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aitech-sd-lab/NLU_lab_7-8/slot-model-pl/training.log

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add NLU solution
2022-05-02 16:00:15 +02:00
2022-05-02 15:44:09,185 ----------------------------------------------------------------------------------------------------
2022-05-02 15:44:09,185 Model: "SequenceTagger(
(embeddings): StackedEmbeddings(
(list_embedding_0): WordEmbeddings('pl')
(list_embedding_1): FlairEmbeddings(
(lm): LanguageModel(
(drop): Dropout(p=0.25, inplace=False)
(encoder): Embedding(1602, 100)
(rnn): LSTM(100, 2048)
(decoder): Linear(in_features=2048, out_features=1602, bias=True)
)
)
(list_embedding_2): FlairEmbeddings(
(lm): LanguageModel(
(drop): Dropout(p=0.25, inplace=False)
(encoder): Embedding(1602, 100)
(rnn): LSTM(100, 2048)
(decoder): Linear(in_features=2048, out_features=1602, bias=True)
)
)
(list_embedding_3): CharacterEmbeddings(
(char_embedding): Embedding(275, 25)
(char_rnn): LSTM(25, 25, bidirectional=True)
)
)
(word_dropout): WordDropout(p=0.05)
(locked_dropout): LockedDropout(p=0.5)
(embedding2nn): Linear(in_features=4446, out_features=4446, bias=True)
(rnn): LSTM(4446, 256, batch_first=True, bidirectional=True)
(linear): Linear(in_features=512, out_features=50, bias=True)
(beta): 1.0
(weights): None
(weight_tensor) None
)"
2022-05-02 15:44:09,185 ----------------------------------------------------------------------------------------------------
2022-05-02 15:44:09,185 Corpus: "Corpus: 735 train + 82 dev + 152 test sentences"
2022-05-02 15:44:09,185 ----------------------------------------------------------------------------------------------------
2022-05-02 15:44:09,185 Parameters:
2022-05-02 15:44:09,185 - learning_rate: "0.1"
2022-05-02 15:44:09,185 - mini_batch_size: "32"
2022-05-02 15:44:09,185 - patience: "3"
2022-05-02 15:44:09,185 - anneal_factor: "0.5"
2022-05-02 15:44:09,185 - max_epochs: "10"
2022-05-02 15:44:09,185 - shuffle: "True"
2022-05-02 15:44:09,185 - train_with_dev: "True"
2022-05-02 15:44:09,185 - batch_growth_annealing: "False"
2022-05-02 15:44:09,185 ----------------------------------------------------------------------------------------------------
2022-05-02 15:44:09,185 Model training base path: "slot-model-pl"
2022-05-02 15:44:09,185 ----------------------------------------------------------------------------------------------------
2022-05-02 15:44:09,185 Device: cpu
2022-05-02 15:44:09,185 ----------------------------------------------------------------------------------------------------
2022-05-02 15:44:09,185 Embeddings storage mode: cpu
2022-05-02 15:44:09,212 ----------------------------------------------------------------------------------------------------
2022-05-02 15:44:12,896 epoch 1 - iter 2/26 - loss 5.40706334 - samples/sec: 17.37 - lr: 0.100000
2022-05-02 15:44:17,195 epoch 1 - iter 4/26 - loss 4.38706093 - samples/sec: 14.89 - lr: 0.100000
