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autotrain-flair-hipe2022-de-hmbert

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  1. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3/best-model.pt +3 -0
  2. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3/dev.tsv +0 -0
  3. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3/final-model.pt +3 -0
  4. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3/loss.tsv +11 -0
  5. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3/test.tsv +0 -0
  6. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3/training.log +242 -0
hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3/best-model.pt ADDED
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hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3/dev.tsv ADDED
The diff for this file is too large to render. See raw diff
 
hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3/final-model.pt ADDED
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hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3/loss.tsv ADDED
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+ EPOCH TIMESTAMP LEARNING_RATE TRAIN_LOSS DEV_LOSS DEV_PRECISION DEV_RECALL DEV_F1 DEV_ACCURACY
2
+ 1 20:55:45 0.0000 0.5998 0.1752 0.6209 0.5801 0.5998 0.4398
3
+ 2 20:57:14 0.0000 0.1545 0.1319 0.6967 0.7490 0.7219 0.5856
4
+ 3 20:58:46 0.0000 0.0860 0.1338 0.7029 0.7326 0.7175 0.5788
5
+ 4 21:00:19 0.0000 0.0521 0.1692 0.7810 0.7584 0.7695 0.6390
6
+ 5 21:01:52 0.0000 0.0374 0.1798 0.7332 0.7780 0.7549 0.6203
7
+ 6 21:03:24 0.0000 0.0231 0.1903 0.7798 0.7725 0.7761 0.6496
8
+ 7 21:04:58 0.0000 0.0131 0.2086 0.7754 0.8045 0.7897 0.6660
9
+ 8 21:06:31 0.0000 0.0092 0.2286 0.7591 0.7959 0.7771 0.6496
10
+ 9 21:08:04 0.0000 0.0052 0.2226 0.7540 0.8030 0.7777 0.6525
11
+ 10 21:09:32 0.0000 0.0031 0.2275 0.7791 0.7967 0.7878 0.6647
hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3/test.tsv ADDED
The diff for this file is too large to render. See raw diff
 
hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3/training.log ADDED
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+ 2023-09-03 20:54:20,902 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 20:54:20,903 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)
7
+ (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)
18
+ (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(
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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
+ )
28
+ (intermediate): BertIntermediate(
29
+ (dense): Linear(in_features=768, out_features=3072, bias=True)
30
+ (intermediate_act_fn): GELUActivation()
31
+ )
32
+ (output): BertOutput(
33
+ (dense): Linear(in_features=3072, out_features=768, bias=True)
34
+ (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
35
+ (dropout): Dropout(p=0.1, inplace=False)
36
+ )
37
+ )
38
+ )
39
+ )
40
+ (pooler): BertPooler(
41
+ (dense): Linear(in_features=768, out_features=768, bias=True)
42
+ (activation): Tanh()
43
+ )
44
+ )
45
+ )
46
+ (locked_dropout): LockedDropout(p=0.5)
47
+ (linear): Linear(in_features=768, out_features=21, bias=True)
48
+ (loss_function): CrossEntropyLoss()
49
+ )"
50
+ 2023-09-03 20:54:20,903 ----------------------------------------------------------------------------------------------------
51
