Hugging Face's logo

stefan-it
/
flair-clean-conll-4

Token Classification
Flair
PyTorch
TensorBoard
English
sequence-tagger-model
hetzner
hetzner-gex44
hetzner-gpu
Model card Files Metrics Community
stefan-it commited on
Commit
6c17c22
•
1 Parent(s): c81de75

Upload ./training.log with huggingface_hub

Browse files
Files changed (1) hide show
  1. training.log +246 -0
training.log ADDED
@@ -0,0 +1,246 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 2023-10-27 18:44:31,683 ----------------------------------------------------------------------------------------------------
2
+ 2023-10-27 18:44:31,685 Model: "SequenceTagger(
3
+ (embeddings): TransformerWordEmbeddings(
4
+ (model): XLMRobertaModel(
5
+ (embeddings): XLMRobertaEmbeddings(
6
+ (word_embeddings): Embedding(250003, 1024)
7
+ (position_embeddings): Embedding(514, 1024, padding_idx=1)
8
+ (token_type_embeddings): Embedding(1, 1024)
9
+ (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
10
+ (dropout): Dropout(p=0.1, inplace=False)
11
+ )
12
+ (encoder): XLMRobertaEncoder(
13
+ (layer): ModuleList(
14
+ (0-23): 24 x XLMRobertaLayer(
15
+ (attention): XLMRobertaAttention(
16
+ (self): XLMRobertaSelfAttention(
17
+ (query): Linear(in_features=1024, out_features=1024, bias=True)
18
+ (key): Linear(in_features=1024, out_features=1024, bias=True)
19
+ (value): Linear(in_features=1024, out_features=1024, bias=True)
20
+ (dropout): Dropout(p=0.1, inplace=False)
21
+ )
22
+ (output): XLMRobertaSelfOutput(
23
+ (dense): Linear(in_features=1024, out_features=1024, bias=True)
24
+ (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
25
+ (dropout): Dropout(p=0.1, inplace=False)
26
+ )
27
+ )
28
+ (intermediate): XLMRobertaIntermediate(
29
+ (dense): Linear(in_features=1024, out_features=4096, bias=True)
30
+ (intermediate_act_fn): GELUActivation()
31
+ )
32
+ (output): XLMRobertaOutput(
33
+ (dense): Linear(in_features=4096, out_features=1024, bias=True)
34
+ (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
35
+ (dropout): Dropout(p=0.1, inplace=False)
36
+ )
37
+ )
38
+ )
39
+ )
40
+ (pooler): XLMRobertaPooler(
41
+ (dense): Linear(in_features=1024, out_features=1024, bias=True)
42
+ (activation): Tanh()
43
+ )
44
+ )
45
+ )
46
+ (locked_dropout): LockedDropout(p=0.5)
47
+ (linear): Linear(in_features=1024, out_features=17, bias=True)
48
+ (loss_function): CrossEntropyLoss()
49
+ )"
50
+ 2023-10-27 18:44:31,685 ----------------------------------------------------------------------------------------------------
51
+ 2023-10-27 18:44:31,685 Corpus: 14903 train + 3449 dev + 3658 test sentences
52
+ 2023-10-27 18:44:31,685 ----------------------------------------------------------------------------------------------------
53
+ 2023-10-27 18:44:31,685 Train: 14903 sentences
54
+ 2023-10-27 18:44:31,685 (train_with_dev=False, train_with_test=False)
55
+ 2023-10-27 18:44:31,685 ----------------------------------------------------------------------------------------------------
56
+ 2023-10-27 18:44:31,685 Training Params:
57
+ 2023-10-27 18:44:31,685 - learning_rate: "5e-06"
58
+ 2023-10-27 18:44:31,685 - mini_batch_size: "4"
59
+ 2023-10-27 18:44:31,685 - max_epochs: "10"
60
+ 2023-10-27 18:44:31,685 - shuffle: "True"
61
+ 2023-10-27 18:44:31,685 ----------------------------------------------------------------------------------------------------
62
+ 2023-10-27 18:44:31,685 Plugins:
63
+ 2023-10-27 18:44:31,685 - TensorboardLogger
64
+ 2023-10-27 18:44:31,686 - LinearScheduler | warmup_fraction: '0.1'
65
+ 2023-10-27 18:44:31,686 ----------------------------------------------------------------------------------------------------
66
+ 2023-10-27 18:44:31,686 Final evaluation on model from best epoch (best-model.pt)
67
+ 2023-10-27 18:44:31,686 - metric: "('micro avg', 'f1-score')"
68
+ 2023-10-27 18:44:31,686 ----------------------------------------------------------------------------------------------------
69
+ 2023-10-27 18:44:31,686 Computation:
70
+ 2023-10-27 18:44:31,686 - compute on device: cuda:0
71
+ 2023-10-27 18:44:31,686 - embedding storage: none
72
+ 2023-10-27 18:44:31,686 ----------------------------------------------------------------------------------------------------
73
+ 2023-10-27 18:44:31,686 Model training base path: "flair-clean-conll-lr5e-06-bs4-4"
74
+ 2023-10-27 18:44:31,686 ----------------------------------------------------------------------------------------------------
75
+ 2023-10-27 18:44:31,686 ----------------------------------------------------------------------------------------------------
76
+ 2023-10-27 18:44:31,686 Logging anything other than scalars to TensorBoard is currently not supported.
