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flair-hipe-2022-topres19th-en

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.1697548119.4c6324b99746.1159.14 +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 13:10:00 0.0000 0.4601 0.0551 0.7398 0.7679 0.7536 0.6298
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+ 2 13:11:22 0.0000 0.0733 0.0556 0.7610 0.8059 0.7828 0.6519
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+ 3 13:12:45 0.0000 0.0471 0.0630 0.7461 0.8059 0.7748 0.6475
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+ 4 13:14:08 0.0000 0.0325 0.0940 0.7702 0.8059 0.7876 0.6564
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+ 5 13:15:33 0.0000 0.0231 0.0967 0.7510 0.8270 0.7871 0.6577
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+ 6 13:16:54 0.0000 0.0149 0.1024 0.7799 0.8523 0.8145 0.6966
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+ 7 13:18:17 0.0000 0.0109 0.1118 0.7817 0.8312 0.8057 0.6864
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+ 8 13:19:40 0.0000 0.0066 0.1239 0.7206 0.8270 0.7701 0.6384
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+ 9 13:20:56 0.0000 0.0041 0.1282 0.7453 0.8397 0.7897 0.6633
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+ 10 13:22:12 0.0000 0.0028 0.1235 0.7683 0.8397 0.8024 0.6838
runs/events.out.tfevents.1697548119.4c6324b99746.1159.14 ADDED
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test.tsv ADDED
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training.log ADDED
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+ 2023-10-17 13:08:39,919 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 13:08:39,921 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 13:08:39,921 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 13:08:39,921 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 13:08:39,921 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 13:08:39,921 Train: 6183 sentences
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+ 2023-10-17 13:08:39,921 (train_with_dev=False, train_with_test=False)
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+ 2023-10-17 13:08:39,922 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 13:08:39,922 Training Params:
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+ 2023-10-17 13:08:39,922 - learning_rate: "3e-05"
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+ 2023-10-17 13:08:39,922 - mini_batch_size: "8"
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+ 2023-10-17 13:08:39,922 - max_epochs: "10"
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+ 2023-10-17 13:08:39,922 - shuffle: "True"
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+ 2023-10-17 13:08:39,922 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 13:08:39,922 Plugins:
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+ 2023-10-17 13:08:39,922 - TensorboardLogger
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+ 2023-10-17 13:08:39,922 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-17 13:08:39,922 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 13:08:39,922 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-17 13:08:39,922 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-17 13:08:39,922 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 13:08:39,922 Computation:
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+ 2023-10-17 13:08:39,922 - compute on device: cuda:0
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+ 2023-10-17 13:08:39,923 - embedding storage: none
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+ 2023-10-17 13:08:39,923 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 13:08:39,923 Model training base path: "hmbench-topres19th/en-hmteams/teams-base-historic-multilingual-discriminator-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4"
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+ 2023-10-17 13:08:39,923 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 13:08:39,923 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 13:08:39,923 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-17 13:08:46,795 epoch 1 - iter 77/773 - loss 2.97835929 - time (sec): 6.87 - samples/sec: 1745.08 - lr: 0.000003 - momentum: 0.000000
