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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-lr3e-05-poolingfirst-layers-1-crfFalse-3/best-model.pt +3 -0
  2. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3/dev.tsv +0 -0
  3. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3/final-model.pt +3 -0
  4. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3/loss.tsv +11 -0
  5. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3/test.tsv +0 -0
  6. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3/training.log +246 -0
hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3/best-model.pt ADDED
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hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3/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-lr3e-05-poolingfirst-layers-1-crfFalse-3/final-model.pt ADDED
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hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3/loss.tsv ADDED
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+ EPOCH TIMESTAMP LEARNING_RATE TRAIN_LOSS DEV_LOSS DEV_PRECISION DEV_RECALL DEV_F1 DEV_ACCURACY
2
+ 1 20:39:31 0.0000 0.6927 0.2011 0.5555 0.5324 0.5437 0.3830
3
+ 2 20:41:04 0.0000 0.1673 0.1313 0.6811 0.7146 0.6974 0.5563
4
+ 3 20:42:38 0.0000 0.0888 0.1300 0.7194 0.7295 0.7244 0.5839
5
+ 4 20:44:11 0.0000 0.0542 0.1429 0.7360 0.7608 0.7482 0.6162
6
+ 5 20:45:45 0.0000 0.0376 0.1670 0.7626 0.7811 0.7717 0.6474
7
+ 6 20:47:19 0.0000 0.0248 0.1714 0.7425 0.7912 0.7661 0.6381
8
+ 7 20:48:52 0.0000 0.0166 0.1972 0.7423 0.8084 0.7740 0.6499
9
+ 8 20:50:25 0.0000 0.0140 0.2024 0.7496 0.8194 0.7830 0.6620
10
+ 9 20:52:00 0.0000 0.0082 0.2061 0.7549 0.8210 0.7865 0.6658
11
+ 10 20:53:33 0.0000 0.0065 0.2070 0.7712 0.8116 0.7909 0.6732
hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3/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-lr3e-05-poolingfirst-layers-1-crfFalse-3/training.log ADDED
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+ 2023-09-03 20:38:01,136 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 20:38:01,137 Model: "SequenceTagger(
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+ (embeddings): TransformerWordEmbeddings(
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+ (model): BertModel(
5
+ (embeddings): BertEmbeddings(
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+ (word_embeddings): Embedding(32001, 768)
7
+ (position_embeddings): Embedding(512, 768)
8
+ (token_type_embeddings): Embedding(2, 768)
9
+ (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
10
+ (dropout): Dropout(p=0.1, inplace=False)
11
+ )
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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(
16
+ (self): BertSelfAttention(
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+ (query): Linear(in_features=768, out_features=768, bias=True)
18
+ (key): Linear(in_features=768, out_features=768, bias=True)
19
+ (value): Linear(in_features=768, out_features=768, bias=True)
20
+ (dropout): Dropout(p=0.1, inplace=False)
21
+ )
22
+ (output): BertSelfOutput(
23
+ (dense): Linear(in_features=768, out_features=768, bias=True)
24
+ (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
25
+ (dropout): Dropout(p=0.1, inplace=False)
26
+ )
27
+ )
28
+ (intermediate): BertIntermediate(
29
+ (dense): Linear(in_features=768, out_features=3072, bias=True)
