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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-lr5e-05-poolingfirst-layers-1-crfFalse-5/best-model.pt +3 -0
  2. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5/dev.tsv +0 -0
  3. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5/final-model.pt +3 -0
  4. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5/loss.tsv +11 -0
  5. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5/test.tsv +0 -0
  6. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5/training.log +244 -0
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5/best-model.pt ADDED
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hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5/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-lr5e-05-poolingfirst-layers-1-crfFalse-5/final-model.pt ADDED
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hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5/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
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+ 1 18:35:09 0.0000 0.4460 0.1578 0.6553 0.7165 0.6845 0.5560
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+ 2 18:38:05 0.0000 0.1356 0.1578 0.7420 0.7658 0.7537 0.6394
4
+ 3 18:41:03 0.0000 0.0897 0.1832 0.7050 0.7910 0.7455 0.6229
5
+ 4 18:44:01 0.0000 0.0616 0.1960 0.7267 0.8024 0.7627 0.6562
6
+ 5 18:46:59 0.0000 0.0474 0.2321 0.7745 0.7887 0.7815 0.6750
7
+ 6 18:49:57 0.0000 0.0324 0.2139 0.7981 0.8150 0.8065 0.7055
8
+ 7 18:52:55 0.0000 0.0215 0.2335 0.7861 0.8041 0.7950 0.6954
9
+ 8 18:55:51 0.0000 0.0165 0.2366 0.8040 0.8247 0.8142 0.7150
10
+ 9 18:58:49 0.0000 0.0098 0.2332 0.8096 0.8207 0.8151 0.7176
11
+ 10 19:01:47 0.0000 0.0061 0.2414 0.8064 0.8184 0.8124 0.7163
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5/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-lr5e-05-poolingfirst-layers-1-crfFalse-5/training.log ADDED
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+ 2023-09-04 18:32:16,492 ----------------------------------------------------------------------------------------------------
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+ 2023-09-04 18:32:16,493 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)
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+ (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ (encoder): BertEncoder(
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+ (layer): ModuleList(
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+ (0-11): 12 x BertLayer(
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+ (attention): BertAttention(
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+ (self): BertSelfAttention(
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+ (query): Linear(in_features=768, out_features=768, bias=True)
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+ (key): Linear(in_features=768, out_features=768, bias=True)
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+ (value): Linear(in_features=768, out_features=768, bias=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
21
+ )
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+ (output): BertSelfOutput(
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+ (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
+ )
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+ (intermediate): BertIntermediate(
29
