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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-2/best-model.pt +3 -0
  2. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2/dev.tsv +0 -0
  3. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2/final-model.pt +3 -0
  4. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2/loss.tsv +11 -0
  5. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2/test.tsv +0 -0
  6. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2/training.log +243 -0
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2/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-2/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-2/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-2/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 12:13:20 0.0000 0.5658 0.1398 0.6653 0.7365 0.6991 0.5728
3
+ 2 12:16:19 0.0000 0.1310 0.1189 0.7513 0.8184 0.7834 0.6703
4
+ 3 12:19:18 0.0000 0.0828 0.1391 0.8122 0.7904 0.8012 0.6928
5
+ 4 12:22:17 0.0000 0.0569 0.1755 0.7952 0.8431 0.8185 0.7146
6
+ 5 12:25:17 0.0000 0.0397 0.1827 0.8017 0.8173 0.8094 0.7096
7
+ 6 12:28:16 0.0000 0.0274 0.1943 0.7741 0.8419 0.8066 0.7023
8
+ 7 12:31:14 0.0000 0.0209 0.2070 0.8160 0.8202 0.8181 0.7167
9
+ 8 12:34:12 0.0000 0.0173 0.1981 0.8051 0.8425 0.8234 0.7246
10
+ 9 12:37:13 0.0000 0.0100 0.2161 0.8304 0.8328 0.8316 0.7329
11
+ 10 12:40:15 0.0000 0.0070 0.2240 0.8173 0.8379 0.8275 0.7300
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2/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-2/training.log ADDED
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+ 2023-09-04 12:10:26,605 ----------------------------------------------------------------------------------------------------
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+ 2023-09-04 12:10:26,606 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)
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)
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+ (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 12:10:26,606 ----------------------------------------------------------------------------------------------------
51
+ 2023-09-04 12:10:26,606 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 12:10:26,606 ----------------------------------------------------------------------------------------------------
54
+ 2023-09-04 12:10:26,606 Train: 5901 sentences
55
+ 2023-09-04 12:10:26,606 (train_with_dev=False, train_with_test=False)
56
+ 2023-09-04 12:10:26,606 ----------------------------------------------------------------------------------------------------
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+ 2023-09-04 12:10:26,606 Training Params:
58
+ 2023-09-04 12:10:26,606 - learning_rate: "3e-05"
59
+ 2023-09-04 12:10:26,606 - mini_batch_size: "4"
60
+ 2023-09-04 12:10:26,607 - max_epochs: "10"
61
+ 2023-09-04 12:10:26,607 - shuffle: "True"
62
+ 2023-09-04 12:10:26,607 ----------------------------------------------------------------------------------------------------
63
+ 2023-09-04 12:10:26,607 Plugins:
64
+ 2023-09-04 12:10:26,607 - LinearScheduler | warmup_fraction: '0.1'
65
