-17 09:52:42,856 epoch 2 - iter 539/773 - loss 0.07587851 - time (sec): 49.34 - samples/sec: 1772.60 - lr: 0.000046 - momentum: 0.000000
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+ 2023-10-17 09:52:50,035 epoch 2 - iter 616/773 - loss 0.07502133 - time (sec): 56.52 - samples/sec: 1778.78 - lr: 0.000046 - momentum: 0.000000
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+ 2023-10-17 09:52:57,050 epoch 2 - iter 693/773 - loss 0.07354922 - time (sec): 63.53 - samples/sec: 1766.03 - lr: 0.000045 - momentum: 0.000000
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+ 2023-10-17 09:53:03,992 epoch 2 - iter 770/773 - loss 0.07444958 - time (sec): 70.48 - samples/sec: 1759.81 - lr: 0.000044 - momentum: 0.000000
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+ 2023-10-17 09:53:04,245 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:53:04,245 EPOCH 2 done: loss 0.0746 - lr: 0.000044
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+ 2023-10-17 09:53:07,171 DEV : loss 0.06132051348686218 - f1-score (micro avg) 0.6713
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+ 2023-10-17 09:53:07,199 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:53:13,887 epoch 3 - iter 77/773 - loss 0.04795621 - time (sec): 6.69 - samples/sec: 1748.40 - lr: 0.000044 - momentum: 0.000000
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+ 2023-10-17 09:53:21,207 epoch 3 - iter 154/773 - loss 0.04943447 - time (sec): 14.01 - samples/sec: 1772.77 - lr: 0.000043 - momentum: 0.000000
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+ 2023-10-17 09:53:29,211 epoch 3 - iter 231/773 - loss 0.04886716 - time (sec): 22.01 - samples/sec: 1734.92 - lr: 0.000043 - momentum: 0.000000
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+ 2023-10-17 09:53:36,821 epoch 3 - iter 308/773 - loss 0.04640129 - time (sec): 29.62 - samples/sec: 1708.16 - lr: 0.000042 - momentum: 0.000000
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+ 2023-10-17 09:53:44,470 epoch 3 - iter 385/773 - loss 0.04816842 - time (sec): 37.27 - samples/sec: 1675.52 - lr: 0.000042 - momentum: 0.000000
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+ 2023-10-17 09:53:52,356 epoch 3 - iter 462/773 - loss 0.05098456 - time (sec): 45.15 - samples/sec: 1662.59 - lr: 0.000041 - momentum: 0.000000
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+ 2023-10-17 09:53:59,926 epoch 3 - iter 539/773 - loss 0.05101299 - time (sec): 52.73 - samples/sec: 1653.10 - lr: 0.000041 - momentum: 0.000000
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+ 2023-10-17 09:54:06,734 epoch 3 - iter 616/773 - loss 0.05078472 - time (sec): 59.53 - samples/sec: 1671.82 - lr: 0.000040 - momentum: 0.000000
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+ 2023-10-17 09:54:13,425 epoch 3 - iter 693/773 - loss 0.05251785 - time (sec): 66.22 - samples/sec: 1666.04 - lr: 0.000039 - momentum: 0.000000
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+ 2023-10-17 09:54:20,384 epoch 3 - iter 770/773 - loss 0.05247431 - time (sec): 73.18 - samples/sec: 1692.98 - lr: 0.000039 - momentum: 0.000000
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+ 2023-10-17 09:54:20,643 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:54:20,643 EPOCH 3 done: loss 0.0524 - lr: 0.000039
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+ 2023-10-17 09:54:23,554 DEV : loss 0.05568350851535797 - f1-score (micro avg) 0.7886
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+ 2023-10-17 09:54:23,582 saving best model
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+ 2023-10-17 09:54:24,988 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:54:31,745 epoch 4 - iter 77/773 - loss 0.03917114 - time (sec): 6.75 - samples/sec: 1903.55 - lr: 0.000038 - momentum: 0.000000