+ (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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+ 2023-09-04 18:32:16,493 ----------------------------------------------------------------------------------------------------
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+ 2023-09-04 18:32:16,493 MultiCorpus: 5901 train + 1287 dev + 1505 test sentences
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+ - 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 18:32:16,493 ----------------------------------------------------------------------------------------------------
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+ 2023-09-04 18:32:16,493 Train: 5901 sentences
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+ 2023-09-04 18:32:16,494 (train_with_dev=False, train_with_test=False)
56
+ 2023-09-04 18:32:16,494 ----------------------------------------------------------------------------------------------------
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+ 2023-09-04 18:32:16,494 Training Params:
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+ 2023-09-04 18:32:16,494 - learning_rate: "5e-05"
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+ 2023-09-04 18:32:16,494 - mini_batch_size: "4"
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+ 2023-09-04 18:32:16,494 - max_epochs: "10"
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+ 2023-09-04 18:32:16,494 - shuffle: "True"
62
+ 2023-09-04 18:32:16,494 ----------------------------------------------------------------------------------------------------
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+ 2023-09-04 18:32:16,494 Plugins:
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+ 2023-09-04 18:32:16,494 - LinearScheduler | warmup_fraction: '0.1'
65
+ 2023-09-04 18:32:16,494 ----------------------------------------------------------------------------------------------------
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+ 2023-09-04 18:32:16,494 Final evaluation on model from best epoch (best-model.pt)
67
+ 2023-09-04 18:32:16,494 - metric: "('micro avg', 'f1-score')"
68
+ 2023-09-04 18:32:16,494 ----------------------------------------------------------------------------------------------------
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+ 2023-09-04 18:32:16,494 Computation:
70
+ 2023-09-04 18:32:16,494 - compute on device: cuda:0
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+ 2023-09-04 18:32:16,494 - embedding storage: none
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+ 2023-09-04 18:32:16,494 ----------------------------------------------------------------------------------------------------
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+ 2023-09-04 18:32:16,494 Model training base path: "hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5"
74
+ 2023-09-04 18:32:16,494 ----------------------------------------------------------------------------------------------------
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+ 2023-09-04 18:32:16,495 ----------------------------------------------------------------------------------------------------
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+ 2023-09-04 18:32:31,865 epoch 1 - iter 147/1476 - loss 2.02496878 - time (sec): 15.37 - samples/sec: 1057.61 - lr: 0.000005 - momentum: 0.000000
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+ 2023-09-04 18:32:47,479 epoch 1 - iter 294/1476 - loss 1.26880940 - time (sec): 30.98 - samples/sec: 1056.42 - lr: 0.000010 - momentum: 0.000000