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+ 2023-10-17 13:08:54,296 epoch 1 - iter 154/773 - loss 1.87469467 - time (sec): 14.37 - samples/sec: 1632.78 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-17 13:09:01,982 epoch 1 - iter 231/773 - loss 1.29875250 - time (sec): 22.06 - samples/sec: 1621.70 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-17 13:09:09,388 epoch 1 - iter 308/773 - loss 1.01072496 - time (sec): 29.46 - samples/sec: 1621.66 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-17 13:09:17,277 epoch 1 - iter 385/773 - loss 0.81975956 - time (sec): 37.35 - samples/sec: 1635.34 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 13:09:25,191 epoch 1 - iter 462/773 - loss 0.70447128 - time (sec): 45.27 - samples/sec: 1627.15 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-17 13:09:32,881 epoch 1 - iter 539/773 - loss 0.62180756 - time (sec): 52.96 - samples/sec: 1620.95 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 13:09:41,089 epoch 1 - iter 616/773 - loss 0.55646217 - time (sec): 61.16 - samples/sec: 1609.64 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 13:09:48,700 epoch 1 - iter 693/773 - loss 0.50448744 - time (sec): 68.78 - samples/sec: 1614.09 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 13:09:56,705 epoch 1 - iter 770/773 - loss 0.46197740 - time (sec): 76.78 - samples/sec: 1611.60 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-17 13:09:57,056 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 13:09:57,057 EPOCH 1 done: loss 0.4601 - lr: 0.000030
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+ 2023-10-17 13:10:00,370 DEV : loss 0.055146388709545135 - f1-score (micro avg) 0.7536
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+ 2023-10-17 13:10:00,402 saving best model
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+ 2023-10-17 13:10:01,040 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 13:10:08,616 epoch 2 - iter 77/773 - loss 0.08891357 - time (sec): 7.57 - samples/sec: 1619.07 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-17 13:10:16,519 epoch 2 - iter 154/773 - loss 0.08025883 - time (sec): 15.48 - samples/sec: 1618.18 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 13:10:23,631 epoch 2 - iter 231/773 - loss 0.07291651 - time (sec): 22.59 - samples/sec: 1636.92 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 13:10:30,834 epoch 2 - iter 308/773 - loss 0.07668126 - time (sec): 29.79 - samples/sec: 1651.60 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 13:10:38,528 epoch 2 - iter 385/773 - loss 0.07749694 - time (sec): 37.48 - samples/sec: 1640.58 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 13:10:46,505 epoch 2 - iter 462/773 - loss 0.07639212 - time (sec): 45.46 - samples/sec: 1618.29 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 13:10:54,438 epoch 2 - iter 539/773 - loss 0.07525245 - time (sec): 53.40 - samples/sec: 1615.54 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 13:11:02,973 epoch 2 - iter 616/773 - loss 0.07570975 - time (sec): 61.93 - samples/sec: 1583.02 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 13:11:11,049 epoch 2 - iter 693/773 - loss 0.07390561 - time (sec): 70.01 - samples/sec: 1586.06 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 13:11:18,885 epoch 2 - iter 770/773 - loss 0.07331939 - time (sec): 77.84 - samples/sec: 1592.96 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 13:11:19,172 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 13:11:19,173 EPOCH 2 done: loss 0.0733 - lr: 0.000027
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+ 2023-10-17 13:11:22,464 DEV : loss 0.0555521659553051 - f1-score (micro avg) 0.7828
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+ 2023-10-17 13:11:22,493 saving best model