+ 2023-09-04 12:10:26,607 ----------------------------------------------------------------------------------------------------
66
+ 2023-09-04 12:10:26,607 Final evaluation on model from best epoch (best-model.pt)
67
+ 2023-09-04 12:10:26,607 - metric: "('micro avg', 'f1-score')"
68
+ 2023-09-04 12:10:26,607 ----------------------------------------------------------------------------------------------------
69
+ 2023-09-04 12:10:26,607 Computation:
70
+ 2023-09-04 12:10:26,607 - compute on device: cuda:0
71
+ 2023-09-04 12:10:26,607 - embedding storage: none
72
+ 2023-09-04 12:10:26,607 ----------------------------------------------------------------------------------------------------
73
+ 2023-09-04 12:10:26,607 Model training base path: "hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2"
74
+ 2023-09-04 12:10:26,607 ----------------------------------------------------------------------------------------------------
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+ 2023-09-04 12:10:26,607 ----------------------------------------------------------------------------------------------------
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+ 2023-09-04 12:10:43,161 epoch 1 - iter 147/1476 - loss 2.73210553 - time (sec): 16.55 - samples/sec: 1069.98 - lr: 0.000003 - momentum: 0.000000
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+ 2023-09-04 12:10:59,843 epoch 1 - iter 294/1476 - loss 1.67128939 - time (sec): 33.23 - samples/sec: 1081.29 - lr: 0.000006 - momentum: 0.000000
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+ 2023-09-04 12:11:15,620 epoch 1 - iter 441/1476 - loss 1.27494730 - time (sec): 49.01 - samples/sec: 1060.62 - lr: 0.000009 - momentum: 0.000000
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+ 2023-09-04 12:11:31,691 epoch 1 - iter 588/1476 - loss 1.03907660 - time (sec): 65.08 - samples/sec: 1055.96 - lr: 0.000012 - momentum: 0.000000
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+ 2023-09-04 12:11:47,984 epoch 1 - iter 735/1476 - loss 0.89724396 - time (sec): 81.38 - samples/sec: 1051.72 - lr: 0.000015 - momentum: 0.000000
81
+ 2023-09-04 12:12:03,754 epoch 1 - iter 882/1476 - loss 0.79290439 - time (sec): 97.15 - samples/sec: 1052.97 - lr: 0.000018 - momentum: 0.000000
82
+ 2023-09-04 12:12:19,631 epoch 1 - iter 1029/1476 - loss 0.71392745 - time (sec): 113.02 - samples/sec: 1049.34 - lr: 0.000021 - momentum: 0.000000
83
+ 2023-09-04 12:12:34,680 epoch 1 - iter 1176/1476 - loss 0.65858203 - time (sec): 128.07 - samples/sec: 1041.23 - lr: 0.000024 - momentum: 0.000000
84
+ 2023-09-04 12:12:50,407 epoch 1 - iter 1323/1476 - loss 0.60824912 - time (sec): 143.80 - samples/sec: 1040.82 - lr: 0.000027 - momentum: 0.000000
85
+ 2023-09-04 12:13:05,993 epoch 1 - iter 1470/1476 - loss 0.56700946 - time (sec): 159.38 - samples/sec: 1040.65 - lr: 0.000030 - momentum: 0.000000
86
+ 2023-09-04 12:13:06,527 ----------------------------------------------------------------------------------------------------
87
+ 2023-09-04 12:13:06,527 EPOCH 1 done: loss 0.5658 - lr: 0.000030
88
+ 2023-09-04 12:13:20,766 DEV : loss 0.1397937834262848 - f1-score (micro avg) 0.6991
