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autotrain-flair-hipe2022-fr-hmbert

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  1. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4/best-model.pt +3 -0
  2. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4/dev.tsv +0 -0
  3. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4/final-model.pt +3 -0
  4. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4/loss.tsv +11 -0
  5. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4/test.tsv +0 -0
  6. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4/training.log +240 -0
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4/best-model.pt ADDED
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hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4/dev.tsv ADDED
The diff for this file is too large to render. See raw diff
 
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4/final-model.pt ADDED
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hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-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 16:05:54 0.0000 0.5415 0.1324 0.6817 0.7640 0.7205 0.5916
3
+ 2 16:08:52 0.0000 0.1272 0.1110 0.7712 0.8225 0.7960 0.6838
4
+ 3 16:11:53 0.0000 0.0794 0.1956 0.7284 0.8001 0.7626 0.6522
5
+ 4 16:14:51 0.0000 0.0551 0.1651 0.8434 0.8419 0.8426 0.7454
6
+ 5 16:17:50 0.0000 0.0419 0.2098 0.8085 0.8270 0.8177 0.7159
7
+ 6 16:20:48 0.0000 0.0267 0.2401 0.8017 0.8265 0.8139 0.7105
8
+ 7 16:23:48 0.0000 0.0208 0.2017 0.8137 0.8408 0.8270 0.7303
9
+ 8 16:26:47 0.0000 0.0151 0.2118 0.8235 0.8419 0.8326 0.7365
10
+ 9 16:29:45 0.0000 0.0111 0.2195 0.8215 0.8408 0.8310 0.7311
11
+ 10 16:32:44 0.0000 0.0064 0.2171 0.8227 0.8396 0.8311 0.7334
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4/test.tsv ADDED
The diff for this file is too large to render. See raw diff
 
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4/training.log ADDED
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+ 2023-09-04 16:02:59,537 ----------------------------------------------------------------------------------------------------
2
+ 2023-09-04 16:02:59,538 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)
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(
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+ (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
+ 2023-09-04 16:02:59,538 ----------------------------------------------------------------------------------------------------
51
+ 2023-09-04 16:02:59,538 MultiCorpus: 5901 train + 1287 dev + 1505 test sentences
52
+ - NER_HIPE_2022 Corpus: 5901 train + 1287 dev + 1505 test sentences - /app/.flair/datasets/ner_hipe_2022/v2.1/hipe2020/fr/with_doc_seperator
53
+ 2023-09-04 16:02:59,538 ----------------------------------------------------------------------------------------------------
54
+ 2023-09-04 16:02:59,538 Train: 5901 sentences
55
+ 2023-09-04 16:02:59,538 (train_with_dev=False, train_with_test=False)
56
+ 2023-09-04 16:02:59,538 ----------------------------------------------------------------------------------------------------
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+ 2023-09-04 16:02:59,538 Training Params:
58
+ 2023-09-04 16:02:59,539 - learning_rate: "3e-05"
59
+ 2023-09-04 16:02:59,539 - mini_batch_size: "4"
60
+ 2023-09-04 16:02:59,539 - max_epochs: "10"
61
+ 2023-09-04 16:02:59,539 - shuffle: "True"
62
+ 2023-09-04 16:02:59,539 ----------------------------------------------------------------------------------------------------
63
+ 2023-09-04 16:02:59,539 Plugins:
64
+ 2023-09-04 16:02:59,539 - LinearScheduler | warmup_fraction: '0.1'
65
+ 2023-09-04 16:02:59,539 ----------------------------------------------------------------------------------------------------
66
+ 2023-09-04 16:02:59,539 Final evaluation on model from best epoch (best-model.pt)
67
+ 2023-09-04 16:02:59,539 - metric: "('micro avg', 'f1-score')"
68
+ 2023-09-04 16:02:59,539 ----------------------------------------------------------------------------------------------------
69
+ 2023-09-04 16:02:59,539 Computation:
70
+ 2023-09-04 16:02:59,539 - compute on device: cuda:0
71
+ 2023-09-04 16:02:59,539 - embedding storage: none
72
+ 2023-09-04 16:02:59,539 ----------------------------------------------------------------------------------------------------
73
+ 2023-09-04 16:02:59,539 Model training base path: "hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4"
74
+ 2023-09-04 16:02:59,539 ----------------------------------------------------------------------------------------------------
75
+ 2023-09-04 16:02:59,539 ----------------------------------------------------------------------------------------------------
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+ 2023-09-04 16:03:15,886 epoch 1 - iter 147/1476 - loss 2.52871055 - time (sec): 16.35 - samples/sec: 1052.18 - lr: 0.000003 - momentum: 0.000000
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+ 2023-09-04 16:03:33,176 epoch 1 - iter 294/1476 - loss 1.57041670 - time (sec): 33.64 - samples/sec: 1076.84 - lr: 0.000006 - momentum: 0.000000
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+ 2023-09-04 16:03:48,492 epoch 1 - iter 441/1476 - loss 1.20260211 - time (sec): 48.95 - samples/sec: 1070.07 - lr: 0.000009 - momentum: 0.000000
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+ 2023-09-04 16:04:03,889 epoch 1 - iter 588/1476 - loss 1.00409702 - time (sec): 64.35 - samples/sec: 1057.63 - lr: 0.000012 - momentum: 0.000000
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+ 2023-09-04 16:04:19,482 epoch 1 - iter 735/1476 - loss 0.86487115 - time (sec): 79.94 - samples/sec: 1050.83 - lr: 0.000015 - momentum: 0.000000
81
+ 2023-09-04 16:04:35,541 epoch 1 - iter 882/1476 - loss 0.76585745 - time (sec): 96.00 - samples/sec: 1048.37 - lr: 0.000018 - momentum: 0.000000
82
+ 2023-09-04 16:04:50,167 epoch 1 - iter 1029/1476 - loss 0.70235927 - time (sec): 110.63 - samples/sec: 1039.11 - lr: 0.000021 - momentum: 0.000000
83
+ 2023-09-04 16:05:06,428 epoch 1 - iter 1176/1476 - loss 0.63956363 - time (sec): 126.89 - samples/sec: 1038.51 - lr: 0.000024 - momentum: 0.000000
84
+ 2023-09-04 16:05:22,090 epoch 1 - iter 1323/1476 - loss 0.58984360 - time (sec): 142.55 - samples/sec: 1036.37 - lr: 0.000027 - momentum: 0.000000
85
