Hugging Face's logo

stefan-it
/
hmbench-icdar-fr-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4

Token Classification
Flair
PyTorch
TensorBoard
French
sequence-tagger-model
Model card Files Metrics Community
stefan-it commited on
Commit
780b0f3
•
1 Parent(s): 96e8e6f

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.1697551833.bce904bcef33.2023.15 +3 -0
  5. test.tsv +0 -0
  6. training.log +237 -0
best-model.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:ecf2b7508130168b57c649f9fd3bd259b74bb6fd5b15872677494201d72ec5f6
3
+ size 440941957
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 14:11:36 0.0000 0.4321 0.0889 0.7612 0.6889 0.7233 0.5778
3
+ 2 14:12:39 0.0000 0.1074 0.0836 0.7299 0.7919 0.7596 0.6318
4
+ 3 14:13:43 0.0000 0.0751 0.1065 0.7611 0.7353 0.7480 0.6121
5
+ 4 14:14:47 0.0000 0.0547 0.1318 0.7152 0.8009 0.7556 0.6232
6
+ 5 14:15:49 0.0000 0.0455 0.1657 0.7342 0.7873 0.7598 0.6322
7
+ 6 14:16:53 0.0000 0.0312 0.1811 0.7426 0.7930 0.7670 0.6408
8
+ 7 14:17:58 0.0000 0.0223 0.1962 0.7381 0.7749 0.7561 0.6250
9
+ 8 14:19:01 0.0000 0.0160 0.2258 0.7430 0.7851 0.7635 0.6332
10
+ 9 14:20:04 0.0000 0.0116 0.2169 0.7216 0.7828 0.7509 0.6206
11
+ 10 14:21:08 0.0000 0.0078 0.2361 0.7256 0.7749 0.7495 0.6171
runs/events.out.tfevents.1697551833.bce904bcef33.2023.15 ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:9600df68cd68af91198542a64cca7b42b9b8a62b5dde08137f4cfa14bdbefbf6
3
+ size 556612
test.tsv ADDED
The diff for this file is too large to render. See raw diff
 
training.log ADDED
@@ -0,0 +1,237 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 2023-10-17 14:10:33,346 ----------------------------------------------------------------------------------------------------
2
+ 2023-10-17 14:10:33,347 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 14:10:33,347 ----------------------------------------------------------------------------------------------------
47
+ 2023-10-17 14:10:33,347 MultiCorpus: 7936 train + 992 dev + 992 test sentences
48
+ - NER_ICDAR_EUROPEANA Corpus: 7936 train + 992 dev + 992 test sentences - /root/.flair/datasets/ner_icdar_europeana/fr
49
+ 2023-10-17 14:10:33,347 ----------------------------------------------------------------------------------------------------
50
+ 2023-10-17 14:10:33,347 Train: 7936 sentences
51
+ 2023-10-17 14:10:33,347 (train_with_dev=False, train_with_test=False)
52
+ 2023-10-17 14:10:33,347 ----------------------------------------------------------------------------------------------------
53
+ 2023-10-17 14:10:33,347 Training Params:
54
+ 2023-10-17 14:10:33,347 - learning_rate: "5e-05"
55
+ 2023-10-17 14:10:33,347 - mini_batch_size: "8"
56
+ 2023-10-17 14:10:33,347 - max_epochs: "10"
57
+ 2023-10-17 14:10:33,348 - shuffle: "True"
58
+ 2023-10-17 14:10:33,348 ----------------------------------------------------------------------------------------------------
59
+ 2023-10-17 14:10:33,348 Plugins:
60
+ 2023-10-17 14:10:33,348 - TensorboardLogger
61
+ 2023-10-17 14:10:33,348 - LinearScheduler | warmup_fraction: '0.1'
62
+ 2023-10-17 14:10:33,348 ----------------------------------------------------------------------------------------------------
63
+ 2023-10-17 14:10:33,348 Final evaluation on model from best epoch (best-model.pt)
64
+ 2023-10-17 14:10:33,348 - metric: "('micro avg', 'f1-score')"
65
+ 2023-10-17 14:10:33,348 ----------------------------------------------------------------------------------------------------
66
+ 2023-10-17 14:10:33,348 Computation:
67
+ 2023-10-17 14:10:33,348 - compute on device: cuda:0
68
+ 2023-10-17 14:10:33,348 - embedding storage: none
69
+ 2023-10-17 14:10:33,348 ----------------------------------------------------------------------------------------------------
70
+ 2023-10-17 14:10:33,348 Model training base path: "hmbench-icdar/fr-hmteams/teams-base-historic-multilingual-discriminator-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4"
71
+ 2023-10-17 14:10:33,348 ----------------------------------------------------------------------------------------------------
72
+ 2023-10-17 14:10:33,348 ----------------------------------------------------------------------------------------------------
73
+ 2023-10-17 14:10:33,348 Logging anything other than scalars to TensorBoard is currently not supported.