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+ 2023-10-17 09:54:38,162 epoch 4 - iter 154/773 - loss 0.03481858 - time (sec): 13.17 - samples/sec: 1862.13 - lr: 0.000038 - momentum: 0.000000
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+ 2023-10-17 09:54:45,104 epoch 4 - iter 231/773 - loss 0.03357236 - time (sec): 20.11 - samples/sec: 1865.14 - lr: 0.000037 - momentum: 0.000000
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+ 2023-10-17 09:54:52,050 epoch 4 - iter 308/773 - loss 0.03530401 - time (sec): 27.06 - samples/sec: 1847.93 - lr: 0.000037 - momentum: 0.000000
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+ 2023-10-17 09:54:58,597 epoch 4 - iter 385/773 - loss 0.03461690 - time (sec): 33.61 - samples/sec: 1857.33 - lr: 0.000036 - momentum: 0.000000
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+ 2023-10-17 09:55:05,499 epoch 4 - iter 462/773 - loss 0.03627980 - time (sec): 40.51 - samples/sec: 1859.14 - lr: 0.000036 - momentum: 0.000000
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+ 2023-10-17 09:55:12,971 epoch 4 - iter 539/773 - loss 0.03653929 - time (sec): 47.98 - samples/sec: 1834.41 - lr: 0.000035 - momentum: 0.000000
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+ 2023-10-17 09:55:19,826 epoch 4 - iter 616/773 - loss 0.03616690 - time (sec): 54.83 - samples/sec: 1818.25 - lr: 0.000034 - momentum: 0.000000
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+ 2023-10-17 09:55:27,192 epoch 4 - iter 693/773 - loss 0.03664448 - time (sec): 62.20 - samples/sec: 1792.69 - lr: 0.000034 - momentum: 0.000000
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+ 2023-10-17 09:55:34,633 epoch 4 - iter 770/773 - loss 0.03696210 - time (sec): 69.64 - samples/sec: 1779.84 - lr: 0.000033 - momentum: 0.000000
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+ 2023-10-17 09:55:34,898 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:55:34,898 EPOCH 4 done: loss 0.0372 - lr: 0.000033
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+ 2023-10-17 09:55:37,813 DEV : loss 0.09010311961174011 - f1-score (micro avg) 0.795
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+ 2023-10-17 09:55:37,844 saving best model
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+ 2023-10-17 09:55:39,252 ----------------------------------------------------------------------------------------------------
133
+ 2023-10-17 09:55:46,196 epoch 5 - iter 77/773 - loss 0.02882609 - time (sec): 6.94 - samples/sec: 1709.68 - lr: 0.000033 - momentum: 0.000000
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+ 2023-10-17 09:55:53,133 epoch 5 - iter 154/773 - loss 0.02484139 - time (sec): 13.88 - samples/sec: 1750.43 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-17 09:56:00,065 epoch 5 - iter 231/773 - loss 0.02494855 - time (sec): 20.81 - samples/sec: 1738.11 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-17 09:56:07,071 epoch 5 - iter 308/773 - loss 0.02438607 - time (sec): 27.81 - samples/sec: 1740.43 - lr: 0.000031 - momentum: 0.000000
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+ 2023-10-17 09:56:14,264 epoch 5 - iter 385/773 - loss 0.02602136 - time (sec): 35.01 - samples/sec: 1753.53 - lr: 0.000031 - momentum: 0.000000
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+ 2023-10-17 09:56:21,203 epoch 5 - iter 462/773 - loss 0.02629902 - time (sec): 41.95 - samples/sec: 1764.17 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-17 09:56:28,542 epoch 5 - iter 539/773 - loss 0.02576945 - time (sec): 49.28 - samples/sec: 1755.14 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 09:56:35,568 epoch 5 - iter 616/773 - loss 0.02604685 - time (sec): 56.31 - samples/sec: 1752.21 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 09:56:42,730 epoch 5 - iter 693/773 - loss 0.02654183 - time (sec): 63.47 - samples/sec: 1763.08 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 09:56:50,088 epoch 5 - iter 770/773 - loss 0.02657177 - time (sec): 70.83 - samples/sec: 1747.09 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 09:56:50,394 ----------------------------------------------------------------------------------------------------