2022-05-02 15:44:20,984 epoch 1 - iter 6/26 - loss 3.63759864 - samples/sec: 16.90 - lr: 0.100000
2022-05-02 15:44:25,378 epoch 1 - iter 8/26 - loss 3.26681995 - samples/sec: 14.57 - lr: 0.100000
2022-05-02 15:44:29,757 epoch 1 - iter 10/26 - loss 3.05881263 - samples/sec: 14.62 - lr: 0.100000
2022-05-02 15:44:40,091 epoch 1 - iter 12/26 - loss 2.53006141 - samples/sec: 6.19 - lr: 0.100000
2022-05-02 15:44:52,707 epoch 1 - iter 14/26 - loss 1.93704781 - samples/sec: 5.07 - lr: 0.100000
2022-05-02 15:45:02,080 epoch 1 - iter 16/26 - loss 1.63138431 - samples/sec: 6.83 - lr: 0.100000
2022-05-02 15:45:14,009 epoch 1 - iter 18/26 - loss 1.40000228 - samples/sec: 5.37 - lr: 0.100000
2022-05-02 15:45:23,287 epoch 1 - iter 20/26 - loss 1.23378287 - samples/sec: 6.90 - lr: 0.100000
2022-05-02 15:45:34,691 epoch 1 - iter 22/26 - loss 1.12719827 - samples/sec: 5.61 - lr: 0.100000
2022-05-02 15:45:40,330 epoch 1 - iter 24/26 - loss 1.13188836 - samples/sec: 11.35 - lr: 0.100000
2022-05-02 15:45:49,236 epoch 1 - iter 26/26 - loss 1.10788376 - samples/sec: 7.19 - lr: 0.100000
2022-05-02 15:45:49,236 ----------------------------------------------------------------------------------------------------
2022-05-02 15:45:49,236 EPOCH 1 done: loss 1.1079 - lr 0.1000000
2022-05-02 15:45:49,236 BAD EPOCHS (no improvement): 0
2022-05-02 15:45:49,237 ----------------------------------------------------------------------------------------------------
2022-05-02 15:45:50,285 epoch 2 - iter 2/26 - loss 1.25997738 - samples/sec: 61.30 - lr: 0.100000
2022-05-02 15:45:51,915 epoch 2 - iter 4/26 - loss 0.91321364 - samples/sec: 39.27 - lr: 0.100000
2022-05-02 15:45:53,048 epoch 2 - iter 6/26 - loss 0.97971206 - samples/sec: 56.50 - lr: 0.100000
2022-05-02 15:45:54,340 epoch 2 - iter 8/26 - loss 0.87838664 - samples/sec: 49.56 - lr: 0.100000
2022-05-02 15:45:55,485 epoch 2 - iter 10/26 - loss 0.86177694 - samples/sec: 55.90 - lr: 0.100000
2022-05-02 15:45:56,488 epoch 2 - iter 12/26 - loss 0.81463133 - samples/sec: 63.85 - lr: 0.100000
2022-05-02 15:45:57,918 epoch 2 - iter 14/26 - loss 0.76334644 - samples/sec: 44.79 - lr: 0.100000
2022-05-02 15:45:59,393 epoch 2 - iter 16/26 - loss 0.78542696 - samples/sec: 43.41 - lr: 0.100000
2022-05-02 15:46:00,673 epoch 2 - iter 18/26 - loss 0.74084630 - samples/sec: 49.99 - lr: 0.100000
2022-05-02 15:46:02,245 epoch 2 - iter 20/26 - loss 0.71586100 - samples/sec: 40.74 - lr: 0.100000
2022-05-02 15:46:04,051 epoch 2 - iter 22/26 - loss 0.71469797 - samples/sec: 35.45 - lr: 0.100000
2022-05-02 15:46:05,132 epoch 2 - iter 24/26 - loss 0.71315625 - samples/sec: 59.21 - lr: 0.100000
2022-05-02 15:46:06,356 epoch 2 - iter 26/26 - loss 0.72439117 - samples/sec: 52.36 - lr: 0.100000
2022-05-02 15:46:06,356 ----------------------------------------------------------------------------------------------------
2022-05-02 15:46:06,356 EPOCH 2 done: loss 0.7244 - lr 0.1000000