+ 2023-09-03 20:54:20,903 MultiCorpus: 3575 train + 1235 dev + 1266 test sentences
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+ - NER_HIPE_2022 Corpus: 3575 train + 1235 dev + 1266 test sentences - /app/.flair/datasets/ner_hipe_2022/v2.1/hipe2020/de/with_doc_seperator
53
+ 2023-09-03 20:54:20,903 ----------------------------------------------------------------------------------------------------
54
+ 2023-09-03 20:54:20,903 Train: 3575 sentences
55
+ 2023-09-03 20:54:20,903 (train_with_dev=False, train_with_test=False)
56
+ 2023-09-03 20:54:20,903 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 20:54:20,904 Training Params:
58
+ 2023-09-03 20:54:20,904 - learning_rate: "5e-05"
59
+ 2023-09-03 20:54:20,904 - mini_batch_size: "8"
60
+ 2023-09-03 20:54:20,904 - max_epochs: "10"
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+ 2023-09-03 20:54:20,904 - shuffle: "True"
62
+ 2023-09-03 20:54:20,904 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 20:54:20,904 Plugins:
64
+ 2023-09-03 20:54:20,904 - LinearScheduler | warmup_fraction: '0.1'
65
+ 2023-09-03 20:54:20,904 ----------------------------------------------------------------------------------------------------
66
+ 2023-09-03 20:54:20,904 Final evaluation on model from best epoch (best-model.pt)
67
+ 2023-09-03 20:54:20,904 - metric: "('micro avg', 'f1-score')"
68
+ 2023-09-03 20:54:20,904 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 20:54:20,904 Computation:
70
+ 2023-09-03 20:54:20,904 - compute on device: cuda:0
71
+ 2023-09-03 20:54:20,904 - embedding storage: none
72
+ 2023-09-03 20:54:20,904 ----------------------------------------------------------------------------------------------------
73
+ 2023-09-03 20:54:20,904 Model training base path: "hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3"
74
+ 2023-09-03 20:54:20,904 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 20:54:20,904 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 20:54:27,726 epoch 1 - iter 44/447 - loss 2.73716447 - time (sec): 6.82 - samples/sec: 1209.49 - lr: 0.000005 - momentum: 0.000000
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+ 2023-09-03 20:54:35,545 epoch 1 - iter 88/447 - loss 1.76275074 - time (sec): 14.64 - samples/sec: 1168.40 - lr: 0.000010 - momentum: 0.000000
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+ 2023-09-03 20:54:42,402 epoch 1 - iter 132/447 - loss 1.36332375 - time (sec): 21.50 - samples/sec: 1163.62 - lr: 0.000015 - momentum: 0.000000
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+ 2023-09-03 20:54:50,104 epoch 1 - iter 176/447 - loss 1.09517221 - time (sec): 29.20 - samples/sec: 1184.11 - lr: 0.000020 - momentum: 0.000000
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+ 2023-09-03 20:54:57,824 epoch 1 - iter 220/447 - loss 0.93311472 - time (sec): 36.92 - samples/sec: 1173.15 - lr: 0.000024 - momentum: 0.000000
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+ 2023-09-03 20:55:05,310 epoch 1 - iter 264/447 - loss 0.82310200 - time (sec): 44.40 - samples/sec: 1170.57 - lr: 0.000029 - momentum: 0.000000
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+ 2023-09-03 20:55:12,130 epoch 1 - iter 308/447 - loss 0.75010593 - time (sec): 51.22 - samples/sec: 1171.28 - lr: 0.000034 - momentum: 0.000000
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+ 2023-09-03 20:55:19,673 epoch 1 - iter 352/447 - loss 0.69595179 - time (sec): 58.77 - samples/sec: 1158.62 - lr: 0.000039 - momentum: 0.000000
