Hugging Face's logo

stefan-it
/
autotrain-flair-georgian-ner-xlm_r_large-bs4-e10-lr5e-06-2

Token Classification
Flair
PyTorch
TensorBoard
English
Georgian
sequence-tagger-model
Model card Files Metrics Community
1
stefan-it commited on
Commit
e9bce1b
·
1 Parent(s): 6977190

Upload ./training.log with huggingface_hub

Browse files
Files changed (1) hide show
  1. training.log +245 -0
training.log ADDED
@@ -0,0 +1,245 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 2023-11-16 00:45:31,082 ----------------------------------------------------------------------------------------------------
2
+ 2023-11-16 00:45:31,084 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=13, bias=True)
48
+ (loss_function): CrossEntropyLoss()
49
+ )"
50
+ 2023-11-16 00:45:31,084 ----------------------------------------------------------------------------------------------------
51
+ 2023-11-16 00:45:31,084 MultiCorpus: 30000 train + 10000 dev + 10000 test sentences
52
+ - ColumnCorpus Corpus: 20000 train + 0 dev + 0 test sentences - /root/.flair/datasets/ner_multi_xtreme/en
53
+ - ColumnCorpus Corpus: 10000 train + 10000 dev + 10000 test sentences - /root/.flair/datasets/ner_multi_xtreme/ka
54
+ 2023-11-16 00:45:31,084 ----------------------------------------------------------------------------------------------------
55
+ 2023-11-16 00:45:31,084 Train: 30000 sentences
56
+ 2023-11-16 00:45:31,084 (train_with_dev=False, train_with_test=False)
57
+ 2023-11-16 00:45:31,084 ----------------------------------------------------------------------------------------------------
58
+ 2023-11-16 00:45:31,084 Training Params:
59
+ 2023-11-16 00:45:31,084 - learning_rate: "5e-06"
60
+ 2023-11-16 00:45:31,084 - mini_batch_size: "4"
61
+ 2023-11-16 00:45:31,084 - max_epochs: "10"
62
+ 2023-11-16 00:45:31,084 - shuffle: "True"
63
+ 2023-11-16 00:45:31,084 ----------------------------------------------------------------------------------------------------
64
+ 2023-11-16 00:45:31,084 Plugins:
65
+ 2023-11-16 00:45:31,084 - TensorboardLogger
66
+ 2023-11-16 00:45:31,084 - LinearScheduler | warmup_fraction: '0.1'
67
+ 2023-11-16 00:45:31,084 ----------------------------------------------------------------------------------------------------
68
+ 2023-11-16 00:45:31,084 Final evaluation on model from best epoch (best-model.pt)
69
+ 2023-11-16 00:45:31,085 - metric: "('micro avg', 'f1-score')"
70
+ 2023-11-16 00:45:31,085 ----------------------------------------------------------------------------------------------------
71
+ 2023-11-16 00:45:31,085 Computation:
72
+ 2023-11-16 00:45:31,085 - compute on device: cuda:0
73
+ 2023-11-16 00:45:31,085 - embedding storage: none
74
+ 2023-11-16 00:45:31,085 ----------------------------------------------------------------------------------------------------
75
+ 2023-11-16 00:45:31,085 Model training base path: "autotrain-flair-georgian-ner-xlm_r_large-bs4-e10-lr5e-06-2"
76
+ 2023-11-16 00:45:31,085 ----------------------------------------------------------------------------------------------------
77
+ 2023-11-16 00:45:31,085 ----------------------------------------------------------------------------------------------------
78
+ 2023-11-16 00:45:31,085 Logging anything other than scalars to TensorBoard is currently not supported.
