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autotrain-flair-hipe2022-de-hmbert

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  1. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4/best-model.pt +3 -0
  2. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4/dev.tsv +0 -0
  3. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4/final-model.pt +3 -0
  4. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4/loss.tsv +11 -0
  5. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4/test.tsv +0 -0
  6. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4/training.log +247 -0
hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4/best-model.pt ADDED
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hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4/dev.tsv ADDED
The diff for this file is too large to render. See raw diff
 
hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4/final-model.pt ADDED
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hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4/loss.tsv ADDED
@@ -0,0 +1,11 @@
 
 
 
 
 
 
 
 
 
 
 
 
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+ EPOCH TIMESTAMP LEARNING_RATE TRAIN_LOSS DEV_LOSS DEV_PRECISION DEV_RECALL DEV_F1 DEV_ACCURACY
2
+ 1 22:05:05 0.0000 0.6427 0.1746 0.6445 0.5997 0.6213 0.4640
3
+ 2 22:06:34 0.0000 0.1485 0.1355 0.6911 0.6943 0.6927 0.5505
4
+ 3 22:08:03 0.0000 0.0893 0.1390 0.7512 0.7154 0.7329 0.5957
5
+ 4 22:09:33 0.0000 0.0516 0.1509 0.7055 0.7717 0.7371 0.6055
6
+ 5 22:11:06 0.0000 0.0358 0.1870 0.7400 0.7967 0.7673 0.6381
7
+ 6 22:12:40 0.0000 0.0226 0.2096 0.7855 0.7647 0.7750 0.6498
8
+ 7 22:14:13 0.0000 0.0157 0.2135 0.7528 0.7998 0.7756 0.6483
9
+ 8 22:15:42 0.0000 0.0103 0.2366 0.7698 0.7897 0.7796 0.6529
10
+ 9 22:17:12 0.0000 0.0058 0.2372 0.7899 0.7936 0.7917 0.6682
11
+ 10 22:18:44 0.0000 0.0037 0.2338 0.7916 0.7959 0.7938 0.6715
hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4/test.tsv ADDED
The diff for this file is too large to render. See raw diff
 
hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4/training.log ADDED
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+ 2023-09-03 22:03:37,162 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 22:03:37,163 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(32001, 768)
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+ (position_embeddings): Embedding(512, 768)
8
+ (token_type_embeddings): Embedding(2, 768)
9
+ (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)
20
+ (dropout): Dropout(p=0.1, inplace=False)
21
+ )
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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)
25
+ (dropout): Dropout(p=0.1, inplace=False)
26
+ )
27
+ )
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+ (intermediate): BertIntermediate(
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+ (dense): Linear(in_features=768, out_features=3072, bias=True)
30
+ (intermediate_act_fn): GELUActivation()
31
+ )
32
+ (output): BertOutput(
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+ (dense): Linear(in_features=3072, out_features=768, bias=True)
34
+ (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
35
+ (dropout): Dropout(p=0.1, inplace=False)
36
+ )
37
+ )
38
+ )
39
+ )
40
+ (pooler): BertPooler(
41
+ (dense): Linear(in_features=768, out_features=768, bias=True)
42
+ (activation): Tanh()
43
+ )
44
+ )
45
+ )
46
+ (locked_dropout): LockedDropout(p=0.5)