77
+ 2023-10-27 18:45:17,500 epoch 1 - iter 372/3726 - loss 2.56008396 - time (sec): 45.81 - samples/sec: 437.21 - lr: 0.000000 - momentum: 0.000000
78
+ 2023-10-27 18:46:02,660 epoch 1 - iter 744/3726 - loss 1.77263965 - time (sec): 90.97 - samples/sec: 440.01 - lr: 0.000001 - momentum: 0.000000
79
+ 2023-10-27 18:46:48,640 epoch 1 - iter 1116/3726 - loss 1.37087487 - time (sec): 136.95 - samples/sec: 443.33 - lr: 0.000001 - momentum: 0.000000
80
+ 2023-10-27 18:47:34,220 epoch 1 - iter 1488/3726 - loss 1.13762799 - time (sec): 182.53 - samples/sec: 445.61 - lr: 0.000002 - momentum: 0.000000
81
+ 2023-10-27 18:48:19,175 epoch 1 - iter 1860/3726 - loss 0.97055504 - time (sec): 227.49 - samples/sec: 446.76 - lr: 0.000002 - momentum: 0.000000
82
+ 2023-10-27 18:49:04,645 epoch 1 - iter 2232/3726 - loss 0.83989727 - time (sec): 272.96 - samples/sec: 449.00 - lr: 0.000003 - momentum: 0.000000
83
+ 2023-10-27 18:49:49,378 epoch 1 - iter 2604/3726 - loss 0.74197502 - time (sec): 317.69 - samples/sec: 450.38 - lr: 0.000003 - momentum: 0.000000
84
+ 2023-10-27 18:50:34,930 epoch 1 - iter 2976/3726 - loss 0.66837341 - time (sec): 363.24 - samples/sec: 449.48 - lr: 0.000004 - momentum: 0.000000
85
+ 2023-10-27 18:51:20,133 epoch 1 - iter 3348/3726 - loss 0.60924784 - time (sec): 408.45 - samples/sec: 448.31 - lr: 0.000004 - momentum: 0.000000
86
+ 2023-10-27 18:52:08,456 epoch 1 - iter 3720/3726 - loss 0.55515076 - time (sec): 456.77 - samples/sec: 447.31 - lr: 0.000005 - momentum: 0.000000
87
+ 2023-10-27 18:52:09,145 ----------------------------------------------------------------------------------------------------
88
+ 2023-10-27 18:52:09,145 EPOCH 1 done: loss 0.5546 - lr: 0.000005
89
+ 2023-10-27 18:52:30,774 DEV : loss 0.10047101974487305 - f1-score (micro avg) 0.931
90
+ 2023-10-27 18:52:30,829 saving best model
91
+ 2023-10-27 18:52:33,017 ----------------------------------------------------------------------------------------------------
92
+ 2023-10-27 18:53:18,587 epoch 2 - iter 372/3726 - loss 0.11546777 - time (sec): 45.57 - samples/sec: 460.22 - lr: 0.000005 - momentum: 0.000000
93
+ 2023-10-27 18:54:05,289 epoch 2 - iter 744/3726 - loss 0.10660754 - time (sec): 92.27 - samples/sec: 449.30 - lr: 0.000005 - momentum: 0.000000
94
+ 2023-10-27 18:54:51,000 epoch 2 - iter 1116/3726 - loss 0.10038157 - time (sec): 137.98 - samples/sec: 455.34 - lr: 0.000005 - momentum: 0.000000
95
+ 2023-10-27 18:55:36,869 epoch 2 - iter 1488/3726 - loss 0.09684308 - time (sec): 183.85 - samples/sec: 451.24 - lr: 0.000005 - momentum: 0.000000
96
+ 2023-10-27 18:56:23,094 epoch 2 - iter 1860/3726 - loss 0.09806463 - time (sec): 230.07 - samples/sec: 450.98 - lr: 0.000005 - momentum: 0.000000
97
+ 2023-10-27 18:57:08,793 epoch 2 - iter 2232/3726 - loss 0.09423182 - time (sec): 275.77 - samples/sec: 450.87 - lr: 0.000005 - momentum: 0.000000
98
+ 2023-10-27 18:57:53,912 epoch 2 - iter 2604/3726 - loss 0.09270500 - time (sec): 320.89 - samples/sec: 452.25 - lr: 0.000005 - momentum: 0.000000
