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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-2/best-model.pt +3 -0
  2. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2/dev.tsv +0 -0
  3. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2/final-model.pt +3 -0
  4. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2/loss.tsv +11 -0
  5. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2/test.tsv +0 -0
  6. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2/training.log +245 -0
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-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-lr5e-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-lr5e-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-lr5e-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
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+ 1 12:44:00 0.0000 0.4835 0.1297 0.7105 0.7297 0.7200 0.5987
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+ 2 12:46:58 0.0000 0.1397 0.1371 0.7056 0.7606 0.7321 0.6058
4
+ 3 12:49:58 0.0000 0.0931 0.1489 0.7734 0.7938 0.7835 0.6771
5
+ 4 12:52:59 0.0000 0.0666 0.1895 0.7628 0.8162 0.7886 0.6789
6
+ 5 12:55:59 0.0000 0.0452 0.1993 0.7743 0.8116 0.7925 0.6875
7
+ 6 12:58:59 0.0000 0.0339 0.2285 0.7729 0.8150 0.7934 0.6898
8
+ 7 13:01:59 0.0000 0.0246 0.2234 0.7878 0.8144 0.8009 0.7012
9
+ 8 13:05:00 0.0000 0.0171 0.2465 0.8220 0.8121 0.8171 0.7191
10
+ 9 13:08:01 0.0000 0.0111 0.2399 0.7873 0.8184 0.8026 0.7036
11
+ 10 13:10:59 0.0000 0.0077 0.2503 0.7953 0.8190 0.8070 0.7090
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-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-lr5e-05-poolingfirst-layers-1-crfFalse-2/training.log ADDED
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+ 2023-09-04 12:41:05,500 ----------------------------------------------------------------------------------------------------
2
+ 2023-09-04 12:41:05,501 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)
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)
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+ (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 12:41:05,501 ----------------------------------------------------------------------------------------------------
51
+ 2023-09-04 12:41:05,501 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 12:41:05,501 ----------------------------------------------------------------------------------------------------
54
+ 2023-09-04 12:41:05,501 Train: 5901 sentences
55
+ 2023-09-04 12:41:05,501 (train_with_dev=False, train_with_test=False)
56
+ 2023-09-04 12:41:05,501 ----------------------------------------------------------------------------------------------------
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+ 2023-09-04 12:41:05,501 Training Params:
58
+ 2023-09-04 12:41:05,501 - learning_rate: "5e-05"
59
+ 2023-09-04 12:41:05,501 - mini_batch_size: "4"
60
+ 2023-09-04 12:41:05,502 - max_epochs: "10"
61
+ 2023-09-04 12:41:05,502 - shuffle: "True"
62
+ 2023-09-04 12:41:05,502 ----------------------------------------------------------------------------------------------------
63
+ 2023-09-04 12:41:05,502 Plugins:
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+ 2023-09-04 12:41:05,502 - LinearScheduler | warmup_fraction: '0.1'
65
+ 2023-09-04 12:41:05,502 ----------------------------------------------------------------------------------------------------
66
+ 2023-09-04 12:41:05,502 Final evaluation on model from best epoch (best-model.pt)
67
+ 2023-09-04 12:41:05,502 - metric: "('micro avg', 'f1-score')"
68
+ 2023-09-04 12:41:05,502 ----------------------------------------------------------------------------------------------------
69
+ 2023-09-04 12:41:05,502 Computation:
70
+ 2023-09-04 12:41:05,502 - compute on device: cuda:0
71
+ 2023-09-04 12:41:05,502 - embedding storage: none
72
+ 2023-09-04 12:41:05,502 ----------------------------------------------------------------------------------------------------
73
+ 2023-09-04 12:41:05,502 Model training base path: "hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2"
74
+ 2023-09-04 12:41:05,502 ----------------------------------------------------------------------------------------------------
75
+ 2023-09-04 12:41:05,502 ----------------------------------------------------------------------------------------------------
76
+ 2023-09-04 12:41:21,950 epoch 1 - iter 147/1476 - loss 2.22313355 - time (sec): 16.45 - samples/sec: 1076.86 - lr: 0.000005 - momentum: 0.000000
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+ 2023-09-04 12:41:38,671 epoch 1 - iter 294/1476 - loss 1.37374230 - time (sec): 33.17 - samples/sec: 1083.45 - lr: 0.000010 - momentum: 0.000000
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+ 2023-09-04 12:41:54,462 epoch 1 - iter 441/1476 - loss 1.05288085 - time (sec): 48.96 - samples/sec: 1061.77 - lr: 0.000015 - momentum: 0.000000
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+ 2023-09-04 12:42:10,552 epoch 1 - iter 588/1476 - loss 0.86208252 - time (sec): 65.05 - samples/sec: 1056.52 - lr: 0.000020 - momentum: 0.000000
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+ 2023-09-04 12:42:26,859 epoch 1 - iter 735/1476 - loss 0.74659437 - time (sec): 81.36 - samples/sec: 1051.97 - lr: 0.000025 - momentum: 0.000000
81
+ 2023-09-04 12:42:42,698 epoch 1 - iter 882/1476 - loss 0.66213367 - time (sec): 97.19 - samples/sec: 1052.44 - lr: 0.000030 - momentum: 0.000000
82
+ 2023-09-04 12:42:58,644 epoch 1 - iter 1029/1476 - loss 0.59907497 - time (sec): 113.14 - samples/sec: 1048.26 - lr: 0.000035 - momentum: 0.000000
83
+ 2023-09-04 12:43:13,763 epoch 1 - iter 1176/1476 - loss 0.55542267 - time (sec): 128.26 - samples/sec: 1039.70 - lr: 0.000040 - momentum: 0.000000
84
+ 2023-09-04 12:43:29,571 epoch 1 - iter 1323/1476 - loss 0.51603021 - time (sec): 144.07 - samples/sec: 1038.88 - lr: 0.000045 - momentum: 0.000000
85
+ 2023-09-04 12:43:45,182 epoch 1 - iter 1470/1476 - loss 0.48435086 - time (sec): 159.68 - samples/sec: 1038.74 - lr: 0.000050 - momentum: 0.000000
86
+ 2023-09-04 12:43:45,717 ----------------------------------------------------------------------------------------------------
87
+ 2023-09-04 12:43:45,717 EPOCH 1 done: loss 0.4835 - lr: 0.000050
88
+ 2023-09-04 12:44:00,022 DEV : loss 0.12973235547542572 - f1-score (micro avg) 0.72