+ 2023-09-04 16:05:39,018 epoch 1 - iter 1470/1476 - loss 0.54297100 - time (sec): 159.48 - samples/sec: 1038.96 - lr: 0.000030 - momentum: 0.000000
86
+ 2023-09-04 16:05:39,700 ----------------------------------------------------------------------------------------------------
87
+ 2023-09-04 16:05:39,700 EPOCH 1 done: loss 0.5415 - lr: 0.000030
88
+ 2023-09-04 16:05:54,073 DEV : loss 0.13243263959884644 - f1-score (micro avg) 0.7205
89
+ 2023-09-04 16:05:54,102 saving best model
90
+ 2023-09-04 16:05:54,575 ----------------------------------------------------------------------------------------------------
91
+ 2023-09-04 16:06:09,565 epoch 2 - iter 147/1476 - loss 0.14267517 - time (sec): 14.99 - samples/sec: 981.96 - lr: 0.000030 - momentum: 0.000000
92
+ 2023-09-04 16:06:24,627 epoch 2 - iter 294/1476 - loss 0.14390133 - time (sec): 30.05 - samples/sec: 997.38 - lr: 0.000029 - momentum: 0.000000
93
+ 2023-09-04 16:06:41,452 epoch 2 - iter 441/1476 - loss 0.13701884 - time (sec): 46.88 - samples/sec: 1028.71 - lr: 0.000029 - momentum: 0.000000
94
+ 2023-09-04 16:06:57,177 epoch 2 - iter 588/1476 - loss 0.13648488 - time (sec): 62.60 - samples/sec: 1030.84 - lr: 0.000029 - momentum: 0.000000
95
+ 2023-09-04 16:07:12,917 epoch 2 - iter 735/1476 - loss 0.13592443 - time (sec): 78.34 - samples/sec: 1030.46 - lr: 0.000028 - momentum: 0.000000
96
+ 2023-09-04 16:07:29,367 epoch 2 - iter 882/1476 - loss 0.13220896 - time (sec): 94.79 - samples/sec: 1032.87 - lr: 0.000028 - momentum: 0.000000
97
+ 2023-09-04 16:07:45,479 epoch 2 - iter 1029/1476 - loss 0.13253062 - time (sec): 110.90 - samples/sec: 1031.40 - lr: 0.000028 - momentum: 0.000000
98
+ 2023-09-04 16:08:00,965 epoch 2 - iter 1176/1476 - loss 0.13007434 - time (sec): 126.39 - samples/sec: 1032.26 - lr: 0.000027 - momentum: 0.000000
99
+ 2023-09-04 16:08:18,698 epoch 2 - iter 1323/1476 - loss 0.12881868 - time (sec): 144.12 - samples/sec: 1038.54 - lr: 0.000027 - momentum: 0.000000
100
+ 2023-09-04 16:08:34,338 epoch 2 - iter 1470/1476 - loss 0.12726562 - time (sec): 159.76 - samples/sec: 1038.50 - lr: 0.000027 - momentum: 0.000000
101
+ 2023-09-04 16:08:34,889 ----------------------------------------------------------------------------------------------------
102
+ 2023-09-04 16:08:34,889 EPOCH 2 done: loss 0.1272 - lr: 0.000027
103
+ 2023-09-04 16:08:52,595 DEV : loss 0.11097484081983566 - f1-score (micro avg) 0.796
104
+ 2023-09-04 16:08:52,624 saving best model
105
+ 2023-09-04 16:08:53,973 ----------------------------------------------------------------------------------------------------
106
+ 2023-09-04 16:09:09,684 epoch 3 - iter 147/1476 - loss 0.08170776 - time (sec): 15.71 - samples/sec: 1040.36 - lr: 0.000026 - momentum: 0.000000
107
+ 2023-09-04 16:09:26,779 epoch 3 - iter 294/1476 - loss 0.07926320 - time (sec): 32.80 - samples/sec: 1035.82 - lr: 0.000026 - momentum: 0.000000
108
+ 2023-09-04 16:09:42,102 epoch 3 - iter 441/1476 - loss 0.07890665 - time (sec): 48.13 - samples/sec: 1029.76 - lr: 0.000026 - momentum: 0.000000
109
+ 2023-09-04 16:09:59,259 epoch 3 - iter 588/1476 - loss 0.08608802 - time (sec): 65.28 - samples/sec: 1033.99 - lr: 0.000025 - momentum: 0.000000