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+ 2023-09-04 18:33:02,995 epoch 1 - iter 441/1476 - loss 0.96784431 - time (sec): 46.50 - samples/sec: 1049.51 - lr: 0.000015 - momentum: 0.000000
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+ 2023-09-04 18:33:18,586 epoch 1 - iter 588/1476 - loss 0.79799888 - time (sec): 62.09 - samples/sec: 1046.33 - lr: 0.000020 - momentum: 0.000000
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+ 2023-09-04 18:33:35,423 epoch 1 - iter 735/1476 - loss 0.69614938 - time (sec): 78.93 - samples/sec: 1039.56 - lr: 0.000025 - momentum: 0.000000
81
+ 2023-09-04 18:33:50,161 epoch 1 - iter 882/1476 - loss 0.62257041 - time (sec): 93.67 - samples/sec: 1034.59 - lr: 0.000030 - momentum: 0.000000
82
+ 2023-09-04 18:34:06,435 epoch 1 - iter 1029/1476 - loss 0.56116519 - time (sec): 109.94 - samples/sec: 1040.82 - lr: 0.000035 - momentum: 0.000000
83
+ 2023-09-04 18:34:22,651 epoch 1 - iter 1176/1476 - loss 0.51110730 - time (sec): 126.16 - samples/sec: 1047.53 - lr: 0.000040 - momentum: 0.000000
84
+ 2023-09-04 18:34:38,035 epoch 1 - iter 1323/1476 - loss 0.47553521 - time (sec): 141.54 - samples/sec: 1049.92 - lr: 0.000045 - momentum: 0.000000
85
+ 2023-09-04 18:34:54,567 epoch 1 - iter 1470/1476 - loss 0.44676404 - time (sec): 158.07 - samples/sec: 1049.10 - lr: 0.000050 - momentum: 0.000000
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+ 2023-09-04 18:34:55,137 ----------------------------------------------------------------------------------------------------
87
+ 2023-09-04 18:34:55,137 EPOCH 1 done: loss 0.4460 - lr: 0.000050
88
+ 2023-09-04 18:35:09,253 DEV : loss 0.15778100490570068 - f1-score (micro avg) 0.6845
89
+ 2023-09-04 18:35:09,281 saving best model
90
+ 2023-09-04 18:35:09,755 ----------------------------------------------------------------------------------------------------
91
+ 2023-09-04 18:35:25,713 epoch 2 - iter 147/1476 - loss 0.13394004 - time (sec): 15.96 - samples/sec: 1051.04 - lr: 0.000049 - momentum: 0.000000
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+ 2023-09-04 18:35:41,381 epoch 2 - iter 294/1476 - loss 0.13903916 - time (sec): 31.62 - samples/sec: 1049.62 - lr: 0.000049 - momentum: 0.000000
93
+ 2023-09-04 18:35:57,492 epoch 2 - iter 441/1476 - loss 0.14187201 - time (sec): 47.74 - samples/sec: 1047.16 - lr: 0.000048 - momentum: 0.000000
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+ 2023-09-04 18:36:12,459 epoch 2 - iter 588/1476 - loss 0.13561766 - time (sec): 62.70 - samples/sec: 1043.82 - lr: 0.000048 - momentum: 0.000000
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+ 2023-09-04 18:36:28,695 epoch 2 - iter 735/1476 - loss 0.13108253 - time (sec): 78.94 - samples/sec: 1061.34 - lr: 0.000047 - momentum: 0.000000
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+ 2023-09-04 18:36:47,533 epoch 2 - iter 882/1476 - loss 0.13561892 - time (sec): 97.78 - samples/sec: 1069.55 - lr: 0.000047 - momentum: 0.000000
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+ 2023-09-04 18:37:02,397 epoch 2 - iter 1029/1476 - loss 0.13427076 - time (sec): 112.64 - samples/sec: 1063.47 - lr: 0.000046 - momentum: 0.000000
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+ 2023-09-04 18:37:18,392 epoch 2 - iter 1176/1476 - loss 0.13412798 - time (sec): 128.64 - samples/sec: 1063.68 - lr: 0.000046 - momentum: 0.000000
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+ 2023-09-04 18:37:32,676 epoch 2 - iter 1323/1476 - loss 0.13591119 - time (sec): 142.92 - samples/sec: 1057.76 - lr: 0.000045 - momentum: 0.000000
