Hugging Face's logo

stefan-it
/
hmbench-topres19th-en-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1

Token Classification
Flair
PyTorch
TensorBoard
English
sequence-tagger-model
Model card Files Metrics Community
stefan-it commited on
Commit
19d3037
•
1 Parent(s): 68141d3

Upload folder using huggingface_hub

Browse files
Files changed (6) hide show
  1. best-model.pt +3 -0
  2. dev.tsv +0 -0
  3. loss.tsv +11 -0
  4. runs/events.out.tfevents.1697532866.4c6324b99746.1159.0 +3 -0
  5. test.tsv +0 -0
  6. training.log +236 -0
best-model.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:2e69b67691228b4bb49fcb1285e43a2321ae989bac310a871e1617dca7b21b2b
3
+ size 440942021
dev.tsv ADDED
The diff for this file is too large to render. See raw diff
 
loss.tsv ADDED
@@ -0,0 +1,11 @@
 
 
 
 
 
 
 
 
 
 
 
 
1
+ EPOCH TIMESTAMP LEARNING_RATE TRAIN_LOSS DEV_LOSS DEV_PRECISION DEV_RECALL DEV_F1 DEV_ACCURACY
2
+ 1 08:56:28 0.0000 0.3357 0.0612 0.6871 0.8059 0.7417 0.6025
3
+ 2 08:58:36 0.0000 0.0831 0.0655 0.7403 0.7215 0.7308 0.5917
4
+ 3 09:00:41 0.0000 0.0528 0.0600 0.7692 0.8439 0.8048 0.6873
5
+ 4 09:02:44 0.0000 0.0350 0.0860 0.6980 0.8776 0.7776 0.6440
6
+ 5 09:04:50 0.0000 0.0242 0.0996 0.7702 0.8059 0.7876 0.6702
7
+ 6 09:07:00 0.0000 0.0170 0.0996 0.7665 0.8312 0.7976 0.6793
8
+ 7 09:09:04 0.0000 0.0122 0.1069 0.8120 0.8017 0.8068 0.6884
9
+ 8 09:11:13 0.0000 0.0077 0.1070 0.7674 0.8354 0.8000 0.6828
10
+ 9 09:13:18 0.0000 0.0045 0.1215 0.7869 0.8101 0.7983 0.6833
11
+ 10 09:15:29 0.0000 0.0034 0.1200 0.7608 0.8186 0.7886 0.6736
runs/events.out.tfevents.1697532866.4c6324b99746.1159.0 ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:89e365d5e2127e27f5c9d660aaaa70212bc479b74f2c0d50ea59d7c5935b824d
3
+ size 864636
test.tsv ADDED
The diff for this file is too large to render. See raw diff
 
training.log ADDED
@@ -0,0 +1,236 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 2023-10-17 08:54:26,109 ----------------------------------------------------------------------------------------------------
2
+ 2023-10-17 08:54:26,111 Model: "SequenceTagger(
3
+ (embeddings): TransformerWordEmbeddings(
4
+ (model): ElectraModel(
5
+ (embeddings): ElectraEmbeddings(
6
+ (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)
10
+ (dropout): Dropout(p=0.1, inplace=False)
11
+ )
12
+ (encoder): ElectraEncoder(
13
+ (layer): ModuleList(
14
+ (0-11): 12 x ElectraLayer(
15
+ (attention): ElectraAttention(
16
+ (self): ElectraSelfAttention(
17
+ (query): Linear(in_features=768, out_features=768, bias=True)
18
+ (key): Linear(in_features=768, out_features=768, bias=True)
19
+ (value): Linear(in_features=768, out_features=768, bias=True)
20
+ (dropout): Dropout(p=0.1, inplace=False)
21
+ )
22
+ (output): ElectraSelfOutput(
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): ElectraIntermediate(
29
+ (dense): Linear(in_features=768, out_features=3072, bias=True)
30
+ (intermediate_act_fn): GELUActivation()
31
+ )
32
+ (output): ElectraOutput(
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
+ )
41
+ )
42
+ (locked_dropout): LockedDropout(p=0.5)
43
+ (linear): Linear(in_features=768, out_features=13, bias=True)
44
+ (loss_function): CrossEntropyLoss()
45
+ )"
46