89
+ 2023-09-04 12:13:20,795 saving best model
90
+ 2023-09-04 12:13:21,266 ----------------------------------------------------------------------------------------------------
91
+ 2023-09-04 12:13:35,988 epoch 2 - iter 147/1476 - loss 0.12585947 - time (sec): 14.72 - samples/sec: 1034.12 - lr: 0.000030 - momentum: 0.000000
92
+ 2023-09-04 12:13:51,523 epoch 2 - iter 294/1476 - loss 0.13634053 - time (sec): 30.26 - samples/sec: 1038.61 - lr: 0.000029 - momentum: 0.000000
93
+ 2023-09-04 12:14:07,067 epoch 2 - iter 441/1476 - loss 0.13629332 - time (sec): 45.80 - samples/sec: 1043.07 - lr: 0.000029 - momentum: 0.000000
94
+ 2023-09-04 12:14:22,956 epoch 2 - iter 588/1476 - loss 0.13335551 - time (sec): 61.69 - samples/sec: 1033.64 - lr: 0.000029 - momentum: 0.000000
95
+ 2023-09-04 12:14:38,328 epoch 2 - iter 735/1476 - loss 0.13773118 - time (sec): 77.06 - samples/sec: 1025.00 - lr: 0.000028 - momentum: 0.000000
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+ 2023-09-04 12:14:54,713 epoch 2 - iter 882/1476 - loss 0.13680546 - time (sec): 93.45 - samples/sec: 1028.06 - lr: 0.000028 - momentum: 0.000000
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+ 2023-09-04 12:15:11,883 epoch 2 - iter 1029/1476 - loss 0.13310658 - time (sec): 110.62 - samples/sec: 1034.77 - lr: 0.000028 - momentum: 0.000000
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+ 2023-09-04 12:15:27,632 epoch 2 - iter 1176/1476 - loss 0.12878942 - time (sec): 126.36 - samples/sec: 1036.13 - lr: 0.000027 - momentum: 0.000000
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+ 2023-09-04 12:15:43,931 epoch 2 - iter 1323/1476 - loss 0.13086266 - time (sec): 142.66 - samples/sec: 1037.71 - lr: 0.000027 - momentum: 0.000000
100
+ 2023-09-04 12:16:00,798 epoch 2 - iter 1470/1476 - loss 0.13097485 - time (sec): 159.53 - samples/sec: 1038.10 - lr: 0.000027 - momentum: 0.000000
101
+ 2023-09-04 12:16:01,459 ----------------------------------------------------------------------------------------------------
102
+ 2023-09-04 12:16:01,459 EPOCH 2 done: loss 0.1310 - lr: 0.000027
103
+ 2023-09-04 12:16:19,224 DEV : loss 0.11885535717010498 - f1-score (micro avg) 0.7834
104
+ 2023-09-04 12:16:19,254 saving best model
105
+ 2023-09-04 12:16:20,601 ----------------------------------------------------------------------------------------------------
106
+ 2023-09-04 12:16:36,100 epoch 3 - iter 147/1476 - loss 0.06204677 - time (sec): 15.50 - samples/sec: 1001.27 - lr: 0.000026 - momentum: 0.000000
107
+ 2023-09-04 12:16:51,968 epoch 3 - iter 294/1476 - loss 0.07796621 - time (sec): 31.37 - samples/sec: 1031.45 - lr: 0.000026 - momentum: 0.000000
108
+ 2023-09-04 12:17:07,754 epoch 3 - iter 441/1476 - loss 0.07999886 - time (sec): 47.15 - samples/sec: 1031.13 - lr: 0.000026 - momentum: 0.000000
109
+ 2023-09-04 12:17:22,838 epoch 3 - iter 588/1476 - loss 0.08270452 - time (sec): 62.24 - samples/sec: 1030.22 - lr: 0.000025 - momentum: 0.000000
110
+ 2023-09-04 12:17:39,545 epoch 3 - iter 735/1476 - loss 0.08305027 - time (sec): 78.94 - samples/sec: 1033.22 - lr: 0.000025 - momentum: 0.000000
111