74
+ 2023-10-17 14:10:39,222 epoch 1 - iter 99/992 - loss 2.56583698 - time (sec): 5.87 - samples/sec: 2821.57 - lr: 0.000005 - momentum: 0.000000
75
+ 2023-10-17 14:10:45,260 epoch 1 - iter 198/992 - loss 1.44067154 - time (sec): 11.91 - samples/sec: 2815.77 - lr: 0.000010 - momentum: 0.000000
76
+ 2023-10-17 14:10:50,904 epoch 1 - iter 297/992 - loss 1.06311481 - time (sec): 17.56 - samples/sec: 2793.50 - lr: 0.000015 - momentum: 0.000000
77
+ 2023-10-17 14:10:56,606 epoch 1 - iter 396/992 - loss 0.85235507 - time (sec): 23.26 - samples/sec: 2791.92 - lr: 0.000020 - momentum: 0.000000
78
+ 2023-10-17 14:11:02,780 epoch 1 - iter 495/992 - loss 0.70397019 - time (sec): 29.43 - samples/sec: 2809.19 - lr: 0.000025 - momentum: 0.000000
79
+ 2023-10-17 14:11:08,842 epoch 1 - iter 594/992 - loss 0.61754102 - time (sec): 35.49 - samples/sec: 2781.20 - lr: 0.000030 - momentum: 0.000000
80
+ 2023-10-17 14:11:14,877 epoch 1 - iter 693/992 - loss 0.55425408 - time (sec): 41.53 - samples/sec: 2767.88 - lr: 0.000035 - momentum: 0.000000
81
+ 2023-10-17 14:11:21,011 epoch 1 - iter 792/992 - loss 0.50304327 - time (sec): 47.66 - samples/sec: 2761.17 - lr: 0.000040 - momentum: 0.000000
82
+ 2023-10-17 14:11:26,898 epoch 1 - iter 891/992 - loss 0.46403461 - time (sec): 53.55 - samples/sec: 2766.86 - lr: 0.000045 - momentum: 0.000000
83
+ 2023-10-17 14:11:33,013 epoch 1 - iter 990/992 - loss 0.43242766 - time (sec): 59.66 - samples/sec: 2743.63 - lr: 0.000050 - momentum: 0.000000
84
+ 2023-10-17 14:11:33,117 ----------------------------------------------------------------------------------------------------
85
+ 2023-10-17 14:11:33,117 EPOCH 1 done: loss 0.4321 - lr: 0.000050
86
+ 2023-10-17 14:11:36,203 DEV : loss 0.08887125551700592 - f1-score (micro avg) 0.7233
87
+ 2023-10-17 14:11:36,224 saving best model
88
+ 2023-10-17 14:11:36,696 ----------------------------------------------------------------------------------------------------
89
+ 2023-10-17 14:11:42,469 epoch 2 - iter 99/992 - loss 0.11598058 - time (sec): 5.77 - samples/sec: 2830.44 - lr: 0.000049 - momentum: 0.000000
90
+ 2023-10-17 14:11:48,383 epoch 2 - iter 198/992 - loss 0.11266582 - time (sec): 11.69 - samples/sec: 2820.53 - lr: 0.000049 - momentum: 0.000000
91
+ 2023-10-17 14:11:54,000 epoch 2 - iter 297/992 - loss 0.11132229 - time (sec): 17.30 - samples/sec: 2803.21 - lr: 0.000048 - momentum: 0.000000
92
+ 2023-10-17 14:11:59,687 epoch 2 - iter 396/992 - loss 0.11223753 - time (sec): 22.99 - samples/sec: 2812.50 - lr: 0.000048 - momentum: 0.000000
93
+ 2023-10-17 14:12:05,527 epoch 2 - iter 495/992 - loss 0.10847458 - time (sec): 28.83 - samples/sec: 2810.26 - lr: 0.000047 - momentum: 0.000000
94
+ 2023-10-17 14:12:11,595 epoch 2 - iter 594/992 - loss 0.10790183 - time (sec): 34.90 - samples/sec: 2794.95 - lr: 0.000047 - momentum: 0.000000
95