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+ 2023-09-03 20:55:27,711 epoch 1 - iter 396/447 - loss 0.64348224 - time (sec): 66.81 - samples/sec: 1154.79 - lr: 0.000044 - momentum: 0.000000
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+ 2023-09-03 20:55:34,411 epoch 1 - iter 440/447 - loss 0.60334405 - time (sec): 73.51 - samples/sec: 1161.93 - lr: 0.000049 - momentum: 0.000000
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+ 2023-09-03 20:55:35,425 ----------------------------------------------------------------------------------------------------
87
+ 2023-09-03 20:55:35,426 EPOCH 1 done: loss 0.5998 - lr: 0.000049
88
+ 2023-09-03 20:55:45,844 DEV : loss 0.17524564266204834 - f1-score (micro avg) 0.5998
89
+ 2023-09-03 20:55:45,870 saving best model
90
+ 2023-09-03 20:55:46,360 ----------------------------------------------------------------------------------------------------
91
+ 2023-09-03 20:55:53,314 epoch 2 - iter 44/447 - loss 0.18708094 - time (sec): 6.95 - samples/sec: 1168.87 - lr: 0.000049 - momentum: 0.000000
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+ 2023-09-03 20:56:00,420 epoch 2 - iter 88/447 - loss 0.17901121 - time (sec): 14.06 - samples/sec: 1169.74 - lr: 0.000049 - momentum: 0.000000
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+ 2023-09-03 20:56:07,576 epoch 2 - iter 132/447 - loss 0.17257848 - time (sec): 21.21 - samples/sec: 1171.26 - lr: 0.000048 - momentum: 0.000000
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+ 2023-09-03 20:56:15,224 epoch 2 - iter 176/447 - loss 0.16490695 - time (sec): 28.86 - samples/sec: 1152.42 - lr: 0.000048 - momentum: 0.000000
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+ 2023-09-03 20:56:22,217 epoch 2 - iter 220/447 - loss 0.16421404 - time (sec): 35.85 - samples/sec: 1156.16 - lr: 0.000047 - momentum: 0.000000
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+ 2023-09-03 20:56:29,655 epoch 2 - iter 264/447 - loss 0.15964720 - time (sec): 43.29 - samples/sec: 1151.14 - lr: 0.000047 - momentum: 0.000000
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+ 2023-09-03 20:56:36,715 epoch 2 - iter 308/447 - loss 0.16020622 - time (sec): 50.35 - samples/sec: 1151.99 - lr: 0.000046 - momentum: 0.000000
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+ 2023-09-03 20:56:45,118 epoch 2 - iter 352/447 - loss 0.15440493 - time (sec): 58.76 - samples/sec: 1141.39 - lr: 0.000046 - momentum: 0.000000
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+ 2023-09-03 20:56:52,814 epoch 2 - iter 396/447 - loss 0.15684350 - time (sec): 66.45 - samples/sec: 1154.44 - lr: 0.000045 - momentum: 0.000000
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+ 2023-09-03 20:57:00,179 epoch 2 - iter 440/447 - loss 0.15425075 - time (sec): 73.82 - samples/sec: 1155.31 - lr: 0.000045 - momentum: 0.000000
101
+ 2023-09-03 20:57:01,430 ----------------------------------------------------------------------------------------------------
102
+ 2023-09-03 20:57:01,430 EPOCH 2 done: loss 0.1545 - lr: 0.000045
103
+ 2023-09-03 20:57:14,374 DEV : loss 0.1318657249212265 - f1-score (micro avg) 0.7219
104
+ 2023-09-03 20:57:14,401 saving best model
105
+ 2023-09-03 20:57:15,722 ----------------------------------------------------------------------------------------------------