99
+ 2023-10-27 18:58:39,792 epoch 2 - iter 2976/3726 - loss 0.08995641 - time (sec): 366.77 - samples/sec: 451.59 - lr: 0.000005 - momentum: 0.000000
100
+ 2023-10-27 18:59:26,428 epoch 2 - iter 3348/3726 - loss 0.08720003 - time (sec): 413.41 - samples/sec: 447.43 - lr: 0.000005 - momentum: 0.000000
101
+ 2023-10-27 19:00:11,611 epoch 2 - iter 3720/3726 - loss 0.08576854 - time (sec): 458.59 - samples/sec: 445.51 - lr: 0.000004 - momentum: 0.000000
102
+ 2023-10-27 19:00:12,346 ----------------------------------------------------------------------------------------------------
103
+ 2023-10-27 19:00:12,347 EPOCH 2 done: loss 0.0857 - lr: 0.000004
104
+ 2023-10-27 19:00:35,384 DEV : loss 0.06790720671415329 - f1-score (micro avg) 0.9571
105
+ 2023-10-27 19:00:35,438 saving best model
106
+ 2023-10-27 19:00:37,853 ----------------------------------------------------------------------------------------------------
107
+ 2023-10-27 19:01:23,535 epoch 3 - iter 372/3726 - loss 0.07374127 - time (sec): 45.68 - samples/sec: 439.85 - lr: 0.000004 - momentum: 0.000000
108
+ 2023-10-27 19:02:08,846 epoch 3 - iter 744/3726 - loss 0.06087161 - time (sec): 90.99 - samples/sec: 440.33 - lr: 0.000004 - momentum: 0.000000
109
+ 2023-10-27 19:02:54,176 epoch 3 - iter 1116/3726 - loss 0.05718760 - time (sec): 136.32 - samples/sec: 444.62 - lr: 0.000004 - momentum: 0.000000
110
+ 2023-10-27 19:03:40,248 epoch 3 - iter 1488/3726 - loss 0.05400073 - time (sec): 182.39 - samples/sec: 447.02 - lr: 0.000004 - momentum: 0.000000
111
+ 2023-10-27 19:04:26,095 epoch 3 - iter 1860/3726 - loss 0.05589718 - time (sec): 228.24 - samples/sec: 447.08 - lr: 0.000004 - momentum: 0.000000
112
+ 2023-10-27 19:05:12,308 epoch 3 - iter 2232/3726 - loss 0.05415602 - time (sec): 274.45 - samples/sec: 445.29 - lr: 0.000004 - momentum: 0.000000
113
+ 2023-10-27 19:05:58,644 epoch 3 - iter 2604/3726 - loss 0.05279672 - time (sec): 320.79 - samples/sec: 445.89 - lr: 0.000004 - momentum: 0.000000
114
+ 2023-10-27 19:06:44,498 epoch 3 - iter 2976/3726 - loss 0.05148716 - time (sec): 366.64 - samples/sec: 446.33 - lr: 0.000004 - momentum: 0.000000
115
+ 2023-10-27 19:07:29,887 epoch 3 - iter 3348/3726 - loss 0.05136170 - time (sec): 412.03 - samples/sec: 446.61 - lr: 0.000004 - momentum: 0.000000
116
+ 2023-10-27 19:08:16,632 epoch 3 - iter 3720/3726 - loss 0.05145199 - time (sec): 458.78 - samples/sec: 445.53 - lr: 0.000004 - momentum: 0.000000
117
+ 2023-10-27 19:08:17,367 ----------------------------------------------------------------------------------------------------
118
+ 2023-10-27 19:08:17,367 EPOCH 3 done: loss 0.0514 - lr: 0.000004
119
+ 2023-10-27 19:08:39,846 DEV : loss 0.052444763481616974 - f1-score (micro avg) 0.9626
120
+ 2023-10-27 19:08:39,896 saving best model
121
+ 2023-10-27 19:08:42,267 ----------------------------------------------------------------------------------------------------