+ 2023-10-17 08:54:26,111 ----------------------------------------------------------------------------------------------------
47
+ 2023-10-17 08:54:26,112 MultiCorpus: 6183 train + 680 dev + 2113 test sentences
48
+ - NER_HIPE_2022 Corpus: 6183 train + 680 dev + 2113 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/topres19th/en/with_doc_seperator
49
+ 2023-10-17 08:54:26,112 ----------------------------------------------------------------------------------------------------
50
+ 2023-10-17 08:54:26,112 Train: 6183 sentences
51
+ 2023-10-17 08:54:26,112 (train_with_dev=False, train_with_test=False)
52
+ 2023-10-17 08:54:26,112 ----------------------------------------------------------------------------------------------------
53
+ 2023-10-17 08:54:26,112 Training Params:
54
+ 2023-10-17 08:54:26,112 - learning_rate: "3e-05"
55
+ 2023-10-17 08:54:26,112 - mini_batch_size: "4"
56
+ 2023-10-17 08:54:26,112 - max_epochs: "10"
57
+ 2023-10-17 08:54:26,112 - shuffle: "True"
58
+ 2023-10-17 08:54:26,112 ----------------------------------------------------------------------------------------------------
59
+ 2023-10-17 08:54:26,112 Plugins:
60
+ 2023-10-17 08:54:26,112 - TensorboardLogger
61
+ 2023-10-17 08:54:26,112 - LinearScheduler | warmup_fraction: '0.1'
62
+ 2023-10-17 08:54:26,113 ----------------------------------------------------------------------------------------------------
63
+ 2023-10-17 08:54:26,113 Final evaluation on model from best epoch (best-model.pt)
64
+ 2023-10-17 08:54:26,113 - metric: "('micro avg', 'f1-score')"
65
+ 2023-10-17 08:54:26,113 ----------------------------------------------------------------------------------------------------
66
+ 2023-10-17 08:54:26,113 Computation:
67
+ 2023-10-17 08:54:26,113 - compute on device: cuda:0
68
+ 2023-10-17 08:54:26,113 - embedding storage: none
69
+ 2023-10-17 08:54:26,113 ----------------------------------------------------------------------------------------------------
70
+ 2023-10-17 08:54:26,113 Model training base path: "hmbench-topres19th/en-hmteams/teams-base-historic-multilingual-discriminator-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1"
71
+ 2023-10-17 08:54:26,113 ----------------------------------------------------------------------------------------------------
72
+ 2023-10-17 08:54:26,113 ----------------------------------------------------------------------------------------------------
73
+ 2023-10-17 08:54:26,113 Logging anything other than scalars to TensorBoard is currently not supported.
74
+ 2023-10-17 08:54:38,761 epoch 1 - iter 154/1546 - loss 2.03813618 - time (sec): 12.65 - samples/sec: 1016.55 - lr: 0.000003 - momentum: 0.000000
75
+ 2023-10-17 08:54:50,424 epoch 1 - iter 308/1546 - loss 1.16576737 - time (sec): 24.31 - samples/sec: 1031.97 - lr: 0.000006 - momentum: 0.000000
76
+ 2023-10-17 08:55:01,974 epoch 1 - iter 462/1546 - loss 0.82780908 - time (sec): 35.86 - samples/sec: 1043.52 - lr: 0.000009 - momentum: 0.000000
77
+ 2023-10-17 08:55:13,456 epoch 1 - iter 616/1546 - loss 0.64844751 - time (sec): 47.34 - samples/sec: 1064.97 - lr: 0.000012 - momentum: 0.000000
78
+ 2023-10-17 08:55:26,298 epoch 1 - iter 770/1546 - loss 0.54447510 - time (sec): 60.18 - samples/sec: 1040.86 - lr: 0.000015 - momentum: 0.000000
79