106
+ 2023-09-03 20:57:22,857 epoch 3 - iter 44/447 - loss 0.09910014 - time (sec): 7.13 - samples/sec: 1142.88 - lr: 0.000044 - momentum: 0.000000
107
+ 2023-09-03 20:57:29,811 epoch 3 - iter 88/447 - loss 0.09095220 - time (sec): 14.09 - samples/sec: 1136.57 - lr: 0.000043 - momentum: 0.000000
108
+ 2023-09-03 20:57:37,844 epoch 3 - iter 132/447 - loss 0.09474706 - time (sec): 22.12 - samples/sec: 1119.14 - lr: 0.000043 - momentum: 0.000000
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+ 2023-09-03 20:57:44,926 epoch 3 - iter 176/447 - loss 0.09662773 - time (sec): 29.20 - samples/sec: 1132.33 - lr: 0.000042 - momentum: 0.000000
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+ 2023-09-03 20:57:52,164 epoch 3 - iter 220/447 - loss 0.09890834 - time (sec): 36.44 - samples/sec: 1126.96 - lr: 0.000042 - momentum: 0.000000
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+ 2023-09-03 20:57:59,695 epoch 3 - iter 264/447 - loss 0.09257574 - time (sec): 43.97 - samples/sec: 1133.04 - lr: 0.000041 - momentum: 0.000000
112
+ 2023-09-03 20:58:07,414 epoch 3 - iter 308/447 - loss 0.09156175 - time (sec): 51.69 - samples/sec: 1127.34 - lr: 0.000041 - momentum: 0.000000
113
+ 2023-09-03 20:58:15,315 epoch 3 - iter 352/447 - loss 0.09061217 - time (sec): 59.59 - samples/sec: 1119.14 - lr: 0.000040 - momentum: 0.000000
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+ 2023-09-03 20:58:23,229 epoch 3 - iter 396/447 - loss 0.08784546 - time (sec): 67.51 - samples/sec: 1118.91 - lr: 0.000040 - momentum: 0.000000
115
+ 2023-09-03 20:58:30,739 epoch 3 - iter 440/447 - loss 0.08704201 - time (sec): 75.02 - samples/sec: 1121.66 - lr: 0.000039 - momentum: 0.000000
116
+ 2023-09-03 20:58:33,106 ----------------------------------------------------------------------------------------------------
117
+ 2023-09-03 20:58:33,107 EPOCH 3 done: loss 0.0860 - lr: 0.000039
118
+ 2023-09-03 20:58:46,618 DEV : loss 0.13379663228988647 - f1-score (micro avg) 0.7175
119
+ 2023-09-03 20:58:46,645 ----------------------------------------------------------------------------------------------------
120
+ 2023-09-03 20:58:54,615 epoch 4 - iter 44/447 - loss 0.06277073 - time (sec): 7.97 - samples/sec: 1022.51 - lr: 0.000038 - momentum: 0.000000
121
+ 2023-09-03 20:59:02,152 epoch 4 - iter 88/447 - loss 0.05648163 - time (sec): 15.51 - samples/sec: 1068.96 - lr: 0.000038 - momentum: 0.000000
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+ 2023-09-03 20:59:09,377 epoch 4 - iter 132/447 - loss 0.05312505 - time (sec): 22.73 - samples/sec: 1090.33 - lr: 0.000037 - momentum: 0.000000
123
+ 2023-09-03 20:59:18,549 epoch 4 - iter 176/447 - loss 0.05349326 - time (sec): 31.90 - samples/sec: 1091.10 - lr: 0.000037 - momentum: 0.000000
124
+ 2023-09-03 20:59:26,198 epoch 4 - iter 220/447 - loss 0.04992607 - time (sec): 39.55 - samples/sec: 1092.82 - lr: 0.000036 - momentum: 0.000000
125
+ 2023-09-03 20:59:33,971 epoch 4 - iter 264/447 - loss 0.05119766 - time (sec): 47.33 - samples/sec: 1091.28 - lr: 0.000036 - momentum: 0.000000
126
+ 2023-09-03 20:59:41,433 epoch 4 - iter 308/447 - loss 0.05149054 - time (sec): 54.79 - samples/sec: 1096.95 - lr: 0.000035 - momentum: 0.000000
127
+ 2023-09-03 20:59:48,965 epoch 4 - iter 352/447 - loss 0.05063399 - time (sec): 62.32 - samples/sec: 1097.67 - lr: 0.000035 - momentum: 0.000000