2022-05-02 15:46:06,356 BAD EPOCHS (no improvement): 0
2022-05-02 15:46:06,356 ----------------------------------------------------------------------------------------------------
2022-05-02 15:46:07,363 epoch 3 - iter 2/26 - loss 0.93262965 - samples/sec: 63.62 - lr: 0.100000
2022-05-02 15:46:08,805 epoch 3 - iter 4/26 - loss 0.66342690 - samples/sec: 44.41 - lr: 0.100000
2022-05-02 15:46:10,199 epoch 3 - iter 6/26 - loss 0.69693404 - samples/sec: 45.93 - lr: 0.100000
2022-05-02 15:46:11,106 epoch 3 - iter 8/26 - loss 0.71254800 - samples/sec: 70.54 - lr: 0.100000
2022-05-02 15:46:12,661 epoch 3 - iter 10/26 - loss 0.68056002 - samples/sec: 41.17 - lr: 0.100000
2022-05-02 15:46:14,195 epoch 3 - iter 12/26 - loss 0.62003628 - samples/sec: 41.75 - lr: 0.100000
2022-05-02 15:46:15,549 epoch 3 - iter 14/26 - loss 0.62764929 - samples/sec: 47.29 - lr: 0.100000
2022-05-02 15:46:16,685 epoch 3 - iter 16/26 - loss 0.64616873 - samples/sec: 56.36 - lr: 0.100000
2022-05-02 15:46:18,469 epoch 3 - iter 18/26 - loss 0.65065601 - samples/sec: 35.88 - lr: 0.100000
2022-05-02 15:46:19,908 epoch 3 - iter 20/26 - loss 0.64878090 - samples/sec: 44.50 - lr: 0.100000
2022-05-02 15:46:21,278 epoch 3 - iter 22/26 - loss 0.63696184 - samples/sec: 46.72 - lr: 0.100000
2022-05-02 15:46:22,587 epoch 3 - iter 24/26 - loss 0.63006250 - samples/sec: 48.92 - lr: 0.100000
2022-05-02 15:46:23,866 epoch 3 - iter 26/26 - loss 0.61985071 - samples/sec: 50.08 - lr: 0.100000
2022-05-02 15:46:23,866 ----------------------------------------------------------------------------------------------------
2022-05-02 15:46:23,866 EPOCH 3 done: loss 0.6199 - lr 0.1000000
2022-05-02 15:46:23,866 BAD EPOCHS (no improvement): 0
2022-05-02 15:46:23,867 ----------------------------------------------------------------------------------------------------
2022-05-02 15:46:25,024 epoch 4 - iter 2/26 - loss 0.82507049 - samples/sec: 55.30 - lr: 0.100000
2022-05-02 15:46:26,129 epoch 4 - iter 4/26 - loss 0.78983147 - samples/sec: 57.98 - lr: 0.100000
2022-05-02 15:46:27,401 epoch 4 - iter 6/26 - loss 0.69410684 - samples/sec: 50.34 - lr: 0.100000
2022-05-02 15:46:28,974 epoch 4 - iter 8/26 - loss 0.62705834 - samples/sec: 40.69 - lr: 0.100000
2022-05-02 15:46:30,301 epoch 4 - iter 10/26 - loss 0.57534194 - samples/sec: 48.26 - lr: 0.100000
2022-05-02 15:46:32,177 epoch 4 - iter 12/26 - loss 0.55566517 - samples/sec: 34.13 - lr: 0.100000
2022-05-02 15:46:33,477 epoch 4 - iter 14/26 - loss 0.56243747 - samples/sec: 49.26 - lr: 0.100000
2022-05-02 15:46:35,204 epoch 4 - iter 16/26 - loss 0.56436807 - samples/sec: 37.07 - lr: 0.100000
2022-05-02 15:46:36,732 epoch 4 - iter 18/26 - loss 0.58195288 - samples/sec: 41.88 - lr: 0.100000
2022-05-02 15:46:38,109 epoch 4 - iter 20/26 - loss 0.58868604 - samples/sec: 46.53 - lr: 0.100000
2022-05-02 15:46:39,677 epoch 4 - iter 22/26 - loss 0.56758502 - samples/sec: 40.87 - lr: 0.100000