110
+ 2023-09-04 16:10:14,751 epoch 3 - iter 735/1476 - loss 0.08554394 - time (sec): 80.78 - samples/sec: 1030.81 - lr: 0.000025 - momentum: 0.000000
111
+ 2023-09-04 16:10:30,631 epoch 3 - iter 882/1476 - loss 0.08417210 - time (sec): 96.66 - samples/sec: 1028.67 - lr: 0.000025 - momentum: 0.000000
112
+ 2023-09-04 16:10:46,240 epoch 3 - iter 1029/1476 - loss 0.08294887 - time (sec): 112.27 - samples/sec: 1032.47 - lr: 0.000024 - momentum: 0.000000
113
+ 2023-09-04 16:11:02,889 epoch 3 - iter 1176/1476 - loss 0.08096675 - time (sec): 128.91 - samples/sec: 1035.49 - lr: 0.000024 - momentum: 0.000000
114
+ 2023-09-04 16:11:18,410 epoch 3 - iter 1323/1476 - loss 0.07939551 - time (sec): 144.43 - samples/sec: 1032.82 - lr: 0.000024 - momentum: 0.000000
115
+ 2023-09-04 16:11:34,662 epoch 3 - iter 1470/1476 - loss 0.07940718 - time (sec): 160.69 - samples/sec: 1032.94 - lr: 0.000023 - momentum: 0.000000
116
+ 2023-09-04 16:11:35,231 ----------------------------------------------------------------------------------------------------
117
+ 2023-09-04 16:11:35,231 EPOCH 3 done: loss 0.0794 - lr: 0.000023
118
+ 2023-09-04 16:11:53,030 DEV : loss 0.19561560451984406 - f1-score (micro avg) 0.7626
119
+ 2023-09-04 16:11:53,059 ----------------------------------------------------------------------------------------------------
120
+ 2023-09-04 16:12:09,713 epoch 4 - iter 147/1476 - loss 0.04872021 - time (sec): 16.65 - samples/sec: 1073.23 - lr: 0.000023 - momentum: 0.000000
121
+ 2023-09-04 16:12:25,032 epoch 4 - iter 294/1476 - loss 0.04571029 - time (sec): 31.97 - samples/sec: 1039.82 - lr: 0.000023 - momentum: 0.000000
122
+ 2023-09-04 16:12:43,266 epoch 4 - iter 441/1476 - loss 0.04514289 - time (sec): 50.21 - samples/sec: 1058.79 - lr: 0.000022 - momentum: 0.000000
123
+ 2023-09-04 16:12:59,665 epoch 4 - iter 588/1476 - loss 0.05058321 - time (sec): 66.60 - samples/sec: 1049.86 - lr: 0.000022 - momentum: 0.000000
124
+ 2023-09-04 16:13:14,986 epoch 4 - iter 735/1476 - loss 0.05049063 - time (sec): 81.93 - samples/sec: 1048.57 - lr: 0.000022 - momentum: 0.000000
125
+ 2023-09-04 16:13:31,978 epoch 4 - iter 882/1476 - loss 0.05456700 - time (sec): 98.92 - samples/sec: 1054.31 - lr: 0.000021 - momentum: 0.000000
126
+ 2023-09-04 16:13:47,311 epoch 4 - iter 1029/1476 - loss 0.05403008 - time (sec): 114.25 - samples/sec: 1049.04 - lr: 0.000021 - momentum: 0.000000
127
+ 2023-09-04 16:14:02,513 epoch 4 - iter 1176/1476 - loss 0.05428571 - time (sec): 129.45 - samples/sec: 1040.02 - lr: 0.000021 - momentum: 0.000000
128
+ 2023-09-04 16:14:17,349 epoch 4 - iter 1323/1476 - loss 0.05492730 - time (sec): 144.29 - samples/sec: 1037.08 - lr: 0.000020 - momentum: 0.000000
129
+ 2023-09-04 16:14:32,781 epoch 4 - iter 1470/1476 - loss 0.05522605 - time (sec): 159.72 - samples/sec: 1038.20 - lr: 0.000020 - momentum: 0.000000
130
+ 2023-09-04 16:14:33,375 ----------------------------------------------------------------------------------------------------
131
+ 2023-09-04 16:14:33,375 EPOCH 4 done: loss 0.0551 - lr: 0.000020
132
+ 2023-09-04 16:14:51,078 DEV : loss 0.1650702804327011 - f1-score (micro avg) 0.8426
133