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+ 2023-09-04 18:37:47,752 epoch 2 - iter 1470/1476 - loss 0.13579392 - time (sec): 158.00 - samples/sec: 1050.81 - lr: 0.000044 - momentum: 0.000000
101
+ 2023-09-04 18:37:48,276 ----------------------------------------------------------------------------------------------------
102
+ 2023-09-04 18:37:48,276 EPOCH 2 done: loss 0.1356 - lr: 0.000044
103
+ 2023-09-04 18:38:05,777 DEV : loss 0.15779592096805573 - f1-score (micro avg) 0.7537
104
+ 2023-09-04 18:38:05,806 saving best model
105
+ 2023-09-04 18:38:07,156 ----------------------------------------------------------------------------------------------------
106
+ 2023-09-04 18:38:23,687 epoch 3 - iter 147/1476 - loss 0.09370866 - time (sec): 16.53 - samples/sec: 1121.34 - lr: 0.000044 - momentum: 0.000000
107
+ 2023-09-04 18:38:39,899 epoch 3 - iter 294/1476 - loss 0.08640762 - time (sec): 32.74 - samples/sec: 1076.59 - lr: 0.000043 - momentum: 0.000000
108
+ 2023-09-04 18:38:55,731 epoch 3 - iter 441/1476 - loss 0.08818612 - time (sec): 48.57 - samples/sec: 1071.39 - lr: 0.000043 - momentum: 0.000000
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+ 2023-09-04 18:39:12,765 epoch 3 - iter 588/1476 - loss 0.09258654 - time (sec): 65.61 - samples/sec: 1070.91 - lr: 0.000042 - momentum: 0.000000
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+ 2023-09-04 18:39:28,046 epoch 3 - iter 735/1476 - loss 0.09352700 - time (sec): 80.89 - samples/sec: 1060.30 - lr: 0.000042 - momentum: 0.000000
111
+ 2023-09-04 18:39:43,669 epoch 3 - iter 882/1476 - loss 0.09080285 - time (sec): 96.51 - samples/sec: 1055.33 - lr: 0.000041 - momentum: 0.000000
112
+ 2023-09-04 18:39:58,875 epoch 3 - iter 1029/1476 - loss 0.08826045 - time (sec): 111.72 - samples/sec: 1051.22 - lr: 0.000041 - momentum: 0.000000
113
+ 2023-09-04 18:40:14,489 epoch 3 - iter 1176/1476 - loss 0.08741064 - time (sec): 127.33 - samples/sec: 1049.94 - lr: 0.000040 - momentum: 0.000000
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+ 2023-09-04 18:40:30,379 epoch 3 - iter 1323/1476 - loss 0.08730511 - time (sec): 143.22 - samples/sec: 1046.98 - lr: 0.000039 - momentum: 0.000000
115
+ 2023-09-04 18:40:45,657 epoch 3 - iter 1470/1476 - loss 0.08954129 - time (sec): 158.50 - samples/sec: 1046.58 - lr: 0.000039 - momentum: 0.000000
116
+ 2023-09-04 18:40:46,196 ----------------------------------------------------------------------------------------------------
117
+ 2023-09-04 18:40:46,196 EPOCH 3 done: loss 0.0897 - lr: 0.000039
118
+ 2023-09-04 18:41:03,627 DEV : loss 0.18318207561969757 - f1-score (micro avg) 0.7455
119
+ 2023-09-04 18:41:03,665 ----------------------------------------------------------------------------------------------------
120
+ 2023-09-04 18:41:19,287 epoch 4 - iter 147/1476 - loss 0.05932984 - time (sec): 15.62 - samples/sec: 1032.40 - lr: 0.000038 - momentum: 0.000000
121
+ 2023-09-04 18:41:37,088 epoch 4 - iter 294/1476 - loss 0.06484502 - time (sec): 33.42 - samples/sec: 1067.25 - lr: 0.000038 - momentum: 0.000000
122
+ 2023-09-04 18:41:52,989 epoch 4 - iter 441/1476 - loss 0.06373582 - time (sec): 49.32 - samples/sec: 1050.18 - lr: 0.000037 - momentum: 0.000000
123
+ 2023-09-04 18:42:07,741 epoch 4 - iter 588/1476 - loss 0.06469185 - time (sec): 64.07 - samples/sec: 1034.35 - lr: 0.000037 - momentum: 0.000000
124