2022-05-02 15:46:41,433 epoch 4 - iter 24/26 - loss 0.55202777 - samples/sec: 36.45 - lr: 0.100000
2022-05-02 15:46:42,227 epoch 4 - iter 26/26 - loss 0.56373496 - samples/sec: 80.65 - lr: 0.100000
2022-05-02 15:46:42,227 ----------------------------------------------------------------------------------------------------
2022-05-02 15:46:42,227 EPOCH 4 done: loss 0.5637 - lr 0.1000000
2022-05-02 15:46:42,227 BAD EPOCHS (no improvement): 0
2022-05-02 15:46:42,228 ----------------------------------------------------------------------------------------------------
2022-05-02 15:46:43,715 epoch 5 - iter 2/26 - loss 0.45379848 - samples/sec: 43.04 - lr: 0.100000
2022-05-02 15:46:44,818 epoch 5 - iter 4/26 - loss 0.54424541 - samples/sec: 58.04 - lr: 0.100000
2022-05-02 15:46:46,795 epoch 5 - iter 6/26 - loss 0.55437849 - samples/sec: 32.38 - lr: 0.100000
2022-05-02 15:46:47,855 epoch 5 - iter 8/26 - loss 0.58448347 - samples/sec: 60.42 - lr: 0.100000
2022-05-02 15:46:49,017 epoch 5 - iter 10/26 - loss 0.57394500 - samples/sec: 55.10 - lr: 0.100000
2022-05-02 15:46:50,144 epoch 5 - iter 12/26 - loss 0.56309941 - samples/sec: 56.82 - lr: 0.100000
2022-05-02 15:46:51,022 epoch 5 - iter 14/26 - loss 0.56087045 - samples/sec: 72.92 - lr: 0.100000
2022-05-02 15:46:52,247 epoch 5 - iter 16/26 - loss 0.54126941 - samples/sec: 52.27 - lr: 0.100000
2022-05-02 15:46:53,517 epoch 5 - iter 18/26 - loss 0.54781672 - samples/sec: 50.41 - lr: 0.100000
2022-05-02 15:46:54,987 epoch 5 - iter 20/26 - loss 0.52409069 - samples/sec: 43.55 - lr: 0.100000
2022-05-02 15:46:56,416 epoch 5 - iter 22/26 - loss 0.51082819 - samples/sec: 44.84 - lr: 0.100000
2022-05-02 15:46:58,077 epoch 5 - iter 24/26 - loss 0.50232400 - samples/sec: 38.55 - lr: 0.100000
2022-05-02 15:46:58,995 epoch 5 - iter 26/26 - loss 0.48588470 - samples/sec: 69.78 - lr: 0.100000
2022-05-02 15:46:58,995 ----------------------------------------------------------------------------------------------------
2022-05-02 15:46:58,995 EPOCH 5 done: loss 0.4859 - lr 0.1000000
2022-05-02 15:46:58,995 BAD EPOCHS (no improvement): 0
2022-05-02 15:46:58,995 ----------------------------------------------------------------------------------------------------
2022-05-02 15:47:00,509 epoch 6 - iter 2/26 - loss 0.52375350 - samples/sec: 42.28 - lr: 0.100000
2022-05-02 15:47:02,103 epoch 6 - iter 4/26 - loss 0.41911038 - samples/sec: 40.18 - lr: 0.100000
2022-05-02 15:47:03,126 epoch 6 - iter 6/26 - loss 0.41424604 - samples/sec: 62.57 - lr: 0.100000
2022-05-02 15:47:04,316 epoch 6 - iter 8/26 - loss 0.39943972 - samples/sec: 53.82 - lr: 0.100000
2022-05-02 15:47:05,798 epoch 6 - iter 10/26 - loss 0.36462904 - samples/sec: 43.20 - lr: 0.100000
2022-05-02 15:47:06,774 epoch 6 - iter 12/26 - loss 0.37187295 - samples/sec: 65.60 - lr: 0.100000
2022-05-02 15:47:07,781 epoch 6 - iter 14/26 - loss 0.40622993 - samples/sec: 63.60 - lr: 0.100000