+ 2023-09-04 12:17:56,400 epoch 3 - iter 882/1476 - loss 0.08112755 - time (sec): 95.80 - samples/sec: 1044.57 - lr: 0.000025 - momentum: 0.000000
112
+ 2023-09-04 12:18:12,029 epoch 3 - iter 1029/1476 - loss 0.07913826 - time (sec): 111.43 - samples/sec: 1042.16 - lr: 0.000024 - momentum: 0.000000
113
+ 2023-09-04 12:18:28,585 epoch 3 - iter 1176/1476 - loss 0.08164670 - time (sec): 127.98 - samples/sec: 1046.96 - lr: 0.000024 - momentum: 0.000000
114
+ 2023-09-04 12:18:44,212 epoch 3 - iter 1323/1476 - loss 0.08079377 - time (sec): 143.61 - samples/sec: 1046.25 - lr: 0.000024 - momentum: 0.000000
115
+ 2023-09-04 12:18:59,661 epoch 3 - iter 1470/1476 - loss 0.08273508 - time (sec): 159.06 - samples/sec: 1042.50 - lr: 0.000023 - momentum: 0.000000
116
+ 2023-09-04 12:19:00,226 ----------------------------------------------------------------------------------------------------
117
+ 2023-09-04 12:19:00,226 EPOCH 3 done: loss 0.0828 - lr: 0.000023
118
+ 2023-09-04 12:19:18,028 DEV : loss 0.13910789787769318 - f1-score (micro avg) 0.8012
119
+ 2023-09-04 12:19:18,056 saving best model
120
+ 2023-09-04 12:19:19,397 ----------------------------------------------------------------------------------------------------
121
+ 2023-09-04 12:19:34,809 epoch 4 - iter 147/1476 - loss 0.04738534 - time (sec): 15.41 - samples/sec: 988.79 - lr: 0.000023 - momentum: 0.000000
122
+ 2023-09-04 12:19:50,202 epoch 4 - iter 294/1476 - loss 0.04829203 - time (sec): 30.80 - samples/sec: 1009.68 - lr: 0.000023 - momentum: 0.000000
123
+ 2023-09-04 12:20:06,249 epoch 4 - iter 441/1476 - loss 0.05048346 - time (sec): 46.85 - samples/sec: 1020.57 - lr: 0.000022 - momentum: 0.000000
124
+ 2023-09-04 12:20:21,504 epoch 4 - iter 588/1476 - loss 0.05401829 - time (sec): 62.11 - samples/sec: 1018.02 - lr: 0.000022 - momentum: 0.000000
125
+ 2023-09-04 12:20:37,695 epoch 4 - iter 735/1476 - loss 0.05389081 - time (sec): 78.30 - samples/sec: 1020.65 - lr: 0.000022 - momentum: 0.000000
126
+ 2023-09-04 12:20:54,439 epoch 4 - iter 882/1476 - loss 0.05215908 - time (sec): 95.04 - samples/sec: 1024.97 - lr: 0.000021 - momentum: 0.000000
127
+ 2023-09-04 12:21:11,831 epoch 4 - iter 1029/1476 - loss 0.05188376 - time (sec): 112.43 - samples/sec: 1034.98 - lr: 0.000021 - momentum: 0.000000
128
+ 2023-09-04 12:21:27,996 epoch 4 - iter 1176/1476 - loss 0.05177284 - time (sec): 128.60 - samples/sec: 1033.94 - lr: 0.000021 - momentum: 0.000000
129
+ 2023-09-04 12:21:44,334 epoch 4 - iter 1323/1476 - loss 0.05555991 - time (sec): 144.94 - samples/sec: 1033.07 - lr: 0.000020 - momentum: 0.000000
130
+ 2023-09-04 12:21:59,639 epoch 4 - iter 1470/1476 - loss 0.05670977 - time (sec): 160.24 - samples/sec: 1034.23 - lr: 0.000020 - momentum: 0.000000
131
+ 2023-09-04 12:22:00,312 ----------------------------------------------------------------------------------------------------
132
+ 2023-09-04 12:22:00,312 EPOCH 4 done: loss 0.0569 - lr: 0.000020
133
+ 2023-09-04 12:22:17,969 DEV : loss 0.1755443960428238 - f1-score (micro avg) 0.8185
134
+ 2023-09-04 12:22:17,998 saving best model
135
+ 2023-09-04 12:22:19,334 ----------------------------------------------------------------------------------------------------