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+ 2023-10-17 13:11:23,970 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 13:11:31,790 epoch 3 - iter 77/773 - loss 0.04302712 - time (sec): 7.82 - samples/sec: 1736.74 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 13:11:39,410 epoch 3 - iter 154/773 - loss 0.04215121 - time (sec): 15.44 - samples/sec: 1646.04 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 13:11:47,665 epoch 3 - iter 231/773 - loss 0.04258486 - time (sec): 23.69 - samples/sec: 1601.66 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 13:11:55,609 epoch 3 - iter 308/773 - loss 0.04530148 - time (sec): 31.64 - samples/sec: 1574.86 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 13:12:04,204 epoch 3 - iter 385/773 - loss 0.04445010 - time (sec): 40.23 - samples/sec: 1585.64 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 13:12:12,128 epoch 3 - iter 462/773 - loss 0.04672927 - time (sec): 48.16 - samples/sec: 1566.26 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 13:12:19,104 epoch 3 - iter 539/773 - loss 0.04829116 - time (sec): 55.13 - samples/sec: 1593.13 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 13:12:26,282 epoch 3 - iter 616/773 - loss 0.04728436 - time (sec): 62.31 - samples/sec: 1603.68 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 13:12:34,349 epoch 3 - iter 693/773 - loss 0.04777688 - time (sec): 70.38 - samples/sec: 1592.63 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 13:12:41,971 epoch 3 - iter 770/773 - loss 0.04728166 - time (sec): 78.00 - samples/sec: 1588.52 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 13:12:42,251 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 13:12:42,252 EPOCH 3 done: loss 0.0471 - lr: 0.000023
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+ 2023-10-17 13:12:45,338 DEV : loss 0.06297728419303894 - f1-score (micro avg) 0.7748
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+ 2023-10-17 13:12:45,370 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 13:12:53,983 epoch 4 - iter 77/773 - loss 0.02732323 - time (sec): 8.61 - samples/sec: 1445.84 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 13:13:02,146 epoch 4 - iter 154/773 - loss 0.03181965 - time (sec): 16.77 - samples/sec: 1493.94 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 13:13:10,153 epoch 4 - iter 231/773 - loss 0.03214623 - time (sec): 24.78 - samples/sec: 1480.43 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 13:13:17,716 epoch 4 - iter 308/773 - loss 0.02980114 - time (sec): 32.34 - samples/sec: 1528.22 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 13:13:25,433 epoch 4 - iter 385/773 - loss 0.02875251 - time (sec): 40.06 - samples/sec: 1551.31 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 13:13:33,344 epoch 4 - iter 462/773 - loss 0.03233554 - time (sec): 47.97 - samples/sec: 1560.68 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 13:13:41,357 epoch 4 - iter 539/773 - loss 0.03122506 - time (sec): 55.99 - samples/sec: 1560.44 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 13:13:49,884 epoch 4 - iter 616/773 - loss 0.03233573 - time (sec): 64.51 - samples/sec: 1539.64 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 13:13:57,748 epoch 4 - iter 693/773 - loss 0.03209738 - time (sec): 72.38 - samples/sec: 1538.03 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 13:14:04,906 epoch 4 - iter 770/773 - loss 0.03230222 - time (sec): 79.53 - samples/sec: 1557.31 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 13:14:05,197 ----------------------------------------------------------------------------------------------------
129
+ 2023-10-17 13:14:05,197 EPOCH 4 done: loss 0.0325 - lr: 0.000020
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+ 2023-10-17 13:14:08,468 DEV : loss 0.09397705644369125 - f1-score (micro avg) 0.7876
131
+ 2023-10-17 13:14:08,497 saving best model
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+ 2023-10-17 13:14:09,959 ----------------------------------------------------------------------------------------------------
133