+ 2023-10-17 14:12:17,710 epoch 2 - iter 693/992 - loss 0.10908683 - time (sec): 41.01 - samples/sec: 2796.29 - lr: 0.000046 - momentum: 0.000000
96
+ 2023-10-17 14:12:23,919 epoch 2 - iter 792/992 - loss 0.10920125 - time (sec): 47.22 - samples/sec: 2773.55 - lr: 0.000046 - momentum: 0.000000
97
+ 2023-10-17 14:12:29,935 epoch 2 - iter 891/992 - loss 0.10838687 - time (sec): 53.24 - samples/sec: 2760.47 - lr: 0.000045 - momentum: 0.000000
98
+ 2023-10-17 14:12:35,919 epoch 2 - iter 990/992 - loss 0.10734045 - time (sec): 59.22 - samples/sec: 2766.07 - lr: 0.000044 - momentum: 0.000000
99
+ 2023-10-17 14:12:36,025 ----------------------------------------------------------------------------------------------------
100
+ 2023-10-17 14:12:36,025 EPOCH 2 done: loss 0.1074 - lr: 0.000044
101
+ 2023-10-17 14:12:39,465 DEV : loss 0.08363784104585648 - f1-score (micro avg) 0.7596
102
+ 2023-10-17 14:12:39,487 saving best model
103
+ 2023-10-17 14:12:40,074 ----------------------------------------------------------------------------------------------------
104
+ 2023-10-17 14:12:46,174 epoch 3 - iter 99/992 - loss 0.07378378 - time (sec): 6.10 - samples/sec: 2782.50 - lr: 0.000044 - momentum: 0.000000
105
+ 2023-10-17 14:12:52,120 epoch 3 - iter 198/992 - loss 0.07492355 - time (sec): 12.04 - samples/sec: 2770.67 - lr: 0.000043 - momentum: 0.000000
106
+ 2023-10-17 14:12:57,854 epoch 3 - iter 297/992 - loss 0.07489005 - time (sec): 17.78 - samples/sec: 2780.55 - lr: 0.000043 - momentum: 0.000000
107
+ 2023-10-17 14:13:03,942 epoch 3 - iter 396/992 - loss 0.07422590 - time (sec): 23.87 - samples/sec: 2761.11 - lr: 0.000042 - momentum: 0.000000
108
+ 2023-10-17 14:13:09,975 epoch 3 - iter 495/992 - loss 0.07723264 - time (sec): 29.90 - samples/sec: 2764.56 - lr: 0.000042 - momentum: 0.000000
109
+ 2023-10-17 14:13:16,155 epoch 3 - iter 594/992 - loss 0.07724225 - time (sec): 36.08 - samples/sec: 2761.92 - lr: 0.000041 - momentum: 0.000000
110
+ 2023-10-17 14:13:22,176 epoch 3 - iter 693/992 - loss 0.07604513 - time (sec): 42.10 - samples/sec: 2774.23 - lr: 0.000041 - momentum: 0.000000
111
+ 2023-10-17 14:13:28,294 epoch 3 - iter 792/992 - loss 0.07536649 - time (sec): 48.22 - samples/sec: 2761.96 - lr: 0.000040 - momentum: 0.000000
112
+ 2023-10-17 14:13:34,071 epoch 3 - iter 891/992 - loss 0.07551830 - time (sec): 53.99 - samples/sec: 2754.35 - lr: 0.000039 - momentum: 0.000000
113
+ 2023-10-17 14:13:39,833 epoch 3 - iter 990/992 - loss 0.07517252 - time (sec): 59.76 - samples/sec: 2739.02 - lr: 0.000039 - momentum: 0.000000
114
+ 2023-10-17 14:13:39,973 ----------------------------------------------------------------------------------------------------
115
+ 2023-10-17 14:13:39,974 EPOCH 3 done: loss 0.0751 - lr: 0.000039
116
+ 2023-10-17 14:13:43,451 DEV : loss 0.1065104603767395 - f1-score (micro avg) 0.748
117
+ 2023-10-17 14:13:43,475 ----------------------------------------------------------------------------------------------------