+ 2023-09-04 18:42:23,824 epoch 4 - iter 735/1476 - loss 0.06386989 - time (sec): 80.16 - samples/sec: 1041.02 - lr: 0.000036 - momentum: 0.000000
125
+ 2023-09-04 18:42:39,119 epoch 4 - iter 882/1476 - loss 0.06295200 - time (sec): 95.45 - samples/sec: 1042.98 - lr: 0.000036 - momentum: 0.000000
126
+ 2023-09-04 18:42:54,189 epoch 4 - iter 1029/1476 - loss 0.06203738 - time (sec): 110.52 - samples/sec: 1037.12 - lr: 0.000035 - momentum: 0.000000
127
+ 2023-09-04 18:43:09,465 epoch 4 - iter 1176/1476 - loss 0.06207364 - time (sec): 125.80 - samples/sec: 1038.26 - lr: 0.000034 - momentum: 0.000000
128
+ 2023-09-04 18:43:25,410 epoch 4 - iter 1323/1476 - loss 0.06202569 - time (sec): 141.74 - samples/sec: 1035.51 - lr: 0.000034 - momentum: 0.000000
129
+ 2023-09-04 18:43:43,032 epoch 4 - iter 1470/1476 - loss 0.06137253 - time (sec): 159.37 - samples/sec: 1040.85 - lr: 0.000033 - momentum: 0.000000
130
+ 2023-09-04 18:43:43,612 ----------------------------------------------------------------------------------------------------
131
+ 2023-09-04 18:43:43,612 EPOCH 4 done: loss 0.0616 - lr: 0.000033
132
+ 2023-09-04 18:44:01,148 DEV : loss 0.19600524008274078 - f1-score (micro avg) 0.7627
133
+ 2023-09-04 18:44:01,177 saving best model
134
+ 2023-09-04 18:44:02,507 ----------------------------------------------------------------------------------------------------
135
+ 2023-09-04 18:44:18,270 epoch 5 - iter 147/1476 - loss 0.04910745 - time (sec): 15.76 - samples/sec: 1067.13 - lr: 0.000033 - momentum: 0.000000
136
+ 2023-09-04 18:44:33,170 epoch 5 - iter 294/1476 - loss 0.04622135 - time (sec): 30.66 - samples/sec: 1030.48 - lr: 0.000032 - momentum: 0.000000
137
+ 2023-09-04 18:44:49,236 epoch 5 - iter 441/1476 - loss 0.04026574 - time (sec): 46.73 - samples/sec: 1032.75 - lr: 0.000032 - momentum: 0.000000
138
+ 2023-09-04 18:45:05,111 epoch 5 - iter 588/1476 - loss 0.04133836 - time (sec): 62.60 - samples/sec: 1036.03 - lr: 0.000031 - momentum: 0.000000
139
+ 2023-09-04 18:45:21,499 epoch 5 - iter 735/1476 - loss 0.04194267 - time (sec): 78.99 - samples/sec: 1039.53 - lr: 0.000031 - momentum: 0.000000
140
+ 2023-09-04 18:45:37,366 epoch 5 - iter 882/1476 - loss 0.04322187 - time (sec): 94.86 - samples/sec: 1042.30 - lr: 0.000030 - momentum: 0.000000
141
+ 2023-09-04 18:45:54,016 epoch 5 - iter 1029/1476 - loss 0.04455356 - time (sec): 111.51 - samples/sec: 1040.79 - lr: 0.000029 - momentum: 0.000000
142
+ 2023-09-04 18:46:10,085 epoch 5 - iter 1176/1476 - loss 0.04656032 - time (sec): 127.58 - samples/sec: 1039.85 - lr: 0.000029 - momentum: 0.000000
143
+ 2023-09-04 18:46:26,129 epoch 5 - iter 1323/1476 - loss 0.04822800 - time (sec): 143.62 - samples/sec: 1042.21 - lr: 0.000028 - momentum: 0.000000
144
+ 2023-09-04 18:46:41,477 epoch 5 - iter 1470/1476 - loss 0.04755280 - time (sec): 158.97 - samples/sec: 1043.00 - lr: 0.000028 - momentum: 0.000000
145
+ 2023-09-04 18:46:42,102 ----------------------------------------------------------------------------------------------------
146
+ 2023-09-04 18:46:42,102 EPOCH 5 done: loss 0.0474 - lr: 0.000028
147
+ 2023-09-04 18:46:59,706 DEV : loss 0.23212496936321259 - f1-score (micro avg) 0.7815
148
+ 2023-09-04 18:46:59,734 saving best model
149
+ 2023-09-04 18:47:01,082 ----------------------------------------------------------------------------------------------------
150
+ 2023-09-04 18:47:16,976 epoch 6 - iter 147/1476 - loss 0.03364735 - time (sec): 15.89 - samples/sec: 1073.41 - lr: 0.000027 - momentum: 0.000000
151