47
+ (linear): Linear(in_features=768, out_features=21, bias=True)
48
+ (loss_function): CrossEntropyLoss()
49
+ )"
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+ 2023-09-03 22:03:37,163 ----------------------------------------------------------------------------------------------------
51
+ 2023-09-03 22:03:37,163 MultiCorpus: 3575 train + 1235 dev + 1266 test sentences
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+ - NER_HIPE_2022 Corpus: 3575 train + 1235 dev + 1266 test sentences - /app/.flair/datasets/ner_hipe_2022/v2.1/hipe2020/de/with_doc_seperator
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+ 2023-09-03 22:03:37,163 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 22:03:37,163 Train: 3575 sentences
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+ 2023-09-03 22:03:37,163 (train_with_dev=False, train_with_test=False)
56
+ 2023-09-03 22:03:37,163 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 22:03:37,163 Training Params:
58
+ 2023-09-03 22:03:37,163 - learning_rate: "5e-05"
59
+ 2023-09-03 22:03:37,163 - mini_batch_size: "8"
60
+ 2023-09-03 22:03:37,163 - max_epochs: "10"
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+ 2023-09-03 22:03:37,163 - shuffle: "True"
62
+ 2023-09-03 22:03:37,163 ----------------------------------------------------------------------------------------------------
63
+ 2023-09-03 22:03:37,163 Plugins:
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+ 2023-09-03 22:03:37,163 - LinearScheduler | warmup_fraction: '0.1'
65
+ 2023-09-03 22:03:37,164 ----------------------------------------------------------------------------------------------------
66
+ 2023-09-03 22:03:37,164 Final evaluation on model from best epoch (best-model.pt)
67
+ 2023-09-03 22:03:37,164 - metric: "('micro avg', 'f1-score')"
68
+ 2023-09-03 22:03:37,164 ----------------------------------------------------------------------------------------------------
69
+ 2023-09-03 22:03:37,164 Computation:
70
+ 2023-09-03 22:03:37,164 - compute on device: cuda:0
71
+ 2023-09-03 22:03:37,164 - embedding storage: none
72
+ 2023-09-03 22:03:37,164 ----------------------------------------------------------------------------------------------------
73
+ 2023-09-03 22:03:37,164 Model training base path: "hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4"
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+ 2023-09-03 22:03:37,164 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 22:03:37,164 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 22:03:44,616 epoch 1 - iter 44/447 - loss 3.00461982 - time (sec): 7.45 - samples/sec: 1175.42 - lr: 0.000005 - momentum: 0.000000
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+ 2023-09-03 22:03:52,433 epoch 1 - iter 88/447 - loss 1.96254236 - time (sec): 15.27 - samples/sec: 1163.70 - lr: 0.000010 - momentum: 0.000000
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+ 2023-09-03 22:03:59,439 epoch 1 - iter 132/447 - loss 1.50254678 - time (sec): 22.27 - samples/sec: 1154.90 - lr: 0.000015 - momentum: 0.000000
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+ 2023-09-03 22:04:07,711 epoch 1 - iter 176/447 - loss 1.21259910 - time (sec): 30.55 - samples/sec: 1129.89 - lr: 0.000020 - momentum: 0.000000
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+ 2023-09-03 22:04:14,819 epoch 1 - iter 220/447 - loss 1.03584482 - time (sec): 37.65 - samples/sec: 1134.34 - lr: 0.000024 - momentum: 0.000000