144
+ 2023-10-17 09:56:50,395 EPOCH 5 done: loss 0.0266 - lr: 0.000028
145
+ 2023-10-17 09:56:53,269 DEV : loss 0.1054750606417656 - f1-score (micro avg) 0.7785
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+ 2023-10-17 09:56:53,298 ----------------------------------------------------------------------------------------------------
147
+ 2023-10-17 09:57:00,470 epoch 6 - iter 77/773 - loss 0.01388745 - time (sec): 7.17 - samples/sec: 1788.67 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 09:57:07,341 epoch 6 - iter 154/773 - loss 0.01126746 - time (sec): 14.04 - samples/sec: 1832.99 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 09:57:14,224 epoch 6 - iter 231/773 - loss 0.01379040 - time (sec): 20.92 - samples/sec: 1817.27 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 09:57:21,174 epoch 6 - iter 308/773 - loss 0.01635429 - time (sec): 27.87 - samples/sec: 1813.88 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 09:57:28,078 epoch 6 - iter 385/773 - loss 0.01738643 - time (sec): 34.78 - samples/sec: 1826.82 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 09:57:34,818 epoch 6 - iter 462/773 - loss 0.01857094 - time (sec): 41.52 - samples/sec: 1809.73 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 09:57:41,802 epoch 6 - iter 539/773 - loss 0.01798259 - time (sec): 48.50 - samples/sec: 1791.72 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 09:57:49,468 epoch 6 - iter 616/773 - loss 0.01750456 - time (sec): 56.17 - samples/sec: 1757.49 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 09:57:56,975 epoch 6 - iter 693/773 - loss 0.01733636 - time (sec): 63.68 - samples/sec: 1748.90 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 09:58:03,735 epoch 6 - iter 770/773 - loss 0.01777726 - time (sec): 70.44 - samples/sec: 1758.81 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 09:58:03,988 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:58:03,989 EPOCH 6 done: loss 0.0177 - lr: 0.000022
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+ 2023-10-17 09:58:07,150 DEV : loss 0.11583945155143738 - f1-score (micro avg) 0.7778
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+ 2023-10-17 09:58:07,202 ----------------------------------------------------------------------------------------------------
161
+ 2023-10-17 09:58:13,949 epoch 7 - iter 77/773 - loss 0.00456103 - time (sec): 6.74 - samples/sec: 1737.20 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 09:58:20,748 epoch 7 - iter 154/773 - loss 0.01048448 - time (sec): 13.54 - samples/sec: 1752.44 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 09:58:27,539 epoch 7 - iter 231/773 - loss 0.01223233 - time (sec): 20.33 - samples/sec: 1781.78 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 09:58:34,652 epoch 7 - iter 308/773 - loss 0.01163819 - time (sec): 27.45 - samples/sec: 1783.17 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 09:58:42,661 epoch 7 - iter 385/773 - loss 0.01140701 - time (sec): 35.46 - samples/sec: 1736.84 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-17 09:58:50,477 epoch 7 - iter 462/773 - loss 0.01053450 - time (sec): 43.27 - samples/sec: 1708.48 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-17 09:58:57,739 epoch 7 - iter 539/773 - loss 0.01010124 - time (sec): 50.53 - samples/sec: 1701.36 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-17 09:59:04,851 epoch 7 - iter 616/773 - loss 0.00993634 - time (sec): 57.65 - samples/sec: 1718.81 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-17 09:59:12,153 epoch 7 - iter 693/773 - loss 0.01039778 - time (sec): 64.95 - samples/sec: 1722.45 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-17 09:59:19,114 epoch 7 - iter 770/773 - loss 0.01064432 - time (sec): 71.91 - samples/sec: 1720.07 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-17 09:59:19,398 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:59:19,398 EPOCH 7 done: loss 0.0106 - lr: 0.000017