30
+ (intermediate_act_fn): GELUActivation()
31
+ )
32
+ (output): BertOutput(
33
+ (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
+ )"
50
+ 2023-09-03 20:38:01,137 ----------------------------------------------------------------------------------------------------
51
+ 2023-09-03 20:38:01,137 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
53
+ 2023-09-03 20:38:01,138 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 20:38:01,138 Train: 3575 sentences
55
+ 2023-09-03 20:38:01,138 (train_with_dev=False, train_with_test=False)
56
+ 2023-09-03 20:38:01,138 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 20:38:01,138 Training Params:
58
+ 2023-09-03 20:38:01,138 - learning_rate: "3e-05"
59
+ 2023-09-03 20:38:01,138 - mini_batch_size: "8"
60
+ 2023-09-03 20:38:01,138 - max_epochs: "10"
61
+ 2023-09-03 20:38:01,138 - shuffle: "True"
62
+ 2023-09-03 20:38:01,138 ----------------------------------------------------------------------------------------------------
63
+ 2023-09-03 20:38:01,138 Plugins:
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+ 2023-09-03 20:38:01,138 - LinearScheduler | warmup_fraction: '0.1'
65
+ 2023-09-03 20:38:01,138 ----------------------------------------------------------------------------------------------------
66
+ 2023-09-03 20:38:01,138 Final evaluation on model from best epoch (best-model.pt)
67
+ 2023-09-03 20:38:01,138 - metric: "('micro avg', 'f1-score')"
68
+ 2023-09-03 20:38:01,138 ----------------------------------------------------------------------------------------------------
69
+ 2023-09-03 20:38:01,138 Computation:
70
+ 2023-09-03 20:38:01,138 - compute on device: cuda:0
71
+ 2023-09-03 20:38:01,138 - embedding storage: none
72
+ 2023-09-03 20:38:01,138 ----------------------------------------------------------------------------------------------------
73
+ 2023-09-03 20:38:01,138 Model training base path: "hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3"
74
+ 2023-09-03 20:38:01,138 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 20:38:01,139 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 20:38:08,256 epoch 1 - iter 44/447 - loss 2.87770422 - time (sec): 7.12 - samples/sec: 1159.34 - lr: 0.000003 - momentum: 0.000000
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+ 2023-09-03 20:38:16,481 epoch 1 - iter 88/447 - loss 2.08319111 - time (sec): 15.34 - samples/sec: 1114.92 - lr: 0.000006 - momentum: 0.000000
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+ 2023-09-03 20:38:23,703 epoch 1 - iter 132/447 - loss 1.58991792 - time (sec): 22.56 - samples/sec: 1108.58 - lr: 0.000009 - momentum: 0.000000
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+ 2023-09-03 20:38:31,844 epoch 1 - iter 176/447 - loss 1.28119630 - time (sec): 30.70 - samples/sec: 1126.04 - lr: 0.000012 - momentum: 0.000000
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+ 2023-09-03 20:38:40,019 epoch 1 - iter 220/447 - loss 1.09200429 - time (sec): 38.88 - samples/sec: 1113.98 - lr: 0.000015 - momentum: 0.000000
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+ 2023-09-03 20:38:47,988 epoch 1 - iter 264/447 - loss 0.96199587 - time (sec): 46.85 - samples/sec: 1109.48 - lr: 0.000018 - momentum: 0.000000
82
+ 2023-09-03 20:38:55,233 epoch 1 - iter 308/447 - loss 0.87404531 - time (sec): 54.09 - samples/sec: 1109.16 - lr: 0.000021 - momentum: 0.000000
