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stefan-it
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flair-co-funer-german_dbmdz_bert_base-bs8-e10-lr5e-05-3

Token Classification
Flair
PyTorch
TensorBoard
German
sequence-tagger-model
hetzner
hetzner-gex44
hetzner-gpu
Model card Files Metrics Community
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+ 2024-03-26 16:00:10,103 ----------------------------------------------------------------------------------------------------
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+ 2024-03-26 16:00:10,103 Model: "SequenceTagger(
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+ (embeddings): TransformerWordEmbeddings(
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+ (model): BertModel(
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+ (embeddings): BertEmbeddings(
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+ (word_embeddings): Embedding(31103, 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): BertEncoder(
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+ (layer): ModuleList(
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+ (0-11): 12 x BertLayer(
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+ (attention): BertAttention(
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+ (self): BertSelfAttention(
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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): BertSelfOutput(
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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): BertIntermediate(
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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): BertOutput(
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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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+ (pooler): BertPooler(
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+ (dense): Linear(in_features=768, out_features=768, bias=True)
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+ (activation): Tanh()
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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=17, bias=True)
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+ (loss_function): CrossEntropyLoss()
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+ )"
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+ 2024-03-26 16:00:10,103 ----------------------------------------------------------------------------------------------------
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+ 2024-03-26 16:00:10,103 Corpus: 758 train + 94 dev + 96 test sentences
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+ 2024-03-26 16:00:10,103 ----------------------------------------------------------------------------------------------------
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+ 2024-03-26 16:00:10,103 Train: 758 sentences
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+ 2024-03-26 16:00:10,103 (train_with_dev=False, train_with_test=False)
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+ 2024-03-26 16:00:10,103 ----------------------------------------------------------------------------------------------------
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+ 2024-03-26 16:00:10,103 Training Params:
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+ 2024-03-26 16:00:10,103 - learning_rate: "5e-05"