+ 2023-09-04 18:47:32,326 epoch 6 - iter 294/1476 - loss 0.03018744 - time (sec): 31.24 - samples/sec: 1039.24 - lr: 0.000027 - momentum: 0.000000
152
+ 2023-09-04 18:47:48,202 epoch 6 - iter 441/1476 - loss 0.03191159 - time (sec): 47.12 - samples/sec: 1037.52 - lr: 0.000026 - momentum: 0.000000
153
+ 2023-09-04 18:48:04,195 epoch 6 - iter 588/1476 - loss 0.03116255 - time (sec): 63.11 - samples/sec: 1034.70 - lr: 0.000026 - momentum: 0.000000
154
+ 2023-09-04 18:48:19,467 epoch 6 - iter 735/1476 - loss 0.03084088 - time (sec): 78.38 - samples/sec: 1029.74 - lr: 0.000025 - momentum: 0.000000
155
+ 2023-09-04 18:48:34,466 epoch 6 - iter 882/1476 - loss 0.03014693 - time (sec): 93.38 - samples/sec: 1029.13 - lr: 0.000024 - momentum: 0.000000
156
+ 2023-09-04 18:48:50,739 epoch 6 - iter 1029/1476 - loss 0.03123867 - time (sec): 109.65 - samples/sec: 1037.34 - lr: 0.000024 - momentum: 0.000000
157
+ 2023-09-04 18:49:06,583 epoch 6 - iter 1176/1476 - loss 0.03092415 - time (sec): 125.50 - samples/sec: 1037.61 - lr: 0.000023 - momentum: 0.000000
158
+ 2023-09-04 18:49:22,506 epoch 6 - iter 1323/1476 - loss 0.03257653 - time (sec): 141.42 - samples/sec: 1037.13 - lr: 0.000023 - momentum: 0.000000
159
+ 2023-09-04 18:49:38,830 epoch 6 - iter 1470/1476 - loss 0.03258600 - time (sec): 157.75 - samples/sec: 1046.61 - lr: 0.000022 - momentum: 0.000000
160
+ 2023-09-04 18:49:39,995 ----------------------------------------------------------------------------------------------------
161
+ 2023-09-04 18:49:39,996 EPOCH 6 done: loss 0.0324 - lr: 0.000022
162
+ 2023-09-04 18:49:57,521 DEV : loss 0.21392114460468292 - f1-score (micro avg) 0.8065
163
+ 2023-09-04 18:49:57,553 saving best model
164
+ 2023-09-04 18:49:58,877 ----------------------------------------------------------------------------------------------------
165
+ 2023-09-04 18:50:14,821 epoch 7 - iter 147/1476 - loss 0.01951757 - time (sec): 15.94 - samples/sec: 1095.92 - lr: 0.000022 - momentum: 0.000000
166
+ 2023-09-04 18:50:32,352 epoch 7 - iter 294/1476 - loss 0.02437779 - time (sec): 33.47 - samples/sec: 1075.47 - lr: 0.000021 - momentum: 0.000000
167
+ 2023-09-04 18:50:48,882 epoch 7 - iter 441/1476 - loss 0.02220397 - time (sec): 50.00 - samples/sec: 1069.54 - lr: 0.000021 - momentum: 0.000000
168
+ 2023-09-04 18:51:05,167 epoch 7 - iter 588/1476 - loss 0.02340795 - time (sec): 66.29 - samples/sec: 1078.67 - lr: 0.000020 - momentum: 0.000000
169
+ 2023-09-04 18:51:19,585 epoch 7 - iter 735/1476 - loss 0.02299156 - time (sec): 80.71 - samples/sec: 1071.88 - lr: 0.000019 - momentum: 0.000000
170
+ 2023-09-04 18:51:36,200 epoch 7 - iter 882/1476 - loss 0.02244956 - time (sec): 97.32 - samples/sec: 1063.15 - lr: 0.000019 - momentum: 0.000000
171
+ 2023-09-04 18:51:51,169 epoch 7 - iter 1029/1476 - loss 0.02224666 - time (sec): 112.29 - samples/sec: 1057.87 - lr: 0.000018 - momentum: 0.000000
172
+ 2023-09-04 18:52:06,591 epoch 7 - iter 1176/1476 - loss 0.02184750 - time (sec): 127.71 - samples/sec: 1052.60 - lr: 0.000018 - momentum: 0.000000
173
+ 2023-09-04 18:52:21,812 epoch 7 - iter 1323/1476 - loss 0.02183100 - time (sec): 142.93 - samples/sec: 1050.66 - lr: 0.000017 - momentum: 0.000000
174
+ 2023-09-04 18:52:37,073 epoch 7 - iter 1470/1476 - loss 0.02153376 - time (sec): 158.19 - samples/sec: 1048.54 - lr: 0.000017 - momentum: 0.000000
175
+ 2023-09-04 18:52:37,629 ----------------------------------------------------------------------------------------------------
176