118
+ 2023-10-17 14:13:49,475 epoch 4 - iter 99/992 - loss 0.04524979 - time (sec): 6.00 - samples/sec: 2803.07 - lr: 0.000038 - momentum: 0.000000
119
+ 2023-10-17 14:13:55,680 epoch 4 - iter 198/992 - loss 0.05478362 - time (sec): 12.20 - samples/sec: 2690.30 - lr: 0.000038 - momentum: 0.000000
120
+ 2023-10-17 14:14:01,531 epoch 4 - iter 297/992 - loss 0.05412289 - time (sec): 18.05 - samples/sec: 2698.58 - lr: 0.000037 - momentum: 0.000000
121
+ 2023-10-17 14:14:07,545 epoch 4 - iter 396/992 - loss 0.05506235 - time (sec): 24.07 - samples/sec: 2730.99 - lr: 0.000037 - momentum: 0.000000
122
+ 2023-10-17 14:14:13,343 epoch 4 - iter 495/992 - loss 0.05562356 - time (sec): 29.87 - samples/sec: 2748.67 - lr: 0.000036 - momentum: 0.000000
123
+ 2023-10-17 14:14:19,477 epoch 4 - iter 594/992 - loss 0.05609226 - time (sec): 36.00 - samples/sec: 2751.59 - lr: 0.000036 - momentum: 0.000000
124
+ 2023-10-17 14:14:25,172 epoch 4 - iter 693/992 - loss 0.05560238 - time (sec): 41.70 - samples/sec: 2758.09 - lr: 0.000035 - momentum: 0.000000
125
+ 2023-10-17 14:14:31,622 epoch 4 - iter 792/992 - loss 0.05713362 - time (sec): 48.15 - samples/sec: 2727.19 - lr: 0.000034 - momentum: 0.000000
126
+ 2023-10-17 14:14:37,562 epoch 4 - iter 891/992 - loss 0.05527867 - time (sec): 54.09 - samples/sec: 2723.46 - lr: 0.000034 - momentum: 0.000000
127
+ 2023-10-17 14:14:43,601 epoch 4 - iter 990/992 - loss 0.05470377 - time (sec): 60.12 - samples/sec: 2723.71 - lr: 0.000033 - momentum: 0.000000
128
+ 2023-10-17 14:14:43,697 ----------------------------------------------------------------------------------------------------
129
+ 2023-10-17 14:14:43,697 EPOCH 4 done: loss 0.0547 - lr: 0.000033
130
+ 2023-10-17 14:14:47,132 DEV : loss 0.13179177045822144 - f1-score (micro avg) 0.7556
131
+ 2023-10-17 14:14:47,153 ----------------------------------------------------------------------------------------------------
132
+ 2023-10-17 14:14:52,850 epoch 5 - iter 99/992 - loss 0.05176063 - time (sec): 5.70 - samples/sec: 2801.03 - lr: 0.000033 - momentum: 0.000000
133
+ 2023-10-17 14:14:58,673 epoch 5 - iter 198/992 - loss 0.04599398 - time (sec): 11.52 - samples/sec: 2830.16 - lr: 0.000032 - momentum: 0.000000
134
+ 2023-10-17 14:15:04,479 epoch 5 - iter 297/992 - loss 0.04300166 - time (sec): 17.32 - samples/sec: 2790.18 - lr: 0.000032 - momentum: 0.000000
135
+ 2023-10-17 14:15:10,429 epoch 5 - iter 396/992 - loss 0.04689564 - time (sec): 23.27 - samples/sec: 2771.19 - lr: 0.000031 - momentum: 0.000000
136
+ 2023-10-17 14:15:16,294 epoch 5 - iter 495/992 - loss 0.04522045 - time (sec): 29.14 - samples/sec: 2782.82 - lr: 0.000031 - momentum: 0.000000
137
+ 2023-10-17 14:15:22,519 epoch 5 - iter 594/992 - loss 0.04434071 - time (sec): 35.36 - samples/sec: 2778.12 - lr: 0.000030 - momentum: 0.000000
138