136
+ 2023-09-04 12:22:35,334 epoch 5 - iter 147/1476 - loss 0.04990612 - time (sec): 16.00 - samples/sec: 1034.91 - lr: 0.000020 - momentum: 0.000000
137
+ 2023-09-04 12:22:51,352 epoch 5 - iter 294/1476 - loss 0.04266641 - time (sec): 32.02 - samples/sec: 1039.65 - lr: 0.000019 - momentum: 0.000000
138
+ 2023-09-04 12:23:07,633 epoch 5 - iter 441/1476 - loss 0.04264386 - time (sec): 48.30 - samples/sec: 1042.34 - lr: 0.000019 - momentum: 0.000000
139
+ 2023-09-04 12:23:23,170 epoch 5 - iter 588/1476 - loss 0.04265371 - time (sec): 63.83 - samples/sec: 1040.25 - lr: 0.000019 - momentum: 0.000000
140
+ 2023-09-04 12:23:38,900 epoch 5 - iter 735/1476 - loss 0.04386486 - time (sec): 79.56 - samples/sec: 1037.46 - lr: 0.000018 - momentum: 0.000000
141
+ 2023-09-04 12:23:54,335 epoch 5 - iter 882/1476 - loss 0.04172884 - time (sec): 95.00 - samples/sec: 1032.30 - lr: 0.000018 - momentum: 0.000000
142
+ 2023-09-04 12:24:10,304 epoch 5 - iter 1029/1476 - loss 0.04002915 - time (sec): 110.97 - samples/sec: 1030.88 - lr: 0.000018 - momentum: 0.000000
143
+ 2023-09-04 12:24:27,674 epoch 5 - iter 1176/1476 - loss 0.04050197 - time (sec): 128.34 - samples/sec: 1035.06 - lr: 0.000017 - momentum: 0.000000
144
+ 2023-09-04 12:24:44,341 epoch 5 - iter 1323/1476 - loss 0.03922365 - time (sec): 145.01 - samples/sec: 1039.37 - lr: 0.000017 - momentum: 0.000000
145
+ 2023-09-04 12:24:59,438 epoch 5 - iter 1470/1476 - loss 0.03983868 - time (sec): 160.10 - samples/sec: 1035.92 - lr: 0.000017 - momentum: 0.000000
146
+ 2023-09-04 12:25:00,020 ----------------------------------------------------------------------------------------------------
147
+ 2023-09-04 12:25:00,020 EPOCH 5 done: loss 0.0397 - lr: 0.000017
148
+ 2023-09-04 12:25:17,700 DEV : loss 0.1826694905757904 - f1-score (micro avg) 0.8094
149
+ 2023-09-04 12:25:17,729 ----------------------------------------------------------------------------------------------------
150
+ 2023-09-04 12:25:32,823 epoch 6 - iter 147/1476 - loss 0.03369327 - time (sec): 15.09 - samples/sec: 989.08 - lr: 0.000016 - momentum: 0.000000
151
+ 2023-09-04 12:25:50,310 epoch 6 - iter 294/1476 - loss 0.02937400 - time (sec): 32.58 - samples/sec: 1047.82 - lr: 0.000016 - momentum: 0.000000
152
+ 2023-09-04 12:26:06,465 epoch 6 - iter 441/1476 - loss 0.02926561 - time (sec): 48.74 - samples/sec: 1047.66 - lr: 0.000016 - momentum: 0.000000
153
+ 2023-09-04 12:26:22,535 epoch 6 - iter 588/1476 - loss 0.02924922 - time (sec): 64.81 - samples/sec: 1040.36 - lr: 0.000015 - momentum: 0.000000
154
+ 2023-09-04 12:26:39,050 epoch 6 - iter 735/1476 - loss 0.02908336 - time (sec): 81.32 - samples/sec: 1044.38 - lr: 0.000015 - momentum: 0.000000
155
+ 2023-09-04 12:26:55,665 epoch 6 - iter 882/1476 - loss 0.02948574 - time (sec): 97.94 - samples/sec: 1043.27 - lr: 0.000015 - momentum: 0.000000
156
+ 2023-09-04 12:27:10,472 epoch 6 - iter 1029/1476 - loss 0.02810481 - time (sec): 112.74 - samples/sec: 1041.26 - lr: 0.000014 - momentum: 0.000000
157
+ 2023-09-04 12:27:26,343 epoch 6 - iter 1176/1476 - loss 0.02607506 - time (sec): 128.61 - samples/sec: 1041.52 - lr: 0.000014 - momentum: 0.000000