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+ 2023-09-03 22:04:22,049 epoch 1 - iter 264/447 - loss 0.91897569 - time (sec): 44.88 - samples/sec: 1134.71 - lr: 0.000029 - momentum: 0.000000
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+ 2023-09-03 22:04:29,426 epoch 1 - iter 308/447 - loss 0.83278018 - time (sec): 52.26 - samples/sec: 1133.34 - lr: 0.000034 - momentum: 0.000000
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+ 2023-09-03 22:04:36,847 epoch 1 - iter 352/447 - loss 0.75967852 - time (sec): 59.68 - samples/sec: 1133.18 - lr: 0.000039 - momentum: 0.000000
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+ 2023-09-03 22:04:43,829 epoch 1 - iter 396/447 - loss 0.69877256 - time (sec): 66.66 - samples/sec: 1134.73 - lr: 0.000044 - momentum: 0.000000
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+ 2023-09-03 22:04:53,046 epoch 1 - iter 440/447 - loss 0.64902061 - time (sec): 75.88 - samples/sec: 1123.32 - lr: 0.000049 - momentum: 0.000000
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+ 2023-09-03 22:04:54,158 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 22:04:54,158 EPOCH 1 done: loss 0.6427 - lr: 0.000049
88
+ 2023-09-03 22:05:05,059 DEV : loss 0.17456364631652832 - f1-score (micro avg) 0.6213
89
+ 2023-09-03 22:05:05,086 saving best model
90
+ 2023-09-03 22:05:05,541 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 22:05:13,911 epoch 2 - iter 44/447 - loss 0.19617148 - time (sec): 8.37 - samples/sec: 1070.23 - lr: 0.000049 - momentum: 0.000000
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+ 2023-09-03 22:05:22,808 epoch 2 - iter 88/447 - loss 0.17967532 - time (sec): 17.27 - samples/sec: 1071.74 - lr: 0.000049 - momentum: 0.000000
93
+ 2023-09-03 22:05:29,712 epoch 2 - iter 132/447 - loss 0.16966610 - time (sec): 24.17 - samples/sec: 1083.68 - lr: 0.000048 - momentum: 0.000000
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+ 2023-09-03 22:05:36,876 epoch 2 - iter 176/447 - loss 0.16937622 - time (sec): 31.33 - samples/sec: 1100.26 - lr: 0.000048 - momentum: 0.000000
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+ 2023-09-03 22:05:44,718 epoch 2 - iter 220/447 - loss 0.16592335 - time (sec): 39.18 - samples/sec: 1099.72 - lr: 0.000047 - momentum: 0.000000
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+ 2023-09-03 22:05:51,731 epoch 2 - iter 264/447 - loss 0.15644282 - time (sec): 46.19 - samples/sec: 1120.15 - lr: 0.000047 - momentum: 0.000000
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+ 2023-09-03 22:05:58,701 epoch 2 - iter 308/447 - loss 0.15370065 - time (sec): 53.16 - samples/sec: 1123.35 - lr: 0.000046 - momentum: 0.000000
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+ 2023-09-03 22:06:05,322 epoch 2 - iter 352/447 - loss 0.15345269 - time (sec): 59.78 - samples/sec: 1133.48 - lr: 0.000046 - momentum: 0.000000
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+ 2023-09-03 22:06:13,741 epoch 2 - iter 396/447 - loss 0.14938778 - time (sec): 68.20 - samples/sec: 1126.58 - lr: 0.000045 - momentum: 0.000000
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+ 2023-09-03 22:06:20,624 epoch 2 - iter 440/447 - loss 0.14932727 - time (sec): 75.08 - samples/sec: 1135.40 - lr: 0.000045 - momentum: 0.000000
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+ 2023-09-03 22:06:21,930 ----------------------------------------------------------------------------------------------------
102