+ 2023-09-04 16:14:51,108 saving best model
134
+ 2023-09-04 16:14:52,441 ----------------------------------------------------------------------------------------------------
135
+ 2023-09-04 16:15:08,589 epoch 5 - iter 147/1476 - loss 0.03825252 - time (sec): 16.15 - samples/sec: 1048.01 - lr: 0.000020 - momentum: 0.000000
136
+ 2023-09-04 16:15:23,847 epoch 5 - iter 294/1476 - loss 0.04275573 - time (sec): 31.40 - samples/sec: 1029.22 - lr: 0.000019 - momentum: 0.000000
137
+ 2023-09-04 16:15:39,381 epoch 5 - iter 441/1476 - loss 0.04602369 - time (sec): 46.94 - samples/sec: 1037.64 - lr: 0.000019 - momentum: 0.000000
138
+ 2023-09-04 16:15:55,702 epoch 5 - iter 588/1476 - loss 0.04441490 - time (sec): 63.26 - samples/sec: 1041.26 - lr: 0.000019 - momentum: 0.000000
139
+ 2023-09-04 16:16:11,802 epoch 5 - iter 735/1476 - loss 0.04204585 - time (sec): 79.36 - samples/sec: 1034.46 - lr: 0.000018 - momentum: 0.000000
140
+ 2023-09-04 16:16:28,332 epoch 5 - iter 882/1476 - loss 0.04087090 - time (sec): 95.89 - samples/sec: 1031.86 - lr: 0.000018 - momentum: 0.000000
141
+ 2023-09-04 16:16:45,623 epoch 5 - iter 1029/1476 - loss 0.04229785 - time (sec): 113.18 - samples/sec: 1036.82 - lr: 0.000018 - momentum: 0.000000
142
+ 2023-09-04 16:17:00,370 epoch 5 - iter 1176/1476 - loss 0.04263744 - time (sec): 127.93 - samples/sec: 1035.84 - lr: 0.000017 - momentum: 0.000000
143
+ 2023-09-04 16:17:16,877 epoch 5 - iter 1323/1476 - loss 0.04146297 - time (sec): 144.43 - samples/sec: 1036.58 - lr: 0.000017 - momentum: 0.000000
144
+ 2023-09-04 16:17:32,485 epoch 5 - iter 1470/1476 - loss 0.04198539 - time (sec): 160.04 - samples/sec: 1035.39 - lr: 0.000017 - momentum: 0.000000
145
+ 2023-09-04 16:17:33,083 ----------------------------------------------------------------------------------------------------
146
+ 2023-09-04 16:17:33,084 EPOCH 5 done: loss 0.0419 - lr: 0.000017
147
+ 2023-09-04 16:17:50,720 DEV : loss 0.20976178348064423 - f1-score (micro avg) 0.8177
148
+ 2023-09-04 16:17:50,748 ----------------------------------------------------------------------------------------------------
149
+ 2023-09-04 16:18:06,872 epoch 6 - iter 147/1476 - loss 0.02798721 - time (sec): 16.12 - samples/sec: 1056.07 - lr: 0.000016 - momentum: 0.000000
150
+ 2023-09-04 16:18:22,910 epoch 6 - iter 294/1476 - loss 0.02620166 - time (sec): 32.16 - samples/sec: 1031.47 - lr: 0.000016 - momentum: 0.000000
151
+ 2023-09-04 16:18:37,959 epoch 6 - iter 441/1476 - loss 0.02418936 - time (sec): 47.21 - samples/sec: 1014.04 - lr: 0.000016 - momentum: 0.000000
152
+ 2023-09-04 16:18:53,437 epoch 6 - iter 588/1476 - loss 0.02380874 - time (sec): 62.69 - samples/sec: 1017.91 - lr: 0.000015 - momentum: 0.000000
153
+ 2023-09-04 16:19:09,360 epoch 6 - iter 735/1476 - loss 0.02310671 - time (sec): 78.61 - samples/sec: 1025.30 - lr: 0.000015 - momentum: 0.000000
154
+ 2023-09-04 16:19:24,285 epoch 6 - iter 882/1476 - loss 0.02262606 - time (sec): 93.54 - samples/sec: 1018.00 - lr: 0.000015 - momentum: 0.000000
155
+ 2023-09-04 16:19:40,267 epoch 6 - iter 1029/1476 - loss 0.02380293 - time (sec): 109.52 - samples/sec: 1024.96 - lr: 0.000014 - momentum: 0.000000