+ 2023-09-04 18:52:37,630 EPOCH 7 done: loss 0.0215 - lr: 0.000017
177
+ 2023-09-04 18:52:55,178 DEV : loss 0.23351676762104034 - f1-score (micro avg) 0.795
178
+ 2023-09-04 18:52:55,207 ----------------------------------------------------------------------------------------------------
179
+ 2023-09-04 18:53:11,133 epoch 8 - iter 147/1476 - loss 0.01873955 - time (sec): 15.92 - samples/sec: 1111.65 - lr: 0.000016 - momentum: 0.000000
180
+ 2023-09-04 18:53:26,238 epoch 8 - iter 294/1476 - loss 0.01828038 - time (sec): 31.03 - samples/sec: 1065.80 - lr: 0.000016 - momentum: 0.000000
181
+ 2023-09-04 18:53:43,208 epoch 8 - iter 441/1476 - loss 0.01943585 - time (sec): 48.00 - samples/sec: 1080.66 - lr: 0.000015 - momentum: 0.000000
182
+ 2023-09-04 18:53:59,356 epoch 8 - iter 588/1476 - loss 0.01803489 - time (sec): 64.15 - samples/sec: 1051.57 - lr: 0.000014 - momentum: 0.000000
183
+ 2023-09-04 18:54:13,863 epoch 8 - iter 735/1476 - loss 0.01864845 - time (sec): 78.66 - samples/sec: 1040.35 - lr: 0.000014 - momentum: 0.000000
184
+ 2023-09-04 18:54:30,715 epoch 8 - iter 882/1476 - loss 0.01910796 - time (sec): 95.51 - samples/sec: 1044.93 - lr: 0.000013 - momentum: 0.000000
185
+ 2023-09-04 18:54:46,188 epoch 8 - iter 1029/1476 - loss 0.01788010 - time (sec): 110.98 - samples/sec: 1044.97 - lr: 0.000013 - momentum: 0.000000
186
+ 2023-09-04 18:55:01,558 epoch 8 - iter 1176/1476 - loss 0.01720474 - time (sec): 126.35 - samples/sec: 1043.20 - lr: 0.000012 - momentum: 0.000000
187
+ 2023-09-04 18:55:17,516 epoch 8 - iter 1323/1476 - loss 0.01643761 - time (sec): 142.31 - samples/sec: 1041.61 - lr: 0.000012 - momentum: 0.000000
188
+ 2023-09-04 18:55:33,778 epoch 8 - iter 1470/1476 - loss 0.01631064 - time (sec): 158.57 - samples/sec: 1045.93 - lr: 0.000011 - momentum: 0.000000
189
+ 2023-09-04 18:55:34,315 ----------------------------------------------------------------------------------------------------
190
+ 2023-09-04 18:55:34,316 EPOCH 8 done: loss 0.0165 - lr: 0.000011
191
+ 2023-09-04 18:55:51,873 DEV : loss 0.236624613404274 - f1-score (micro avg) 0.8142
192
+ 2023-09-04 18:55:51,903 saving best model
193
+ 2023-09-04 18:55:53,246 ----------------------------------------------------------------------------------------------------
194
+ 2023-09-04 18:56:08,828 epoch 9 - iter 147/1476 - loss 0.00759325 - time (sec): 15.58 - samples/sec: 1032.05 - lr: 0.000011 - momentum: 0.000000
195
+ 2023-09-04 18:56:24,222 epoch 9 - iter 294/1476 - loss 0.01207807 - time (sec): 30.97 - samples/sec: 1043.57 - lr: 0.000010 - momentum: 0.000000
196
+ 2023-09-04 18:56:38,895 epoch 9 - iter 441/1476 - loss 0.01034734 - time (sec): 45.65 - samples/sec: 1031.06 - lr: 0.000009 - momentum: 0.000000
197
+ 2023-09-04 18:56:55,462 epoch 9 - iter 588/1476 - loss 0.01218309 - time (sec): 62.21 - samples/sec: 1032.18 - lr: 0.000009 - momentum: 0.000000
198
+ 2023-09-04 18:57:10,826 epoch 9 - iter 735/1476 - loss 0.01127190 - time (sec): 77.58 - samples/sec: 1028.39 - lr: 0.000008 - momentum: 0.000000
199
+ 2023-09-04 18:57:26,532 epoch 9 - iter 882/1476 - loss 0.01045626 - time (sec): 93.28 - samples/sec: 1029.01 - lr: 0.000008 - momentum: 0.000000
200
+ 2023-09-04 18:57:42,855 epoch 9 - iter 1029/1476 - loss 0.00995076 - time (sec): 109.61 - samples/sec: 1038.75 - lr: 0.000007 - momentum: 0.000000
201
+ 2023-09-04 18:58:00,059 epoch 9 - iter 1176/1476 - loss 0.01067922 - time (sec): 126.81 - samples/sec: 1042.80 - lr: 0.000007 - momentum: 0.000000
202