83
+ 2023-09-03 20:39:03,247 epoch 1 - iter 352/447 - loss 0.80847572 - time (sec): 62.11 - samples/sec: 1096.31 - lr: 0.000024 - momentum: 0.000000
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+ 2023-09-03 20:39:11,801 epoch 1 - iter 396/447 - loss 0.74550168 - time (sec): 70.66 - samples/sec: 1091.77 - lr: 0.000027 - momentum: 0.000000
85
+ 2023-09-03 20:39:18,882 epoch 1 - iter 440/447 - loss 0.69760897 - time (sec): 77.74 - samples/sec: 1098.60 - lr: 0.000029 - momentum: 0.000000
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+ 2023-09-03 20:39:19,940 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 20:39:19,941 EPOCH 1 done: loss 0.6927 - lr: 0.000029
88
+ 2023-09-03 20:39:31,129 DEV : loss 0.20112653076648712 - f1-score (micro avg) 0.5437
89
+ 2023-09-03 20:39:31,155 saving best model
90
+ 2023-09-03 20:39:31,666 ----------------------------------------------------------------------------------------------------
91
+ 2023-09-03 20:39:38,965 epoch 2 - iter 44/447 - loss 0.22207458 - time (sec): 7.30 - samples/sec: 1113.39 - lr: 0.000030 - momentum: 0.000000
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+ 2023-09-03 20:39:46,446 epoch 2 - iter 88/447 - loss 0.20682482 - time (sec): 14.78 - samples/sec: 1112.65 - lr: 0.000029 - momentum: 0.000000
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+ 2023-09-03 20:39:53,979 epoch 2 - iter 132/447 - loss 0.19761271 - time (sec): 22.31 - samples/sec: 1113.63 - lr: 0.000029 - momentum: 0.000000
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+ 2023-09-03 20:40:02,048 epoch 2 - iter 176/447 - loss 0.18235153 - time (sec): 30.38 - samples/sec: 1094.80 - lr: 0.000029 - momentum: 0.000000
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+ 2023-09-03 20:40:09,373 epoch 2 - iter 220/447 - loss 0.18191485 - time (sec): 37.71 - samples/sec: 1099.40 - lr: 0.000028 - momentum: 0.000000
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+ 2023-09-03 20:40:17,138 epoch 2 - iter 264/447 - loss 0.17612210 - time (sec): 45.47 - samples/sec: 1096.00 - lr: 0.000028 - momentum: 0.000000
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+ 2023-09-03 20:40:24,509 epoch 2 - iter 308/447 - loss 0.17518942 - time (sec): 52.84 - samples/sec: 1097.74 - lr: 0.000028 - momentum: 0.000000
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+ 2023-09-03 20:40:33,331 epoch 2 - iter 352/447 - loss 0.17065693 - time (sec): 61.66 - samples/sec: 1087.57 - lr: 0.000027 - momentum: 0.000000
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+ 2023-09-03 20:40:41,403 epoch 2 - iter 396/447 - loss 0.17009897 - time (sec): 69.74 - samples/sec: 1100.08 - lr: 0.000027 - momentum: 0.000000
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+ 2023-09-03 20:40:49,096 epoch 2 - iter 440/447 - loss 0.16745490 - time (sec): 77.43 - samples/sec: 1101.42 - lr: 0.000027 - momentum: 0.000000
101
+ 2023-09-03 20:40:50,403 ----------------------------------------------------------------------------------------------------
102
+ 2023-09-03 20:40:50,404 EPOCH 2 done: loss 0.1673 - lr: 0.000027
103
+ 2023-09-03 20:41:04,047 DEV : loss 0.1312825232744217 - f1-score (micro avg) 0.6974
104
+ 2023-09-03 20:41:04,073 saving best model
105