156
+ 2023-09-04 16:19:57,468 epoch 6 - iter 1176/1476 - loss 0.02415468 - time (sec): 126.72 - samples/sec: 1034.15 - lr: 0.000014 - momentum: 0.000000
157
+ 2023-09-04 16:20:14,977 epoch 6 - iter 1323/1476 - loss 0.02619189 - time (sec): 144.23 - samples/sec: 1039.67 - lr: 0.000014 - momentum: 0.000000
158
+ 2023-09-04 16:20:30,489 epoch 6 - iter 1470/1476 - loss 0.02681190 - time (sec): 159.74 - samples/sec: 1038.55 - lr: 0.000013 - momentum: 0.000000
159
+ 2023-09-04 16:20:31,039 ----------------------------------------------------------------------------------------------------
160
+ 2023-09-04 16:20:31,039 EPOCH 6 done: loss 0.0267 - lr: 0.000013
161
+ 2023-09-04 16:20:48,771 DEV : loss 0.24007707834243774 - f1-score (micro avg) 0.8139
162
+ 2023-09-04 16:20:48,800 ----------------------------------------------------------------------------------------------------
163
+ 2023-09-04 16:21:03,952 epoch 7 - iter 147/1476 - loss 0.01359181 - time (sec): 15.15 - samples/sec: 1018.09 - lr: 0.000013 - momentum: 0.000000
164
+ 2023-09-04 16:21:19,105 epoch 7 - iter 294/1476 - loss 0.01439497 - time (sec): 30.30 - samples/sec: 992.79 - lr: 0.000013 - momentum: 0.000000
165
+ 2023-09-04 16:21:35,949 epoch 7 - iter 441/1476 - loss 0.01973463 - time (sec): 47.15 - samples/sec: 1005.82 - lr: 0.000012 - momentum: 0.000000
166
+ 2023-09-04 16:21:51,925 epoch 7 - iter 588/1476 - loss 0.01951830 - time (sec): 63.12 - samples/sec: 1005.42 - lr: 0.000012 - momentum: 0.000000
167
+ 2023-09-04 16:22:07,812 epoch 7 - iter 735/1476 - loss 0.01914368 - time (sec): 79.01 - samples/sec: 1012.24 - lr: 0.000012 - momentum: 0.000000
168
+ 2023-09-04 16:22:23,956 epoch 7 - iter 882/1476 - loss 0.02067535 - time (sec): 95.15 - samples/sec: 1015.65 - lr: 0.000011 - momentum: 0.000000
169
+ 2023-09-04 16:22:39,475 epoch 7 - iter 1029/1476 - loss 0.02053550 - time (sec): 110.67 - samples/sec: 1019.44 - lr: 0.000011 - momentum: 0.000000
170
+ 2023-09-04 16:22:55,533 epoch 7 - iter 1176/1476 - loss 0.02017769 - time (sec): 126.73 - samples/sec: 1020.77 - lr: 0.000011 - momentum: 0.000000
171
+ 2023-09-04 16:23:13,472 epoch 7 - iter 1323/1476 - loss 0.01993715 - time (sec): 144.67 - samples/sec: 1031.90 - lr: 0.000010 - momentum: 0.000000
172
+ 2023-09-04 16:23:29,311 epoch 7 - iter 1470/1476 - loss 0.02079057 - time (sec): 160.51 - samples/sec: 1030.96 - lr: 0.000010 - momentum: 0.000000
173
+ 2023-09-04 16:23:30,168 ----------------------------------------------------------------------------------------------------
174
+ 2023-09-04 16:23:30,168 EPOCH 7 done: loss 0.0208 - lr: 0.000010
175
+ 2023-09-04 16:23:47,994 DEV : loss 0.20170645415782928 - f1-score (micro avg) 0.827
176
+ 2023-09-04 16:23:48,023 ----------------------------------------------------------------------------------------------------
177
+ 2023-09-04 16:24:05,138 epoch 8 - iter 147/1476 - loss 0.01368119 - time (sec): 17.11 - samples/sec: 1033.58 - lr: 0.000010 - momentum: 0.000000
178
+ 2023-09-04 16:24:20,283 epoch 8 - iter 294/1476 - loss 0.01483621 - time (sec): 32.26 - samples/sec: 1013.05 - lr: 0.000009 - momentum: 0.000000
179