+ 2023-10-17 13:14:17,907 epoch 5 - iter 77/773 - loss 0.02528085 - time (sec): 7.94 - samples/sec: 1628.49 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 13:14:25,737 epoch 5 - iter 154/773 - loss 0.02527426 - time (sec): 15.77 - samples/sec: 1580.91 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-17 13:14:33,774 epoch 5 - iter 231/773 - loss 0.02362748 - time (sec): 23.81 - samples/sec: 1552.99 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-17 13:14:41,673 epoch 5 - iter 308/773 - loss 0.02239320 - time (sec): 31.71 - samples/sec: 1559.77 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-17 13:14:49,222 epoch 5 - iter 385/773 - loss 0.02330606 - time (sec): 39.26 - samples/sec: 1554.33 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-17 13:14:56,980 epoch 5 - iter 462/773 - loss 0.02385192 - time (sec): 47.02 - samples/sec: 1562.03 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-17 13:15:05,089 epoch 5 - iter 539/773 - loss 0.02449892 - time (sec): 55.13 - samples/sec: 1542.51 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-17 13:15:13,885 epoch 5 - iter 616/773 - loss 0.02394531 - time (sec): 63.92 - samples/sec: 1527.94 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-17 13:15:21,845 epoch 5 - iter 693/773 - loss 0.02348430 - time (sec): 71.88 - samples/sec: 1546.02 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-17 13:15:29,820 epoch 5 - iter 770/773 - loss 0.02290727 - time (sec): 79.86 - samples/sec: 1550.24 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-17 13:15:30,108 ----------------------------------------------------------------------------------------------------
144
+ 2023-10-17 13:15:30,108 EPOCH 5 done: loss 0.0231 - lr: 0.000017
145
+ 2023-10-17 13:15:33,581 DEV : loss 0.09673922508955002 - f1-score (micro avg) 0.7871
146
+ 2023-10-17 13:15:33,614 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 13:15:41,113 epoch 6 - iter 77/773 - loss 0.01282970 - time (sec): 7.50 - samples/sec: 1576.16 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-17 13:15:48,997 epoch 6 - iter 154/773 - loss 0.01426688 - time (sec): 15.38 - samples/sec: 1607.45 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-17 13:15:57,056 epoch 6 - iter 231/773 - loss 0.01542611 - time (sec): 23.44 - samples/sec: 1597.10 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-17 13:16:04,734 epoch 6 - iter 308/773 - loss 0.01498317 - time (sec): 31.12 - samples/sec: 1618.59 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 13:16:11,813 epoch 6 - iter 385/773 - loss 0.01554285 - time (sec): 38.20 - samples/sec: 1621.76 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 13:16:19,174 epoch 6 - iter 462/773 - loss 0.01570361 - time (sec): 45.56 - samples/sec: 1618.77 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 13:16:26,803 epoch 6 - iter 539/773 - loss 0.01538039 - time (sec): 53.19 - samples/sec: 1608.65 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-17 13:16:34,412 epoch 6 - iter 616/773 - loss 0.01501943 - time (sec): 60.80 - samples/sec: 1610.50 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-17 13:16:42,341 epoch 6 - iter 693/773 - loss 0.01478103 - time (sec): 68.72 - samples/sec: 1614.46 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-17 13:16:50,931 epoch 6 - iter 770/773 - loss 0.01499341 - time (sec): 77.31 - samples/sec: 1601.28 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-17 13:16:51,209 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 13:16:51,209 EPOCH 6 done: loss 0.0149 - lr: 0.000013
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+ 2023-10-17 13:16:54,440 DEV : loss 0.10239903628826141 - f1-score (micro avg) 0.8145
160
+ 2023-10-17 13:16:54,472 saving best model
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+ 2023-10-17 13:16:55,963 ----------------------------------------------------------------------------------------------------
162