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+ 2023-10-17 09:59:22,806 DEV : loss 0.11872334033250809 - f1-score (micro avg) 0.818
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+ 2023-10-17 09:59:22,836 saving best model
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+ 2023-10-17 09:59:24,267 ----------------------------------------------------------------------------------------------------
176
+ 2023-10-17 09:59:31,418 epoch 8 - iter 77/773 - loss 0.01473996 - time (sec): 7.14 - samples/sec: 1732.87 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-17 09:59:39,170 epoch 8 - iter 154/773 - loss 0.01203286 - time (sec): 14.90 - samples/sec: 1696.02 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-17 09:59:46,392 epoch 8 - iter 231/773 - loss 0.01161199 - time (sec): 22.12 - samples/sec: 1689.65 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 09:59:53,350 epoch 8 - iter 308/773 - loss 0.00966991 - time (sec): 29.08 - samples/sec: 1702.11 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-17 10:00:00,653 epoch 8 - iter 385/773 - loss 0.00955744 - time (sec): 36.38 - samples/sec: 1690.94 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-17 10:00:08,701 epoch 8 - iter 462/773 - loss 0.00952576 - time (sec): 44.43 - samples/sec: 1680.49 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-17 10:00:16,534 epoch 8 - iter 539/773 - loss 0.00909750 - time (sec): 52.26 - samples/sec: 1676.63 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-17 10:00:24,416 epoch 8 - iter 616/773 - loss 0.00903899 - time (sec): 60.14 - samples/sec: 1654.18 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-17 10:00:31,889 epoch 8 - iter 693/773 - loss 0.00893252 - time (sec): 67.61 - samples/sec: 1641.23 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-17 10:00:39,288 epoch 8 - iter 770/773 - loss 0.00862898 - time (sec): 75.01 - samples/sec: 1652.22 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-17 10:00:39,604 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 10:00:39,605 EPOCH 8 done: loss 0.0086 - lr: 0.000011
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+ 2023-10-17 10:00:42,778 DEV : loss 0.12361815571784973 - f1-score (micro avg) 0.778
189
+ 2023-10-17 10:00:42,809 ----------------------------------------------------------------------------------------------------
190
+ 2023-10-17 10:00:50,878 epoch 9 - iter 77/773 - loss 0.00424221 - time (sec): 8.07 - samples/sec: 1562.76 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-17 10:00:58,265 epoch 9 - iter 154/773 - loss 0.00382206 - time (sec): 15.45 - samples/sec: 1587.14 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-17 10:01:05,562 epoch 9 - iter 231/773 - loss 0.00494659 - time (sec): 22.75 - samples/sec: 1643.92 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-17 10:01:13,223 epoch 9 - iter 308/773 - loss 0.00538922 - time (sec): 30.41 - samples/sec: 1615.47 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-17 10:01:20,951 epoch 9 - iter 385/773 - loss 0.00585671 - time (sec): 38.14 - samples/sec: 1622.67 - lr: 0.000008 - momentum: 0.000000
195
+ 2023-10-17 10:01:28,146 epoch 9 - iter 462/773 - loss 0.00592916 - time (sec): 45.33 - samples/sec: 1632.48 - lr: 0.000008 - momentum: 0.000000