+ 2023-10-17 14:15:28,400 epoch 5 - iter 693/992 - loss 0.04509966 - time (sec): 41.25 - samples/sec: 2783.70 - lr: 0.000029 - momentum: 0.000000
139
+ 2023-10-17 14:15:34,527 epoch 5 - iter 792/992 - loss 0.04488398 - time (sec): 47.37 - samples/sec: 2777.19 - lr: 0.000029 - momentum: 0.000000
140
+ 2023-10-17 14:15:40,289 epoch 5 - iter 891/992 - loss 0.04506764 - time (sec): 53.13 - samples/sec: 2779.31 - lr: 0.000028 - momentum: 0.000000
141
+ 2023-10-17 14:15:46,075 epoch 5 - iter 990/992 - loss 0.04560631 - time (sec): 58.92 - samples/sec: 2776.63 - lr: 0.000028 - momentum: 0.000000
142
+ 2023-10-17 14:15:46,209 ----------------------------------------------------------------------------------------------------
143
+ 2023-10-17 14:15:46,209 EPOCH 5 done: loss 0.0455 - lr: 0.000028
144
+ 2023-10-17 14:15:49,653 DEV : loss 0.16570182144641876 - f1-score (micro avg) 0.7598
145
+ 2023-10-17 14:15:49,677 saving best model
146
+ 2023-10-17 14:15:50,266 ----------------------------------------------------------------------------------------------------
147
+ 2023-10-17 14:15:56,488 epoch 6 - iter 99/992 - loss 0.03548701 - time (sec): 6.22 - samples/sec: 2691.46 - lr: 0.000027 - momentum: 0.000000
148
+ 2023-10-17 14:16:02,509 epoch 6 - iter 198/992 - loss 0.03164832 - time (sec): 12.24 - samples/sec: 2707.48 - lr: 0.000027 - momentum: 0.000000
149
+ 2023-10-17 14:16:08,671 epoch 6 - iter 297/992 - loss 0.03207736 - time (sec): 18.40 - samples/sec: 2692.61 - lr: 0.000026 - momentum: 0.000000
150
+ 2023-10-17 14:16:14,599 epoch 6 - iter 396/992 - loss 0.03177863 - time (sec): 24.33 - samples/sec: 2718.45 - lr: 0.000026 - momentum: 0.000000
151
+ 2023-10-17 14:16:20,727 epoch 6 - iter 495/992 - loss 0.03190660 - time (sec): 30.46 - samples/sec: 2725.37 - lr: 0.000025 - momentum: 0.000000
152
+ 2023-10-17 14:16:26,730 epoch 6 - iter 594/992 - loss 0.03263403 - time (sec): 36.46 - samples/sec: 2715.91 - lr: 0.000024 - momentum: 0.000000
153
+ 2023-10-17 14:16:32,715 epoch 6 - iter 693/992 - loss 0.03190880 - time (sec): 42.45 - samples/sec: 2743.76 - lr: 0.000024 - momentum: 0.000000
154
+ 2023-10-17 14:16:38,628 epoch 6 - iter 792/992 - loss 0.03117610 - time (sec): 48.36 - samples/sec: 2738.62 - lr: 0.000023 - momentum: 0.000000
155
+ 2023-10-17 14:16:44,371 epoch 6 - iter 891/992 - loss 0.03097983 - time (sec): 54.10 - samples/sec: 2738.45 - lr: 0.000023 - momentum: 0.000000
156
+ 2023-10-17 14:16:50,118 epoch 6 - iter 990/992 - loss 0.03122063 - time (sec): 59.85 - samples/sec: 2734.90 - lr: 0.000022 - momentum: 0.000000
157
+ 2023-10-17 14:16:50,219 ----------------------------------------------------------------------------------------------------
158
+ 2023-10-17 14:16:50,219 EPOCH 6 done: loss 0.0312 - lr: 0.000022
159
+ 2023-10-17 14:16:53,684 DEV : loss 0.18113002181053162 - f1-score (micro avg) 0.767
160
+ 2023-10-17 14:16:53,705 saving best model