158
+ 2023-09-04 12:27:41,942 epoch 6 - iter 1323/1476 - loss 0.02644226 - time (sec): 144.21 - samples/sec: 1038.75 - lr: 0.000014 - momentum: 0.000000
159
+ 2023-09-04 12:27:57,884 epoch 6 - iter 1470/1476 - loss 0.02747315 - time (sec): 160.15 - samples/sec: 1036.31 - lr: 0.000013 - momentum: 0.000000
160
+ 2023-09-04 12:27:58,407 ----------------------------------------------------------------------------------------------------
161
+ 2023-09-04 12:27:58,408 EPOCH 6 done: loss 0.0274 - lr: 0.000013
162
+ 2023-09-04 12:28:16,034 DEV : loss 0.19426430761814117 - f1-score (micro avg) 0.8066
163
+ 2023-09-04 12:28:16,070 ----------------------------------------------------------------------------------------------------
164
+ 2023-09-04 12:28:33,027 epoch 7 - iter 147/1476 - loss 0.02184037 - time (sec): 16.96 - samples/sec: 1008.97 - lr: 0.000013 - momentum: 0.000000
165
+ 2023-09-04 12:28:47,857 epoch 7 - iter 294/1476 - loss 0.01928947 - time (sec): 31.79 - samples/sec: 1011.09 - lr: 0.000013 - momentum: 0.000000
166
+ 2023-09-04 12:29:04,927 epoch 7 - iter 441/1476 - loss 0.01813981 - time (sec): 48.86 - samples/sec: 1034.77 - lr: 0.000012 - momentum: 0.000000
167
+ 2023-09-04 12:29:22,279 epoch 7 - iter 588/1476 - loss 0.01750973 - time (sec): 66.21 - samples/sec: 1047.02 - lr: 0.000012 - momentum: 0.000000
168
+ 2023-09-04 12:29:37,447 epoch 7 - iter 735/1476 - loss 0.01906498 - time (sec): 81.38 - samples/sec: 1037.15 - lr: 0.000012 - momentum: 0.000000
169
+ 2023-09-04 12:29:52,572 epoch 7 - iter 882/1476 - loss 0.01833867 - time (sec): 96.50 - samples/sec: 1033.81 - lr: 0.000011 - momentum: 0.000000
170
+ 2023-09-04 12:30:07,790 epoch 7 - iter 1029/1476 - loss 0.01953989 - time (sec): 111.72 - samples/sec: 1037.08 - lr: 0.000011 - momentum: 0.000000
171
+ 2023-09-04 12:30:23,159 epoch 7 - iter 1176/1476 - loss 0.02082342 - time (sec): 127.09 - samples/sec: 1035.03 - lr: 0.000011 - momentum: 0.000000
172
+ 2023-09-04 12:30:38,922 epoch 7 - iter 1323/1476 - loss 0.02014967 - time (sec): 142.85 - samples/sec: 1033.23 - lr: 0.000010 - momentum: 0.000000
173
+ 2023-09-04 12:30:56,155 epoch 7 - iter 1470/1476 - loss 0.02095818 - time (sec): 160.08 - samples/sec: 1036.30 - lr: 0.000010 - momentum: 0.000000
174
+ 2023-09-04 12:30:56,696 ----------------------------------------------------------------------------------------------------
175
+ 2023-09-04 12:30:56,696 EPOCH 7 done: loss 0.0209 - lr: 0.000010
176
+ 2023-09-04 12:31:14,641 DEV : loss 0.20697009563446045 - f1-score (micro avg) 0.8181
177
+ 2023-09-04 12:31:14,671 ----------------------------------------------------------------------------------------------------
178
+ 2023-09-04 12:31:30,467 epoch 8 - iter 147/1476 - loss 0.02008496 - time (sec): 15.79 - samples/sec: 1063.45 - lr: 0.000010 - momentum: 0.000000
179
+ 2023-09-04 12:31:47,018 epoch 8 - iter 294/1476 - loss 0.01871448 - time (sec): 32.35 - samples/sec: 1043.87 - lr: 0.000009 - momentum: 0.000000
180
+ 2023-09-04 12:32:04,749 epoch 8 - iter 441/1476 - loss 0.02232604 - time (sec): 50.08 - samples/sec: 1061.88 - lr: 0.000009 - momentum: 0.000000