196
+ 2023-10-17 10:01:35,335 epoch 9 - iter 539/773 - loss 0.00543064 - time (sec): 52.52 - samples/sec: 1654.27 - lr: 0.000007 - momentum: 0.000000
197
+ 2023-10-17 10:01:42,299 epoch 9 - iter 616/773 - loss 0.00549350 - time (sec): 59.49 - samples/sec: 1664.43 - lr: 0.000007 - momentum: 0.000000
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+ 2023-10-17 10:01:49,520 epoch 9 - iter 693/773 - loss 0.00513001 - time (sec): 66.71 - samples/sec: 1683.75 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-17 10:01:56,696 epoch 9 - iter 770/773 - loss 0.00549165 - time (sec): 73.89 - samples/sec: 1675.81 - lr: 0.000006 - momentum: 0.000000
200
+ 2023-10-17 10:01:56,967 ----------------------------------------------------------------------------------------------------
201
+ 2023-10-17 10:01:56,967 EPOCH 9 done: loss 0.0055 - lr: 0.000006
202
+ 2023-10-17 10:01:59,842 DEV : loss 0.12155171483755112 - f1-score (micro avg) 0.7975
203
+ 2023-10-17 10:01:59,871 ----------------------------------------------------------------------------------------------------
204
+ 2023-10-17 10:02:06,780 epoch 10 - iter 77/773 - loss 0.00669265 - time (sec): 6.91 - samples/sec: 1812.77 - lr: 0.000005 - momentum: 0.000000
205
+ 2023-10-17 10:02:13,747 epoch 10 - iter 154/773 - loss 0.00465994 - time (sec): 13.87 - samples/sec: 1786.79 - lr: 0.000005 - momentum: 0.000000
206
+ 2023-10-17 10:02:20,720 epoch 10 - iter 231/773 - loss 0.00329970 - time (sec): 20.85 - samples/sec: 1815.30 - lr: 0.000004 - momentum: 0.000000
207
+ 2023-10-17 10:02:27,633 epoch 10 - iter 308/773 - loss 0.00309264 - time (sec): 27.76 - samples/sec: 1810.49 - lr: 0.000003 - momentum: 0.000000
208
+ 2023-10-17 10:02:34,497 epoch 10 - iter 385/773 - loss 0.00320190 - time (sec): 34.62 - samples/sec: 1805.14 - lr: 0.000003 - momentum: 0.000000
209
+ 2023-10-17 10:02:41,428 epoch 10 - iter 462/773 - loss 0.00367371 - time (sec): 41.56 - samples/sec: 1786.35 - lr: 0.000002 - momentum: 0.000000
210
+ 2023-10-17 10:02:48,391 epoch 10 - iter 539/773 - loss 0.00352687 - time (sec): 48.52 - samples/sec: 1793.81 - lr: 0.000002 - momentum: 0.000000
211
+ 2023-10-17 10:02:55,392 epoch 10 - iter 616/773 - loss 0.00364918 - time (sec): 55.52 - samples/sec: 1780.19 - lr: 0.000001 - momentum: 0.000000
212
+ 2023-10-17 10:03:02,317 epoch 10 - iter 693/773 - loss 0.00341373 - time (sec): 62.44 - samples/sec: 1784.63 - lr: 0.000001 - momentum: 0.000000
213
+ 2023-10-17 10:03:09,513 epoch 10 - iter 770/773 - loss 0.00316098 - time (sec): 69.64 - samples/sec: 1778.30 - lr: 0.000000 - momentum: 0.000000
214
+ 2023-10-17 10:03:09,772 ----------------------------------------------------------------------------------------------------
215
+ 2023-10-17 10:03:09,772 EPOCH 10 done: loss 0.0031 - lr: 0.000000
216
+ 2023-10-17 10:03:12,617 DEV : loss 0.12296666949987411 - f1-score (micro avg) 0.7942
217
+ 2023-10-17 10:03:13,213 ----------------------------------------------------------------------------------------------------
218
+ 2023-10-17 10:03:13,215 Loading model from best epoch ...
219
+ 2023-10-17 10:03:15,488 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
220
+ 2023-10-17 10:03:23,976
221
+ Results:
222
+ - F-score (micro) 0.7995
223
+ - F-score (macro) 0.7004
224
+ - Accuracy 0.6805
225
+
226
+ By class:
227
+ precision recall f1-score support
228
+
229
+ LOC 0.8866 0.8182 0.8510 946
230
+ BUILDING 0.6207 0.4865 0.5455 185
231
+ STREET 0.7551 0.6607 0.7048 56
232
+
233
+ micro avg 0.8444 0.7591 0.7995 1187
234
+ macro avg 0.7541 0.6551 0.7004 1187
235
+ weighted avg 0.8390 0.7591 0.7965 1187
236
+
237
+ 2023-10-17 10:03:23,976 ----------------------------------------------------------------------------------------------------