+ 2023-10-17 08:55:38,367 epoch 1 - iter 924/1546 - loss 0.47295779 - time (sec): 72.25 - samples/sec: 1037.01 - lr: 0.000018 - momentum: 0.000000
80
+ 2023-10-17 08:55:50,285 epoch 1 - iter 1078/1546 - loss 0.43087146 - time (sec): 84.17 - samples/sec: 1030.26 - lr: 0.000021 - momentum: 0.000000
81
+ 2023-10-17 08:56:01,700 epoch 1 - iter 1232/1546 - loss 0.39626057 - time (sec): 95.59 - samples/sec: 1033.71 - lr: 0.000024 - momentum: 0.000000
82
+ 2023-10-17 08:56:13,219 epoch 1 - iter 1386/1546 - loss 0.36195701 - time (sec): 107.10 - samples/sec: 1041.77 - lr: 0.000027 - momentum: 0.000000
83
+ 2023-10-17 08:56:25,365 epoch 1 - iter 1540/1546 - loss 0.33627435 - time (sec): 119.25 - samples/sec: 1039.81 - lr: 0.000030 - momentum: 0.000000
84
+ 2023-10-17 08:56:25,820 ----------------------------------------------------------------------------------------------------
85
+ 2023-10-17 08:56:25,821 EPOCH 1 done: loss 0.3357 - lr: 0.000030
86
+ 2023-10-17 08:56:28,051 DEV : loss 0.06119954213500023 - f1-score (micro avg) 0.7417
87
+ 2023-10-17 08:56:28,079 saving best model
88
+ 2023-10-17 08:56:28,614 ----------------------------------------------------------------------------------------------------
89
+ 2023-10-17 08:56:40,142 epoch 2 - iter 154/1546 - loss 0.10453700 - time (sec): 11.53 - samples/sec: 1025.22 - lr: 0.000030 - momentum: 0.000000
90
+ 2023-10-17 08:56:52,687 epoch 2 - iter 308/1546 - loss 0.08704820 - time (sec): 24.07 - samples/sec: 1003.63 - lr: 0.000029 - momentum: 0.000000
91
+ 2023-10-17 08:57:05,545 epoch 2 - iter 462/1546 - loss 0.08423983 - time (sec): 36.93 - samples/sec: 1020.87 - lr: 0.000029 - momentum: 0.000000
92
+ 2023-10-17 08:57:17,766 epoch 2 - iter 616/1546 - loss 0.08543159 - time (sec): 49.15 - samples/sec: 1019.96 - lr: 0.000029 - momentum: 0.000000
93
+ 2023-10-17 08:57:30,051 epoch 2 - iter 770/1546 - loss 0.08700069 - time (sec): 61.43 - samples/sec: 1021.39 - lr: 0.000028 - momentum: 0.000000
94
+ 2023-10-17 08:57:42,483 epoch 2 - iter 924/1546 - loss 0.08706791 - time (sec): 73.87 - samples/sec: 1014.33 - lr: 0.000028 - momentum: 0.000000
95
+ 2023-10-17 08:57:55,337 epoch 2 - iter 1078/1546 - loss 0.08512417 - time (sec): 86.72 - samples/sec: 1008.52 - lr: 0.000028 - momentum: 0.000000
96
+ 2023-10-17 08:58:07,949 epoch 2 - iter 1232/1546 - loss 0.08386350 - time (sec): 99.33 - samples/sec: 1012.09 - lr: 0.000027 - momentum: 0.000000
97
+ 2023-10-17 08:58:20,787 epoch 2 - iter 1386/1546 - loss 0.08241631 - time (sec): 112.17 - samples/sec: 1000.28 - lr: 0.000027 - momentum: 0.000000
98
+ 2023-10-17 08:58:32,838 epoch 2 - iter 1540/1546 - loss 0.08294873 - time (sec): 124.22 - samples/sec: 998.40 - lr: 0.000027 - momentum: 0.000000
99
+ 2023-10-17 08:58:33,293 ----------------------------------------------------------------------------------------------------
100
+ 2023-10-17 08:58:33,293 EPOCH 2 done: loss 0.0831 - lr: 0.000027
101
+ 2023-10-17 08:58:36,749 DEV : loss 0.06554654985666275 - f1-score (micro avg) 0.7308
102
+ 2023-10-17 08:58:36,779 ----------------------------------------------------------------------------------------------------
103
+ 2023-10-17 08:58:48,693 epoch 3 - iter 154/1546 - loss 0.05336694 - time (sec): 11.91 - samples/sec: 981.36 - lr: 0.000026 - momentum: 0.000000