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+ 2024-03-26 16:00:10,103 - mini_batch_size: "8"
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+ 2024-03-26 16:00:10,103 - max_epochs: "10"
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+ 2024-03-26 16:00:10,103 - shuffle: "True"
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+ 2024-03-26 16:00:10,103 ----------------------------------------------------------------------------------------------------
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+ 2024-03-26 16:00:10,103 Plugins:
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+ 2024-03-26 16:00:10,103 - TensorboardLogger
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+ 2024-03-26 16:00:10,103 - LinearScheduler | warmup_fraction: '0.1'
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+ 2024-03-26 16:00:10,103 ----------------------------------------------------------------------------------------------------
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+ 2024-03-26 16:00:10,103 Final evaluation on model from best epoch (best-model.pt)
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+ 2024-03-26 16:00:10,103 - metric: "('micro avg', 'f1-score')"
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+ 2024-03-26 16:00:10,103 ----------------------------------------------------------------------------------------------------
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+ 2024-03-26 16:00:10,103 Computation:
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+ 2024-03-26 16:00:10,103 - compute on device: cuda:0
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+ 2024-03-26 16:00:10,103 - embedding storage: none
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+ 2024-03-26 16:00:10,103 ----------------------------------------------------------------------------------------------------
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+ 2024-03-26 16:00:10,103 Model training base path: "flair-co-funer-german_dbmdz_bert_base-bs8-e10-lr5e-05-3"
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+ 2024-03-26 16:00:10,103 ----------------------------------------------------------------------------------------------------
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+ 2024-03-26 16:00:10,103 ----------------------------------------------------------------------------------------------------
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+ 2024-03-26 16:00:10,103 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2024-03-26 16:00:11,464 epoch 1 - iter 9/95 - loss 3.38937223 - time (sec): 1.36 - samples/sec: 2344.72 - lr: 0.000004 - momentum: 0.000000
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+ 2024-03-26 16:00:13,275 epoch 1 - iter 18/95 - loss 3.23427345 - time (sec): 3.17 - samples/sec: 1991.11 - lr: 0.000009 - momentum: 0.000000
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+ 2024-03-26 16:00:15,187 epoch 1 - iter 27/95 - loss 2.95063236 - time (sec): 5.08 - samples/sec: 1943.51 - lr: 0.000014 - momentum: 0.000000
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+ 2024-03-26 16:00:16,548 epoch 1 - iter 36/95 - loss 2.68191715 - time (sec): 6.44 - samples/sec: 1963.25 - lr: 0.000018 - momentum: 0.000000
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+ 2024-03-26 16:00:18,440 epoch 1 - iter 45/95 - loss 2.45788459 - time (sec): 8.34 - samples/sec: 1945.86 - lr: 0.000023 - momentum: 0.000000