+ 2023-09-03 20:41:05,397 ----------------------------------------------------------------------------------------------------
106
+ 2023-09-03 20:41:12,799 epoch 3 - iter 44/447 - loss 0.09600557 - time (sec): 7.40 - samples/sec: 1101.65 - lr: 0.000026 - momentum: 0.000000
107
+ 2023-09-03 20:41:19,987 epoch 3 - iter 88/447 - loss 0.09164265 - time (sec): 14.59 - samples/sec: 1097.53 - lr: 0.000026 - momentum: 0.000000
108
+ 2023-09-03 20:41:28,276 epoch 3 - iter 132/447 - loss 0.09931759 - time (sec): 22.88 - samples/sec: 1082.09 - lr: 0.000026 - momentum: 0.000000
109
+ 2023-09-03 20:41:35,534 epoch 3 - iter 176/447 - loss 0.10190165 - time (sec): 30.14 - samples/sec: 1097.25 - lr: 0.000025 - momentum: 0.000000
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+ 2023-09-03 20:41:42,936 epoch 3 - iter 220/447 - loss 0.10106481 - time (sec): 37.54 - samples/sec: 1094.01 - lr: 0.000025 - momentum: 0.000000
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+ 2023-09-03 20:41:50,590 epoch 3 - iter 264/447 - loss 0.09587819 - time (sec): 45.19 - samples/sec: 1102.46 - lr: 0.000025 - momentum: 0.000000
112
+ 2023-09-03 20:41:58,415 epoch 3 - iter 308/447 - loss 0.09330796 - time (sec): 53.02 - samples/sec: 1099.13 - lr: 0.000024 - momentum: 0.000000
113
+ 2023-09-03 20:42:06,439 epoch 3 - iter 352/447 - loss 0.09333750 - time (sec): 61.04 - samples/sec: 1092.55 - lr: 0.000024 - momentum: 0.000000
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+ 2023-09-03 20:42:14,445 epoch 3 - iter 396/447 - loss 0.09009942 - time (sec): 69.05 - samples/sec: 1093.94 - lr: 0.000024 - momentum: 0.000000
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+ 2023-09-03 20:42:22,031 epoch 3 - iter 440/447 - loss 0.09024854 - time (sec): 76.63 - samples/sec: 1097.99 - lr: 0.000023 - momentum: 0.000000
116
+ 2023-09-03 20:42:24,430 ----------------------------------------------------------------------------------------------------
117
+ 2023-09-03 20:42:24,430 EPOCH 3 done: loss 0.0888 - lr: 0.000023
118
+ 2023-09-03 20:42:37,993 DEV : loss 0.12996716797351837 - f1-score (micro avg) 0.7244
119
+ 2023-09-03 20:42:38,020 saving best model
120
+ 2023-09-03 20:42:39,386 ----------------------------------------------------------------------------------------------------
121
+ 2023-09-03 20:42:47,336 epoch 4 - iter 44/447 - loss 0.06489612 - time (sec): 7.95 - samples/sec: 1025.16 - lr: 0.000023 - momentum: 0.000000
122
+ 2023-09-03 20:42:54,857 epoch 4 - iter 88/447 - loss 0.06116370 - time (sec): 15.47 - samples/sec: 1071.52 - lr: 0.000023 - momentum: 0.000000
123
+ 2023-09-03 20:43:02,087 epoch 4 - iter 132/447 - loss 0.05795912 - time (sec): 22.70 - samples/sec: 1091.87 - lr: 0.000022 - momentum: 0.000000
124
+ 2023-09-03 20:43:11,201 epoch 4 - iter 176/447 - loss 0.05949996 - time (sec): 31.81 - samples/sec: 1094.21 - lr: 0.000022 - momentum: 0.000000
125
+ 2023-09-03 20:43:18,807 epoch 4 - iter 220/447 - loss 0.05640865 - time (sec): 39.42 - samples/sec: 1096.50 - lr: 0.000022 - momentum: 0.000000
126
+ 2023-09-03 20:43:26,552 epoch 4 - iter 264/447 - loss 0.05636115 - time (sec): 47.16 - samples/sec: 1095.01 - lr: 0.000021 - momentum: 0.000000
127
+ 2023-09-03 20:43:34,015 epoch 4 - iter 308/447 - loss 0.05540446 - time (sec): 54.63 - samples/sec: 1100.16 - lr: 0.000021 - momentum: 0.000000
128