2022-05-02 15:47:08,846 epoch 6 - iter 16/26 - loss 0.42953310 - samples/sec: 60.13 - lr: 0.100000
2022-05-02 15:47:10,187 epoch 6 - iter 18/26 - loss 0.41096443 - samples/sec: 47.72 - lr: 0.100000
2022-05-02 15:47:11,212 epoch 6 - iter 20/26 - loss 0.42107760 - samples/sec: 62.50 - lr: 0.100000
2022-05-02 15:47:12,138 epoch 6 - iter 22/26 - loss 0.42309019 - samples/sec: 69.15 - lr: 0.100000
2022-05-02 15:47:13,311 epoch 6 - iter 24/26 - loss 0.42768651 - samples/sec: 54.57 - lr: 0.100000
2022-05-02 15:47:14,615 epoch 6 - iter 26/26 - loss 0.42251539 - samples/sec: 49.12 - lr: 0.100000
2022-05-02 15:47:14,615 ----------------------------------------------------------------------------------------------------
2022-05-02 15:47:14,615 EPOCH 6 done: loss 0.4225 - lr 0.1000000
2022-05-02 15:47:14,615 BAD EPOCHS (no improvement): 0
2022-05-02 15:47:14,615 ----------------------------------------------------------------------------------------------------
2022-05-02 15:47:15,953 epoch 7 - iter 2/26 - loss 0.42888915 - samples/sec: 47.86 - lr: 0.100000
2022-05-02 15:47:16,988 epoch 7 - iter 4/26 - loss 0.46386105 - samples/sec: 61.89 - lr: 0.100000
2022-05-02 15:47:17,972 epoch 7 - iter 6/26 - loss 0.45750826 - samples/sec: 65.04 - lr: 0.100000
2022-05-02 15:47:19,035 epoch 7 - iter 8/26 - loss 0.45111557 - samples/sec: 60.26 - lr: 0.100000
2022-05-02 15:47:20,138 epoch 7 - iter 10/26 - loss 0.44598492 - samples/sec: 58.08 - lr: 0.100000
2022-05-02 15:47:21,221 epoch 7 - iter 12/26 - loss 0.43062620 - samples/sec: 59.11 - lr: 0.100000
2022-05-02 15:47:22,486 epoch 7 - iter 14/26 - loss 0.43319146 - samples/sec: 50.61 - lr: 0.100000
2022-05-02 15:47:23,844 epoch 7 - iter 16/26 - loss 0.40657923 - samples/sec: 47.16 - lr: 0.100000
2022-05-02 15:47:25,007 epoch 7 - iter 18/26 - loss 0.41484192 - samples/sec: 55.05 - lr: 0.100000
2022-05-02 15:47:26,325 epoch 7 - iter 20/26 - loss 0.41555710 - samples/sec: 48.58 - lr: 0.100000
2022-05-02 15:47:27,600 epoch 7 - iter 22/26 - loss 0.40336973 - samples/sec: 50.21 - lr: 0.100000
2022-05-02 15:47:29,044 epoch 7 - iter 24/26 - loss 0.39532046 - samples/sec: 44.33 - lr: 0.100000
2022-05-02 15:47:30,078 epoch 7 - iter 26/26 - loss 0.38841035 - samples/sec: 61.93 - lr: 0.100000
2022-05-02 15:47:30,079 ----------------------------------------------------------------------------------------------------
2022-05-02 15:47:30,079 EPOCH 7 done: loss 0.3884 - lr 0.1000000
2022-05-02 15:47:30,079 BAD EPOCHS (no improvement): 0
2022-05-02 15:47:30,079 ----------------------------------------------------------------------------------------------------
2022-05-02 15:47:31,357 epoch 8 - iter 2/26 - loss 0.41543718 - samples/sec: 50.11 - lr: 0.100000
2022-05-02 15:47:32,538 epoch 8 - iter 4/26 - loss 0.32899498 - samples/sec: 54.19 - lr: 0.100000
2022-05-02 15:47:33,686 epoch 8 - iter 6/26 - loss 0.35113539 - samples/sec: 55.79 - lr: 0.100000