89
+ 2023-09-04 12:44:00,050 saving best model
90
+ 2023-09-04 12:44:00,523 ----------------------------------------------------------------------------------------------------
91
+ 2023-09-04 12:44:15,272 epoch 2 - iter 147/1476 - loss 0.13043275 - time (sec): 14.75 - samples/sec: 1032.13 - lr: 0.000049 - momentum: 0.000000
92
+ 2023-09-04 12:44:30,831 epoch 2 - iter 294/1476 - loss 0.13817560 - time (sec): 30.31 - samples/sec: 1036.87 - lr: 0.000049 - momentum: 0.000000
93
+ 2023-09-04 12:44:46,416 epoch 2 - iter 441/1476 - loss 0.13982802 - time (sec): 45.89 - samples/sec: 1040.96 - lr: 0.000048 - momentum: 0.000000
94
+ 2023-09-04 12:45:02,296 epoch 2 - iter 588/1476 - loss 0.13827499 - time (sec): 61.77 - samples/sec: 1032.24 - lr: 0.000048 - momentum: 0.000000
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+ 2023-09-04 12:45:17,688 epoch 2 - iter 735/1476 - loss 0.14321867 - time (sec): 77.16 - samples/sec: 1023.62 - lr: 0.000047 - momentum: 0.000000
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+ 2023-09-04 12:45:34,057 epoch 2 - iter 882/1476 - loss 0.14413832 - time (sec): 93.53 - samples/sec: 1027.10 - lr: 0.000047 - momentum: 0.000000
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+ 2023-09-04 12:45:51,209 epoch 2 - iter 1029/1476 - loss 0.13943760 - time (sec): 110.69 - samples/sec: 1034.12 - lr: 0.000046 - momentum: 0.000000
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+ 2023-09-04 12:46:06,983 epoch 2 - iter 1176/1476 - loss 0.13462370 - time (sec): 126.46 - samples/sec: 1035.36 - lr: 0.000046 - momentum: 0.000000
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+ 2023-09-04 12:46:23,330 epoch 2 - iter 1323/1476 - loss 0.13833582 - time (sec): 142.81 - samples/sec: 1036.67 - lr: 0.000045 - momentum: 0.000000
100
+ 2023-09-04 12:46:40,221 epoch 2 - iter 1470/1476 - loss 0.13957066 - time (sec): 159.70 - samples/sec: 1037.02 - lr: 0.000044 - momentum: 0.000000
101
+ 2023-09-04 12:46:40,879 ----------------------------------------------------------------------------------------------------
102
+ 2023-09-04 12:46:40,879 EPOCH 2 done: loss 0.1397 - lr: 0.000044
103
+ 2023-09-04 12:46:58,622 DEV : loss 0.13705389201641083 - f1-score (micro avg) 0.7321
104
+ 2023-09-04 12:46:58,651 saving best model
105
+ 2023-09-04 12:46:59,986 ----------------------------------------------------------------------------------------------------
106
+ 2023-09-04 12:47:15,499 epoch 3 - iter 147/1476 - loss 0.07281328 - time (sec): 15.51 - samples/sec: 1000.37 - lr: 0.000044 - momentum: 0.000000
107
+ 2023-09-04 12:47:31,881 epoch 3 - iter 294/1476 - loss 0.08498610 - time (sec): 31.89 - samples/sec: 1014.38 - lr: 0.000043 - momentum: 0.000000
108
+ 2023-09-04 12:47:47,727 epoch 3 - iter 441/1476 - loss 0.09120280 - time (sec): 47.74 - samples/sec: 1018.43 - lr: 0.000043 - momentum: 0.000000
109
+ 2023-09-04 12:48:02,893 epoch 3 - iter 588/1476 - loss 0.08916578 - time (sec): 62.91 - samples/sec: 1019.25 - lr: 0.000042 - momentum: 0.000000
110
+ 2023-09-04 12:48:19,680 epoch 3 - iter 735/1476 - loss 0.09238527 - time (sec): 79.69 - samples/sec: 1023.49 - lr: 0.000042 - momentum: 0.000000