79
+ 2023-11-16 00:47:04,086 epoch 1 - iter 750/7500 - loss 3.20421711 - time (sec): 93.00 - samples/sec: 254.76 - lr: 0.000000 - momentum: 0.000000
80
+ 2023-11-16 00:48:36,937 epoch 1 - iter 1500/7500 - loss 2.53046773 - time (sec): 185.85 - samples/sec: 256.83 - lr: 0.000001 - momentum: 0.000000
81
+ 2023-11-16 00:50:07,872 epoch 1 - iter 2250/7500 - loss 2.16462133 - time (sec): 276.79 - samples/sec: 258.95 - lr: 0.000001 - momentum: 0.000000
82
+ 2023-11-16 00:51:39,794 epoch 1 - iter 3000/7500 - loss 1.89258823 - time (sec): 368.71 - samples/sec: 258.86 - lr: 0.000002 - momentum: 0.000000
83
+ 2023-11-16 00:53:11,581 epoch 1 - iter 3750/7500 - loss 1.66150429 - time (sec): 460.49 - samples/sec: 259.63 - lr: 0.000002 - momentum: 0.000000
84
+ 2023-11-16 00:54:42,341 epoch 1 - iter 4500/7500 - loss 1.47974045 - time (sec): 551.25 - samples/sec: 261.00 - lr: 0.000003 - momentum: 0.000000
85
+ 2023-11-16 00:56:14,348 epoch 1 - iter 5250/7500 - loss 1.33779802 - time (sec): 643.26 - samples/sec: 261.74 - lr: 0.000003 - momentum: 0.000000
86
+ 2023-11-16 00:57:44,891 epoch 1 - iter 6000/7500 - loss 1.23224942 - time (sec): 733.80 - samples/sec: 262.47 - lr: 0.000004 - momentum: 0.000000
87
+ 2023-11-16 00:59:17,561 epoch 1 - iter 6750/7500 - loss 1.14528322 - time (sec): 826.47 - samples/sec: 262.22 - lr: 0.000004 - momentum: 0.000000
88
+ 2023-11-16 01:00:50,271 epoch 1 - iter 7500/7500 - loss 1.07465936 - time (sec): 919.18 - samples/sec: 261.97 - lr: 0.000005 - momentum: 0.000000
89
+ 2023-11-16 01:00:50,273 ----------------------------------------------------------------------------------------------------
90
+ 2023-11-16 01:00:50,273 EPOCH 1 done: loss 1.0747 - lr: 0.000005
91
+ 2023-11-16 01:01:17,256 DEV : loss 0.2856157124042511 - f1-score (micro avg) 0.8045
92
+ 2023-11-16 01:01:18,998 saving best model
93
+ 2023-11-16 01:01:20,796 ----------------------------------------------------------------------------------------------------
94
+ 2023-11-16 01:02:54,269 epoch 2 - iter 750/7500 - loss 0.40158514 - time (sec): 93.47 - samples/sec: 252.67 - lr: 0.000005 - momentum: 0.000000
95
+ 2023-11-16 01:04:26,600 epoch 2 - iter 1500/7500 - loss 0.40578126 - time (sec): 185.80 - samples/sec: 257.91 - lr: 0.000005 - momentum: 0.000000
96
+ 2023-11-16 01:06:01,665 epoch 2 - iter 2250/7500 - loss 0.40182467 - time (sec): 280.87 - samples/sec: 256.06 - lr: 0.000005 - momentum: 0.000000
97
+ 2023-11-16 01:07:35,409 epoch 2 - iter 3000/7500 - loss 0.40425251 - time (sec): 374.61 - samples/sec: 255.46 - lr: 0.000005 - momentum: 0.000000
98
+ 2023-11-16 01:09:06,889 epoch 2 - iter 3750/7500 - loss 0.40579040 - time (sec): 466.09 - samples/sec: 256.59 - lr: 0.000005 - momentum: 0.000000
99
+ 2023-11-16 01:10:37,461 epoch 2 - iter 4500/7500 - loss 0.40296543 - time (sec): 556.66 - samples/sec: 257.85 - lr: 0.000005 - momentum: 0.000000
100
+ 2023-11-16 01:12:10,834 epoch 2 - iter 5250/7500 - loss 0.39886416 - time (sec): 650.03 - samples/sec: 258.59 - lr: 0.000005 - momentum: 0.000000
101
+ 2023-11-16 01:13:43,058 epoch 2 - iter 6000/7500 - loss 0.40163262 - time (sec): 742.26 - samples/sec: 258.99 - lr: 0.000005 - momentum: 0.000000
102
+ 2023-11-16 01:15:15,768 epoch 2 - iter 6750/7500 - loss 0.39984468 - time (sec): 834.97 - samples/sec: 259.04 - lr: 0.000005 - momentum: 0.000000
103
+ 2023-11-16 01:16:48,990 epoch 2 - iter 7500/7500 - loss 0.39673334 - time (sec): 928.19 - samples/sec: 259.43 - lr: 0.000004 - momentum: 0.000000