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+ 2024-03-26 16:00:19,794 epoch 1 - iter 54/95 - loss 2.29457257 - time (sec): 9.69 - samples/sec: 1969.63 - lr: 0.000028 - momentum: 0.000000
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+ 2024-03-26 16:00:21,041 epoch 1 - iter 63/95 - loss 2.14298789 - time (sec): 10.94 - samples/sec: 1996.55 - lr: 0.000033 - momentum: 0.000000
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+ 2024-03-26 16:00:22,974 epoch 1 - iter 72/95 - loss 1.95332289 - time (sec): 12.87 - samples/sec: 1982.77 - lr: 0.000037 - momentum: 0.000000
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+ 2024-03-26 16:00:24,934 epoch 1 - iter 81/95 - loss 1.78824283 - time (sec): 14.83 - samples/sec: 1968.68 - lr: 0.000042 - momentum: 0.000000
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+ 2024-03-26 16:00:26,454 epoch 1 - iter 90/95 - loss 1.66300792 - time (sec): 16.35 - samples/sec: 1986.71 - lr: 0.000047 - momentum: 0.000000
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+ 2024-03-26 16:00:27,491 ----------------------------------------------------------------------------------------------------
88
+ 2024-03-26 16:00:27,491 EPOCH 1 done: loss 1.5900 - lr: 0.000047
89
+ 2024-03-26 16:00:28,295 DEV : loss 0.44570106267929077 - f1-score (micro avg) 0.687
90
+ 2024-03-26 16:00:28,296 saving best model
91
+ 2024-03-26 16:00:28,557 ----------------------------------------------------------------------------------------------------
92
+ 2024-03-26 16:00:29,915 epoch 2 - iter 9/95 - loss 0.52755606 - time (sec): 1.36 - samples/sec: 2018.97 - lr: 0.000050 - momentum: 0.000000
93
+ 2024-03-26 16:00:31,744 epoch 2 - iter 18/95 - loss 0.39877224 - time (sec): 3.19 - samples/sec: 1917.26 - lr: 0.000049 - momentum: 0.000000
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+ 2024-03-26 16:00:32,910 epoch 2 - iter 27/95 - loss 0.39086480 - time (sec): 4.35 - samples/sec: 1971.50 - lr: 0.000048 - momentum: 0.000000
95
+ 2024-03-26 16:00:35,150 epoch 2 - iter 36/95 - loss 0.36615692 - time (sec): 6.59 - samples/sec: 1923.29 - lr: 0.000048 - momentum: 0.000000
96
+ 2024-03-26 16:00:37,076 epoch 2 - iter 45/95 - loss 0.36188341 - time (sec): 8.52 - samples/sec: 1930.42 - lr: 0.000047 - momentum: 0.000000
97
+ 2024-03-26 16:00:39,227 epoch 2 - iter 54/95 - loss 0.34938497 - time (sec): 10.67 - samples/sec: 1901.63 - lr: 0.000047 - momentum: 0.000000
98
+ 2024-03-26 16:00:41,225 epoch 2 - iter 63/95 - loss 0.33873827 - time (sec): 12.67 - samples/sec: 1855.75 - lr: 0.000046 - momentum: 0.000000
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+ 2024-03-26 16:00:42,730 epoch 2 - iter 72/95 - loss 0.34109897 - time (sec): 14.17 - samples/sec: 1864.02 - lr: 0.000046 - momentum: 0.000000
100
+ 2024-03-26 16:00:44,172 epoch 2 - iter 81/95 - loss 0.34624828 - time (sec): 15.61 - samples/sec: 1886.66 - lr: 0.000045 - momentum: 0.000000
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+ 2024-03-26 16:00:46,384 epoch 2 - iter 90/95 - loss 0.33363333 - time (sec): 17.83 - samples/sec: 1860.80 - lr: 0.000045 - momentum: 0.000000
102
+ 2024-03-26 16:00:47,022 ----------------------------------------------------------------------------------------------------
103
+ 2024-03-26 16:00:47,022 EPOCH 2 done: loss 0.3302 - lr: 0.000045
104
+ 2024-03-26 16:00:47,911 DEV : loss 0.24601905047893524 - f1-score (micro avg) 0.8349