+ 2023-09-03 22:06:21,930 EPOCH 2 done: loss 0.1485 - lr: 0.000045
103
+ 2023-09-03 22:06:34,648 DEV : loss 0.1355467289686203 - f1-score (micro avg) 0.6927
104
+ 2023-09-03 22:06:34,674 saving best model
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+ 2023-09-03 22:06:35,991 ----------------------------------------------------------------------------------------------------
106
+ 2023-09-03 22:06:43,373 epoch 3 - iter 44/447 - loss 0.11084828 - time (sec): 7.38 - samples/sec: 1159.33 - lr: 0.000044 - momentum: 0.000000
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+ 2023-09-03 22:06:51,829 epoch 3 - iter 88/447 - loss 0.09617220 - time (sec): 15.84 - samples/sec: 1127.10 - lr: 0.000043 - momentum: 0.000000
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+ 2023-09-03 22:06:59,166 epoch 3 - iter 132/447 - loss 0.08541625 - time (sec): 23.17 - samples/sec: 1131.09 - lr: 0.000043 - momentum: 0.000000
109
+ 2023-09-03 22:07:06,261 epoch 3 - iter 176/447 - loss 0.08791110 - time (sec): 30.27 - samples/sec: 1138.58 - lr: 0.000042 - momentum: 0.000000
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+ 2023-09-03 22:07:12,844 epoch 3 - iter 220/447 - loss 0.08725166 - time (sec): 36.85 - samples/sec: 1141.37 - lr: 0.000042 - momentum: 0.000000
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+ 2023-09-03 22:07:20,076 epoch 3 - iter 264/447 - loss 0.08591226 - time (sec): 44.08 - samples/sec: 1146.82 - lr: 0.000041 - momentum: 0.000000
112
+ 2023-09-03 22:07:27,072 epoch 3 - iter 308/447 - loss 0.08861516 - time (sec): 51.08 - samples/sec: 1149.11 - lr: 0.000041 - momentum: 0.000000
113
+ 2023-09-03 22:07:34,653 epoch 3 - iter 352/447 - loss 0.08519341 - time (sec): 58.66 - samples/sec: 1151.20 - lr: 0.000040 - momentum: 0.000000
114
+ 2023-09-03 22:07:41,422 epoch 3 - iter 396/447 - loss 0.08896865 - time (sec): 65.43 - samples/sec: 1159.03 - lr: 0.000040 - momentum: 0.000000
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+ 2023-09-03 22:07:49,673 epoch 3 - iter 440/447 - loss 0.08973667 - time (sec): 73.68 - samples/sec: 1157.81 - lr: 0.000039 - momentum: 0.000000
116
+ 2023-09-03 22:07:50,684 ----------------------------------------------------------------------------------------------------
117
+ 2023-09-03 22:07:50,684 EPOCH 3 done: loss 0.0893 - lr: 0.000039
118
+ 2023-09-03 22:08:03,119 DEV : loss 0.13895265758037567 - f1-score (micro avg) 0.7329
119
+ 2023-09-03 22:08:03,146 saving best model
120
+ 2023-09-03 22:08:04,473 ----------------------------------------------------------------------------------------------------
121
+ 2023-09-03 22:08:11,591 epoch 4 - iter 44/447 - loss 0.04648237 - time (sec): 7.12 - samples/sec: 1186.01 - lr: 0.000038 - momentum: 0.000000
122
+ 2023-09-03 22:08:18,309 epoch 4 - iter 88/447 - loss 0.05241541 - time (sec): 13.84 - samples/sec: 1186.27 - lr: 0.000038 - momentum: 0.000000
123
+ 2023-09-03 22:08:25,668 epoch 4 - iter 132/447 - loss 0.04548737 - time (sec): 21.19 - samples/sec: 1178.24 - lr: 0.000037 - momentum: 0.000000
124
+ 2023-09-03 22:08:32,467 epoch 4 - iter 176/447 - loss 0.04325888 - time (sec): 27.99 - samples/sec: 1190.52 - lr: 0.000037 - momentum: 0.000000
125
+ 2023-09-03 22:08:42,149 epoch 4 - iter 220/447 - loss 0.04812558 - time (sec): 37.68 - samples/sec: 1151.18 - lr: 0.000036 - momentum: 0.000000
126
+ 2023-09-03 22:08:49,472 epoch 4 - iter 264/447 - loss 0.04853272 - time (sec): 45.00 - samples/sec: 1154.30 - lr: 0.000036 - momentum: 0.000000