122
+ 2023-10-27 19:09:28,802 epoch 4 - iter 372/3726 - loss 0.03871254 - time (sec): 46.53 - samples/sec: 454.07 - lr: 0.000004 - momentum: 0.000000
123
+ 2023-10-27 19:10:14,349 epoch 4 - iter 744/3726 - loss 0.04116213 - time (sec): 92.08 - samples/sec: 449.79 - lr: 0.000004 - momentum: 0.000000
124
+ 2023-10-27 19:11:00,008 epoch 4 - iter 1116/3726 - loss 0.04020488 - time (sec): 137.74 - samples/sec: 454.42 - lr: 0.000004 - momentum: 0.000000
125
+ 2023-10-27 19:11:45,448 epoch 4 - iter 1488/3726 - loss 0.04174189 - time (sec): 183.18 - samples/sec: 445.99 - lr: 0.000004 - momentum: 0.000000
126
+ 2023-10-27 19:12:31,270 epoch 4 - iter 1860/3726 - loss 0.04211643 - time (sec): 229.00 - samples/sec: 444.26 - lr: 0.000004 - momentum: 0.000000
127
+ 2023-10-27 19:13:16,951 epoch 4 - iter 2232/3726 - loss 0.04082069 - time (sec): 274.68 - samples/sec: 443.61 - lr: 0.000004 - momentum: 0.000000
128
+ 2023-10-27 19:14:02,493 epoch 4 - iter 2604/3726 - loss 0.03901976 - time (sec): 320.22 - samples/sec: 446.31 - lr: 0.000004 - momentum: 0.000000
129
+ 2023-10-27 19:14:48,002 epoch 4 - iter 2976/3726 - loss 0.03797498 - time (sec): 365.73 - samples/sec: 446.53 - lr: 0.000003 - momentum: 0.000000
130
+ 2023-10-27 19:15:33,644 epoch 4 - iter 3348/3726 - loss 0.03778630 - time (sec): 411.37 - samples/sec: 445.67 - lr: 0.000003 - momentum: 0.000000
131
+ 2023-10-27 19:16:19,632 epoch 4 - iter 3720/3726 - loss 0.03709369 - time (sec): 457.36 - samples/sec: 446.74 - lr: 0.000003 - momentum: 0.000000
132
+ 2023-10-27 19:16:20,375 ----------------------------------------------------------------------------------------------------
133
+ 2023-10-27 19:16:20,375 EPOCH 4 done: loss 0.0372 - lr: 0.000003
134
+ 2023-10-27 19:16:43,692 DEV : loss 0.05450737103819847 - f1-score (micro avg) 0.9647
135
+ 2023-10-27 19:16:43,745 saving best model
136
+ 2023-10-27 19:16:46,767 ----------------------------------------------------------------------------------------------------
137
+ 2023-10-27 19:17:32,579 epoch 5 - iter 372/3726 - loss 0.02845766 - time (sec): 45.81 - samples/sec: 459.66 - lr: 0.000003 - momentum: 0.000000
138
+ 2023-10-27 19:18:18,467 epoch 5 - iter 744/3726 - loss 0.02421414 - time (sec): 91.70 - samples/sec: 448.91 - lr: 0.000003 - momentum: 0.000000
139
+ 2023-10-27 19:19:04,523 epoch 5 - iter 1116/3726 - loss 0.02470444 - time (sec): 137.75 - samples/sec: 447.01 - lr: 0.000003 - momentum: 0.000000
140
+ 2023-10-27 19:19:50,020 epoch 5 - iter 1488/3726 - loss 0.02877377 - time (sec): 183.25 - samples/sec: 446.70 - lr: 0.000003 - momentum: 0.000000
141
+ 2023-10-27 19:20:35,406 epoch 5 - iter 1860/3726 - loss 0.02956343 - time (sec): 228.64 - samples/sec: 445.95 - lr: 0.000003 - momentum: 0.000000
142
+ 2023-10-27 19:21:21,375 epoch 5 - iter 2232/3726 - loss 0.02911303 - time (sec): 274.61 - samples/sec: 444.00 - lr: 0.000003 - momentum: 0.000000
143