111
+ 2023-09-04 12:48:36,558 epoch 3 - iter 882/1476 - loss 0.09045454 - time (sec): 96.57 - samples/sec: 1036.22 - lr: 0.000041 - momentum: 0.000000
112
+ 2023-09-04 12:48:52,241 epoch 3 - iter 1029/1476 - loss 0.08793924 - time (sec): 112.25 - samples/sec: 1034.49 - lr: 0.000041 - momentum: 0.000000
113
+ 2023-09-04 12:49:08,955 epoch 3 - iter 1176/1476 - loss 0.09159878 - time (sec): 128.97 - samples/sec: 1038.96 - lr: 0.000040 - momentum: 0.000000
114
+ 2023-09-04 12:49:24,711 epoch 3 - iter 1323/1476 - loss 0.09066876 - time (sec): 144.72 - samples/sec: 1038.19 - lr: 0.000039 - momentum: 0.000000
115
+ 2023-09-04 12:49:40,188 epoch 3 - iter 1470/1476 - loss 0.09316735 - time (sec): 160.20 - samples/sec: 1035.06 - lr: 0.000039 - momentum: 0.000000
116
+ 2023-09-04 12:49:40,758 ----------------------------------------------------------------------------------------------------
117
+ 2023-09-04 12:49:40,758 EPOCH 3 done: loss 0.0931 - lr: 0.000039
118
+ 2023-09-04 12:49:58,561 DEV : loss 0.1488744020462036 - f1-score (micro avg) 0.7835
119
+ 2023-09-04 12:49:58,602 saving best model
120
+ 2023-09-04 12:49:59,944 ----------------------------------------------------------------------------------------------------
121
+ 2023-09-04 12:50:15,389 epoch 4 - iter 147/1476 - loss 0.05437294 - time (sec): 15.44 - samples/sec: 986.70 - lr: 0.000038 - momentum: 0.000000
122
+ 2023-09-04 12:50:30,759 epoch 4 - iter 294/1476 - loss 0.05695284 - time (sec): 30.81 - samples/sec: 1009.35 - lr: 0.000038 - momentum: 0.000000
123
+ 2023-09-04 12:50:46,761 epoch 4 - iter 441/1476 - loss 0.06071564 - time (sec): 46.82 - samples/sec: 1021.33 - lr: 0.000037 - momentum: 0.000000
124
+ 2023-09-04 12:51:02,028 epoch 4 - iter 588/1476 - loss 0.06089200 - time (sec): 62.08 - samples/sec: 1018.38 - lr: 0.000037 - momentum: 0.000000
125
+ 2023-09-04 12:51:18,219 epoch 4 - iter 735/1476 - loss 0.06175334 - time (sec): 78.27 - samples/sec: 1020.96 - lr: 0.000036 - momentum: 0.000000
126
+ 2023-09-04 12:51:35,354 epoch 4 - iter 882/1476 - loss 0.06353415 - time (sec): 95.41 - samples/sec: 1021.01 - lr: 0.000036 - momentum: 0.000000
127
+ 2023-09-04 12:51:52,839 epoch 4 - iter 1029/1476 - loss 0.06422628 - time (sec): 112.89 - samples/sec: 1030.76 - lr: 0.000035 - momentum: 0.000000
128
+ 2023-09-04 12:52:08,729 epoch 4 - iter 1176/1476 - loss 0.06363828 - time (sec): 128.78 - samples/sec: 1032.44 - lr: 0.000034 - momentum: 0.000000
129
+ 2023-09-04 12:52:25,193 epoch 4 - iter 1323/1476 - loss 0.06681448 - time (sec): 145.25 - samples/sec: 1030.85 - lr: 0.000034 - momentum: 0.000000
130
+ 2023-09-04 12:52:40,592 epoch 4 - iter 1470/1476 - loss 0.06664854 - time (sec): 160.65 - samples/sec: 1031.61 - lr: 0.000033 - momentum: 0.000000
131
+ 2023-09-04 12:52:41,271 ----------------------------------------------------------------------------------------------------
132
+ 2023-09-04 12:52:41,271 EPOCH 4 done: loss 0.0666 - lr: 0.000033
133
+ 2023-09-04 12:52:59,139 DEV : loss 0.18947024643421173 - f1-score (micro avg) 0.7886
134
+ 2023-09-04 12:52:59,167 saving best model
135
+ 2023-09-04 12:53:00,515 ----------------------------------------------------------------------------------------------------