+ 2023-09-03 20:43:41,538 epoch 4 - iter 352/447 - loss 0.05375133 - time (sec): 62.15 - samples/sec: 1100.67 - lr: 0.000021 - momentum: 0.000000
129
+ 2023-09-03 20:43:49,896 epoch 4 - iter 396/447 - loss 0.05323583 - time (sec): 70.51 - samples/sec: 1096.64 - lr: 0.000020 - momentum: 0.000000
130
+ 2023-09-03 20:43:57,109 epoch 4 - iter 440/447 - loss 0.05404656 - time (sec): 77.72 - samples/sec: 1098.37 - lr: 0.000020 - momentum: 0.000000
131
+ 2023-09-03 20:43:58,214 ----------------------------------------------------------------------------------------------------
132
+ 2023-09-03 20:43:58,214 EPOCH 4 done: loss 0.0542 - lr: 0.000020
133
+ 2023-09-03 20:44:11,846 DEV : loss 0.1428946554660797 - f1-score (micro avg) 0.7482
134
+ 2023-09-03 20:44:11,873 saving best model
135
+ 2023-09-03 20:44:13,216 ----------------------------------------------------------------------------------------------------
136
+ 2023-09-03 20:44:21,181 epoch 5 - iter 44/447 - loss 0.04681179 - time (sec): 7.96 - samples/sec: 1062.71 - lr: 0.000020 - momentum: 0.000000
137
+ 2023-09-03 20:44:28,455 epoch 5 - iter 88/447 - loss 0.04231390 - time (sec): 15.24 - samples/sec: 1086.61 - lr: 0.000019 - momentum: 0.000000
138
+ 2023-09-03 20:44:35,815 epoch 5 - iter 132/447 - loss 0.03921651 - time (sec): 22.60 - samples/sec: 1105.78 - lr: 0.000019 - momentum: 0.000000
139
+ 2023-09-03 20:44:43,028 epoch 5 - iter 176/447 - loss 0.04019439 - time (sec): 29.81 - samples/sec: 1104.55 - lr: 0.000019 - momentum: 0.000000
140
+ 2023-09-03 20:44:51,795 epoch 5 - iter 220/447 - loss 0.03832566 - time (sec): 38.58 - samples/sec: 1092.74 - lr: 0.000018 - momentum: 0.000000
141
+ 2023-09-03 20:44:58,968 epoch 5 - iter 264/447 - loss 0.03844560 - time (sec): 45.75 - samples/sec: 1096.05 - lr: 0.000018 - momentum: 0.000000
142
+ 2023-09-03 20:45:06,387 epoch 5 - iter 308/447 - loss 0.03728014 - time (sec): 53.17 - samples/sec: 1098.71 - lr: 0.000018 - momentum: 0.000000
143
+ 2023-09-03 20:45:15,425 epoch 5 - iter 352/447 - loss 0.03837852 - time (sec): 62.21 - samples/sec: 1095.01 - lr: 0.000017 - momentum: 0.000000
144
+ 2023-09-03 20:45:23,478 epoch 5 - iter 396/447 - loss 0.03707734 - time (sec): 70.26 - samples/sec: 1099.26 - lr: 0.000017 - momentum: 0.000000
145
+ 2023-09-03 20:45:31,293 epoch 5 - iter 440/447 - loss 0.03780499 - time (sec): 78.08 - samples/sec: 1093.09 - lr: 0.000017 - momentum: 0.000000
146
+ 2023-09-03 20:45:32,316 ----------------------------------------------------------------------------------------------------
147
+ 2023-09-03 20:45:32,316 EPOCH 5 done: loss 0.0376 - lr: 0.000017
148
+ 2023-09-03 20:45:45,882 DEV : loss 0.16702891886234283 - f1-score (micro avg) 0.7717
149
+ 2023-09-03 20:45:45,912 saving best model
150
+ 2023-09-03 20:45:47,259 ----------------------------------------------------------------------------------------------------
151
+ 2023-09-03 20:45:56,238 epoch 6 - iter 44/447 - loss 0.02478206 - time (sec): 8.98 - samples/sec: 1071.87 - lr: 0.000016 - momentum: 0.000000
152
+ 2023-09-03 20:46:03,630 epoch 6 - iter 88/447 - loss 0.02103582 - time (sec): 16.37 - samples/sec: 1088.79 - lr: 0.000016 - momentum: 0.000000
153
+ 2023-09-03 20:46:11,965 epoch 6 - iter 132/447 - loss 0.02046341 - time (sec): 24.70 - samples/sec: 1078.23 - lr: 0.000016 - momentum: 0.000000
154
+ 2023-09-03 20:46:19,805 epoch 6 - iter 176/447 - loss 0.02077589 - time (sec): 32.55 - samples/sec: 1088.12 - lr: 0.000015 - momentum: 0.000000