161
+ 2023-10-17 14:16:54,157 ----------------------------------------------------------------------------------------------------
162
+ 2023-10-17 14:16:59,988 epoch 7 - iter 99/992 - loss 0.02597015 - time (sec): 5.83 - samples/sec: 2684.60 - lr: 0.000022 - momentum: 0.000000
163
+ 2023-10-17 14:17:06,231 epoch 7 - iter 198/992 - loss 0.02169306 - time (sec): 12.07 - samples/sec: 2712.41 - lr: 0.000021 - momentum: 0.000000
164
+ 2023-10-17 14:17:12,255 epoch 7 - iter 297/992 - loss 0.02197468 - time (sec): 18.10 - samples/sec: 2712.38 - lr: 0.000021 - momentum: 0.000000
165
+ 2023-10-17 14:17:18,365 epoch 7 - iter 396/992 - loss 0.02241929 - time (sec): 24.21 - samples/sec: 2694.37 - lr: 0.000020 - momentum: 0.000000
166
+ 2023-10-17 14:17:24,253 epoch 7 - iter 495/992 - loss 0.02267823 - time (sec): 30.10 - samples/sec: 2714.40 - lr: 0.000019 - momentum: 0.000000
167
+ 2023-10-17 14:17:30,023 epoch 7 - iter 594/992 - loss 0.02246580 - time (sec): 35.87 - samples/sec: 2715.96 - lr: 0.000019 - momentum: 0.000000
168
+ 2023-10-17 14:17:35,851 epoch 7 - iter 693/992 - loss 0.02190549 - time (sec): 41.69 - samples/sec: 2716.69 - lr: 0.000018 - momentum: 0.000000
169
+ 2023-10-17 14:17:41,839 epoch 7 - iter 792/992 - loss 0.02218982 - time (sec): 47.68 - samples/sec: 2718.42 - lr: 0.000018 - momentum: 0.000000
170
+ 2023-10-17 14:17:47,671 epoch 7 - iter 891/992 - loss 0.02196221 - time (sec): 53.51 - samples/sec: 2725.79 - lr: 0.000017 - momentum: 0.000000
171
+ 2023-10-17 14:17:54,071 epoch 7 - iter 990/992 - loss 0.02230131 - time (sec): 59.91 - samples/sec: 2731.72 - lr: 0.000017 - momentum: 0.000000
172
+ 2023-10-17 14:17:54,194 ----------------------------------------------------------------------------------------------------
173
+ 2023-10-17 14:17:54,194 EPOCH 7 done: loss 0.0223 - lr: 0.000017
174
+ 2023-10-17 14:17:58,301 DEV : loss 0.19615165889263153 - f1-score (micro avg) 0.7561
175
+ 2023-10-17 14:17:58,323 ----------------------------------------------------------------------------------------------------
176
+ 2023-10-17 14:18:04,361 epoch 8 - iter 99/992 - loss 0.01117805 - time (sec): 6.04 - samples/sec: 2648.12 - lr: 0.000016 - momentum: 0.000000
177
+ 2023-10-17 14:18:10,390 epoch 8 - iter 198/992 - loss 0.01283703 - time (sec): 12.07 - samples/sec: 2680.35 - lr: 0.000016 - momentum: 0.000000
178
+ 2023-10-17 14:18:16,340 epoch 8 - iter 297/992 - loss 0.01697295 - time (sec): 18.02 - samples/sec: 2662.18 - lr: 0.000015 - momentum: 0.000000
179
+ 2023-10-17 14:18:22,180 epoch 8 - iter 396/992 - loss 0.01691545 - time (sec): 23.86 - samples/sec: 2686.17 - lr: 0.000014 - momentum: 0.000000
180
+ 2023-10-17 14:18:28,167 epoch 8 - iter 495/992 - loss 0.01590682 - time (sec): 29.84 - samples/sec: 2703.03 - lr: 0.000014 - momentum: 0.000000
181