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+ 2024-03-26 16:00:47,912 saving best model
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+ 2024-03-26 16:00:48,351 ----------------------------------------------------------------------------------------------------
107
+ 2024-03-26 16:00:49,982 epoch 3 - iter 9/95 - loss 0.17646890 - time (sec): 1.63 - samples/sec: 1833.52 - lr: 0.000044 - momentum: 0.000000
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+ 2024-03-26 16:00:51,766 epoch 3 - iter 18/95 - loss 0.16766463 - time (sec): 3.41 - samples/sec: 1855.01 - lr: 0.000043 - momentum: 0.000000
109
+ 2024-03-26 16:00:52,956 epoch 3 - iter 27/95 - loss 0.18728041 - time (sec): 4.60 - samples/sec: 2030.12 - lr: 0.000043 - momentum: 0.000000
110
+ 2024-03-26 16:00:54,515 epoch 3 - iter 36/95 - loss 0.18521714 - time (sec): 6.16 - samples/sec: 2016.24 - lr: 0.000042 - momentum: 0.000000
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+ 2024-03-26 16:00:55,916 epoch 3 - iter 45/95 - loss 0.19008186 - time (sec): 7.56 - samples/sec: 2026.74 - lr: 0.000042 - momentum: 0.000000
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+ 2024-03-26 16:00:57,902 epoch 3 - iter 54/95 - loss 0.18491639 - time (sec): 9.55 - samples/sec: 1979.28 - lr: 0.000041 - momentum: 0.000000
113
+ 2024-03-26 16:00:59,891 epoch 3 - iter 63/95 - loss 0.18437887 - time (sec): 11.54 - samples/sec: 1929.51 - lr: 0.000041 - momentum: 0.000000
114
+ 2024-03-26 16:01:01,728 epoch 3 - iter 72/95 - loss 0.18892877 - time (sec): 13.37 - samples/sec: 1912.41 - lr: 0.000040 - momentum: 0.000000
115
+ 2024-03-26 16:01:03,733 epoch 3 - iter 81/95 - loss 0.18190291 - time (sec): 15.38 - samples/sec: 1885.10 - lr: 0.000040 - momentum: 0.000000
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+ 2024-03-26 16:01:05,682 epoch 3 - iter 90/95 - loss 0.19017568 - time (sec): 17.33 - samples/sec: 1886.81 - lr: 0.000039 - momentum: 0.000000
117
+ 2024-03-26 16:01:06,770 ----------------------------------------------------------------------------------------------------
118
+ 2024-03-26 16:01:06,770 EPOCH 3 done: loss 0.1854 - lr: 0.000039
119
+ 2024-03-26 16:01:07,662 DEV : loss 0.17745639383792877 - f1-score (micro avg) 0.8735
120
+ 2024-03-26 16:01:07,663 saving best model
121
+ 2024-03-26 16:01:08,092 ----------------------------------------------------------------------------------------------------
122
+ 2024-03-26 16:01:09,387 epoch 4 - iter 9/95 - loss 0.12389405 - time (sec): 1.29 - samples/sec: 2146.08 - lr: 0.000039 - momentum: 0.000000
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+ 2024-03-26 16:01:11,232 epoch 4 - iter 18/95 - loss 0.12111894 - time (sec): 3.14 - samples/sec: 1958.29 - lr: 0.000038 - momentum: 0.000000
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+ 2024-03-26 16:01:13,234 epoch 4 - iter 27/95 - loss 0.11487951 - time (sec): 5.14 - samples/sec: 1878.49 - lr: 0.000037 - momentum: 0.000000
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+ 2024-03-26 16:01:14,716 epoch 4 - iter 36/95 - loss 0.11435733 - time (sec): 6.62 - samples/sec: 1894.36 - lr: 0.000037 - momentum: 0.000000
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+ 2024-03-26 16:01:17,154 epoch 4 - iter 45/95 - loss 0.11185218 - time (sec): 9.06 - samples/sec: 1823.85 - lr: 0.000036 - momentum: 0.000000
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+ 2024-03-26 16:01:19,006 epoch 4 - iter 54/95 - loss 0.11138127 - time (sec): 10.91 - samples/sec: 1808.45 - lr: 0.000036 - momentum: 0.000000