155
+ 2023-09-03 20:46:27,589 epoch 6 - iter 220/447 - loss 0.02191523 - time (sec): 40.33 - samples/sec: 1100.70 - lr: 0.000015 - momentum: 0.000000
156
+ 2023-09-03 20:46:34,770 epoch 6 - iter 264/447 - loss 0.02187664 - time (sec): 47.51 - samples/sec: 1101.25 - lr: 0.000015 - momentum: 0.000000
157
+ 2023-09-03 20:46:42,207 epoch 6 - iter 308/447 - loss 0.02081148 - time (sec): 54.95 - samples/sec: 1097.02 - lr: 0.000014 - momentum: 0.000000
158
+ 2023-09-03 20:46:49,455 epoch 6 - iter 352/447 - loss 0.02299955 - time (sec): 62.19 - samples/sec: 1097.25 - lr: 0.000014 - momentum: 0.000000
159
+ 2023-09-03 20:46:57,197 epoch 6 - iter 396/447 - loss 0.02460398 - time (sec): 69.94 - samples/sec: 1100.45 - lr: 0.000014 - momentum: 0.000000
160
+ 2023-09-03 20:47:04,047 epoch 6 - iter 440/447 - loss 0.02491084 - time (sec): 76.79 - samples/sec: 1105.01 - lr: 0.000013 - momentum: 0.000000
161
+ 2023-09-03 20:47:05,871 ----------------------------------------------------------------------------------------------------
162
+ 2023-09-03 20:47:05,871 EPOCH 6 done: loss 0.0248 - lr: 0.000013
163
+ 2023-09-03 20:47:19,571 DEV : loss 0.17136597633361816 - f1-score (micro avg) 0.7661
164
+ 2023-09-03 20:47:19,598 ----------------------------------------------------------------------------------------------------
165
+ 2023-09-03 20:47:27,827 epoch 7 - iter 44/447 - loss 0.01929350 - time (sec): 8.23 - samples/sec: 1117.71 - lr: 0.000013 - momentum: 0.000000
166
+ 2023-09-03 20:47:37,459 epoch 7 - iter 88/447 - loss 0.02096872 - time (sec): 17.86 - samples/sec: 1062.49 - lr: 0.000013 - momentum: 0.000000
167
+ 2023-09-03 20:47:45,301 epoch 7 - iter 132/447 - loss 0.01814244 - time (sec): 25.70 - samples/sec: 1075.58 - lr: 0.000012 - momentum: 0.000000
168
+ 2023-09-03 20:47:53,168 epoch 7 - iter 176/447 - loss 0.01890478 - time (sec): 33.57 - samples/sec: 1095.58 - lr: 0.000012 - momentum: 0.000000
169
+ 2023-09-03 20:48:01,735 epoch 7 - iter 220/447 - loss 0.01724343 - time (sec): 42.14 - samples/sec: 1083.88 - lr: 0.000012 - momentum: 0.000000
170
+ 2023-09-03 20:48:08,869 epoch 7 - iter 264/447 - loss 0.01632175 - time (sec): 49.27 - samples/sec: 1081.89 - lr: 0.000011 - momentum: 0.000000
171
+ 2023-09-03 20:48:15,789 epoch 7 - iter 308/447 - loss 0.01615000 - time (sec): 56.19 - samples/sec: 1086.63 - lr: 0.000011 - momentum: 0.000000
172
+ 2023-09-03 20:48:23,495 epoch 7 - iter 352/447 - loss 0.01669215 - time (sec): 63.90 - samples/sec: 1084.54 - lr: 0.000011 - momentum: 0.000000
173
+ 2023-09-03 20:48:30,350 epoch 7 - iter 396/447 - loss 0.01623588 - time (sec): 70.75 - samples/sec: 1089.73 - lr: 0.000010 - momentum: 0.000000
174
+ 2023-09-03 20:48:37,401 epoch 7 - iter 440/447 - loss 0.01680044 - time (sec): 77.80 - samples/sec: 1092.99 - lr: 0.000010 - momentum: 0.000000
175
+ 2023-09-03 20:48:38,836 ----------------------------------------------------------------------------------------------------
176
+ 2023-09-03 20:48:38,836 EPOCH 7 done: loss 0.0166 - lr: 0.000010
177
+ 2023-09-03 20:48:52,421 DEV : loss 0.19717402756214142 - f1-score (micro avg) 0.774
178
+ 2023-09-03 20:48:52,448 saving best model
179
+ 2023-09-03 20:48:53,789 ----------------------------------------------------------------------------------------------------
180