127
+ 2023-09-03 22:08:55,986 epoch 4 - iter 308/447 - loss 0.04945678 - time (sec): 51.51 - samples/sec: 1160.71 - lr: 0.000035 - momentum: 0.000000
128
+ 2023-09-03 22:09:03,030 epoch 4 - iter 352/447 - loss 0.04862237 - time (sec): 58.56 - samples/sec: 1158.25 - lr: 0.000035 - momentum: 0.000000
129
+ 2023-09-03 22:09:11,999 epoch 4 - iter 396/447 - loss 0.04969455 - time (sec): 67.52 - samples/sec: 1144.81 - lr: 0.000034 - momentum: 0.000000
130
+ 2023-09-03 22:09:19,277 epoch 4 - iter 440/447 - loss 0.05061967 - time (sec): 74.80 - samples/sec: 1139.39 - lr: 0.000033 - momentum: 0.000000
131
+ 2023-09-03 22:09:20,415 ----------------------------------------------------------------------------------------------------
132
+ 2023-09-03 22:09:20,415 EPOCH 4 done: loss 0.0516 - lr: 0.000033
133
+ 2023-09-03 22:09:33,207 DEV : loss 0.15090885758399963 - f1-score (micro avg) 0.7371
134
+ 2023-09-03 22:09:33,233 saving best model
135
+ 2023-09-03 22:09:34,589 ----------------------------------------------------------------------------------------------------
136
+ 2023-09-03 22:09:42,646 epoch 5 - iter 44/447 - loss 0.03586095 - time (sec): 8.06 - samples/sec: 1113.62 - lr: 0.000033 - momentum: 0.000000
137
+ 2023-09-03 22:09:50,282 epoch 5 - iter 88/447 - loss 0.03374525 - time (sec): 15.69 - samples/sec: 1100.55 - lr: 0.000032 - momentum: 0.000000
138
+ 2023-09-03 22:09:57,990 epoch 5 - iter 132/447 - loss 0.03495845 - time (sec): 23.40 - samples/sec: 1116.01 - lr: 0.000032 - momentum: 0.000000
139
+ 2023-09-03 22:10:05,484 epoch 5 - iter 176/447 - loss 0.03476096 - time (sec): 30.89 - samples/sec: 1122.55 - lr: 0.000031 - momentum: 0.000000
140
+ 2023-09-03 22:10:12,600 epoch 5 - iter 220/447 - loss 0.03837830 - time (sec): 38.01 - samples/sec: 1121.96 - lr: 0.000031 - momentum: 0.000000
141
+ 2023-09-03 22:10:20,517 epoch 5 - iter 264/447 - loss 0.03833497 - time (sec): 45.93 - samples/sec: 1116.17 - lr: 0.000030 - momentum: 0.000000
142
+ 2023-09-03 22:10:29,701 epoch 5 - iter 308/447 - loss 0.03847519 - time (sec): 55.11 - samples/sec: 1099.20 - lr: 0.000030 - momentum: 0.000000
143
+ 2023-09-03 22:10:36,611 epoch 5 - iter 352/447 - loss 0.03810102 - time (sec): 62.02 - samples/sec: 1105.93 - lr: 0.000029 - momentum: 0.000000
144
+ 2023-09-03 22:10:44,324 epoch 5 - iter 396/447 - loss 0.03738290 - time (sec): 69.73 - samples/sec: 1100.88 - lr: 0.000028 - momentum: 0.000000
145
+ 2023-09-03 22:10:52,171 epoch 5 - iter 440/447 - loss 0.03626288 - time (sec): 77.58 - samples/sec: 1100.48 - lr: 0.000028 - momentum: 0.000000
146
+ 2023-09-03 22:10:53,227 ----------------------------------------------------------------------------------------------------
147
+ 2023-09-03 22:10:53,227 EPOCH 5 done: loss 0.0358 - lr: 0.000028
148
+ 2023-09-03 22:11:06,510 DEV : loss 0.18703238666057587 - f1-score (micro avg) 0.7673
149
+ 2023-09-03 22:11:06,536 saving best model
150
+ 2023-09-03 22:11:07,864 ----------------------------------------------------------------------------------------------------
151
+ 2023-09-03 22:11:15,663 epoch 6 - iter 44/447 - loss 0.01850349 - time (sec): 7.80 - samples/sec: 1103.69 - lr: 0.000027 - momentum: 0.000000
152
+ 2023-09-03 22:11:23,891 epoch 6 - iter 88/447 - loss 0.01852900 - time (sec): 16.03 - samples/sec: 1099.21 - lr: 0.000027 - momentum: 0.000000
153
+ 2023-09-03 22:11:31,268 epoch 6 - iter 132/447 - loss 0.01746134 - time (sec): 23.40 - samples/sec: 1109.71 - lr: 0.000026 - momentum: 0.000000