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+ 2024-03-26 16:01:20,943 epoch 4 - iter 63/95 - loss 0.10945874 - time (sec): 12.85 - samples/sec: 1789.84 - lr: 0.000035 - momentum: 0.000000
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+ 2024-03-26 16:01:22,821 epoch 4 - iter 72/95 - loss 0.11053287 - time (sec): 14.73 - samples/sec: 1806.53 - lr: 0.000035 - momentum: 0.000000
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+ 2024-03-26 16:01:24,846 epoch 4 - iter 81/95 - loss 0.11929813 - time (sec): 16.75 - samples/sec: 1804.79 - lr: 0.000034 - momentum: 0.000000
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+ 2024-03-26 16:01:25,824 epoch 4 - iter 90/95 - loss 0.11963430 - time (sec): 17.73 - samples/sec: 1844.53 - lr: 0.000034 - momentum: 0.000000
132
+ 2024-03-26 16:01:26,849 ----------------------------------------------------------------------------------------------------
133
+ 2024-03-26 16:01:26,849 EPOCH 4 done: loss 0.1195 - lr: 0.000034
134
+ 2024-03-26 16:01:27,742 DEV : loss 0.17124532163143158 - f1-score (micro avg) 0.9038
135
+ 2024-03-26 16:01:27,743 saving best model
136
+ 2024-03-26 16:01:28,185 ----------------------------------------------------------------------------------------------------
137
+ 2024-03-26 16:01:30,060 epoch 5 - iter 9/95 - loss 0.09160459 - time (sec): 1.87 - samples/sec: 1834.63 - lr: 0.000033 - momentum: 0.000000
138
+ 2024-03-26 16:01:31,476 epoch 5 - iter 18/95 - loss 0.08544367 - time (sec): 3.29 - samples/sec: 1902.36 - lr: 0.000032 - momentum: 0.000000
139
+ 2024-03-26 16:01:32,833 epoch 5 - iter 27/95 - loss 0.10039035 - time (sec): 4.65 - samples/sec: 1943.76 - lr: 0.000032 - momentum: 0.000000
140
+ 2024-03-26 16:01:34,716 epoch 5 - iter 36/95 - loss 0.09956787 - time (sec): 6.53 - samples/sec: 1879.63 - lr: 0.000031 - momentum: 0.000000
141
+ 2024-03-26 16:01:36,923 epoch 5 - iter 45/95 - loss 0.09508086 - time (sec): 8.74 - samples/sec: 1863.49 - lr: 0.000031 - momentum: 0.000000
142
+ 2024-03-26 16:01:39,355 epoch 5 - iter 54/95 - loss 0.09118724 - time (sec): 11.17 - samples/sec: 1818.57 - lr: 0.000030 - momentum: 0.000000
143
+ 2024-03-26 16:01:41,013 epoch 5 - iter 63/95 - loss 0.08797836 - time (sec): 12.83 - samples/sec: 1811.31 - lr: 0.000030 - momentum: 0.000000
144
+ 2024-03-26 16:01:42,784 epoch 5 - iter 72/95 - loss 0.08582414 - time (sec): 14.60 - samples/sec: 1811.74 - lr: 0.000029 - momentum: 0.000000
145
+ 2024-03-26 16:01:44,986 epoch 5 - iter 81/95 - loss 0.08720160 - time (sec): 16.80 - samples/sec: 1796.16 - lr: 0.000029 - momentum: 0.000000
146
+ 2024-03-26 16:01:46,370 epoch 5 - iter 90/95 - loss 0.08919404 - time (sec): 18.18 - samples/sec: 1812.62 - lr: 0.000028 - momentum: 0.000000
147
+ 2024-03-26 16:01:47,136 ----------------------------------------------------------------------------------------------------
148
+ 2024-03-26 16:01:47,136 EPOCH 5 done: loss 0.0873 - lr: 0.000028
149
+ 2024-03-26 16:01:48,034 DEV : loss 0.16974209249019623 - f1-score (micro avg) 0.9198
150
+ 2024-03-26 16:01:48,035 saving best model
151
+ 2024-03-26 16:01:48,479 ----------------------------------------------------------------------------------------------------
152
+ 2024-03-26 16:01:50,417 epoch 6 - iter 9/95 - loss 0.05813380 - time (sec): 1.94 - samples/sec: 1801.23 - lr: 0.000027 - momentum: 0.000000
153