+ 2023-09-03 20:49:01,326 epoch 8 - iter 44/447 - loss 0.01434840 - time (sec): 7.54 - samples/sec: 1147.20 - lr: 0.000010 - momentum: 0.000000
181
+ 2023-09-03 20:49:08,666 epoch 8 - iter 88/447 - loss 0.01870681 - time (sec): 14.88 - samples/sec: 1128.20 - lr: 0.000009 - momentum: 0.000000
182
+ 2023-09-03 20:49:17,985 epoch 8 - iter 132/447 - loss 0.01633533 - time (sec): 24.20 - samples/sec: 1106.26 - lr: 0.000009 - momentum: 0.000000
183
+ 2023-09-03 20:49:26,291 epoch 8 - iter 176/447 - loss 0.01577803 - time (sec): 32.50 - samples/sec: 1083.08 - lr: 0.000009 - momentum: 0.000000
184
+ 2023-09-03 20:49:33,737 epoch 8 - iter 220/447 - loss 0.01504455 - time (sec): 39.95 - samples/sec: 1091.08 - lr: 0.000008 - momentum: 0.000000
185
+ 2023-09-03 20:49:42,284 epoch 8 - iter 264/447 - loss 0.01516367 - time (sec): 48.49 - samples/sec: 1079.56 - lr: 0.000008 - momentum: 0.000000
186
+ 2023-09-03 20:49:49,916 epoch 8 - iter 308/447 - loss 0.01545841 - time (sec): 56.13 - samples/sec: 1085.42 - lr: 0.000008 - momentum: 0.000000
187
+ 2023-09-03 20:49:56,975 epoch 8 - iter 352/447 - loss 0.01481127 - time (sec): 63.18 - samples/sec: 1090.63 - lr: 0.000007 - momentum: 0.000000
188
+ 2023-09-03 20:50:04,674 epoch 8 - iter 396/447 - loss 0.01435214 - time (sec): 70.88 - samples/sec: 1092.42 - lr: 0.000007 - momentum: 0.000000
189
+ 2023-09-03 20:50:11,484 epoch 8 - iter 440/447 - loss 0.01417938 - time (sec): 77.69 - samples/sec: 1098.64 - lr: 0.000007 - momentum: 0.000000
190
+ 2023-09-03 20:50:12,491 ----------------------------------------------------------------------------------------------------
191
+ 2023-09-03 20:50:12,491 EPOCH 8 done: loss 0.0140 - lr: 0.000007
192
+ 2023-09-03 20:50:25,651 DEV : loss 0.20242349803447723 - f1-score (micro avg) 0.783
193
+ 2023-09-03 20:50:25,677 saving best model
194
+ 2023-09-03 20:50:26,989 ----------------------------------------------------------------------------------------------------
195
+ 2023-09-03 20:50:35,061 epoch 9 - iter 44/447 - loss 0.00420510 - time (sec): 8.07 - samples/sec: 1012.68 - lr: 0.000006 - momentum: 0.000000
196
+ 2023-09-03 20:50:44,482 epoch 9 - iter 88/447 - loss 0.00343905 - time (sec): 17.49 - samples/sec: 1000.84 - lr: 0.000006 - momentum: 0.000000
197
+ 2023-09-03 20:50:52,543 epoch 9 - iter 132/447 - loss 0.00358262 - time (sec): 25.55 - samples/sec: 1019.71 - lr: 0.000006 - momentum: 0.000000
198
+ 2023-09-03 20:51:00,192 epoch 9 - iter 176/447 - loss 0.00573962 - time (sec): 33.20 - samples/sec: 1037.28 - lr: 0.000005 - momentum: 0.000000
199
+ 2023-09-03 20:51:07,255 epoch 9 - iter 220/447 - loss 0.00698964 - time (sec): 40.27 - samples/sec: 1062.03 - lr: 0.000005 - momentum: 0.000000
200
+ 2023-09-03 20:51:14,345 epoch 9 - iter 264/447 - loss 0.00672782 - time (sec): 47.35 - samples/sec: 1074.40 - lr: 0.000005 - momentum: 0.000000
201
+ 2023-09-03 20:51:21,666 epoch 9 - iter 308/447 - loss 0.00617531 - time (sec): 54.68 - samples/sec: 1078.60 - lr: 0.000004 - momentum: 0.000000
202
+ 2023-09-03 20:51:28,836 epoch 9 - iter 352/447 - loss 0.00669845 - time (sec): 61.85 - samples/sec: 1086.03 - lr: 0.000004 - momentum: 0.000000
203
+ 2023-09-03 20:51:37,727 epoch 9 - iter 396/447 - loss 0.00732645 - time (sec): 70.74 - samples/sec: 1089.07 - lr: 0.000004 - momentum: 0.000000
204