154
+ 2023-09-03 22:11:40,403 epoch 6 - iter 176/447 - loss 0.01744129 - time (sec): 32.54 - samples/sec: 1101.18 - lr: 0.000026 - momentum: 0.000000
155
+ 2023-09-03 22:11:47,974 epoch 6 - iter 220/447 - loss 0.01967293 - time (sec): 40.11 - samples/sec: 1081.05 - lr: 0.000025 - momentum: 0.000000
156
+ 2023-09-03 22:11:55,216 epoch 6 - iter 264/447 - loss 0.01841148 - time (sec): 47.35 - samples/sec: 1087.19 - lr: 0.000025 - momentum: 0.000000
157
+ 2023-09-03 22:12:03,093 epoch 6 - iter 308/447 - loss 0.01855352 - time (sec): 55.23 - samples/sec: 1085.87 - lr: 0.000024 - momentum: 0.000000
158
+ 2023-09-03 22:12:10,507 epoch 6 - iter 352/447 - loss 0.02055176 - time (sec): 62.64 - samples/sec: 1086.07 - lr: 0.000023 - momentum: 0.000000
159
+ 2023-09-03 22:12:18,228 epoch 6 - iter 396/447 - loss 0.02136484 - time (sec): 70.36 - samples/sec: 1093.17 - lr: 0.000023 - momentum: 0.000000
160
+ 2023-09-03 22:12:26,017 epoch 6 - iter 440/447 - loss 0.02271157 - time (sec): 78.15 - samples/sec: 1091.54 - lr: 0.000022 - momentum: 0.000000
161
+ 2023-09-03 22:12:27,092 ----------------------------------------------------------------------------------------------------
162
+ 2023-09-03 22:12:27,092 EPOCH 6 done: loss 0.0226 - lr: 0.000022
163
+ 2023-09-03 22:12:40,208 DEV : loss 0.2095835655927658 - f1-score (micro avg) 0.775
164
+ 2023-09-03 22:12:40,235 saving best model
165
+ 2023-09-03 22:12:41,848 ----------------------------------------------------------------------------------------------------
166
+ 2023-09-03 22:12:51,322 epoch 7 - iter 44/447 - loss 0.02179964 - time (sec): 9.47 - samples/sec: 1048.43 - lr: 0.000022 - momentum: 0.000000
167
+ 2023-09-03 22:12:58,904 epoch 7 - iter 88/447 - loss 0.01967603 - time (sec): 17.05 - samples/sec: 1046.02 - lr: 0.000021 - momentum: 0.000000
168
+ 2023-09-03 22:13:06,822 epoch 7 - iter 132/447 - loss 0.01671162 - time (sec): 24.97 - samples/sec: 1064.77 - lr: 0.000021 - momentum: 0.000000
169
+ 2023-09-03 22:13:14,693 epoch 7 - iter 176/447 - loss 0.01631688 - time (sec): 32.84 - samples/sec: 1078.88 - lr: 0.000020 - momentum: 0.000000
170
+ 2023-09-03 22:13:22,185 epoch 7 - iter 220/447 - loss 0.01540574 - time (sec): 40.34 - samples/sec: 1089.38 - lr: 0.000020 - momentum: 0.000000
171
+ 2023-09-03 22:13:29,431 epoch 7 - iter 264/447 - loss 0.01610182 - time (sec): 47.58 - samples/sec: 1085.33 - lr: 0.000019 - momentum: 0.000000
172
+ 2023-09-03 22:13:36,929 epoch 7 - iter 308/447 - loss 0.01612637 - time (sec): 55.08 - samples/sec: 1091.99 - lr: 0.000018 - momentum: 0.000000
173
+ 2023-09-03 22:13:44,464 epoch 7 - iter 352/447 - loss 0.01597313 - time (sec): 62.61 - samples/sec: 1091.97 - lr: 0.000018 - momentum: 0.000000
174
+ 2023-09-03 22:13:51,503 epoch 7 - iter 396/447 - loss 0.01617994 - time (sec): 69.65 - samples/sec: 1096.51 - lr: 0.000017 - momentum: 0.000000
175
+ 2023-09-03 22:13:59,075 epoch 7 - iter 440/447 - loss 0.01580620 - time (sec): 77.23 - samples/sec: 1106.63 - lr: 0.000017 - momentum: 0.000000
176
+ 2023-09-03 22:14:00,041 ----------------------------------------------------------------------------------------------------
177
+ 2023-09-03 22:14:00,041 EPOCH 7 done: loss 0.0157 - lr: 0.000017
178
+ 2023-09-03 22:14:12,985 DEV : loss 0.21346184611320496 - f1-score (micro avg) 0.7756
179
+ 2023-09-03 22:14:13,012 saving best model
180