128
+ 2023-09-03 20:59:57,322 epoch 4 - iter 396/447 - loss 0.05013942 - time (sec): 70.68 - samples/sec: 1094.04 - lr: 0.000034 - momentum: 0.000000
129
+ 2023-09-03 21:00:04,533 epoch 4 - iter 440/447 - loss 0.05169724 - time (sec): 77.89 - samples/sec: 1096.02 - lr: 0.000033 - momentum: 0.000000
130
+ 2023-09-03 21:00:05,636 ----------------------------------------------------------------------------------------------------
131
+ 2023-09-03 21:00:05,636 EPOCH 4 done: loss 0.0521 - lr: 0.000033
132
+ 2023-09-03 21:00:19,119 DEV : loss 0.1692018061876297 - f1-score (micro avg) 0.7695
133
+ 2023-09-03 21:00:19,153 saving best model
134
+ 2023-09-03 21:00:20,479 ----------------------------------------------------------------------------------------------------
135
+ 2023-09-03 21:00:28,391 epoch 5 - iter 44/447 - loss 0.03984379 - time (sec): 7.91 - samples/sec: 1069.76 - lr: 0.000033 - momentum: 0.000000
136
+ 2023-09-03 21:00:35,671 epoch 5 - iter 88/447 - loss 0.03527843 - time (sec): 15.19 - samples/sec: 1089.96 - lr: 0.000032 - momentum: 0.000000
137
+ 2023-09-03 21:00:43,033 epoch 5 - iter 132/447 - loss 0.03633554 - time (sec): 22.55 - samples/sec: 1107.96 - lr: 0.000032 - momentum: 0.000000
138
+ 2023-09-03 21:00:50,223 epoch 5 - iter 176/447 - loss 0.03854755 - time (sec): 29.74 - samples/sec: 1107.05 - lr: 0.000031 - momentum: 0.000000
139
+ 2023-09-03 21:00:58,991 epoch 5 - iter 220/447 - loss 0.03856388 - time (sec): 38.51 - samples/sec: 1094.62 - lr: 0.000031 - momentum: 0.000000
140
+ 2023-09-03 21:01:06,149 epoch 5 - iter 264/447 - loss 0.03879315 - time (sec): 45.67 - samples/sec: 1098.02 - lr: 0.000030 - momentum: 0.000000
141
+ 2023-09-03 21:01:13,548 epoch 5 - iter 308/447 - loss 0.03806475 - time (sec): 53.07 - samples/sec: 1100.83 - lr: 0.000030 - momentum: 0.000000
142
+ 2023-09-03 21:01:22,543 epoch 5 - iter 352/447 - loss 0.03944462 - time (sec): 62.06 - samples/sec: 1097.56 - lr: 0.000029 - momentum: 0.000000
143
+ 2023-09-03 21:01:30,581 epoch 5 - iter 396/447 - loss 0.03814469 - time (sec): 70.10 - samples/sec: 1101.77 - lr: 0.000028 - momentum: 0.000000
144
+ 2023-09-03 21:01:38,343 epoch 5 - iter 440/447 - loss 0.03774412 - time (sec): 77.86 - samples/sec: 1096.09 - lr: 0.000028 - momentum: 0.000000
145
+ 2023-09-03 21:01:39,351 ----------------------------------------------------------------------------------------------------
146
+ 2023-09-03 21:01:39,351 EPOCH 5 done: loss 0.0374 - lr: 0.000028
147
+ 2023-09-03 21:01:52,412 DEV : loss 0.17978209257125854 - f1-score (micro avg) 0.7549
148
+ 2023-09-03 21:01:52,439 ----------------------------------------------------------------------------------------------------
149
+ 2023-09-03 21:02:01,732 epoch 6 - iter 44/447 - loss 0.02980735 - time (sec): 9.29 - samples/sec: 1035.64 - lr: 0.000027 - momentum: 0.000000
150
+ 2023-09-03 21:02:09,110 epoch 6 - iter 88/447 - loss 0.02423965 - time (sec): 16.67 - samples/sec: 1069.15 - lr: 0.000027 - momentum: 0.000000
151
+ 2023-09-03 21:02:17,431 epoch 6 - iter 132/447 - loss 0.02298144 - time (sec): 24.99 - samples/sec: 1065.88 - lr: 0.000026 - momentum: 0.000000
152
+ 2023-09-03 21:02:25,242 epoch 6 - iter 176/447 - loss 0.02273349 - time (sec): 32.80 - samples/sec: 1079.58 - lr: 0.000026 - momentum: 0.000000
153