2022-05-02 15:47:34,725 epoch 8 - iter 8/26 - loss 0.38507402 - samples/sec: 61.58 - lr: 0.100000
2022-05-02 15:47:35,995 epoch 8 - iter 10/26 - loss 0.42831411 - samples/sec: 50.42 - lr: 0.100000
2022-05-02 15:47:37,049 epoch 8 - iter 12/26 - loss 0.39097058 - samples/sec: 60.79 - lr: 0.100000
2022-05-02 15:47:38,008 epoch 8 - iter 14/26 - loss 0.37596686 - samples/sec: 66.72 - lr: 0.100000
2022-05-02 15:47:39,462 epoch 8 - iter 16/26 - loss 0.37649604 - samples/sec: 44.05 - lr: 0.100000
2022-05-02 15:47:40,655 epoch 8 - iter 18/26 - loss 0.37892339 - samples/sec: 53.64 - lr: 0.100000
2022-05-02 15:47:42,031 epoch 8 - iter 20/26 - loss 0.35924042 - samples/sec: 46.54 - lr: 0.100000
2022-05-02 15:47:43,123 epoch 8 - iter 22/26 - loss 0.35480360 - samples/sec: 58.65 - lr: 0.100000
2022-05-02 15:47:44,286 epoch 8 - iter 24/26 - loss 0.34975662 - samples/sec: 55.03 - lr: 0.100000
2022-05-02 15:47:45,065 epoch 8 - iter 26/26 - loss 0.34695374 - samples/sec: 82.23 - lr: 0.100000
2022-05-02 15:47:45,065 ----------------------------------------------------------------------------------------------------
2022-05-02 15:47:45,065 EPOCH 8 done: loss 0.3470 - lr 0.1000000
2022-05-02 15:47:45,065 BAD EPOCHS (no improvement): 0
2022-05-02 15:47:45,066 ----------------------------------------------------------------------------------------------------
2022-05-02 15:47:46,190 epoch 9 - iter 2/26 - loss 0.25508414 - samples/sec: 56.93 - lr: 0.100000
2022-05-02 15:47:47,453 epoch 9 - iter 4/26 - loss 0.32180418 - samples/sec: 50.72 - lr: 0.100000
2022-05-02 15:47:48,461 epoch 9 - iter 6/26 - loss 0.40408790 - samples/sec: 63.48 - lr: 0.100000
2022-05-02 15:47:49,701 epoch 9 - iter 8/26 - loss 0.39779257 - samples/sec: 51.64 - lr: 0.100000
2022-05-02 15:47:51,048 epoch 9 - iter 10/26 - loss 0.36724150 - samples/sec: 47.52 - lr: 0.100000
2022-05-02 15:47:51,922 epoch 9 - iter 12/26 - loss 0.35932055 - samples/sec: 73.33 - lr: 0.100000
2022-05-02 15:47:53,117 epoch 9 - iter 14/26 - loss 0.34947437 - samples/sec: 53.57 - lr: 0.100000
2022-05-02 15:47:54,265 epoch 9 - iter 16/26 - loss 0.32652718 - samples/sec: 55.77 - lr: 0.100000
2022-05-02 15:47:55,487 epoch 9 - iter 18/26 - loss 0.32168879 - samples/sec: 52.41 - lr: 0.100000
2022-05-02 15:47:56,483 epoch 9 - iter 20/26 - loss 0.32835642 - samples/sec: 64.28 - lr: 0.100000
2022-05-02 15:47:57,790 epoch 9 - iter 22/26 - loss 0.33238740 - samples/sec: 48.98 - lr: 0.100000
2022-05-02 15:47:59,047 epoch 9 - iter 24/26 - loss 0.32465148 - samples/sec: 50.93 - lr: 0.100000
2022-05-02 15:48:00,176 epoch 9 - iter 26/26 - loss 0.30912264 - samples/sec: 56.73 - lr: 0.100000
2022-05-02 15:48:00,176 ----------------------------------------------------------------------------------------------------
2022-05-02 15:48:00,176 EPOCH 9 done: loss 0.3091 - lr 0.1000000
2022-05-02 15:48:00,176 BAD EPOCHS (no improvement): 0