+ 2023-09-03 20:51:45,718 epoch 9 - iter 440/447 - loss 0.00784726 - time (sec): 78.73 - samples/sec: 1083.79 - lr: 0.000003 - momentum: 0.000000
205
+ 2023-09-03 20:51:46,743 ----------------------------------------------------------------------------------------------------
206
+ 2023-09-03 20:51:46,744 EPOCH 9 done: loss 0.0082 - lr: 0.000003
207
+ 2023-09-03 20:52:00,034 DEV : loss 0.20611871778964996 - f1-score (micro avg) 0.7865
208
+ 2023-09-03 20:52:00,063 saving best model
209
+ 2023-09-03 20:52:01,421 ----------------------------------------------------------------------------------------------------
210
+ 2023-09-03 20:52:09,460 epoch 10 - iter 44/447 - loss 0.00677502 - time (sec): 8.04 - samples/sec: 1121.79 - lr: 0.000003 - momentum: 0.000000
211
+ 2023-09-03 20:52:16,836 epoch 10 - iter 88/447 - loss 0.01068836 - time (sec): 15.41 - samples/sec: 1122.36 - lr: 0.000003 - momentum: 0.000000
212
+ 2023-09-03 20:52:24,450 epoch 10 - iter 132/447 - loss 0.00821975 - time (sec): 23.03 - samples/sec: 1117.70 - lr: 0.000002 - momentum: 0.000000
213
+ 2023-09-03 20:52:32,639 epoch 10 - iter 176/447 - loss 0.00812287 - time (sec): 31.22 - samples/sec: 1109.39 - lr: 0.000002 - momentum: 0.000000
214
+ 2023-09-03 20:52:39,919 epoch 10 - iter 220/447 - loss 0.00725474 - time (sec): 38.50 - samples/sec: 1108.00 - lr: 0.000002 - momentum: 0.000000
215
+ 2023-09-03 20:52:47,348 epoch 10 - iter 264/447 - loss 0.00845142 - time (sec): 45.93 - samples/sec: 1106.12 - lr: 0.000001 - momentum: 0.000000
216
+ 2023-09-03 20:52:55,128 epoch 10 - iter 308/447 - loss 0.00775298 - time (sec): 53.71 - samples/sec: 1106.39 - lr: 0.000001 - momentum: 0.000000
217
+ 2023-09-03 20:53:04,946 epoch 10 - iter 352/447 - loss 0.00703352 - time (sec): 63.52 - samples/sec: 1096.54 - lr: 0.000001 - momentum: 0.000000
218
+ 2023-09-03 20:53:12,202 epoch 10 - iter 396/447 - loss 0.00696440 - time (sec): 70.78 - samples/sec: 1095.09 - lr: 0.000000 - momentum: 0.000000
219
+ 2023-09-03 20:53:19,146 epoch 10 - iter 440/447 - loss 0.00637139 - time (sec): 77.72 - samples/sec: 1097.19 - lr: 0.000000 - momentum: 0.000000
220
+ 2023-09-03 20:53:20,209 ----------------------------------------------------------------------------------------------------
221
+ 2023-09-03 20:53:20,209 EPOCH 10 done: loss 0.0065 - lr: 0.000000
222
+ 2023-09-03 20:53:33,415 DEV : loss 0.20700332522392273 - f1-score (micro avg) 0.7909
223
+ 2023-09-03 20:53:33,442 saving best model
224
+ 2023-09-03 20:53:35,340 ----------------------------------------------------------------------------------------------------
225
+ 2023-09-03 20:53:35,342 Loading model from best epoch ...
226
+ 2023-09-03 20:53:37,132 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
227
+ 2023-09-03 20:53:47,867
228
+ Results:
229
+ - F-score (micro) 0.7443
230
+ - F-score (macro) 0.6732
231
+ - Accuracy 0.6117
232
+
233
+ By class:
234
+ precision recall f1-score support
235
+
236
+ loc 0.8439 0.8523 0.8481 596
237
+ pers 0.6263 0.7297 0.6741 333
238
+ org 0.5455 0.5909 0.5673 132
239
+ prod 0.6364 0.5303 0.5785 66
240
+ time 0.6491 0.7551 0.6981 49
241
+
242
+ micro avg 0.7237 0.7662 0.7443 1176
243
+ macro avg 0.6602 0.6917 0.6732 1176
244
+ weighted avg 0.7290 0.7662 0.7459 1176
245
+
246
+ 2023-09-03 20:53:47,867 ----------------------------------------------------------------------------------------------------