+ 2023-10-17 13:17:03,813 epoch 7 - iter 77/773 - loss 0.00993657 - time (sec): 7.85 - samples/sec: 1575.71 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-17 13:17:11,672 epoch 7 - iter 154/773 - loss 0.01083244 - time (sec): 15.71 - samples/sec: 1559.76 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-17 13:17:20,126 epoch 7 - iter 231/773 - loss 0.01440738 - time (sec): 24.16 - samples/sec: 1534.55 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-17 13:17:28,474 epoch 7 - iter 308/773 - loss 0.01310777 - time (sec): 32.51 - samples/sec: 1509.11 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-17 13:17:36,571 epoch 7 - iter 385/773 - loss 0.01226929 - time (sec): 40.61 - samples/sec: 1518.86 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-17 13:17:44,264 epoch 7 - iter 462/773 - loss 0.01168054 - time (sec): 48.30 - samples/sec: 1512.85 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-17 13:17:51,543 epoch 7 - iter 539/773 - loss 0.01131055 - time (sec): 55.58 - samples/sec: 1550.49 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-17 13:17:59,150 epoch 7 - iter 616/773 - loss 0.01118152 - time (sec): 63.18 - samples/sec: 1570.31 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-17 13:18:06,493 epoch 7 - iter 693/773 - loss 0.01095252 - time (sec): 70.53 - samples/sec: 1583.36 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-17 13:18:14,096 epoch 7 - iter 770/773 - loss 0.01093922 - time (sec): 78.13 - samples/sec: 1584.50 - lr: 0.000010 - momentum: 0.000000
172
+ 2023-10-17 13:18:14,408 ----------------------------------------------------------------------------------------------------
173
+ 2023-10-17 13:18:14,408 EPOCH 7 done: loss 0.0109 - lr: 0.000010
174
+ 2023-10-17 13:18:17,632 DEV : loss 0.11184219270944595 - f1-score (micro avg) 0.8057
175
+ 2023-10-17 13:18:17,665 ----------------------------------------------------------------------------------------------------
176
+ 2023-10-17 13:18:25,406 epoch 8 - iter 77/773 - loss 0.00400052 - time (sec): 7.74 - samples/sec: 1593.59 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-17 13:18:33,177 epoch 8 - iter 154/773 - loss 0.00447152 - time (sec): 15.51 - samples/sec: 1569.21 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-17 13:18:40,878 epoch 8 - iter 231/773 - loss 0.00490799 - time (sec): 23.21 - samples/sec: 1565.41 - lr: 0.000009 - momentum: 0.000000
179
+ 2023-10-17 13:18:48,509 epoch 8 - iter 308/773 - loss 0.00494344 - time (sec): 30.84 - samples/sec: 1567.92 - lr: 0.000009 - momentum: 0.000000
180
+ 2023-10-17 13:18:56,212 epoch 8 - iter 385/773 - loss 0.00625829 - time (sec): 38.54 - samples/sec: 1578.24 - lr: 0.000008 - momentum: 0.000000
181
+ 2023-10-17 13:19:04,489 epoch 8 - iter 462/773 - loss 0.00613140 - time (sec): 46.82 - samples/sec: 1564.49 - lr: 0.000008 - momentum: 0.000000
182
+ 2023-10-17 13:19:13,691 epoch 8 - iter 539/773 - loss 0.00633907 - time (sec): 56.02 - samples/sec: 1552.09 - lr: 0.000008 - momentum: 0.000000
183
+ 2023-10-17 13:19:22,168 epoch 8 - iter 616/773 - loss 0.00615248 - time (sec): 64.50 - samples/sec: 1546.11 - lr: 0.000007 - momentum: 0.000000
184
+ 2023-10-17 13:19:30,578 epoch 8 - iter 693/773 - loss 0.00641877 - time (sec): 72.91 - samples/sec: 1533.68 - lr: 0.000007 - momentum: 0.000000
185
+ 2023-10-17 13:19:37,585 epoch 8 - iter 770/773 - loss 0.00648931 - time (sec): 79.92 - samples/sec: 1550.50 - lr: 0.000007 - momentum: 0.000000
186
+ 2023-10-17 13:19:37,865 ----------------------------------------------------------------------------------------------------
187
+ 2023-10-17 13:19:37,865 EPOCH 8 done: loss 0.0066 - lr: 0.000007
188
+ 2023-10-17 13:19:40,914 DEV : loss 0.1238672062754631 - f1-score (micro avg) 0.7701
189
+ 2023-10-17 13:19:40,948 ----------------------------------------------------------------------------------------------------
190
+ 2023-10-17 13:19:47,862 epoch 9 - iter 77/773 - loss 0.00422951 - time (sec): 6.91 - samples/sec: 1776.61 - lr: 0.000006 - momentum: 0.000000
191
+ 2023-10-17 13:19:55,147 epoch 9 - iter 154/773 - loss 0.00410486 - time (sec): 14.20 - samples/sec: 1755.62 - lr: 0.000006 - momentum: 0.000000
192
+ 2023-10-17 13:20:02,671 epoch 9 - iter 231/773 - loss 0.00365045 - time (sec): 21.72 - samples/sec: 1765.62 - lr: 0.000006 - momentum: 0.000000
193