104
+ 2023-10-17 08:59:01,019 epoch 3 - iter 308/1546 - loss 0.05246151 - time (sec): 24.24 - samples/sec: 1024.44 - lr: 0.000026 - momentum: 0.000000
105
+ 2023-10-17 08:59:13,098 epoch 3 - iter 462/1546 - loss 0.04970324 - time (sec): 36.32 - samples/sec: 1051.46 - lr: 0.000026 - momentum: 0.000000
106
+ 2023-10-17 08:59:25,184 epoch 3 - iter 616/1546 - loss 0.04604475 - time (sec): 48.40 - samples/sec: 1045.28 - lr: 0.000025 - momentum: 0.000000
107
+ 2023-10-17 08:59:37,027 epoch 3 - iter 770/1546 - loss 0.04734557 - time (sec): 60.25 - samples/sec: 1036.50 - lr: 0.000025 - momentum: 0.000000
108
+ 2023-10-17 08:59:48,854 epoch 3 - iter 924/1546 - loss 0.04896834 - time (sec): 72.07 - samples/sec: 1041.64 - lr: 0.000025 - momentum: 0.000000
109
+ 2023-10-17 09:00:00,733 epoch 3 - iter 1078/1546 - loss 0.05098204 - time (sec): 83.95 - samples/sec: 1038.22 - lr: 0.000024 - momentum: 0.000000
110
+ 2023-10-17 09:00:13,133 epoch 3 - iter 1232/1546 - loss 0.05011942 - time (sec): 96.35 - samples/sec: 1032.97 - lr: 0.000024 - momentum: 0.000000
111
+ 2023-10-17 09:00:25,611 epoch 3 - iter 1386/1546 - loss 0.05242302 - time (sec): 108.83 - samples/sec: 1013.79 - lr: 0.000024 - momentum: 0.000000
112
+ 2023-10-17 09:00:37,699 epoch 3 - iter 1540/1546 - loss 0.05287108 - time (sec): 120.92 - samples/sec: 1024.65 - lr: 0.000023 - momentum: 0.000000
113
+ 2023-10-17 09:00:38,164 ----------------------------------------------------------------------------------------------------
114
+ 2023-10-17 09:00:38,164 EPOCH 3 done: loss 0.0528 - lr: 0.000023
115
+ 2023-10-17 09:00:41,078 DEV : loss 0.06000832840800285 - f1-score (micro avg) 0.8048
116
+ 2023-10-17 09:00:41,106 saving best model
117
+ 2023-10-17 09:00:42,500 ----------------------------------------------------------------------------------------------------
118
+ 2023-10-17 09:00:54,331 epoch 4 - iter 154/1546 - loss 0.03629651 - time (sec): 11.83 - samples/sec: 1087.02 - lr: 0.000023 - momentum: 0.000000
119
+ 2023-10-17 09:01:06,052 epoch 4 - iter 308/1546 - loss 0.03234710 - time (sec): 23.55 - samples/sec: 1041.51 - lr: 0.000023 - momentum: 0.000000
120
+ 2023-10-17 09:01:18,027 epoch 4 - iter 462/1546 - loss 0.03335157 - time (sec): 35.52 - samples/sec: 1056.04 - lr: 0.000022 - momentum: 0.000000
121
+ 2023-10-17 09:01:30,170 epoch 4 - iter 616/1546 - loss 0.03256761 - time (sec): 47.67 - samples/sec: 1049.02 - lr: 0.000022 - momentum: 0.000000
122
+ 2023-10-17 09:01:42,048 epoch 4 - iter 770/1546 - loss 0.03247698 - time (sec): 59.54 - samples/sec: 1048.24 - lr: 0.000022 - momentum: 0.000000
123
+ 2023-10-17 09:01:53,943 epoch 4 - iter 924/1546 - loss 0.03387399 - time (sec): 71.44 - samples/sec: 1054.17 - lr: 0.000021 - momentum: 0.000000
124
+ 2023-10-17 09:02:05,990 epoch 4 - iter 1078/1546 - loss 0.03389742 - time (sec): 83.49 - samples/sec: 1054.23 - lr: 0.000021 - momentum: 0.000000
125
+ 2023-10-17 09:02:17,845 epoch 4 - iter 1232/1546 - loss 0.03395574 - time (sec): 95.34 - samples/sec: 1045.74 - lr: 0.000021 - momentum: 0.000000
126
+ 2023-10-17 09:02:29,692 epoch 4 - iter 1386/1546 - loss 0.03458275 - time (sec): 107.19 - samples/sec: 1040.29 - lr: 0.000020 - momentum: 0.000000
127