181
+ 2023-09-04 12:32:19,968 epoch 8 - iter 588/1476 - loss 0.02350058 - time (sec): 65.30 - samples/sec: 1041.33 - lr: 0.000009 - momentum: 0.000000
182
+ 2023-09-04 12:32:35,689 epoch 8 - iter 735/1476 - loss 0.02124328 - time (sec): 81.02 - samples/sec: 1034.13 - lr: 0.000008 - momentum: 0.000000
183
+ 2023-09-04 12:32:50,743 epoch 8 - iter 882/1476 - loss 0.02070320 - time (sec): 96.07 - samples/sec: 1032.53 - lr: 0.000008 - momentum: 0.000000
184
+ 2023-09-04 12:33:06,653 epoch 8 - iter 1029/1476 - loss 0.01859722 - time (sec): 111.98 - samples/sec: 1028.60 - lr: 0.000008 - momentum: 0.000000
185
+ 2023-09-04 12:33:21,480 epoch 8 - iter 1176/1476 - loss 0.01802054 - time (sec): 126.81 - samples/sec: 1027.72 - lr: 0.000007 - momentum: 0.000000
186
+ 2023-09-04 12:33:38,044 epoch 8 - iter 1323/1476 - loss 0.01771451 - time (sec): 143.37 - samples/sec: 1032.78 - lr: 0.000007 - momentum: 0.000000
187
+ 2023-09-04 12:33:54,419 epoch 8 - iter 1470/1476 - loss 0.01726761 - time (sec): 159.75 - samples/sec: 1037.79 - lr: 0.000007 - momentum: 0.000000
188
+ 2023-09-04 12:33:55,015 ----------------------------------------------------------------------------------------------------
189
+ 2023-09-04 12:33:55,016 EPOCH 8 done: loss 0.0173 - lr: 0.000007
190
+ 2023-09-04 12:34:12,904 DEV : loss 0.19811831414699554 - f1-score (micro avg) 0.8234
191
+ 2023-09-04 12:34:12,933 saving best model
192
+ 2023-09-04 12:34:14,315 ----------------------------------------------------------------------------------------------------
193
+ 2023-09-04 12:34:30,008 epoch 9 - iter 147/1476 - loss 0.01313589 - time (sec): 15.69 - samples/sec: 992.22 - lr: 0.000006 - momentum: 0.000000
194
+ 2023-09-04 12:34:46,798 epoch 9 - iter 294/1476 - loss 0.00980602 - time (sec): 32.48 - samples/sec: 1014.79 - lr: 0.000006 - momentum: 0.000000
195
+ 2023-09-04 12:35:01,711 epoch 9 - iter 441/1476 - loss 0.00813046 - time (sec): 47.39 - samples/sec: 1024.95 - lr: 0.000006 - momentum: 0.000000
196
+ 2023-09-04 12:35:17,496 epoch 9 - iter 588/1476 - loss 0.00810265 - time (sec): 63.18 - samples/sec: 1032.87 - lr: 0.000005 - momentum: 0.000000
197
+ 2023-09-04 12:35:35,018 epoch 9 - iter 735/1476 - loss 0.00874325 - time (sec): 80.70 - samples/sec: 1032.76 - lr: 0.000005 - momentum: 0.000000
198
+ 2023-09-04 12:35:50,521 epoch 9 - iter 882/1476 - loss 0.00780631 - time (sec): 96.20 - samples/sec: 1028.83 - lr: 0.000005 - momentum: 0.000000
199
+ 2023-09-04 12:36:06,994 epoch 9 - iter 1029/1476 - loss 0.00794049 - time (sec): 112.68 - samples/sec: 1030.11 - lr: 0.000004 - momentum: 0.000000
200
+ 2023-09-04 12:36:22,645 epoch 9 - iter 1176/1476 - loss 0.00837359 - time (sec): 128.33 - samples/sec: 1025.73 - lr: 0.000004 - momentum: 0.000000
201
+ 2023-09-04 12:36:38,140 epoch 9 - iter 1323/1476 - loss 0.00873700 - time (sec): 143.82 - samples/sec: 1029.57 - lr: 0.000004 - momentum: 0.000000
202
+ 2023-09-04 12:36:55,034 epoch 9 - iter 1470/1476 - loss 0.00966166 - time (sec): 160.72 - samples/sec: 1030.15 - lr: 0.000003 - momentum: 0.000000
203
+ 2023-09-04 12:36:55,729 ----------------------------------------------------------------------------------------------------