+ 2023-09-03 21:02:33,012 epoch 6 - iter 220/447 - loss 0.02157790 - time (sec): 40.57 - samples/sec: 1094.10 - lr: 0.000025 - momentum: 0.000000
154
+ 2023-09-03 21:02:40,172 epoch 6 - iter 264/447 - loss 0.02048096 - time (sec): 47.73 - samples/sec: 1096.13 - lr: 0.000025 - momentum: 0.000000
155
+ 2023-09-03 21:02:47,619 epoch 6 - iter 308/447 - loss 0.02034238 - time (sec): 55.18 - samples/sec: 1092.41 - lr: 0.000024 - momentum: 0.000000
156
+ 2023-09-03 21:02:54,850 epoch 6 - iter 352/447 - loss 0.02168193 - time (sec): 62.41 - samples/sec: 1093.46 - lr: 0.000023 - momentum: 0.000000
157
+ 2023-09-03 21:03:02,551 epoch 6 - iter 396/447 - loss 0.02256137 - time (sec): 70.11 - samples/sec: 1097.72 - lr: 0.000023 - momentum: 0.000000
158
+ 2023-09-03 21:03:09,393 epoch 6 - iter 440/447 - loss 0.02303606 - time (sec): 76.95 - samples/sec: 1102.62 - lr: 0.000022 - momentum: 0.000000
159
+ 2023-09-03 21:03:11,217 ----------------------------------------------------------------------------------------------------
160
+ 2023-09-03 21:03:11,217 EPOCH 6 done: loss 0.0231 - lr: 0.000022
161
+ 2023-09-03 21:03:24,374 DEV : loss 0.19026526808738708 - f1-score (micro avg) 0.7761
162
+ 2023-09-03 21:03:24,400 saving best model
163
+ 2023-09-03 21:03:25,705 ----------------------------------------------------------------------------------------------------
164
+ 2023-09-03 21:03:33,900 epoch 7 - iter 44/447 - loss 0.01327631 - time (sec): 8.19 - samples/sec: 1122.36 - lr: 0.000022 - momentum: 0.000000
165
+ 2023-09-03 21:03:43,494 epoch 7 - iter 88/447 - loss 0.01391578 - time (sec): 17.79 - samples/sec: 1066.78 - lr: 0.000021 - momentum: 0.000000
166
+ 2023-09-03 21:03:51,315 epoch 7 - iter 132/447 - loss 0.01429568 - time (sec): 25.61 - samples/sec: 1079.50 - lr: 0.000021 - momentum: 0.000000
167
+ 2023-09-03 21:03:59,158 epoch 7 - iter 176/447 - loss 0.01322758 - time (sec): 33.45 - samples/sec: 1099.43 - lr: 0.000020 - momentum: 0.000000
168
+ 2023-09-03 21:04:07,980 epoch 7 - iter 220/447 - loss 0.01222787 - time (sec): 42.27 - samples/sec: 1080.35 - lr: 0.000020 - momentum: 0.000000
169
+ 2023-09-03 21:04:15,077 epoch 7 - iter 264/447 - loss 0.01097556 - time (sec): 49.37 - samples/sec: 1079.67 - lr: 0.000019 - momentum: 0.000000
170
+ 2023-09-03 21:04:21,966 epoch 7 - iter 308/447 - loss 0.01200888 - time (sec): 56.26 - samples/sec: 1085.28 - lr: 0.000018 - momentum: 0.000000
171
+ 2023-09-03 21:04:29,651 epoch 7 - iter 352/447 - loss 0.01374890 - time (sec): 63.94 - samples/sec: 1083.72 - lr: 0.000018 - momentum: 0.000000
172
+ 2023-09-03 21:04:36,475 epoch 7 - iter 396/447 - loss 0.01349475 - time (sec): 70.77 - samples/sec: 1089.46 - lr: 0.000017 - momentum: 0.000000
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+ 2023-09-03 21:04:43,497 epoch 7 - iter 440/447 - loss 0.01326615 - time (sec): 77.79 - samples/sec: 1093.14 - lr: 0.000017 - momentum: 0.000000
174
+ 2023-09-03 21:04:44,921 ----------------------------------------------------------------------------------------------------
175
+ 2023-09-03 21:04:44,921 EPOCH 7 done: loss 0.0131 - lr: 0.000017
176
+ 2023-09-03 21:04:58,031 DEV : loss 0.20857642590999603 - f1-score (micro avg) 0.7897
177
+ 2023-09-03 21:04:58,060 saving best model
178
+ 2023-09-03 21:04:59,399 ----------------------------------------------------------------------------------------------------