+ 2023-10-27 19:22:06,761 epoch 5 - iter 2604/3726 - loss 0.02983439 - time (sec): 319.99 - samples/sec: 446.51 - lr: 0.000003 - momentum: 0.000000
144
+ 2023-10-27 19:22:52,640 epoch 5 - iter 2976/3726 - loss 0.02953542 - time (sec): 365.87 - samples/sec: 447.29 - lr: 0.000003 - momentum: 0.000000
145
+ 2023-10-27 19:23:37,972 epoch 5 - iter 3348/3726 - loss 0.02932321 - time (sec): 411.20 - samples/sec: 448.46 - lr: 0.000003 - momentum: 0.000000
146
+ 2023-10-27 19:24:24,955 epoch 5 - iter 3720/3726 - loss 0.02916844 - time (sec): 458.19 - samples/sec: 445.99 - lr: 0.000003 - momentum: 0.000000
147
+ 2023-10-27 19:24:25,687 ----------------------------------------------------------------------------------------------------
148
+ 2023-10-27 19:24:25,688 EPOCH 5 done: loss 0.0292 - lr: 0.000003
149
+ 2023-10-27 19:24:48,031 DEV : loss 0.05429258942604065 - f1-score (micro avg) 0.9647
150
+ 2023-10-27 19:24:48,084 saving best model
151
+ 2023-10-27 19:24:50,724 ----------------------------------------------------------------------------------------------------
152
+ 2023-10-27 19:25:36,994 epoch 6 - iter 372/3726 - loss 0.01703796 - time (sec): 46.27 - samples/sec: 456.14 - lr: 0.000003 - momentum: 0.000000
153
+ 2023-10-27 19:26:22,882 epoch 6 - iter 744/3726 - loss 0.02169011 - time (sec): 92.16 - samples/sec: 454.83 - lr: 0.000003 - momentum: 0.000000
154
+ 2023-10-27 19:27:07,925 epoch 6 - iter 1116/3726 - loss 0.02127737 - time (sec): 137.20 - samples/sec: 451.49 - lr: 0.000003 - momentum: 0.000000
155
+ 2023-10-27 19:27:53,410 epoch 6 - iter 1488/3726 - loss 0.02157007 - time (sec): 182.68 - samples/sec: 452.54 - lr: 0.000003 - momentum: 0.000000
156
+ 2023-10-27 19:28:39,521 epoch 6 - iter 1860/3726 - loss 0.02169603 - time (sec): 228.79 - samples/sec: 449.67 - lr: 0.000003 - momentum: 0.000000
157
+ 2023-10-27 19:29:24,934 epoch 6 - iter 2232/3726 - loss 0.02182002 - time (sec): 274.21 - samples/sec: 451.13 - lr: 0.000002 - momentum: 0.000000
158
+ 2023-10-27 19:30:11,317 epoch 6 - iter 2604/3726 - loss 0.02225228 - time (sec): 320.59 - samples/sec: 448.74 - lr: 0.000002 - momentum: 0.000000
159
+ 2023-10-27 19:30:56,485 epoch 6 - iter 2976/3726 - loss 0.02130458 - time (sec): 365.76 - samples/sec: 450.41 - lr: 0.000002 - momentum: 0.000000
160
+ 2023-10-27 19:31:42,001 epoch 6 - iter 3348/3726 - loss 0.02151336 - time (sec): 411.28 - samples/sec: 448.25 - lr: 0.000002 - momentum: 0.000000
161
+ 2023-10-27 19:32:28,009 epoch 6 - iter 3720/3726 - loss 0.02092486 - time (sec): 457.28 - samples/sec: 446.72 - lr: 0.000002 - momentum: 0.000000
162
+ 2023-10-27 19:32:28,753 ----------------------------------------------------------------------------------------------------
163
+ 2023-10-27 19:32:28,753 EPOCH 6 done: loss 0.0209 - lr: 0.000002
164
+ 2023-10-27 19:32:52,231 DEV : loss 0.052240729331970215 - f1-score (micro avg) 0.9692
165
+ 2023-10-27 19:32:52,286 saving best model
166