136
+ 2023-09-04 12:53:16,577 epoch 5 - iter 147/1476 - loss 0.04297170 - time (sec): 16.06 - samples/sec: 1030.92 - lr: 0.000033 - momentum: 0.000000
137
+ 2023-09-04 12:53:32,604 epoch 5 - iter 294/1476 - loss 0.04431455 - time (sec): 32.09 - samples/sec: 1037.33 - lr: 0.000032 - momentum: 0.000000
138
+ 2023-09-04 12:53:48,491 epoch 5 - iter 441/1476 - loss 0.04484576 - time (sec): 47.97 - samples/sec: 1049.34 - lr: 0.000032 - momentum: 0.000000
139
+ 2023-09-04 12:54:04,010 epoch 5 - iter 588/1476 - loss 0.04376185 - time (sec): 63.49 - samples/sec: 1045.83 - lr: 0.000031 - momentum: 0.000000
140
+ 2023-09-04 12:54:19,680 epoch 5 - iter 735/1476 - loss 0.04368058 - time (sec): 79.16 - samples/sec: 1042.72 - lr: 0.000031 - momentum: 0.000000
141
+ 2023-09-04 12:54:35,189 epoch 5 - iter 882/1476 - loss 0.04539124 - time (sec): 94.67 - samples/sec: 1035.86 - lr: 0.000030 - momentum: 0.000000
142
+ 2023-09-04 12:54:51,200 epoch 5 - iter 1029/1476 - loss 0.04406403 - time (sec): 110.68 - samples/sec: 1033.53 - lr: 0.000029 - momentum: 0.000000
143
+ 2023-09-04 12:55:08,769 epoch 5 - iter 1176/1476 - loss 0.04473760 - time (sec): 128.25 - samples/sec: 1035.75 - lr: 0.000029 - momentum: 0.000000
144
+ 2023-09-04 12:55:25,502 epoch 5 - iter 1323/1476 - loss 0.04353630 - time (sec): 144.99 - samples/sec: 1039.51 - lr: 0.000028 - momentum: 0.000000
145
+ 2023-09-04 12:55:40,746 epoch 5 - iter 1470/1476 - loss 0.04533154 - time (sec): 160.23 - samples/sec: 1035.10 - lr: 0.000028 - momentum: 0.000000
146
+ 2023-09-04 12:55:41,340 ----------------------------------------------------------------------------------------------------
147
+ 2023-09-04 12:55:41,340 EPOCH 5 done: loss 0.0452 - lr: 0.000028
148
+ 2023-09-04 12:55:59,294 DEV : loss 0.19925478100776672 - f1-score (micro avg) 0.7925
149
+ 2023-09-04 12:55:59,323 saving best model
150
+ 2023-09-04 12:56:00,672 ----------------------------------------------------------------------------------------------------
151
+ 2023-09-04 12:56:15,887 epoch 6 - iter 147/1476 - loss 0.03566906 - time (sec): 15.21 - samples/sec: 981.26 - lr: 0.000027 - momentum: 0.000000
152
+ 2023-09-04 12:56:33,489 epoch 6 - iter 294/1476 - loss 0.03245913 - time (sec): 32.82 - samples/sec: 1040.29 - lr: 0.000027 - momentum: 0.000000
153
+ 2023-09-04 12:56:49,628 epoch 6 - iter 441/1476 - loss 0.03754273 - time (sec): 48.95 - samples/sec: 1042.98 - lr: 0.000026 - momentum: 0.000000
154
+ 2023-09-04 12:57:05,764 epoch 6 - iter 588/1476 - loss 0.04214083 - time (sec): 65.09 - samples/sec: 1035.81 - lr: 0.000026 - momentum: 0.000000
155
+ 2023-09-04 12:57:22,270 epoch 6 - iter 735/1476 - loss 0.03917241 - time (sec): 81.60 - samples/sec: 1040.84 - lr: 0.000025 - momentum: 0.000000
156
+ 2023-09-04 12:57:38,968 epoch 6 - iter 882/1476 - loss 0.03938033 - time (sec): 98.29 - samples/sec: 1039.46 - lr: 0.000024 - momentum: 0.000000
157
+ 2023-09-04 12:57:53,807 epoch 6 - iter 1029/1476 - loss 0.03736740 - time (sec): 113.13 - samples/sec: 1037.66 - lr: 0.000024 - momentum: 0.000000
158