+ 2023-10-17 09:02:41,652 epoch 4 - iter 1540/1546 - loss 0.03469566 - time (sec): 119.15 - samples/sec: 1040.31 - lr: 0.000020 - momentum: 0.000000
128
+ 2023-10-17 09:02:42,104 ----------------------------------------------------------------------------------------------------
129
+ 2023-10-17 09:02:42,104 EPOCH 4 done: loss 0.0350 - lr: 0.000020
130
+ 2023-10-17 09:02:44,928 DEV : loss 0.08604831993579865 - f1-score (micro avg) 0.7776
131
+ 2023-10-17 09:02:44,958 ----------------------------------------------------------------------------------------------------
132
+ 2023-10-17 09:02:57,022 epoch 5 - iter 154/1546 - loss 0.02284007 - time (sec): 12.06 - samples/sec: 983.65 - lr: 0.000020 - momentum: 0.000000
133
+ 2023-10-17 09:03:09,189 epoch 5 - iter 308/1546 - loss 0.01803104 - time (sec): 24.23 - samples/sec: 1002.50 - lr: 0.000019 - momentum: 0.000000
134
+ 2023-10-17 09:03:21,262 epoch 5 - iter 462/1546 - loss 0.01912710 - time (sec): 36.30 - samples/sec: 996.31 - lr: 0.000019 - momentum: 0.000000
135
+ 2023-10-17 09:03:33,353 epoch 5 - iter 616/1546 - loss 0.01989178 - time (sec): 48.39 - samples/sec: 1000.35 - lr: 0.000019 - momentum: 0.000000
136
+ 2023-10-17 09:03:45,469 epoch 5 - iter 770/1546 - loss 0.02252057 - time (sec): 60.51 - samples/sec: 1014.50 - lr: 0.000018 - momentum: 0.000000
137
+ 2023-10-17 09:03:57,436 epoch 5 - iter 924/1546 - loss 0.02304384 - time (sec): 72.48 - samples/sec: 1021.05 - lr: 0.000018 - momentum: 0.000000
138
+ 2023-10-17 09:04:09,542 epoch 5 - iter 1078/1546 - loss 0.02187336 - time (sec): 84.58 - samples/sec: 1022.70 - lr: 0.000018 - momentum: 0.000000
139
+ 2023-10-17 09:04:21,529 epoch 5 - iter 1232/1546 - loss 0.02255478 - time (sec): 96.57 - samples/sec: 1021.76 - lr: 0.000017 - momentum: 0.000000
140
+ 2023-10-17 09:04:33,945 epoch 5 - iter 1386/1546 - loss 0.02290087 - time (sec): 108.98 - samples/sec: 1026.82 - lr: 0.000017 - momentum: 0.000000
141
+ 2023-10-17 09:04:46,517 epoch 5 - iter 1540/1546 - loss 0.02398018 - time (sec): 121.56 - samples/sec: 1018.04 - lr: 0.000017 - momentum: 0.000000
142
+ 2023-10-17 09:04:47,010 ----------------------------------------------------------------------------------------------------
143
+ 2023-10-17 09:04:47,010 EPOCH 5 done: loss 0.0242 - lr: 0.000017
144
+ 2023-10-17 09:04:50,104 DEV : loss 0.09960237890481949 - f1-score (micro avg) 0.7876
145
+ 2023-10-17 09:04:50,137 ----------------------------------------------------------------------------------------------------
146
+ 2023-10-17 09:05:02,656 epoch 6 - iter 154/1546 - loss 0.01677459 - time (sec): 12.52 - samples/sec: 1024.75 - lr: 0.000016 - momentum: 0.000000
147
+ 2023-10-17 09:05:14,857 epoch 6 - iter 308/1546 - loss 0.01372105 - time (sec): 24.72 - samples/sec: 1041.31 - lr: 0.000016 - momentum: 0.000000
148
+ 2023-10-17 09:05:27,217 epoch 6 - iter 462/1546 - loss 0.01421126 - time (sec): 37.08 - samples/sec: 1025.57 - lr: 0.000016 - momentum: 0.000000
149
+ 2023-10-17 09:05:39,731 epoch 6 - iter 616/1546 - loss 0.01600038 - time (sec): 49.59 - samples/sec: 1019.56 - lr: 0.000015 - momentum: 0.000000
150
+ 2023-10-17 09:05:52,141 epoch 6 - iter 770/1546 - loss 0.01705876 - time (sec): 62.00 - samples/sec: 1024.70 - lr: 0.000015 - momentum: 0.000000
151