+ 2023-09-03 22:14:14,349 ----------------------------------------------------------------------------------------------------
181
+ 2023-09-03 22:14:21,624 epoch 8 - iter 44/447 - loss 0.00559374 - time (sec): 7.27 - samples/sec: 1181.34 - lr: 0.000016 - momentum: 0.000000
182
+ 2023-09-03 22:14:29,176 epoch 8 - iter 88/447 - loss 0.00569671 - time (sec): 14.83 - samples/sec: 1155.86 - lr: 0.000016 - momentum: 0.000000
183
+ 2023-09-03 22:14:36,035 epoch 8 - iter 132/447 - loss 0.01018177 - time (sec): 21.68 - samples/sec: 1169.06 - lr: 0.000015 - momentum: 0.000000
184
+ 2023-09-03 22:14:43,018 epoch 8 - iter 176/447 - loss 0.01136583 - time (sec): 28.67 - samples/sec: 1168.77 - lr: 0.000015 - momentum: 0.000000
185
+ 2023-09-03 22:14:50,415 epoch 8 - iter 220/447 - loss 0.01046024 - time (sec): 36.06 - samples/sec: 1158.72 - lr: 0.000014 - momentum: 0.000000
186
+ 2023-09-03 22:14:57,393 epoch 8 - iter 264/447 - loss 0.00983952 - time (sec): 43.04 - samples/sec: 1166.38 - lr: 0.000013 - momentum: 0.000000
187
+ 2023-09-03 22:15:04,599 epoch 8 - iter 308/447 - loss 0.00938640 - time (sec): 50.25 - samples/sec: 1163.21 - lr: 0.000013 - momentum: 0.000000
188
+ 2023-09-03 22:15:13,018 epoch 8 - iter 352/447 - loss 0.01105031 - time (sec): 58.67 - samples/sec: 1152.82 - lr: 0.000012 - momentum: 0.000000
189
+ 2023-09-03 22:15:21,211 epoch 8 - iter 396/447 - loss 0.01042212 - time (sec): 66.86 - samples/sec: 1147.29 - lr: 0.000012 - momentum: 0.000000
190
+ 2023-09-03 22:15:28,131 epoch 8 - iter 440/447 - loss 0.01008377 - time (sec): 73.78 - samples/sec: 1153.04 - lr: 0.000011 - momentum: 0.000000
191
+ 2023-09-03 22:15:29,423 ----------------------------------------------------------------------------------------------------
192
+ 2023-09-03 22:15:29,423 EPOCH 8 done: loss 0.0103 - lr: 0.000011
193
+ 2023-09-03 22:15:42,125 DEV : loss 0.23662017285823822 - f1-score (micro avg) 0.7796
194
+ 2023-09-03 22:15:42,152 saving best model
195
+ 2023-09-03 22:15:43,477 ----------------------------------------------------------------------------------------------------
196
+ 2023-09-03 22:15:50,431 epoch 9 - iter 44/447 - loss 0.00145890 - time (sec): 6.95 - samples/sec: 1193.03 - lr: 0.000011 - momentum: 0.000000
197
+ 2023-09-03 22:15:58,205 epoch 9 - iter 88/447 - loss 0.00495495 - time (sec): 14.73 - samples/sec: 1152.95 - lr: 0.000010 - momentum: 0.000000
198
+ 2023-09-03 22:16:04,837 epoch 9 - iter 132/447 - loss 0.00690696 - time (sec): 21.36 - samples/sec: 1166.87 - lr: 0.000010 - momentum: 0.000000
199
+ 2023-09-03 22:16:12,174 epoch 9 - iter 176/447 - loss 0.00870116 - time (sec): 28.70 - samples/sec: 1159.17 - lr: 0.000009 - momentum: 0.000000
200
+ 2023-09-03 22:16:20,096 epoch 9 - iter 220/447 - loss 0.00803870 - time (sec): 36.62 - samples/sec: 1148.72 - lr: 0.000008 - momentum: 0.000000
201
+ 2023-09-03 22:16:27,725 epoch 9 - iter 264/447 - loss 0.00678798 - time (sec): 44.25 - samples/sec: 1140.18 - lr: 0.000008 - momentum: 0.000000
202
+ 2023-09-03 22:16:34,699 epoch 9 - iter 308/447 - loss 0.00678808 - time (sec): 51.22 - samples/sec: 1151.45 - lr: 0.000007 - momentum: 0.000000
203
+ 2023-09-03 22:16:43,889 epoch 9 - iter 352/447 - loss 0.00678058 - time (sec): 60.41 - samples/sec: 1143.77 - lr: 0.000007 - momentum: 0.000000
204
+ 2023-09-03 22:16:51,250 epoch 9 - iter 396/447 - loss 0.00610103 - time (sec): 67.77 - samples/sec: 1141.24 - lr: 0.000006 - momentum: 0.000000
205
+ 2023-09-03 22:16:58,407 epoch 9 - iter 440/447 - loss 0.00581456 - time (sec): 74.93 - samples/sec: 1140.58 - lr: 0.000006 - momentum: 0.000000