+ 2023-09-04 12:58:09,831 epoch 6 - iter 1176/1476 - loss 0.03518827 - time (sec): 129.16 - samples/sec: 1037.13 - lr: 0.000023 - momentum: 0.000000
159
+ 2023-09-04 12:58:25,528 epoch 6 - iter 1323/1476 - loss 0.03450183 - time (sec): 144.85 - samples/sec: 1034.15 - lr: 0.000023 - momentum: 0.000000
160
+ 2023-09-04 12:58:41,496 epoch 6 - iter 1470/1476 - loss 0.03399637 - time (sec): 160.82 - samples/sec: 1032.01 - lr: 0.000022 - momentum: 0.000000
161
+ 2023-09-04 12:58:42,028 ----------------------------------------------------------------------------------------------------
162
+ 2023-09-04 12:58:42,029 EPOCH 6 done: loss 0.0339 - lr: 0.000022
163
+ 2023-09-04 12:58:59,939 DEV : loss 0.2285047173500061 - f1-score (micro avg) 0.7934
164
+ 2023-09-04 12:58:59,968 saving best model
165
+ 2023-09-04 12:59:01,300 ----------------------------------------------------------------------------------------------------
166
+ 2023-09-04 12:59:18,284 epoch 7 - iter 147/1476 - loss 0.02070811 - time (sec): 16.98 - samples/sec: 1007.35 - lr: 0.000022 - momentum: 0.000000
167
+ 2023-09-04 12:59:33,102 epoch 7 - iter 294/1476 - loss 0.01999558 - time (sec): 31.80 - samples/sec: 1010.62 - lr: 0.000021 - momentum: 0.000000
168
+ 2023-09-04 12:59:50,186 epoch 7 - iter 441/1476 - loss 0.02505522 - time (sec): 48.88 - samples/sec: 1034.16 - lr: 0.000021 - momentum: 0.000000
169
+ 2023-09-04 13:00:07,527 epoch 7 - iter 588/1476 - loss 0.02337657 - time (sec): 66.23 - samples/sec: 1046.75 - lr: 0.000020 - momentum: 0.000000
170
+ 2023-09-04 13:00:22,653 epoch 7 - iter 735/1476 - loss 0.02335444 - time (sec): 81.35 - samples/sec: 1037.46 - lr: 0.000019 - momentum: 0.000000
171
+ 2023-09-04 13:00:37,830 epoch 7 - iter 882/1476 - loss 0.02581999 - time (sec): 96.53 - samples/sec: 1033.52 - lr: 0.000019 - momentum: 0.000000
172
+ 2023-09-04 13:00:53,075 epoch 7 - iter 1029/1476 - loss 0.02632303 - time (sec): 111.77 - samples/sec: 1036.57 - lr: 0.000018 - momentum: 0.000000
173
+ 2023-09-04 13:01:08,477 epoch 7 - iter 1176/1476 - loss 0.02644004 - time (sec): 127.18 - samples/sec: 1034.32 - lr: 0.000018 - momentum: 0.000000
174
+ 2023-09-04 13:01:24,297 epoch 7 - iter 1323/1476 - loss 0.02509027 - time (sec): 143.00 - samples/sec: 1032.18 - lr: 0.000017 - momentum: 0.000000
175
+ 2023-09-04 13:01:41,491 epoch 7 - iter 1470/1476 - loss 0.02464981 - time (sec): 160.19 - samples/sec: 1035.62 - lr: 0.000017 - momentum: 0.000000
176
+ 2023-09-04 13:01:42,039 ----------------------------------------------------------------------------------------------------
177
+ 2023-09-04 13:01:42,040 EPOCH 7 done: loss 0.0246 - lr: 0.000017
178
+ 2023-09-04 13:01:59,891 DEV : loss 0.2234429568052292 - f1-score (micro avg) 0.8009
179
+ 2023-09-04 13:01:59,920 saving best model
180
+ 2023-09-04 13:02:01,248 ----------------------------------------------------------------------------------------------------
181
+ 2023-09-04 13:02:17,045 epoch 8 - iter 147/1476 - loss 0.02104970 - time (sec): 15.80 - samples/sec: 1063.41 - lr: 0.000016 - momentum: 0.000000
182
+ 2023-09-04 13:02:33,539 epoch 8 - iter 294/1476 - loss 0.01905570 - time (sec): 32.29 - samples/sec: 1045.70 - lr: 0.000016 - momentum: 0.000000
183