+ 2023-10-27 19:32:55,357 ----------------------------------------------------------------------------------------------------
167
+ 2023-10-27 19:33:41,068 epoch 7 - iter 372/3726 - loss 0.02108834 - time (sec): 45.71 - samples/sec: 450.92 - lr: 0.000002 - momentum: 0.000000
168
+ 2023-10-27 19:34:26,441 epoch 7 - iter 744/3726 - loss 0.01802067 - time (sec): 91.08 - samples/sec: 447.77 - lr: 0.000002 - momentum: 0.000000
169
+ 2023-10-27 19:35:12,147 epoch 7 - iter 1116/3726 - loss 0.01632005 - time (sec): 136.79 - samples/sec: 445.34 - lr: 0.000002 - momentum: 0.000000
170
+ 2023-10-27 19:35:57,225 epoch 7 - iter 1488/3726 - loss 0.01518131 - time (sec): 181.87 - samples/sec: 444.13 - lr: 0.000002 - momentum: 0.000000
171
+ 2023-10-27 19:36:42,626 epoch 7 - iter 1860/3726 - loss 0.01620599 - time (sec): 227.27 - samples/sec: 445.22 - lr: 0.000002 - momentum: 0.000000
172
+ 2023-10-27 19:37:28,781 epoch 7 - iter 2232/3726 - loss 0.01630849 - time (sec): 273.42 - samples/sec: 446.32 - lr: 0.000002 - momentum: 0.000000
173
+ 2023-10-27 19:38:14,783 epoch 7 - iter 2604/3726 - loss 0.01643476 - time (sec): 319.42 - samples/sec: 447.07 - lr: 0.000002 - momentum: 0.000000
174
+ 2023-10-27 19:39:00,647 epoch 7 - iter 2976/3726 - loss 0.01586704 - time (sec): 365.29 - samples/sec: 447.99 - lr: 0.000002 - momentum: 0.000000
175
+ 2023-10-27 19:39:46,421 epoch 7 - iter 3348/3726 - loss 0.01547939 - time (sec): 411.06 - samples/sec: 448.19 - lr: 0.000002 - momentum: 0.000000
176
+ 2023-10-27 19:40:32,073 epoch 7 - iter 3720/3726 - loss 0.01513403 - time (sec): 456.71 - samples/sec: 447.41 - lr: 0.000002 - momentum: 0.000000
177
+ 2023-10-27 19:40:32,785 ----------------------------------------------------------------------------------------------------
178
+ 2023-10-27 19:40:32,785 EPOCH 7 done: loss 0.0151 - lr: 0.000002
179
+ 2023-10-27 19:40:55,727 DEV : loss 0.050821226090192795 - f1-score (micro avg) 0.9714
180
+ 2023-10-27 19:40:55,781 saving best model
181
+ 2023-10-27 19:40:58,713 ----------------------------------------------------------------------------------------------------
182
+ 2023-10-27 19:41:44,020 epoch 8 - iter 372/3726 - loss 0.01532970 - time (sec): 45.30 - samples/sec: 450.04 - lr: 0.000002 - momentum: 0.000000
183
+ 2023-10-27 19:42:29,890 epoch 8 - iter 744/3726 - loss 0.01208467 - time (sec): 91.18 - samples/sec: 452.34 - lr: 0.000002 - momentum: 0.000000
184
+ 2023-10-27 19:43:16,086 epoch 8 - iter 1116/3726 - loss 0.01337061 - time (sec): 137.37 - samples/sec: 446.08 - lr: 0.000002 - momentum: 0.000000
185
+ 2023-10-27 19:44:02,334 epoch 8 - iter 1488/3726 - loss 0.01184764 - time (sec): 183.62 - samples/sec: 444.41 - lr: 0.000001 - momentum: 0.000000
186
+ 2023-10-27 19:44:48,481 epoch 8 - iter 1860/3726 - loss 0.01162265 - time (sec): 229.77 - samples/sec: 444.22 - lr: 0.000001 - momentum: 0.000000
187
+ 2023-10-27 19:45:33,897 epoch 8 - iter 2232/3726 - loss 0.01116723 - time (sec): 275.18 - samples/sec: 447.92 - lr: 0.000001 - momentum: 0.000000