+ 2023-10-17 13:20:10,380 epoch 9 - iter 308/773 - loss 0.00392863 - time (sec): 29.43 - samples/sec: 1757.76 - lr: 0.000005 - momentum: 0.000000
194
+ 2023-10-17 13:20:17,497 epoch 9 - iter 385/773 - loss 0.00446919 - time (sec): 36.55 - samples/sec: 1704.88 - lr: 0.000005 - momentum: 0.000000
195
+ 2023-10-17 13:20:24,563 epoch 9 - iter 462/773 - loss 0.00449122 - time (sec): 43.61 - samples/sec: 1700.22 - lr: 0.000005 - momentum: 0.000000
196
+ 2023-10-17 13:20:31,806 epoch 9 - iter 539/773 - loss 0.00450468 - time (sec): 50.86 - samples/sec: 1698.95 - lr: 0.000004 - momentum: 0.000000
197
+ 2023-10-17 13:20:39,046 epoch 9 - iter 616/773 - loss 0.00433734 - time (sec): 58.10 - samples/sec: 1694.55 - lr: 0.000004 - momentum: 0.000000
198
+ 2023-10-17 13:20:46,096 epoch 9 - iter 693/773 - loss 0.00402114 - time (sec): 65.15 - samples/sec: 1695.80 - lr: 0.000004 - momentum: 0.000000
199
+ 2023-10-17 13:20:53,731 epoch 9 - iter 770/773 - loss 0.00408108 - time (sec): 72.78 - samples/sec: 1699.18 - lr: 0.000003 - momentum: 0.000000
200
+ 2023-10-17 13:20:54,012 ----------------------------------------------------------------------------------------------------
201
+ 2023-10-17 13:20:54,013 EPOCH 9 done: loss 0.0041 - lr: 0.000003
202
+ 2023-10-17 13:20:56,844 DEV : loss 0.12823046743869781 - f1-score (micro avg) 0.7897
203
+ 2023-10-17 13:20:56,875 ----------------------------------------------------------------------------------------------------
204
+ 2023-10-17 13:21:04,003 epoch 10 - iter 77/773 - loss 0.00299248 - time (sec): 7.13 - samples/sec: 1816.45 - lr: 0.000003 - momentum: 0.000000
205
+ 2023-10-17 13:21:11,274 epoch 10 - iter 154/773 - loss 0.00431058 - time (sec): 14.40 - samples/sec: 1760.63 - lr: 0.000003 - momentum: 0.000000
206
+ 2023-10-17 13:21:18,785 epoch 10 - iter 231/773 - loss 0.00336600 - time (sec): 21.91 - samples/sec: 1768.91 - lr: 0.000002 - momentum: 0.000000
207
+ 2023-10-17 13:21:26,524 epoch 10 - iter 308/773 - loss 0.00313090 - time (sec): 29.65 - samples/sec: 1721.62 - lr: 0.000002 - momentum: 0.000000
208
+ 2023-10-17 13:21:33,931 epoch 10 - iter 385/773 - loss 0.00278220 - time (sec): 37.05 - samples/sec: 1705.96 - lr: 0.000002 - momentum: 0.000000
209
+ 2023-10-17 13:21:40,847 epoch 10 - iter 462/773 - loss 0.00299155 - time (sec): 43.97 - samples/sec: 1724.70 - lr: 0.000001 - momentum: 0.000000
210
+ 2023-10-17 13:21:48,002 epoch 10 - iter 539/773 - loss 0.00280852 - time (sec): 51.12 - samples/sec: 1716.44 - lr: 0.000001 - momentum: 0.000000
211
+ 2023-10-17 13:21:54,933 epoch 10 - iter 616/773 - loss 0.00285345 - time (sec): 58.06 - samples/sec: 1706.62 - lr: 0.000001 - momentum: 0.000000
212
+ 2023-10-17 13:22:02,114 epoch 10 - iter 693/773 - loss 0.00280881 - time (sec): 65.24 - samples/sec: 1701.66 - lr: 0.000000 - momentum: 0.000000
213
+ 2023-10-17 13:22:09,626 epoch 10 - iter 770/773 - loss 0.00273836 - time (sec): 72.75 - samples/sec: 1701.49 - lr: 0.000000 - momentum: 0.000000
214
+ 2023-10-17 13:22:09,899 ----------------------------------------------------------------------------------------------------
215
+ 2023-10-17 13:22:09,899 EPOCH 10 done: loss 0.0028 - lr: 0.000000
216
+ 2023-10-17 13:22:12,830 DEV : loss 0.12347615510225296 - f1-score (micro avg) 0.8024
217
+ 2023-10-17 13:22:13,409 ----------------------------------------------------------------------------------------------------
218
+ 2023-10-17 13:22:13,411 Loading model from best epoch ...
219
+ 2023-10-17 13:22:15,686 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 13:22:24,113
221
+ Results:
222
+ - F-score (micro) 0.8043
223
+ - F-score (macro) 0.7283
224
+ - Accuracy 0.691
225
+
226
+ By class:
227
+ precision recall f1-score support
228
+
229
+ LOC 0.8384 0.8499 0.8441 946
230
+ BUILDING 0.6218 0.6486 0.6349 185
231
+ STREET 0.6667 0.7500 0.7059 56
232
+
233
+ micro avg 0.7951 0.8138 0.8043 1187
234
+ macro avg 0.7089 0.7495 0.7283 1187
235
+ weighted avg 0.7965 0.8138 0.8050 1187
236
+
237
+ 2023-10-17 13:22:24,113 ----------------------------------------------------------------------------------------------------