+ 2023-09-04 13:02:51,720 epoch 8 - iter 441/1476 - loss 0.02143358 - time (sec): 50.47 - samples/sec: 1053.59 - lr: 0.000015 - momentum: 0.000000
184
+ 2023-09-04 13:03:06,995 epoch 8 - iter 588/1476 - loss 0.02318846 - time (sec): 65.75 - samples/sec: 1034.19 - lr: 0.000014 - momentum: 0.000000
185
+ 2023-09-04 13:03:22,725 epoch 8 - iter 735/1476 - loss 0.02159453 - time (sec): 81.48 - samples/sec: 1028.31 - lr: 0.000014 - momentum: 0.000000
186
+ 2023-09-04 13:03:37,894 epoch 8 - iter 882/1476 - loss 0.02077588 - time (sec): 96.64 - samples/sec: 1026.41 - lr: 0.000013 - momentum: 0.000000
187
+ 2023-09-04 13:03:53,907 epoch 8 - iter 1029/1476 - loss 0.01921124 - time (sec): 112.66 - samples/sec: 1022.41 - lr: 0.000013 - momentum: 0.000000
188
+ 2023-09-04 13:04:08,936 epoch 8 - iter 1176/1476 - loss 0.01898697 - time (sec): 127.69 - samples/sec: 1020.65 - lr: 0.000012 - momentum: 0.000000
189
+ 2023-09-04 13:04:25,679 epoch 8 - iter 1323/1476 - loss 0.01816890 - time (sec): 144.43 - samples/sec: 1025.22 - lr: 0.000012 - momentum: 0.000000
190
+ 2023-09-04 13:04:42,147 epoch 8 - iter 1470/1476 - loss 0.01707363 - time (sec): 160.90 - samples/sec: 1030.37 - lr: 0.000011 - momentum: 0.000000
191
+ 2023-09-04 13:04:42,746 ----------------------------------------------------------------------------------------------------
192
+ 2023-09-04 13:04:42,746 EPOCH 8 done: loss 0.0171 - lr: 0.000011
193
+ 2023-09-04 13:05:00,631 DEV : loss 0.2465333193540573 - f1-score (micro avg) 0.8171
194
+ 2023-09-04 13:05:00,660 saving best model
195
+ 2023-09-04 13:05:01,997 ----------------------------------------------------------------------------------------------------
196
+ 2023-09-04 13:05:17,734 epoch 9 - iter 147/1476 - loss 0.00882656 - time (sec): 15.74 - samples/sec: 989.49 - lr: 0.000011 - momentum: 0.000000
197
+ 2023-09-04 13:05:34,580 epoch 9 - iter 294/1476 - loss 0.01220668 - time (sec): 32.58 - samples/sec: 1011.69 - lr: 0.000010 - momentum: 0.000000
198
+ 2023-09-04 13:05:49,519 epoch 9 - iter 441/1476 - loss 0.01041928 - time (sec): 47.52 - samples/sec: 1022.24 - lr: 0.000009 - momentum: 0.000000
199
+ 2023-09-04 13:06:05,393 epoch 9 - iter 588/1476 - loss 0.01239141 - time (sec): 63.39 - samples/sec: 1029.38 - lr: 0.000009 - momentum: 0.000000
200
+ 2023-09-04 13:06:22,377 epoch 9 - iter 735/1476 - loss 0.01291391 - time (sec): 80.38 - samples/sec: 1036.93 - lr: 0.000008 - momentum: 0.000000
201
+ 2023-09-04 13:06:37,918 epoch 9 - iter 882/1476 - loss 0.01164990 - time (sec): 95.92 - samples/sec: 1031.89 - lr: 0.000008 - momentum: 0.000000
202
+ 2023-09-04 13:06:54,500 epoch 9 - iter 1029/1476 - loss 0.01012837 - time (sec): 112.50 - samples/sec: 1031.72 - lr: 0.000007 - momentum: 0.000000
203
+ 2023-09-04 13:07:10,209 epoch 9 - iter 1176/1476 - loss 0.01002218 - time (sec): 128.21 - samples/sec: 1026.68 - lr: 0.000007 - momentum: 0.000000
204
+ 2023-09-04 13:07:25,675 epoch 9 - iter 1323/1476 - loss 0.00974478 - time (sec): 143.68 - samples/sec: 1030.62 - lr: 0.000006 - momentum: 0.000000
205
+ 2023-09-04 13:07:42,531 epoch 9 - iter 1470/1476 - loss 0.01086558 - time (sec): 160.53 - samples/sec: 1031.34 - lr: 0.000006 - momentum: 0.000000