179
+ 2023-09-03 21:05:06,891 epoch 8 - iter 44/447 - loss 0.00956675 - time (sec): 7.49 - samples/sec: 1153.98 - lr: 0.000016 - momentum: 0.000000
180
+ 2023-09-03 21:05:14,219 epoch 8 - iter 88/447 - loss 0.00759491 - time (sec): 14.82 - samples/sec: 1132.50 - lr: 0.000016 - momentum: 0.000000
181
+ 2023-09-03 21:05:23,510 epoch 8 - iter 132/447 - loss 0.00798829 - time (sec): 24.11 - samples/sec: 1110.17 - lr: 0.000015 - momentum: 0.000000
182
+ 2023-09-03 21:05:31,762 epoch 8 - iter 176/447 - loss 0.00858372 - time (sec): 32.36 - samples/sec: 1087.71 - lr: 0.000015 - momentum: 0.000000
183
+ 2023-09-03 21:05:39,184 epoch 8 - iter 220/447 - loss 0.00883033 - time (sec): 39.78 - samples/sec: 1095.55 - lr: 0.000014 - momentum: 0.000000
184
+ 2023-09-03 21:05:47,717 epoch 8 - iter 264/447 - loss 0.00944911 - time (sec): 48.32 - samples/sec: 1083.50 - lr: 0.000013 - momentum: 0.000000
185
+ 2023-09-03 21:05:55,417 epoch 8 - iter 308/447 - loss 0.00948601 - time (sec): 56.02 - samples/sec: 1087.55 - lr: 0.000013 - momentum: 0.000000
186
+ 2023-09-03 21:06:02,467 epoch 8 - iter 352/447 - loss 0.00892223 - time (sec): 63.07 - samples/sec: 1092.67 - lr: 0.000012 - momentum: 0.000000
187
+ 2023-09-03 21:06:10,154 epoch 8 - iter 396/447 - loss 0.00886820 - time (sec): 70.75 - samples/sec: 1094.43 - lr: 0.000012 - momentum: 0.000000
188
+ 2023-09-03 21:06:16,959 epoch 8 - iter 440/447 - loss 0.00915381 - time (sec): 77.56 - samples/sec: 1100.56 - lr: 0.000011 - momentum: 0.000000
189
+ 2023-09-03 21:06:17,966 ----------------------------------------------------------------------------------------------------
190
+ 2023-09-03 21:06:17,967 EPOCH 8 done: loss 0.0092 - lr: 0.000011
191
+ 2023-09-03 21:06:31,579 DEV : loss 0.2286161482334137 - f1-score (micro avg) 0.7771
192
+ 2023-09-03 21:06:31,605 ----------------------------------------------------------------------------------------------------
193
+ 2023-09-03 21:06:39,659 epoch 9 - iter 44/447 - loss 0.00534548 - time (sec): 8.05 - samples/sec: 1015.04 - lr: 0.000011 - momentum: 0.000000
194
+ 2023-09-03 21:06:48,777 epoch 9 - iter 88/447 - loss 0.00425359 - time (sec): 17.17 - samples/sec: 1019.60 - lr: 0.000010 - momentum: 0.000000
195
+ 2023-09-03 21:06:56,849 epoch 9 - iter 132/447 - loss 0.00444618 - time (sec): 25.24 - samples/sec: 1032.27 - lr: 0.000010 - momentum: 0.000000
196
+ 2023-09-03 21:07:04,488 epoch 9 - iter 176/447 - loss 0.00461089 - time (sec): 32.88 - samples/sec: 1047.40 - lr: 0.000009 - momentum: 0.000000
197
+ 2023-09-03 21:07:11,516 epoch 9 - iter 220/447 - loss 0.00539607 - time (sec): 39.91 - samples/sec: 1071.49 - lr: 0.000008 - momentum: 0.000000
198
+ 2023-09-03 21:07:18,585 epoch 9 - iter 264/447 - loss 0.00492824 - time (sec): 46.98 - samples/sec: 1083.02 - lr: 0.000008 - momentum: 0.000000
199
+ 2023-09-03 21:07:25,897 epoch 9 - iter 308/447 - loss 0.00432453 - time (sec): 54.29 - samples/sec: 1086.25 - lr: 0.000007 - momentum: 0.000000
200
+ 2023-09-03 21:07:33,042 epoch 9 - iter 352/447 - loss 0.00427809 - time (sec): 61.44 - samples/sec: 1093.30 - lr: 0.000007 - momentum: 0.000000
201
+ 2023-09-03 21:07:41,918 epoch 9 - iter 396/447 - loss 0.00449438 - time (sec): 70.31 - samples/sec: 1095.67 - lr: 0.000006 - momentum: 0.000000
202
+ 2023-09-03 21:07:49,889 epoch 9 - iter 440/447 - loss 0.00470024 - time (sec): 78.28 - samples/sec: 1089.96 - lr: 0.000006 - momentum: 0.000000