188
+ 2023-10-27 19:46:19,563 epoch 8 - iter 2604/3726 - loss 0.01237156 - time (sec): 320.85 - samples/sec: 448.45 - lr: 0.000001 - momentum: 0.000000
189
+ 2023-10-27 19:47:06,257 epoch 8 - iter 2976/3726 - loss 0.01216515 - time (sec): 367.54 - samples/sec: 445.88 - lr: 0.000001 - momentum: 0.000000
190
+ 2023-10-27 19:47:51,860 epoch 8 - iter 3348/3726 - loss 0.01228458 - time (sec): 413.15 - samples/sec: 445.41 - lr: 0.000001 - momentum: 0.000000
191
+ 2023-10-27 19:48:37,302 epoch 8 - iter 3720/3726 - loss 0.01173645 - time (sec): 458.59 - samples/sec: 445.55 - lr: 0.000001 - momentum: 0.000000
192
+ 2023-10-27 19:48:38,037 ----------------------------------------------------------------------------------------------------
193
+ 2023-10-27 19:48:38,038 EPOCH 8 done: loss 0.0117 - lr: 0.000001
194
+ 2023-10-27 19:49:01,125 DEV : loss 0.05222569778561592 - f1-score (micro avg) 0.9726
195
+ 2023-10-27 19:49:01,178 saving best model
196
+ 2023-10-27 19:49:03,903 ----------------------------------------------------------------------------------------------------
197
+ 2023-10-27 19:49:49,349 epoch 9 - iter 372/3726 - loss 0.00486713 - time (sec): 45.44 - samples/sec: 455.66 - lr: 0.000001 - momentum: 0.000000
198
+ 2023-10-27 19:50:34,984 epoch 9 - iter 744/3726 - loss 0.00545741 - time (sec): 91.08 - samples/sec: 453.89 - lr: 0.000001 - momentum: 0.000000
199
+ 2023-10-27 19:51:20,189 epoch 9 - iter 1116/3726 - loss 0.00787820 - time (sec): 136.28 - samples/sec: 452.07 - lr: 0.000001 - momentum: 0.000000
200
+ 2023-10-27 19:52:05,623 epoch 9 - iter 1488/3726 - loss 0.00691940 - time (sec): 181.72 - samples/sec: 451.59 - lr: 0.000001 - momentum: 0.000000
201
+ 2023-10-27 19:52:51,095 epoch 9 - iter 1860/3726 - loss 0.00761910 - time (sec): 227.19 - samples/sec: 452.40 - lr: 0.000001 - momentum: 0.000000
202
+ 2023-10-27 19:53:36,717 epoch 9 - iter 2232/3726 - loss 0.00762123 - time (sec): 272.81 - samples/sec: 450.93 - lr: 0.000001 - momentum: 0.000000
203
+ 2023-10-27 19:54:23,451 epoch 9 - iter 2604/3726 - loss 0.00784988 - time (sec): 319.55 - samples/sec: 448.92 - lr: 0.000001 - momentum: 0.000000
204
+ 2023-10-27 19:55:08,672 epoch 9 - iter 2976/3726 - loss 0.00844099 - time (sec): 364.77 - samples/sec: 449.81 - lr: 0.000001 - momentum: 0.000000
205
+ 2023-10-27 19:55:53,980 epoch 9 - iter 3348/3726 - loss 0.00836439 - time (sec): 410.07 - samples/sec: 449.23 - lr: 0.000001 - momentum: 0.000000
206
+ 2023-10-27 19:56:39,471 epoch 9 - iter 3720/3726 - loss 0.00807807 - time (sec): 455.57 - samples/sec: 448.18 - lr: 0.000001 - momentum: 0.000000
207
+ 2023-10-27 19:56:40,233 ----------------------------------------------------------------------------------------------------
208
+ 2023-10-27 19:56:40,233 EPOCH 9 done: loss 0.0081 - lr: 0.000001
209
+ 2023-10-27 19:57:03,898 DEV : loss 0.05321481078863144 - f1-score (micro avg) 0.9742
210
+ 2023-10-27 19:57:03,949 saving best model