+ 2023-10-17 14:18:34,030 epoch 8 - iter 594/992 - loss 0.01525603 - time (sec): 35.71 - samples/sec: 2727.45 - lr: 0.000013 - momentum: 0.000000
182
+ 2023-10-17 14:18:39,887 epoch 8 - iter 693/992 - loss 0.01579587 - time (sec): 41.56 - samples/sec: 2718.25 - lr: 0.000013 - momentum: 0.000000
183
+ 2023-10-17 14:18:46,164 epoch 8 - iter 792/992 - loss 0.01527372 - time (sec): 47.84 - samples/sec: 2719.87 - lr: 0.000012 - momentum: 0.000000
184
+ 2023-10-17 14:18:52,001 epoch 8 - iter 891/992 - loss 0.01527021 - time (sec): 53.68 - samples/sec: 2733.81 - lr: 0.000012 - momentum: 0.000000
185
+ 2023-10-17 14:18:58,203 epoch 8 - iter 990/992 - loss 0.01606507 - time (sec): 59.88 - samples/sec: 2733.60 - lr: 0.000011 - momentum: 0.000000
186
+ 2023-10-17 14:18:58,303 ----------------------------------------------------------------------------------------------------
187
+ 2023-10-17 14:18:58,303 EPOCH 8 done: loss 0.0160 - lr: 0.000011
188
+ 2023-10-17 14:19:01,691 DEV : loss 0.22577238082885742 - f1-score (micro avg) 0.7635
189
+ 2023-10-17 14:19:01,712 ----------------------------------------------------------------------------------------------------
190
+ 2023-10-17 14:19:07,524 epoch 9 - iter 99/992 - loss 0.00989044 - time (sec): 5.81 - samples/sec: 2697.63 - lr: 0.000011 - momentum: 0.000000
191
+ 2023-10-17 14:19:13,330 epoch 9 - iter 198/992 - loss 0.01505293 - time (sec): 11.62 - samples/sec: 2783.58 - lr: 0.000010 - momentum: 0.000000
192
+ 2023-10-17 14:19:19,081 epoch 9 - iter 297/992 - loss 0.01350984 - time (sec): 17.37 - samples/sec: 2745.86 - lr: 0.000009 - momentum: 0.000000
193
+ 2023-10-17 14:19:25,043 epoch 9 - iter 396/992 - loss 0.01226420 - time (sec): 23.33 - samples/sec: 2731.47 - lr: 0.000009 - momentum: 0.000000
194
+ 2023-10-17 14:19:30,941 epoch 9 - iter 495/992 - loss 0.01239703 - time (sec): 29.23 - samples/sec: 2743.58 - lr: 0.000008 - momentum: 0.000000
195
+ 2023-10-17 14:19:37,003 epoch 9 - iter 594/992 - loss 0.01210630 - time (sec): 35.29 - samples/sec: 2739.17 - lr: 0.000008 - momentum: 0.000000
196
+ 2023-10-17 14:19:43,129 epoch 9 - iter 693/992 - loss 0.01202169 - time (sec): 41.42 - samples/sec: 2746.32 - lr: 0.000007 - momentum: 0.000000
197
+ 2023-10-17 14:19:49,259 epoch 9 - iter 792/992 - loss 0.01182813 - time (sec): 47.55 - samples/sec: 2749.42 - lr: 0.000007 - momentum: 0.000000
198
+ 2023-10-17 14:19:55,176 epoch 9 - iter 891/992 - loss 0.01183421 - time (sec): 53.46 - samples/sec: 2746.32 - lr: 0.000006 - momentum: 0.000000
199
+ 2023-10-17 14:20:01,241 epoch 9 - iter 990/992 - loss 0.01157965 - time (sec): 59.53 - samples/sec: 2748.33 - lr: 0.000006 - momentum: 0.000000
200
+ 2023-10-17 14:20:01,395 ----------------------------------------------------------------------------------------------------
201
+ 2023-10-17 14:20:01,395 EPOCH 9 done: loss 0.0116 - lr: 0.000006
202
+ 2023-10-17 14:20:04,942 DEV : loss 0.21687279641628265 - f1-score (micro avg) 0.7509