203
+ 2023-09-03 21:07:50,912 ----------------------------------------------------------------------------------------------------
204
+ 2023-09-03 21:07:50,913 EPOCH 9 done: loss 0.0052 - lr: 0.000006
205
+ 2023-09-03 21:08:04,501 DEV : loss 0.22261452674865723 - f1-score (micro avg) 0.7777
206
+ 2023-09-03 21:08:04,528 ----------------------------------------------------------------------------------------------------
207
+ 2023-09-03 21:08:12,555 epoch 10 - iter 44/447 - loss 0.00479591 - time (sec): 8.03 - samples/sec: 1123.45 - lr: 0.000005 - momentum: 0.000000
208
+ 2023-09-03 21:08:19,833 epoch 10 - iter 88/447 - loss 0.00658392 - time (sec): 15.30 - samples/sec: 1130.43 - lr: 0.000005 - momentum: 0.000000
209
+ 2023-09-03 21:08:27,230 epoch 10 - iter 132/447 - loss 0.00460342 - time (sec): 22.70 - samples/sec: 1133.81 - lr: 0.000004 - momentum: 0.000000
210
+ 2023-09-03 21:08:35,120 epoch 10 - iter 176/447 - loss 0.00429682 - time (sec): 30.59 - samples/sec: 1132.08 - lr: 0.000003 - momentum: 0.000000
211
+ 2023-09-03 21:08:41,797 epoch 10 - iter 220/447 - loss 0.00420025 - time (sec): 37.27 - samples/sec: 1144.52 - lr: 0.000003 - momentum: 0.000000
212
+ 2023-09-03 21:08:48,910 epoch 10 - iter 264/447 - loss 0.00362146 - time (sec): 44.38 - samples/sec: 1144.62 - lr: 0.000002 - momentum: 0.000000
213
+ 2023-09-03 21:08:56,331 epoch 10 - iter 308/447 - loss 0.00361059 - time (sec): 51.80 - samples/sec: 1147.07 - lr: 0.000002 - momentum: 0.000000
214
+ 2023-09-03 21:09:05,620 epoch 10 - iter 352/447 - loss 0.00336808 - time (sec): 61.09 - samples/sec: 1140.21 - lr: 0.000001 - momentum: 0.000000
215
+ 2023-09-03 21:09:12,495 epoch 10 - iter 396/447 - loss 0.00329159 - time (sec): 67.97 - samples/sec: 1140.42 - lr: 0.000001 - momentum: 0.000000
216
+ 2023-09-03 21:09:19,057 epoch 10 - iter 440/447 - loss 0.00311645 - time (sec): 74.53 - samples/sec: 1144.22 - lr: 0.000000 - momentum: 0.000000
217
+ 2023-09-03 21:09:20,071 ----------------------------------------------------------------------------------------------------
218
+ 2023-09-03 21:09:20,072 EPOCH 10 done: loss 0.0031 - lr: 0.000000
219
+ 2023-09-03 21:09:32,827 DEV : loss 0.2274623066186905 - f1-score (micro avg) 0.7878
220
+ 2023-09-03 21:09:33,320 ----------------------------------------------------------------------------------------------------
221
+ 2023-09-03 21:09:33,321 Loading model from best epoch ...
222
+ 2023-09-03 21:09:35,181 SequenceTagger predicts: Dictionary with 21 tags: O, S-loc, B-loc, E-loc, I-loc, S-pers, B-pers, E-pers, I-pers, S-org, B-org, E-org, I-org, S-prod, B-prod, E-prod, I-prod, S-time, B-time, E-time, I-time
223
+ 2023-09-03 21:09:45,092
224
+ Results:
225
+ - F-score (micro) 0.7335
226
+ - F-score (macro) 0.6578
227
+ - Accuracy 0.5978
228
+
229
+ By class:
230
+ precision recall f1-score support
231
+
232
+ loc 0.8115 0.8523 0.8314 596
233
+ pers 0.6434 0.7477 0.6917 333
234
+ org 0.4839 0.4545 0.4687 132
235
+ prod 0.5690 0.5000 0.5323 66
236
+ time 0.7358 0.7959 0.7647 49
237
+
238
+ micro avg 0.7123 0.7560 0.7335 1176
239
+ macro avg 0.6487 0.6701 0.6578 1176
240
+ weighted avg 0.7104 0.7560 0.7316 1176
241
+
242
+ 2023-09-03 21:09:45,092 ----------------------------------------------------------------------------------------------------