+ 2024-03-26 16:01:51,968 epoch 6 - iter 18/95 - loss 0.05561430 - time (sec): 3.49 - samples/sec: 1823.72 - lr: 0.000027 - momentum: 0.000000
154
+ 2024-03-26 16:01:53,872 epoch 6 - iter 27/95 - loss 0.05571319 - time (sec): 5.39 - samples/sec: 1834.08 - lr: 0.000026 - momentum: 0.000000
155
+ 2024-03-26 16:01:55,443 epoch 6 - iter 36/95 - loss 0.05560469 - time (sec): 6.96 - samples/sec: 1831.74 - lr: 0.000026 - momentum: 0.000000
156
+ 2024-03-26 16:01:56,891 epoch 6 - iter 45/95 - loss 0.05722084 - time (sec): 8.41 - samples/sec: 1869.84 - lr: 0.000025 - momentum: 0.000000
157
+ 2024-03-26 16:01:58,341 epoch 6 - iter 54/95 - loss 0.05800404 - time (sec): 9.86 - samples/sec: 1867.99 - lr: 0.000025 - momentum: 0.000000
158
+ 2024-03-26 16:01:59,615 epoch 6 - iter 63/95 - loss 0.05975631 - time (sec): 11.13 - samples/sec: 1930.92 - lr: 0.000024 - momentum: 0.000000
159
+ 2024-03-26 16:02:01,857 epoch 6 - iter 72/95 - loss 0.06417836 - time (sec): 13.38 - samples/sec: 1898.32 - lr: 0.000024 - momentum: 0.000000
160
+ 2024-03-26 16:02:03,435 epoch 6 - iter 81/95 - loss 0.06173608 - time (sec): 14.95 - samples/sec: 1916.22 - lr: 0.000023 - momentum: 0.000000
161
+ 2024-03-26 16:02:05,134 epoch 6 - iter 90/95 - loss 0.06379710 - time (sec): 16.65 - samples/sec: 1935.46 - lr: 0.000023 - momentum: 0.000000
162
+ 2024-03-26 16:02:06,397 ----------------------------------------------------------------------------------------------------
163
+ 2024-03-26 16:02:06,397 EPOCH 6 done: loss 0.0636 - lr: 0.000023
164
+ 2024-03-26 16:02:07,293 DEV : loss 0.1660703867673874 - f1-score (micro avg) 0.936
165
+ 2024-03-26 16:02:07,294 saving best model
166
+ 2024-03-26 16:02:07,738 ----------------------------------------------------------------------------------------------------
167
+ 2024-03-26 16:02:09,621 epoch 7 - iter 9/95 - loss 0.04045283 - time (sec): 1.88 - samples/sec: 1688.44 - lr: 0.000022 - momentum: 0.000000
168
+ 2024-03-26 16:02:11,655 epoch 7 - iter 18/95 - loss 0.02841548 - time (sec): 3.91 - samples/sec: 1674.65 - lr: 0.000021 - momentum: 0.000000
169
+ 2024-03-26 16:02:13,183 epoch 7 - iter 27/95 - loss 0.02627311 - time (sec): 5.44 - samples/sec: 1797.34 - lr: 0.000021 - momentum: 0.000000
170
+ 2024-03-26 16:02:15,125 epoch 7 - iter 36/95 - loss 0.02722641 - time (sec): 7.38 - samples/sec: 1784.42 - lr: 0.000020 - momentum: 0.000000
171
+ 2024-03-26 16:02:17,497 epoch 7 - iter 45/95 - loss 0.03259541 - time (sec): 9.76 - samples/sec: 1777.38 - lr: 0.000020 - momentum: 0.000000
172
+ 2024-03-26 16:02:19,014 epoch 7 - iter 54/95 - loss 0.03491364 - time (sec): 11.27 - samples/sec: 1784.27 - lr: 0.000019 - momentum: 0.000000
173
+ 2024-03-26 16:02:21,194 epoch 7 - iter 63/95 - loss 0.04056925 - time (sec): 13.45 - samples/sec: 1790.05 - lr: 0.000019 - momentum: 0.000000
174
+ 2024-03-26 16:02:22,983 epoch 7 - iter 72/95 - loss 0.04684255 - time (sec): 15.24 - samples/sec: 1795.92 - lr: 0.000018 - momentum: 0.000000
175
+ 2024-03-26 16:02:24,403 epoch 7 - iter 81/95 - loss 0.04395515 - time (sec): 16.66 - samples/sec: 1807.79 - lr: 0.000018 - momentum: 0.000000
176
+ 2024-03-26 16:02:26,375 epoch 7 - iter 90/95 - loss 0.04605325 - time (sec): 18.63 - samples/sec: 1787.55 - lr: 0.000017 - momentum: 0.000000
177
+ 2024-03-26 16:02:26,860 ----------------------------------------------------------------------------------------------------
178
+ 2024-03-26 16:02:26,860 EPOCH 7 done: loss 0.0469 - lr: 0.000017
179