+ 2023-09-04 16:24:36,338 epoch 8 - iter 441/1476 - loss 0.01441544 - time (sec): 48.31 - samples/sec: 1028.54 - lr: 0.000009 - momentum: 0.000000
180
+ 2023-09-04 16:24:51,632 epoch 8 - iter 588/1476 - loss 0.01356112 - time (sec): 63.61 - samples/sec: 1023.44 - lr: 0.000009 - momentum: 0.000000
181
+ 2023-09-04 16:25:08,937 epoch 8 - iter 735/1476 - loss 0.01543348 - time (sec): 80.91 - samples/sec: 1025.66 - lr: 0.000008 - momentum: 0.000000
182
+ 2023-09-04 16:25:25,043 epoch 8 - iter 882/1476 - loss 0.01437888 - time (sec): 97.02 - samples/sec: 1026.18 - lr: 0.000008 - momentum: 0.000000
183
+ 2023-09-04 16:25:39,764 epoch 8 - iter 1029/1476 - loss 0.01391930 - time (sec): 111.74 - samples/sec: 1023.92 - lr: 0.000008 - momentum: 0.000000
184
+ 2023-09-04 16:25:56,352 epoch 8 - iter 1176/1476 - loss 0.01365890 - time (sec): 128.33 - samples/sec: 1026.47 - lr: 0.000007 - momentum: 0.000000
185
+ 2023-09-04 16:26:12,375 epoch 8 - iter 1323/1476 - loss 0.01346635 - time (sec): 144.35 - samples/sec: 1028.51 - lr: 0.000007 - momentum: 0.000000
186
+ 2023-09-04 16:26:28,802 epoch 8 - iter 1470/1476 - loss 0.01514444 - time (sec): 160.78 - samples/sec: 1031.04 - lr: 0.000007 - momentum: 0.000000
187
+ 2023-09-04 16:26:29,417 ----------------------------------------------------------------------------------------------------
188
+ 2023-09-04 16:26:29,417 EPOCH 8 done: loss 0.0151 - lr: 0.000007
189
+ 2023-09-04 16:26:47,229 DEV : loss 0.21184813976287842 - f1-score (micro avg) 0.8326
190
+ 2023-09-04 16:26:47,258 ----------------------------------------------------------------------------------------------------
191
+ 2023-09-04 16:27:03,378 epoch 9 - iter 147/1476 - loss 0.00710163 - time (sec): 16.12 - samples/sec: 1048.44 - lr: 0.000006 - momentum: 0.000000
192
+ 2023-09-04 16:27:19,755 epoch 9 - iter 294/1476 - loss 0.00801837 - time (sec): 32.50 - samples/sec: 1037.91 - lr: 0.000006 - momentum: 0.000000
193
+ 2023-09-04 16:27:34,665 epoch 9 - iter 441/1476 - loss 0.00580640 - time (sec): 47.40 - samples/sec: 1026.45 - lr: 0.000006 - momentum: 0.000000
194
+ 2023-09-04 16:27:49,985 epoch 9 - iter 588/1476 - loss 0.00837084 - time (sec): 62.73 - samples/sec: 1019.41 - lr: 0.000005 - momentum: 0.000000
195
+ 2023-09-04 16:28:06,208 epoch 9 - iter 735/1476 - loss 0.01021569 - time (sec): 78.95 - samples/sec: 1012.55 - lr: 0.000005 - momentum: 0.000000
196
+ 2023-09-04 16:28:23,064 epoch 9 - iter 882/1476 - loss 0.01022452 - time (sec): 95.80 - samples/sec: 1017.33 - lr: 0.000005 - momentum: 0.000000
197
+ 2023-09-04 16:28:39,930 epoch 9 - iter 1029/1476 - loss 0.00955407 - time (sec): 112.67 - samples/sec: 1024.97 - lr: 0.000004 - momentum: 0.000000
198
+ 2023-09-04 16:28:55,018 epoch 9 - iter 1176/1476 - loss 0.01011015 - time (sec): 127.76 - samples/sec: 1023.93 - lr: 0.000004 - momentum: 0.000000
199
+ 2023-09-04 16:29:10,672 epoch 9 - iter 1323/1476 - loss 0.01074550 - time (sec): 143.41 - samples/sec: 1023.30 - lr: 0.000004 - momentum: 0.000000
200
+ 2023-09-04 16:29:27,485 epoch 9 - iter 1470/1476 - loss 0.01108833 - time (sec): 160.23 - samples/sec: 1034.99 - lr: 0.000003 - momentum: 0.000000
201