+ 2023-10-17 09:06:04,991 epoch 6 - iter 924/1546 - loss 0.01729932 - time (sec): 74.85 - samples/sec: 1003.82 - lr: 0.000015 - momentum: 0.000000
152
+ 2023-10-17 09:06:17,860 epoch 6 - iter 1078/1546 - loss 0.01687148 - time (sec): 87.72 - samples/sec: 990.68 - lr: 0.000014 - momentum: 0.000000
153
+ 2023-10-17 09:06:30,814 epoch 6 - iter 1232/1546 - loss 0.01640763 - time (sec): 100.67 - samples/sec: 980.55 - lr: 0.000014 - momentum: 0.000000
154
+ 2023-10-17 09:06:44,004 epoch 6 - iter 1386/1546 - loss 0.01674242 - time (sec): 113.86 - samples/sec: 978.03 - lr: 0.000014 - momentum: 0.000000
155
+ 2023-10-17 09:06:57,512 epoch 6 - iter 1540/1546 - loss 0.01702449 - time (sec): 127.37 - samples/sec: 972.60 - lr: 0.000013 - momentum: 0.000000
156
+ 2023-10-17 09:06:58,036 ----------------------------------------------------------------------------------------------------
157
+ 2023-10-17 09:06:58,037 EPOCH 6 done: loss 0.0170 - lr: 0.000013
158
+ 2023-10-17 09:07:00,854 DEV : loss 0.09961654990911484 - f1-score (micro avg) 0.7976
159
+ 2023-10-17 09:07:00,882 ----------------------------------------------------------------------------------------------------
160
+ 2023-10-17 09:07:14,298 epoch 7 - iter 154/1546 - loss 0.00495503 - time (sec): 13.41 - samples/sec: 873.42 - lr: 0.000013 - momentum: 0.000000
161
+ 2023-10-17 09:07:26,536 epoch 7 - iter 308/1546 - loss 0.01087828 - time (sec): 25.65 - samples/sec: 925.23 - lr: 0.000013 - momentum: 0.000000
162
+ 2023-10-17 09:07:38,433 epoch 7 - iter 462/1546 - loss 0.01334889 - time (sec): 37.55 - samples/sec: 964.89 - lr: 0.000012 - momentum: 0.000000
163
+ 2023-10-17 09:07:50,256 epoch 7 - iter 616/1546 - loss 0.01321695 - time (sec): 49.37 - samples/sec: 991.28 - lr: 0.000012 - momentum: 0.000000
164
+ 2023-10-17 09:08:01,813 epoch 7 - iter 770/1546 - loss 0.01313843 - time (sec): 60.93 - samples/sec: 1010.71 - lr: 0.000012 - momentum: 0.000000
165
+ 2023-10-17 09:08:13,352 epoch 7 - iter 924/1546 - loss 0.01154668 - time (sec): 72.47 - samples/sec: 1020.16 - lr: 0.000011 - momentum: 0.000000
166
+ 2023-10-17 09:08:24,980 epoch 7 - iter 1078/1546 - loss 0.01103876 - time (sec): 84.10 - samples/sec: 1022.36 - lr: 0.000011 - momentum: 0.000000
167
+ 2023-10-17 09:08:36,738 epoch 7 - iter 1232/1546 - loss 0.01096499 - time (sec): 95.85 - samples/sec: 1033.68 - lr: 0.000011 - momentum: 0.000000
168
+ 2023-10-17 09:08:48,467 epoch 7 - iter 1386/1546 - loss 0.01135222 - time (sec): 107.58 - samples/sec: 1039.84 - lr: 0.000010 - momentum: 0.000000
169
+ 2023-10-17 09:09:01,265 epoch 7 - iter 1540/1546 - loss 0.01229847 - time (sec): 120.38 - samples/sec: 1027.48 - lr: 0.000010 - momentum: 0.000000
170
+ 2023-10-17 09:09:01,773 ----------------------------------------------------------------------------------------------------
171
+ 2023-10-17 09:09:01,774 EPOCH 7 done: loss 0.0122 - lr: 0.000010
172
+ 2023-10-17 09:09:04,924 DEV : loss 0.10686086863279343 - f1-score (micro avg) 0.8068
173
+ 2023-10-17 09:09:04,958 saving best model
174
+ 2023-10-17 09:09:06,388 ----------------------------------------------------------------------------------------------------
175
+ 2023-10-17 09:09:18,894 epoch 8 - iter 154/1546 - loss 0.00730795 - time (sec): 12.50 - samples/sec: 990.35 - lr: 0.000010 - momentum: 0.000000