211
+ 2023-10-27 19:57:06,562 ----------------------------------------------------------------------------------------------------
212
+ 2023-10-27 19:57:52,150 epoch 10 - iter 372/3726 - loss 0.00368318 - time (sec): 45.59 - samples/sec: 447.26 - lr: 0.000001 - momentum: 0.000000
213
+ 2023-10-27 19:58:37,058 epoch 10 - iter 744/3726 - loss 0.00490381 - time (sec): 90.49 - samples/sec: 441.64 - lr: 0.000000 - momentum: 0.000000
214
+ 2023-10-27 19:59:22,616 epoch 10 - iter 1116/3726 - loss 0.00591526 - time (sec): 136.05 - samples/sec: 444.19 - lr: 0.000000 - momentum: 0.000000
215
+ 2023-10-27 20:00:08,343 epoch 10 - iter 1488/3726 - loss 0.00634553 - time (sec): 181.78 - samples/sec: 443.45 - lr: 0.000000 - momentum: 0.000000
216
+ 2023-10-27 20:00:54,241 epoch 10 - iter 1860/3726 - loss 0.00584248 - time (sec): 227.68 - samples/sec: 441.45 - lr: 0.000000 - momentum: 0.000000
217
+ 2023-10-27 20:01:40,141 epoch 10 - iter 2232/3726 - loss 0.00572180 - time (sec): 273.58 - samples/sec: 443.18 - lr: 0.000000 - momentum: 0.000000
218
+ 2023-10-27 20:02:25,686 epoch 10 - iter 2604/3726 - loss 0.00598793 - time (sec): 319.12 - samples/sec: 445.80 - lr: 0.000000 - momentum: 0.000000
219
+ 2023-10-27 20:03:11,659 epoch 10 - iter 2976/3726 - loss 0.00597232 - time (sec): 365.09 - samples/sec: 445.70 - lr: 0.000000 - momentum: 0.000000
220
+ 2023-10-27 20:03:57,027 epoch 10 - iter 3348/3726 - loss 0.00599237 - time (sec): 410.46 - samples/sec: 447.67 - lr: 0.000000 - momentum: 0.000000
221
+ 2023-10-27 20:04:42,462 epoch 10 - iter 3720/3726 - loss 0.00636112 - time (sec): 455.90 - samples/sec: 448.08 - lr: 0.000000 - momentum: 0.000000
222
+ 2023-10-27 20:04:43,220 ----------------------------------------------------------------------------------------------------
223
+ 2023-10-27 20:04:43,220 EPOCH 10 done: loss 0.0064 - lr: 0.000000
224
+ 2023-10-27 20:05:06,157 DEV : loss 0.05384046211838722 - f1-score (micro avg) 0.9737
225
+ 2023-10-27 20:05:08,560 ----------------------------------------------------------------------------------------------------
226
+ 2023-10-27 20:05:08,562 Loading model from best epoch ...
227
+ 2023-10-27 20:05:16,264 SequenceTagger predicts: Dictionary with 17 tags: O, S-ORG, B-ORG, E-ORG, I-ORG, S-PER, B-PER, E-PER, I-PER, S-LOC, B-LOC, E-LOC, I-LOC, S-MISC, B-MISC, E-MISC, I-MISC
228
+ 2023-10-27 20:05:38,883
229
+ Results:
230
+ - F-score (micro) 0.9696
231
+ - F-score (macro) 0.9645
232
+ - Accuracy 0.9555
233
+
234
+ By class:
235
+ precision recall f1-score support
236
+
237
+ ORG 0.9665 0.9665 0.9665 1909
238
+ PER 0.9956 0.9956 0.9956 1591
239
+ LOC 0.9723 0.9674 0.9698 1413
240
+ MISC 0.9117 0.9409 0.9261 812
241
+
242
+ micro avg 0.9680 0.9712 0.9696 5725
243
+ macro avg 0.9615 0.9676 0.9645 5725
244
+ weighted avg 0.9682 0.9712 0.9697 5725
245
+
246
+ 2023-10-27 20:05:38,883 ----------------------------------------------------------------------------------------------------