206
+ 2023-09-03 22:16:59,353 ----------------------------------------------------------------------------------------------------
207
+ 2023-09-03 22:16:59,354 EPOCH 9 done: loss 0.0058 - lr: 0.000006
208
+ 2023-09-03 22:17:12,218 DEV : loss 0.23721420764923096 - f1-score (micro avg) 0.7917
209
+ 2023-09-03 22:17:12,245 saving best model
210
+ 2023-09-03 22:17:13,563 ----------------------------------------------------------------------------------------------------
211
+ 2023-09-03 22:17:22,436 epoch 10 - iter 44/447 - loss 0.00717064 - time (sec): 8.87 - samples/sec: 1114.23 - lr: 0.000005 - momentum: 0.000000
212
+ 2023-09-03 22:17:30,722 epoch 10 - iter 88/447 - loss 0.00593374 - time (sec): 17.16 - samples/sec: 1081.01 - lr: 0.000005 - momentum: 0.000000
213
+ 2023-09-03 22:17:38,148 epoch 10 - iter 132/447 - loss 0.00468114 - time (sec): 24.58 - samples/sec: 1090.66 - lr: 0.000004 - momentum: 0.000000
214
+ 2023-09-03 22:17:45,211 epoch 10 - iter 176/447 - loss 0.00470797 - time (sec): 31.65 - samples/sec: 1099.93 - lr: 0.000003 - momentum: 0.000000
215
+ 2023-09-03 22:17:52,787 epoch 10 - iter 220/447 - loss 0.00429535 - time (sec): 39.22 - samples/sec: 1104.04 - lr: 0.000003 - momentum: 0.000000
216
+ 2023-09-03 22:17:59,720 epoch 10 - iter 264/447 - loss 0.00404627 - time (sec): 46.16 - samples/sec: 1111.62 - lr: 0.000002 - momentum: 0.000000
217
+ 2023-09-03 22:18:07,149 epoch 10 - iter 308/447 - loss 0.00420055 - time (sec): 53.58 - samples/sec: 1110.36 - lr: 0.000002 - momentum: 0.000000
218
+ 2023-09-03 22:18:15,187 epoch 10 - iter 352/447 - loss 0.00415827 - time (sec): 61.62 - samples/sec: 1105.23 - lr: 0.000001 - momentum: 0.000000
219
+ 2023-09-03 22:18:22,241 epoch 10 - iter 396/447 - loss 0.00376964 - time (sec): 68.68 - samples/sec: 1113.03 - lr: 0.000001 - momentum: 0.000000
220
+ 2023-09-03 22:18:30,062 epoch 10 - iter 440/447 - loss 0.00369437 - time (sec): 76.50 - samples/sec: 1118.14 - lr: 0.000000 - momentum: 0.000000
221
+ 2023-09-03 22:18:31,120 ----------------------------------------------------------------------------------------------------
222
+ 2023-09-03 22:18:31,121 EPOCH 10 done: loss 0.0037 - lr: 0.000000
223
+ 2023-09-03 22:18:44,578 DEV : loss 0.23381954431533813 - f1-score (micro avg) 0.7938
224
+ 2023-09-03 22:18:44,605 saving best model
225
+ 2023-09-03 22:18:46,420 ----------------------------------------------------------------------------------------------------
226
+ 2023-09-03 22:18:46,422 Loading model from best epoch ...
227
+ 2023-09-03 22:18:48,216 SequenceTagger predicts: Dictionary with 21 tags: O, S-loc, B-loc, E-loc, I-loc, S-pers, B-pers, E-pers, I-pers, S-org, B-org, E-org, I-org, S-prod, B-prod, E-prod, I-prod, S-time, B-time, E-time, I-time
228
+ 2023-09-03 22:18:58,912
229
+ Results:
230
+ - F-score (micro) 0.7375
231
+ - F-score (macro) 0.6636
232
+ - Accuracy 0.6048
233
+
234
+ By class:
235
+ precision recall f1-score support
236
+
237
+ loc 0.8358 0.8540 0.8448 596
238
+ pers 0.6085 0.7327 0.6649 333
239
+ org 0.5500 0.5000 0.5238 132
240
+ prod 0.6200 0.4697 0.5345 66
241
+ time 0.7091 0.7959 0.7500 49
242
+
243
+ micro avg 0.7198 0.7560 0.7375 1176
244
+ macro avg 0.6647 0.6705 0.6636 1176
245
+ weighted avg 0.7220 0.7560 0.7365 1176
246
+
247
+ 2023-09-03 22:18:58,912 ----------------------------------------------------------------------------------------------------