+ 2024-03-26 16:02:27,749 DEV : loss 0.18657518923282623 - f1-score (micro avg) 0.9354
180
+ 2024-03-26 16:02:27,750 ----------------------------------------------------------------------------------------------------
181
+ 2024-03-26 16:02:29,622 epoch 8 - iter 9/95 - loss 0.02945220 - time (sec): 1.87 - samples/sec: 1713.38 - lr: 0.000016 - momentum: 0.000000
182
+ 2024-03-26 16:02:32,102 epoch 8 - iter 18/95 - loss 0.02065644 - time (sec): 4.35 - samples/sec: 1702.09 - lr: 0.000016 - momentum: 0.000000
183
+ 2024-03-26 16:02:33,863 epoch 8 - iter 27/95 - loss 0.02067654 - time (sec): 6.11 - samples/sec: 1740.79 - lr: 0.000015 - momentum: 0.000000
184
+ 2024-03-26 16:02:35,488 epoch 8 - iter 36/95 - loss 0.02424997 - time (sec): 7.74 - samples/sec: 1713.45 - lr: 0.000015 - momentum: 0.000000
185
+ 2024-03-26 16:02:36,997 epoch 8 - iter 45/95 - loss 0.02289045 - time (sec): 9.25 - samples/sec: 1746.67 - lr: 0.000014 - momentum: 0.000000
186
+ 2024-03-26 16:02:38,662 epoch 8 - iter 54/95 - loss 0.02432025 - time (sec): 10.91 - samples/sec: 1767.82 - lr: 0.000014 - momentum: 0.000000
187
+ 2024-03-26 16:02:40,854 epoch 8 - iter 63/95 - loss 0.03266905 - time (sec): 13.10 - samples/sec: 1766.14 - lr: 0.000013 - momentum: 0.000000
188
+ 2024-03-26 16:02:43,099 epoch 8 - iter 72/95 - loss 0.03310354 - time (sec): 15.35 - samples/sec: 1748.47 - lr: 0.000013 - momentum: 0.000000
189
+ 2024-03-26 16:02:44,752 epoch 8 - iter 81/95 - loss 0.03687701 - time (sec): 17.00 - samples/sec: 1752.00 - lr: 0.000012 - momentum: 0.000000
190
+ 2024-03-26 16:02:46,046 epoch 8 - iter 90/95 - loss 0.03730740 - time (sec): 18.30 - samples/sec: 1794.50 - lr: 0.000012 - momentum: 0.000000
191
+ 2024-03-26 16:02:46,945 ----------------------------------------------------------------------------------------------------
192
+ 2024-03-26 16:02:46,945 EPOCH 8 done: loss 0.0362 - lr: 0.000012
193
+ 2024-03-26 16:02:47,878 DEV : loss 0.18976689875125885 - f1-score (micro avg) 0.9344
194
+ 2024-03-26 16:02:47,880 ----------------------------------------------------------------------------------------------------
195
+ 2024-03-26 16:02:49,859 epoch 9 - iter 9/95 - loss 0.01635759 - time (sec): 1.98 - samples/sec: 1782.87 - lr: 0.000011 - momentum: 0.000000
196
+ 2024-03-26 16:02:51,582 epoch 9 - iter 18/95 - loss 0.02304365 - time (sec): 3.70 - samples/sec: 1808.51 - lr: 0.000010 - momentum: 0.000000
197
+ 2024-03-26 16:02:53,458 epoch 9 - iter 27/95 - loss 0.02506509 - time (sec): 5.58 - samples/sec: 1832.02 - lr: 0.000010 - momentum: 0.000000
198
+ 2024-03-26 16:02:55,313 epoch 9 - iter 36/95 - loss 0.02234485 - time (sec): 7.43 - samples/sec: 1827.49 - lr: 0.000009 - momentum: 0.000000
199
+ 2024-03-26 16:02:57,570 epoch 9 - iter 45/95 - loss 0.02061792 - time (sec): 9.69 - samples/sec: 1749.48 - lr: 0.000009 - momentum: 0.000000
200
+ 2024-03-26 16:02:59,495 epoch 9 - iter 54/95 - loss 0.02488325 - time (sec): 11.61 - samples/sec: 1738.19 - lr: 0.000008 - momentum: 0.000000
201
+ 2024-03-26 16:03:01,384 epoch 9 - iter 63/95 - loss 0.02441510 - time (sec): 13.50 - samples/sec: 1748.69 - lr: 0.000008 - momentum: 0.000000
202
+ 2024-03-26 16:03:03,255 epoch 9 - iter 72/95 - loss 0.02401047 - time (sec): 15.37 - samples/sec: 1752.87 - lr: 0.000007 - momentum: 0.000000
203
+ 2024-03-26 16:03:04,501 epoch 9 - iter 81/95 - loss 0.02516190 - time (sec): 16.62 - samples/sec: 1776.36 - lr: 0.000007 - momentum: 0.000000
204