2022-05-02 15:48:00,176 ----------------------------------------------------------------------------------------------------
2022-05-02 15:48:01,533 epoch 10 - iter 2/26 - loss 0.34254425 - samples/sec: 47.18 - lr: 0.100000
2022-05-02 15:48:02,801 epoch 10 - iter 4/26 - loss 0.37900189 - samples/sec: 50.52 - lr: 0.100000
2022-05-02 15:48:03,912 epoch 10 - iter 6/26 - loss 0.33156605 - samples/sec: 57.61 - lr: 0.100000
2022-05-02 15:48:05,257 epoch 10 - iter 8/26 - loss 0.30826664 - samples/sec: 47.58 - lr: 0.100000
2022-05-02 15:48:06,496 epoch 10 - iter 10/26 - loss 0.32724932 - samples/sec: 51.71 - lr: 0.100000
2022-05-02 15:48:07,790 epoch 10 - iter 12/26 - loss 0.30998078 - samples/sec: 49.46 - lr: 0.100000
2022-05-02 15:48:09,009 epoch 10 - iter 14/26 - loss 0.30504032 - samples/sec: 52.52 - lr: 0.100000
2022-05-02 15:48:10,539 epoch 10 - iter 16/26 - loss 0.28721872 - samples/sec: 41.87 - lr: 0.100000
2022-05-02 15:48:11,646 epoch 10 - iter 18/26 - loss 0.29072309 - samples/sec: 57.84 - lr: 0.100000
2022-05-02 15:48:12,706 epoch 10 - iter 20/26 - loss 0.30101217 - samples/sec: 60.40 - lr: 0.100000
2022-05-02 15:48:13,994 epoch 10 - iter 22/26 - loss 0.30494834 - samples/sec: 49.71 - lr: 0.100000
2022-05-02 15:48:15,298 epoch 10 - iter 24/26 - loss 0.31061478 - samples/sec: 49.09 - lr: 0.100000
2022-05-02 15:48:16,150 epoch 10 - iter 26/26 - loss 0.31141102 - samples/sec: 75.23 - lr: 0.100000
2022-05-02 15:48:16,150 ----------------------------------------------------------------------------------------------------
2022-05-02 15:48:16,150 EPOCH 10 done: loss 0.3114 - lr 0.1000000
2022-05-02 15:48:16,150 BAD EPOCHS (no improvement): 1
2022-05-02 15:48:46,752 ----------------------------------------------------------------------------------------------------
2022-05-02 15:48:46,768 Testing using last state of model ...
2022-05-02 15:49:06,966 0.3 0.1846 0.2286 0.1364
2022-05-02 15:49:06,967
Results:
- F-score (micro) 0.2286
- F-score (macro) 0.1296
- Accuracy 0.1364
By class:
precision recall f1-score support
quantity 0.3571 0.8333 0.5000 6
title 0.2857 0.2000 0.2353 10
goal 0.0000 0.0000 0.0000 10
time 0.4000 0.2222 0.2857 9
date 0.6667 0.5000 0.5714 4
area 0.0000 0.0000 0.0000 3
interval 0.0000 0.0000 0.0000 1
movie 0.0000 0.0000 0.0000 3
phone 0.0000 0.0000 0.0000 3
seat 0.0000 0.0000 0.0000 2
hour 0.0000 0.0000 0.0000 2
row 0.0000 0.0000 0.0000 2
ticketnumber 0.0000 0.0000 0.0000 2
name 1.0000 1.0000 1.0000 1
e-mail 0.0000 0.0000 0.0000 2
ticketsnumber 0.0000 0.0000 0.0000 1
sit_place 0.0000 0.0000 0.0000 1
email 0.0000 0.0000 0.0000 1
bankAccountNumber 0.0000 0.0000 0.0000 1
issue 0.0000 0.0000 0.0000 1
micro avg 0.3000 0.1846 0.2286 65
macro avg 0.1355 0.1378 0.1296 65
weighted avg 0.1887 0.1846 0.1725 65
samples avg 0.1364 0.1364 0.1364 65
2022-05-02 15:49:06,967 ----------------------------------------------------------------------------------------------------
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