+ 2023-09-04 18:58:15,164 epoch 9 - iter 1323/1476 - loss 0.01024615 - time (sec): 141.92 - samples/sec: 1039.94 - lr: 0.000006 - momentum: 0.000000
203
+ 2023-09-04 18:58:31,083 epoch 9 - iter 1470/1476 - loss 0.00981814 - time (sec): 157.84 - samples/sec: 1045.59 - lr: 0.000006 - momentum: 0.000000
204
+ 2023-09-04 18:58:32,178 ----------------------------------------------------------------------------------------------------
205
+ 2023-09-04 18:58:32,178 EPOCH 9 done: loss 0.0098 - lr: 0.000006
206
+ 2023-09-04 18:58:49,793 DEV : loss 0.23322366178035736 - f1-score (micro avg) 0.8151
207
+ 2023-09-04 18:58:49,822 saving best model
208
+ 2023-09-04 18:58:51,190 ----------------------------------------------------------------------------------------------------
209
+ 2023-09-04 18:59:06,247 epoch 10 - iter 147/1476 - loss 0.00382879 - time (sec): 15.05 - samples/sec: 1019.35 - lr: 0.000005 - momentum: 0.000000
210
+ 2023-09-04 18:59:22,908 epoch 10 - iter 294/1476 - loss 0.00462447 - time (sec): 31.72 - samples/sec: 1038.24 - lr: 0.000004 - momentum: 0.000000
211
+ 2023-09-04 18:59:39,127 epoch 10 - iter 441/1476 - loss 0.00486403 - time (sec): 47.94 - samples/sec: 1033.28 - lr: 0.000004 - momentum: 0.000000
212
+ 2023-09-04 18:59:55,441 epoch 10 - iter 588/1476 - loss 0.00414586 - time (sec): 64.25 - samples/sec: 1038.11 - lr: 0.000003 - momentum: 0.000000
213
+ 2023-09-04 19:00:12,033 epoch 10 - iter 735/1476 - loss 0.00439257 - time (sec): 80.84 - samples/sec: 1047.43 - lr: 0.000003 - momentum: 0.000000
214
+ 2023-09-04 19:00:26,973 epoch 10 - iter 882/1476 - loss 0.00473245 - time (sec): 95.78 - samples/sec: 1047.58 - lr: 0.000002 - momentum: 0.000000
215
+ 2023-09-04 19:00:41,737 epoch 10 - iter 1029/1476 - loss 0.00555172 - time (sec): 110.55 - samples/sec: 1047.61 - lr: 0.000002 - momentum: 0.000000
216
+ 2023-09-04 19:00:57,891 epoch 10 - iter 1176/1476 - loss 0.00552654 - time (sec): 126.70 - samples/sec: 1044.10 - lr: 0.000001 - momentum: 0.000000
217
+ 2023-09-04 19:01:14,234 epoch 10 - iter 1323/1476 - loss 0.00571332 - time (sec): 143.04 - samples/sec: 1050.96 - lr: 0.000001 - momentum: 0.000000
218
+ 2023-09-04 19:01:29,292 epoch 10 - iter 1470/1476 - loss 0.00609596 - time (sec): 158.10 - samples/sec: 1048.52 - lr: 0.000000 - momentum: 0.000000
219
+ 2023-09-04 19:01:29,892 ----------------------------------------------------------------------------------------------------
220
+ 2023-09-04 19:01:29,893 EPOCH 10 done: loss 0.0061 - lr: 0.000000
221
+ 2023-09-04 19:01:47,566 DEV : loss 0.24142222106456757 - f1-score (micro avg) 0.8124
222
+ 2023-09-04 19:01:48,087 ----------------------------------------------------------------------------------------------------
223
+ 2023-09-04 19:01:48,088 Loading model from best epoch ...
224
+ 2023-09-04 19:01:49,921 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
225
+ 2023-09-04 19:02:04,516
226
+ Results:
227
+ - F-score (micro) 0.7816
228
+ - F-score (macro) 0.6773
229
+ - Accuracy 0.6675
230
+
231
+ By class:
232
+ precision recall f1-score support
233
+
234
+ loc 0.8361 0.8741 0.8547 858
235
+ pers 0.7482 0.7858 0.7666 537
236
+ org 0.4859 0.5227 0.5036 132
237
+ time 0.5303 0.6481 0.5833 54
238
+ prod 0.7222 0.6393 0.6783 61
239
+
240
+ micro avg 0.7632 0.8009 0.7816 1642
241
+ macro avg 0.6646 0.6940 0.6773 1642
242
+ weighted avg 0.7649 0.8009 0.7822 1642
243
+
244
+ 2023-09-04 19:02:04,516 ----------------------------------------------------------------------------------------------------