+ 2024-03-26 16:03:05,902 epoch 9 - iter 90/95 - loss 0.02891991 - time (sec): 18.02 - samples/sec: 1798.48 - lr: 0.000006 - momentum: 0.000000
205
+ 2024-03-26 16:03:06,844 ----------------------------------------------------------------------------------------------------
206
+ 2024-03-26 16:03:06,844 EPOCH 9 done: loss 0.0284 - lr: 0.000006
207
+ 2024-03-26 16:03:07,796 DEV : loss 0.2133364975452423 - f1-score (micro avg) 0.9383
208
+ 2024-03-26 16:03:07,797 saving best model
209
+ 2024-03-26 16:03:08,256 ----------------------------------------------------------------------------------------------------
210
+ 2024-03-26 16:03:10,399 epoch 10 - iter 9/95 - loss 0.00852392 - time (sec): 2.14 - samples/sec: 1780.84 - lr: 0.000005 - momentum: 0.000000
211
+ 2024-03-26 16:03:11,661 epoch 10 - iter 18/95 - loss 0.00945942 - time (sec): 3.40 - samples/sec: 1902.77 - lr: 0.000005 - momentum: 0.000000
212
+ 2024-03-26 16:03:12,973 epoch 10 - iter 27/95 - loss 0.03452703 - time (sec): 4.71 - samples/sec: 2011.60 - lr: 0.000004 - momentum: 0.000000
213
+ 2024-03-26 16:03:14,328 epoch 10 - iter 36/95 - loss 0.02966500 - time (sec): 6.07 - samples/sec: 2032.32 - lr: 0.000004 - momentum: 0.000000
214
+ 2024-03-26 16:03:16,198 epoch 10 - iter 45/95 - loss 0.02385615 - time (sec): 7.94 - samples/sec: 2005.69 - lr: 0.000003 - momentum: 0.000000
215
+ 2024-03-26 16:03:17,776 epoch 10 - iter 54/95 - loss 0.02232956 - time (sec): 9.52 - samples/sec: 1991.74 - lr: 0.000003 - momentum: 0.000000
216
+ 2024-03-26 16:03:20,299 epoch 10 - iter 63/95 - loss 0.02272554 - time (sec): 12.04 - samples/sec: 1909.44 - lr: 0.000002 - momentum: 0.000000
217
+ 2024-03-26 16:03:21,585 epoch 10 - iter 72/95 - loss 0.02126761 - time (sec): 13.33 - samples/sec: 1913.48 - lr: 0.000002 - momentum: 0.000000
218
+ 2024-03-26 16:03:23,927 epoch 10 - iter 81/95 - loss 0.02015630 - time (sec): 15.67 - samples/sec: 1859.08 - lr: 0.000001 - momentum: 0.000000
219
+ 2024-03-26 16:03:26,158 epoch 10 - iter 90/95 - loss 0.02166406 - time (sec): 17.90 - samples/sec: 1837.32 - lr: 0.000001 - momentum: 0.000000
220
+ 2024-03-26 16:03:27,216 ----------------------------------------------------------------------------------------------------
221
+ 2024-03-26 16:03:27,216 EPOCH 10 done: loss 0.0211 - lr: 0.000001
222
+ 2024-03-26 16:03:28,123 DEV : loss 0.21451087296009064 - f1-score (micro avg) 0.9331
223
+ 2024-03-26 16:03:28,392 ----------------------------------------------------------------------------------------------------
224
+ 2024-03-26 16:03:28,392 Loading model from best epoch ...
225
+ 2024-03-26 16:03:29,252 SequenceTagger predicts: Dictionary with 17 tags: O, S-Unternehmen, B-Unternehmen, E-Unternehmen, I-Unternehmen, S-Auslagerung, B-Auslagerung, E-Auslagerung, I-Auslagerung, S-Ort, B-Ort, E-Ort, I-Ort, S-Software, B-Software, E-Software, I-Software
226
+ 2024-03-26 16:03:30,117
227
+ Results:
228
+ - F-score (micro) 0.907
229
+ - F-score (macro) 0.9177
230
+ - Accuracy 0.8345
231
+
232
+ By class:
233
+ precision recall f1-score support
234
+
235
+ Unternehmen 0.8520 0.8872 0.8692 266
236
+ Auslagerung 0.8958 0.9317 0.9134 249
237
+ Ort 0.9565 0.9851 0.9706 134
238
+
239
+ micro avg 0.8902 0.9245 0.9070 649
240
+ macro avg 0.9014 0.9347 0.9177 649
241
+ weighted avg 0.8904 0.9245 0.9071 649
242
+
243
+ 2024-03-26 16:03:30,117 ----------------------------------------------------------------------------------------------------