+ 2023-09-04 16:29:28,075 ----------------------------------------------------------------------------------------------------
202
+ 2023-09-04 16:29:28,076 EPOCH 9 done: loss 0.0111 - lr: 0.000003
203
+ 2023-09-04 16:29:45,841 DEV : loss 0.21952657401561737 - f1-score (micro avg) 0.831
204
+ 2023-09-04 16:29:45,870 ----------------------------------------------------------------------------------------------------
205
+ 2023-09-04 16:30:01,686 epoch 10 - iter 147/1476 - loss 0.00192668 - time (sec): 15.81 - samples/sec: 1026.31 - lr: 0.000003 - momentum: 0.000000
206
+ 2023-09-04 16:30:17,496 epoch 10 - iter 294/1476 - loss 0.00589079 - time (sec): 31.62 - samples/sec: 1037.17 - lr: 0.000003 - momentum: 0.000000
207
+ 2023-09-04 16:30:32,320 epoch 10 - iter 441/1476 - loss 0.00609051 - time (sec): 46.45 - samples/sec: 1027.66 - lr: 0.000002 - momentum: 0.000000
208
+ 2023-09-04 16:30:49,236 epoch 10 - iter 588/1476 - loss 0.00804938 - time (sec): 63.36 - samples/sec: 1040.72 - lr: 0.000002 - momentum: 0.000000
209
+ 2023-09-04 16:31:06,789 epoch 10 - iter 735/1476 - loss 0.00869197 - time (sec): 80.92 - samples/sec: 1043.44 - lr: 0.000002 - momentum: 0.000000
210
+ 2023-09-04 16:31:22,199 epoch 10 - iter 882/1476 - loss 0.00811293 - time (sec): 96.33 - samples/sec: 1042.35 - lr: 0.000001 - momentum: 0.000000
211
+ 2023-09-04 16:31:38,238 epoch 10 - iter 1029/1476 - loss 0.00720321 - time (sec): 112.37 - samples/sec: 1044.89 - lr: 0.000001 - momentum: 0.000000
212
+ 2023-09-04 16:31:55,863 epoch 10 - iter 1176/1476 - loss 0.00680639 - time (sec): 129.99 - samples/sec: 1044.88 - lr: 0.000001 - momentum: 0.000000
213
+ 2023-09-04 16:32:11,597 epoch 10 - iter 1323/1476 - loss 0.00675417 - time (sec): 145.73 - samples/sec: 1039.65 - lr: 0.000000 - momentum: 0.000000
214
+ 2023-09-04 16:32:26,367 epoch 10 - iter 1470/1476 - loss 0.00641702 - time (sec): 160.50 - samples/sec: 1032.89 - lr: 0.000000 - momentum: 0.000000
215
+ 2023-09-04 16:32:26,963 ----------------------------------------------------------------------------------------------------
216
+ 2023-09-04 16:32:26,963 EPOCH 10 done: loss 0.0064 - lr: 0.000000
217
+ 2023-09-04 16:32:44,868 DEV : loss 0.2171323001384735 - f1-score (micro avg) 0.8311
218
+ 2023-09-04 16:32:45,400 ----------------------------------------------------------------------------------------------------
219
+ 2023-09-04 16:32:45,401 Loading model from best epoch ...
220
+ 2023-09-04 16:32:47,539 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-time, B-time, E-time, I-time, S-prod, B-prod, E-prod, I-prod
221
+ 2023-09-04 16:33:02,401
222
+ Results:
223
+ - F-score (micro) 0.7883
224
+ - F-score (macro) 0.6744
225
+ - Accuracy 0.6716
226
+
227
+ By class:
228
+ precision recall f1-score support
229
+
230
+ loc 0.8560 0.8520 0.8540 858
231
+ pers 0.7569 0.8119 0.7835 537
232
+ org 0.5917 0.5379 0.5635 132
233
+ time 0.4167 0.5556 0.4762 54
234
+ prod 0.7193 0.6721 0.6949 61
235
+
236
+ micro avg 0.7796 0.7972 0.7883 1642
237
+ macro avg 0.6681 0.6859 0.6744 1642
238
+ weighted avg 0.7828 0.7972 0.7892 1642
239
+
240
+ 2023-09-04 16:33:02,402 ----------------------------------------------------------------------------------------------------