176
+ 2023-10-17 09:09:31,198 epoch 8 - iter 308/1546 - loss 0.00668288 - time (sec): 24.80 - samples/sec: 1018.51 - lr: 0.000009 - momentum: 0.000000
177
+ 2023-10-17 09:09:43,852 epoch 8 - iter 462/1546 - loss 0.00776588 - time (sec): 37.46 - samples/sec: 997.70 - lr: 0.000009 - momentum: 0.000000
178
+ 2023-10-17 09:09:56,384 epoch 8 - iter 616/1546 - loss 0.00746374 - time (sec): 49.99 - samples/sec: 990.02 - lr: 0.000009 - momentum: 0.000000
179
+ 2023-10-17 09:10:08,876 epoch 8 - iter 770/1546 - loss 0.00675501 - time (sec): 62.48 - samples/sec: 984.51 - lr: 0.000008 - momentum: 0.000000
180
+ 2023-10-17 09:10:21,661 epoch 8 - iter 924/1546 - loss 0.00715765 - time (sec): 75.27 - samples/sec: 991.92 - lr: 0.000008 - momentum: 0.000000
181
+ 2023-10-17 09:10:34,171 epoch 8 - iter 1078/1546 - loss 0.00694728 - time (sec): 87.78 - samples/sec: 998.22 - lr: 0.000008 - momentum: 0.000000
182
+ 2023-10-17 09:10:46,699 epoch 8 - iter 1232/1546 - loss 0.00699978 - time (sec): 100.30 - samples/sec: 991.84 - lr: 0.000007 - momentum: 0.000000
183
+ 2023-10-17 09:10:58,637 epoch 8 - iter 1386/1546 - loss 0.00720570 - time (sec): 112.24 - samples/sec: 988.67 - lr: 0.000007 - momentum: 0.000000
184
+ 2023-10-17 09:11:10,512 epoch 8 - iter 1540/1546 - loss 0.00770745 - time (sec): 124.12 - samples/sec: 998.57 - lr: 0.000007 - momentum: 0.000000
185
+ 2023-10-17 09:11:10,965 ----------------------------------------------------------------------------------------------------
186
+ 2023-10-17 09:11:10,965 EPOCH 8 done: loss 0.0077 - lr: 0.000007
187
+ 2023-10-17 09:11:13,892 DEV : loss 0.10704014450311661 - f1-score (micro avg) 0.8
188
+ 2023-10-17 09:11:13,922 ----------------------------------------------------------------------------------------------------
189
+ 2023-10-17 09:11:25,981 epoch 9 - iter 154/1546 - loss 0.00378941 - time (sec): 12.05 - samples/sec: 1045.81 - lr: 0.000006 - momentum: 0.000000
190
+ 2023-10-17 09:11:37,802 epoch 9 - iter 308/1546 - loss 0.00283362 - time (sec): 23.88 - samples/sec: 1027.32 - lr: 0.000006 - momentum: 0.000000
191
+ 2023-10-17 09:11:50,093 epoch 9 - iter 462/1546 - loss 0.00396993 - time (sec): 36.17 - samples/sec: 1034.15 - lr: 0.000006 - momentum: 0.000000
192
+ 2023-10-17 09:12:03,236 epoch 9 - iter 616/1546 - loss 0.00400059 - time (sec): 49.31 - samples/sec: 996.36 - lr: 0.000005 - momentum: 0.000000
193
+ 2023-10-17 09:12:15,466 epoch 9 - iter 770/1546 - loss 0.00385112 - time (sec): 61.54 - samples/sec: 1005.67 - lr: 0.000005 - momentum: 0.000000
194
+ 2023-10-17 09:12:27,700 epoch 9 - iter 924/1546 - loss 0.00459493 - time (sec): 73.77 - samples/sec: 1003.17 - lr: 0.000005 - momentum: 0.000000
195
+ 2023-10-17 09:12:39,832 epoch 9 - iter 1078/1546 - loss 0.00412718 - time (sec): 85.91 - samples/sec: 1011.44 - lr: 0.000004 - momentum: 0.000000
196
+ 2023-10-17 09:12:51,879 epoch 9 - iter 1232/1546 - loss 0.00405342 - time (sec): 97.95 - samples/sec: 1010.84 - lr: 0.000004 - momentum: 0.000000
197
+ 2023-10-17 09:13:03,846 epoch 9 - iter 1386/1546 - loss 0.00404944 - time (sec): 109.92 - samples/sec: 1021.84 - lr: 0.000004 - momentum: 0.000000
198
+ 2023-10-17 09:13:15,705 epoch 9 - iter 1540/1546 - loss 0.00452713 - time (sec): 121.78 - samples/sec: 1016.74 - lr: 0.000003 - momentum: 0.000000