206
+ 2023-09-04 13:07:43,222 ----------------------------------------------------------------------------------------------------
207
+ 2023-09-04 13:07:43,222 EPOCH 9 done: loss 0.0111 - lr: 0.000006
208
+ 2023-09-04 13:08:01,008 DEV : loss 0.23992028832435608 - f1-score (micro avg) 0.8026
209
+ 2023-09-04 13:08:01,037 ----------------------------------------------------------------------------------------------------
210
+ 2023-09-04 13:08:17,870 epoch 10 - iter 147/1476 - loss 0.00738594 - time (sec): 16.83 - samples/sec: 1054.01 - lr: 0.000005 - momentum: 0.000000
211
+ 2023-09-04 13:08:33,223 epoch 10 - iter 294/1476 - loss 0.00717515 - time (sec): 32.19 - samples/sec: 1046.63 - lr: 0.000004 - momentum: 0.000000
212
+ 2023-09-04 13:08:47,936 epoch 10 - iter 441/1476 - loss 0.00881687 - time (sec): 46.90 - samples/sec: 1050.10 - lr: 0.000004 - momentum: 0.000000
213
+ 2023-09-04 13:09:03,864 epoch 10 - iter 588/1476 - loss 0.00781440 - time (sec): 62.83 - samples/sec: 1046.86 - lr: 0.000003 - momentum: 0.000000
214
+ 2023-09-04 13:09:19,628 epoch 10 - iter 735/1476 - loss 0.00754620 - time (sec): 78.59 - samples/sec: 1041.24 - lr: 0.000003 - momentum: 0.000000
215
+ 2023-09-04 13:09:37,111 epoch 10 - iter 882/1476 - loss 0.00777399 - time (sec): 96.07 - samples/sec: 1048.14 - lr: 0.000002 - momentum: 0.000000
216
+ 2023-09-04 13:09:52,104 epoch 10 - iter 1029/1476 - loss 0.00746587 - time (sec): 111.07 - samples/sec: 1039.50 - lr: 0.000002 - momentum: 0.000000
217
+ 2023-09-04 13:10:08,750 epoch 10 - iter 1176/1476 - loss 0.00697918 - time (sec): 127.71 - samples/sec: 1037.41 - lr: 0.000001 - momentum: 0.000000
218
+ 2023-09-04 13:10:25,067 epoch 10 - iter 1323/1476 - loss 0.00814534 - time (sec): 144.03 - samples/sec: 1037.56 - lr: 0.000001 - momentum: 0.000000
219
+ 2023-09-04 13:10:41,038 epoch 10 - iter 1470/1476 - loss 0.00775839 - time (sec): 160.00 - samples/sec: 1036.73 - lr: 0.000000 - momentum: 0.000000
220
+ 2023-09-04 13:10:41,631 ----------------------------------------------------------------------------------------------------
221
+ 2023-09-04 13:10:41,631 EPOCH 10 done: loss 0.0077 - lr: 0.000000
222
+ 2023-09-04 13:10:59,474 DEV : loss 0.2502734065055847 - f1-score (micro avg) 0.807
223
+ 2023-09-04 13:11:00,040 ----------------------------------------------------------------------------------------------------
224
+ 2023-09-04 13:11:00,042 Loading model from best epoch ...
225
+ 2023-09-04 13:11:01,899 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
226
+ 2023-09-04 13:11:16,645
227
+ Results:
228
+ - F-score (micro) 0.7859
229
+ - F-score (macro) 0.6745
230
+ - Accuracy 0.6703
231
+
232
+ By class:
233
+ precision recall f1-score support
234
+
235
+ loc 0.8637 0.8566 0.8602 858
236
+ pers 0.7371 0.7989 0.7668 537
237
+ org 0.5739 0.5000 0.5344 132
238
+ prod 0.6032 0.6230 0.6129 61
239
+ time 0.5556 0.6481 0.5983 54
240
+
241
+ micro avg 0.7784 0.7935 0.7859 1642
242
+ macro avg 0.6667 0.6853 0.6745 1642
243
+ weighted avg 0.7792 0.7935 0.7856 1642
244
+
245
+ 2023-09-04 13:11:16,645 ----------------------------------------------------------------------------------------------------