204
+ 2023-09-04 12:36:55,729 EPOCH 9 done: loss 0.0100 - lr: 0.000003
205
+ 2023-09-04 12:37:13,594 DEV : loss 0.21610967814922333 - f1-score (micro avg) 0.8316
206
+ 2023-09-04 12:37:13,628 saving best model
207
+ 2023-09-04 12:37:15,830 ----------------------------------------------------------------------------------------------------
208
+ 2023-09-04 12:37:32,731 epoch 10 - iter 147/1476 - loss 0.01124360 - time (sec): 16.90 - samples/sec: 1049.82 - lr: 0.000003 - momentum: 0.000000
209
+ 2023-09-04 12:37:48,228 epoch 10 - iter 294/1476 - loss 0.00846166 - time (sec): 32.40 - samples/sec: 1039.80 - lr: 0.000003 - momentum: 0.000000
210
+ 2023-09-04 12:38:03,099 epoch 10 - iter 441/1476 - loss 0.00738758 - time (sec): 47.27 - samples/sec: 1041.90 - lr: 0.000002 - momentum: 0.000000
211
+ 2023-09-04 12:38:19,174 epoch 10 - iter 588/1476 - loss 0.00716507 - time (sec): 63.34 - samples/sec: 1038.33 - lr: 0.000002 - momentum: 0.000000
212
+ 2023-09-04 12:38:35,118 epoch 10 - iter 735/1476 - loss 0.00703400 - time (sec): 79.29 - samples/sec: 1032.10 - lr: 0.000002 - momentum: 0.000000
213
+ 2023-09-04 12:38:52,583 epoch 10 - iter 882/1476 - loss 0.00810549 - time (sec): 96.75 - samples/sec: 1040.79 - lr: 0.000001 - momentum: 0.000000
214
+ 2023-09-04 12:39:07,706 epoch 10 - iter 1029/1476 - loss 0.00746135 - time (sec): 111.87 - samples/sec: 1032.00 - lr: 0.000001 - momentum: 0.000000
215
+ 2023-09-04 12:39:24,342 epoch 10 - iter 1176/1476 - loss 0.00751286 - time (sec): 128.51 - samples/sec: 1030.97 - lr: 0.000001 - momentum: 0.000000
216
+ 2023-09-04 12:39:40,699 epoch 10 - iter 1323/1476 - loss 0.00775825 - time (sec): 144.87 - samples/sec: 1031.56 - lr: 0.000000 - momentum: 0.000000
217
+ 2023-09-04 12:39:56,750 epoch 10 - iter 1470/1476 - loss 0.00705547 - time (sec): 160.92 - samples/sec: 1030.82 - lr: 0.000000 - momentum: 0.000000
218
+ 2023-09-04 12:39:57,343 ----------------------------------------------------------------------------------------------------
219
+ 2023-09-04 12:39:57,343 EPOCH 10 done: loss 0.0070 - lr: 0.000000
220
+ 2023-09-04 12:40:15,130 DEV : loss 0.22397613525390625 - f1-score (micro avg) 0.8275
221
+ 2023-09-04 12:40:15,633 ----------------------------------------------------------------------------------------------------
222
+ 2023-09-04 12:40:15,635 Loading model from best epoch ...
223
+ 2023-09-04 12:40:17,463 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
224
+ 2023-09-04 12:40:32,158
225
+ Results:
226
+ - F-score (micro) 0.8052
227
+ - F-score (macro) 0.7066
228
+ - Accuracy 0.6966
229
+
230
+ By class:
231
+ precision recall f1-score support
232
+
233
+ loc 0.8786 0.8776 0.8781 858
234
+ pers 0.7470 0.8082 0.7764 537
235
+ org 0.6061 0.6061 0.6061 132
236
+ time 0.5303 0.6481 0.5833 54
237
+ prod 0.7069 0.6721 0.6891 61
238
+
239
+ micro avg 0.7928 0.8179 0.8052 1642
240
+ macro avg 0.6938 0.7224 0.7066 1642
241
+ weighted avg 0.7958 0.8179 0.8063 1642
242
+
243
+ 2023-09-04 12:40:32,158 ----------------------------------------------------------------------------------------------------