203
+ 2023-10-17 14:20:04,963 ----------------------------------------------------------------------------------------------------
204
+ 2023-10-17 14:20:10,822 epoch 10 - iter 99/992 - loss 0.00708023 - time (sec): 5.86 - samples/sec: 2779.19 - lr: 0.000005 - momentum: 0.000000
205
+ 2023-10-17 14:20:16,971 epoch 10 - iter 198/992 - loss 0.00762459 - time (sec): 12.01 - samples/sec: 2789.93 - lr: 0.000004 - momentum: 0.000000
206
+ 2023-10-17 14:20:22,928 epoch 10 - iter 297/992 - loss 0.00709401 - time (sec): 17.96 - samples/sec: 2760.24 - lr: 0.000004 - momentum: 0.000000
207
+ 2023-10-17 14:20:28,815 epoch 10 - iter 396/992 - loss 0.00681691 - time (sec): 23.85 - samples/sec: 2739.23 - lr: 0.000003 - momentum: 0.000000
208
+ 2023-10-17 14:20:34,724 epoch 10 - iter 495/992 - loss 0.00708234 - time (sec): 29.76 - samples/sec: 2750.97 - lr: 0.000003 - momentum: 0.000000
209
+ 2023-10-17 14:20:40,813 epoch 10 - iter 594/992 - loss 0.00757164 - time (sec): 35.85 - samples/sec: 2743.70 - lr: 0.000002 - momentum: 0.000000
210
+ 2023-10-17 14:20:46,718 epoch 10 - iter 693/992 - loss 0.00802286 - time (sec): 41.75 - samples/sec: 2750.56 - lr: 0.000002 - momentum: 0.000000
211
+ 2023-10-17 14:20:52,723 epoch 10 - iter 792/992 - loss 0.00785135 - time (sec): 47.76 - samples/sec: 2749.46 - lr: 0.000001 - momentum: 0.000000
212
+ 2023-10-17 14:20:58,677 epoch 10 - iter 891/992 - loss 0.00789678 - time (sec): 53.71 - samples/sec: 2743.95 - lr: 0.000001 - momentum: 0.000000
213
+ 2023-10-17 14:21:04,642 epoch 10 - iter 990/992 - loss 0.00784035 - time (sec): 59.68 - samples/sec: 2743.80 - lr: 0.000000 - momentum: 0.000000
214
+ 2023-10-17 14:21:04,763 ----------------------------------------------------------------------------------------------------
215
+ 2023-10-17 14:21:04,763 EPOCH 10 done: loss 0.0078 - lr: 0.000000
216
+ 2023-10-17 14:21:08,189 DEV : loss 0.23605531454086304 - f1-score (micro avg) 0.7495
217
+ 2023-10-17 14:21:08,645 ----------------------------------------------------------------------------------------------------
218
+ 2023-10-17 14:21:08,646 Loading model from best epoch ...
219
+ 2023-10-17 14:21:10,060 SequenceTagger predicts: Dictionary with 13 tags: O, S-PER, B-PER, E-PER, I-PER, S-LOC, B-LOC, E-LOC, I-LOC, S-ORG, B-ORG, E-ORG, I-ORG
220
+ 2023-10-17 14:21:13,417
221
+ Results:
222
+ - F-score (micro) 0.7731
223
+ - F-score (macro) 0.6853
224
+ - Accuracy 0.6466
225
+
226
+ By class:
227
+ precision recall f1-score support
228
+
229
+ LOC 0.8043 0.8534 0.8281 655
230
+ PER 0.7066 0.8206 0.7593 223
231
+ ORG 0.5474 0.4094 0.4685 127
232
+
233
+ micro avg 0.7569 0.7900 0.7731 1005
234
+ macro avg 0.6861 0.6945 0.6853 1005
235
+ weighted avg 0.7502 0.7900 0.7674 1005
236
+
237
+ 2023-10-17 14:21:13,417 ----------------------------------------------------------------------------------------------------