199
+ 2023-10-17 09:13:16,160 ----------------------------------------------------------------------------------------------------
200
+ 2023-10-17 09:13:16,160 EPOCH 9 done: loss 0.0045 - lr: 0.000003
201
+ 2023-10-17 09:13:18,878 DEV : loss 0.12154770642518997 - f1-score (micro avg) 0.7983
202
+ 2023-10-17 09:13:18,904 ----------------------------------------------------------------------------------------------------
203
+ 2023-10-17 09:13:30,793 epoch 10 - iter 154/1546 - loss 0.00274083 - time (sec): 11.89 - samples/sec: 1053.40 - lr: 0.000003 - momentum: 0.000000
204
+ 2023-10-17 09:13:42,788 epoch 10 - iter 308/1546 - loss 0.00390665 - time (sec): 23.88 - samples/sec: 1038.05 - lr: 0.000003 - momentum: 0.000000
205
+ 2023-10-17 09:13:54,809 epoch 10 - iter 462/1546 - loss 0.00330672 - time (sec): 35.90 - samples/sec: 1054.09 - lr: 0.000002 - momentum: 0.000000
206
+ 2023-10-17 09:14:07,461 epoch 10 - iter 616/1546 - loss 0.00342831 - time (sec): 48.56 - samples/sec: 1035.09 - lr: 0.000002 - momentum: 0.000000
207
+ 2023-10-17 09:14:20,606 epoch 10 - iter 770/1546 - loss 0.00329853 - time (sec): 61.70 - samples/sec: 1013.00 - lr: 0.000002 - momentum: 0.000000
208
+ 2023-10-17 09:14:33,362 epoch 10 - iter 924/1546 - loss 0.00299964 - time (sec): 74.46 - samples/sec: 996.99 - lr: 0.000001 - momentum: 0.000000
209
+ 2023-10-17 09:14:46,754 epoch 10 - iter 1078/1546 - loss 0.00313900 - time (sec): 87.85 - samples/sec: 990.71 - lr: 0.000001 - momentum: 0.000000
210
+ 2023-10-17 09:14:59,125 epoch 10 - iter 1232/1546 - loss 0.00324827 - time (sec): 100.22 - samples/sec: 986.19 - lr: 0.000001 - momentum: 0.000000
211
+ 2023-10-17 09:15:12,274 epoch 10 - iter 1386/1546 - loss 0.00311186 - time (sec): 113.37 - samples/sec: 983.00 - lr: 0.000000 - momentum: 0.000000
212
+ 2023-10-17 09:15:25,848 epoch 10 - iter 1540/1546 - loss 0.00341137 - time (sec): 126.94 - samples/sec: 975.57 - lr: 0.000000 - momentum: 0.000000
213
+ 2023-10-17 09:15:26,351 ----------------------------------------------------------------------------------------------------
214
+ 2023-10-17 09:15:26,351 EPOCH 10 done: loss 0.0034 - lr: 0.000000
215
+ 2023-10-17 09:15:29,674 DEV : loss 0.11997128278017044 - f1-score (micro avg) 0.7886
216
+ 2023-10-17 09:15:30,263 ----------------------------------------------------------------------------------------------------
217
+ 2023-10-17 09:15:30,265 Loading model from best epoch ...
218
+ 2023-10-17 09:15:32,732 SequenceTagger predicts: Dictionary with 13 tags: O, S-LOC, B-LOC, E-LOC, I-LOC, S-BUILDING, B-BUILDING, E-BUILDING, I-BUILDING, S-STREET, B-STREET, E-STREET, I-STREET
219
+ 2023-10-17 09:15:40,874
220
+ Results:
221
+ - F-score (micro) 0.8096
222
+ - F-score (macro) 0.7186
223
+ - Accuracy 0.6984
224
+
225
+ By class:
226
+ precision recall f1-score support
227
+
228
+ LOC 0.8731 0.8362 0.8542 946
229
+ BUILDING 0.6806 0.5297 0.5957 185
230
+ STREET 0.6667 0.7500 0.7059 56
231
+
232
+ micro avg 0.8365 0.7843 0.8096 1187
233
+ macro avg 0.7401 0.7053 0.7186 1187
234
+ weighted avg 0.8333 0.7843 0.8069 1187
235
+
236
+ 2023-10-17 09:15:40,875 ----------------------------------------------------------------------------------------------------