104
+ 2023-11-16 01:16:48,992 ----------------------------------------------------------------------------------------------------
105
+ 2023-11-16 01:16:48,992 EPOCH 2 done: loss 0.3967 - lr: 0.000004
106
+ 2023-11-16 01:17:16,238 DEV : loss 0.27984410524368286 - f1-score (micro avg) 0.8635
107
+ 2023-11-16 01:17:18,468 saving best model
108
+ 2023-11-16 01:17:21,478 ----------------------------------------------------------------------------------------------------
109
+ 2023-11-16 01:18:56,267 epoch 3 - iter 750/7500 - loss 0.34788380 - time (sec): 94.78 - samples/sec: 252.61 - lr: 0.000004 - momentum: 0.000000
110
+ 2023-11-16 01:20:29,227 epoch 3 - iter 1500/7500 - loss 0.35790619 - time (sec): 187.74 - samples/sec: 257.86 - lr: 0.000004 - momentum: 0.000000
111
+ 2023-11-16 01:22:00,989 epoch 3 - iter 2250/7500 - loss 0.36275974 - time (sec): 279.51 - samples/sec: 256.05 - lr: 0.000004 - momentum: 0.000000
112
+ 2023-11-16 01:23:32,112 epoch 3 - iter 3000/7500 - loss 0.35448466 - time (sec): 370.63 - samples/sec: 257.61 - lr: 0.000004 - momentum: 0.000000
113
+ 2023-11-16 01:25:04,514 epoch 3 - iter 3750/7500 - loss 0.35784162 - time (sec): 463.03 - samples/sec: 258.62 - lr: 0.000004 - momentum: 0.000000
114
+ 2023-11-16 01:26:37,957 epoch 3 - iter 4500/7500 - loss 0.35491385 - time (sec): 556.48 - samples/sec: 259.13 - lr: 0.000004 - momentum: 0.000000
115
+ 2023-11-16 01:28:09,894 epoch 3 - iter 5250/7500 - loss 0.35268653 - time (sec): 648.41 - samples/sec: 259.90 - lr: 0.000004 - momentum: 0.000000
116
+ 2023-11-16 01:29:43,647 epoch 3 - iter 6000/7500 - loss 0.35432380 - time (sec): 742.17 - samples/sec: 259.26 - lr: 0.000004 - momentum: 0.000000
117
+ 2023-11-16 01:31:15,583 epoch 3 - iter 6750/7500 - loss 0.35073442 - time (sec): 834.10 - samples/sec: 259.69 - lr: 0.000004 - momentum: 0.000000
118
+ 2023-11-16 01:32:47,991 epoch 3 - iter 7500/7500 - loss 0.34845272 - time (sec): 926.51 - samples/sec: 259.90 - lr: 0.000004 - momentum: 0.000000
119
+ 2023-11-16 01:32:47,993 ----------------------------------------------------------------------------------------------------
120
+ 2023-11-16 01:32:47,993 EPOCH 3 done: loss 0.3485 - lr: 0.000004
121
+ 2023-11-16 01:33:14,670 DEV : loss 0.2744104862213135 - f1-score (micro avg) 0.8834
122
+ 2023-11-16 01:33:16,359 saving best model
123
+ 2023-11-16 01:33:18,657 ----------------------------------------------------------------------------------------------------
124
+ 2023-11-16 01:34:49,914 epoch 4 - iter 750/7500 - loss 0.29966012 - time (sec): 91.25 - samples/sec: 265.65 - lr: 0.000004 - momentum: 0.000000
125
+ 2023-11-16 01:36:21,479 epoch 4 - iter 1500/7500 - loss 0.29512059 - time (sec): 182.82 - samples/sec: 262.70 - lr: 0.000004 - momentum: 0.000000
126
+ 2023-11-16 01:37:53,047 epoch 4 - iter 2250/7500 - loss 0.29934339 - time (sec): 274.38 - samples/sec: 262.65 - lr: 0.000004 - momentum: 0.000000
127
+ 2023-11-16 01:39:25,499 epoch 4 - iter 3000/7500 - loss 0.29881097 - time (sec): 366.84 - samples/sec: 263.27 - lr: 0.000004 - momentum: 0.000000
128
+ 2023-11-16 01:40:56,808 epoch 4 - iter 3750/7500 - loss 0.29543460 - time (sec): 458.15 - samples/sec: 264.24 - lr: 0.000004 - momentum: 0.000000
129
+ 2023-11-16 01:42:30,166 epoch 4 - iter 4500/7500 - loss 0.29266567 - time (sec): 551.50 - samples/sec: 262.68 - lr: 0.000004 - momentum: 0.000000
130
+ 2023-11-16 01:44:03,529 epoch 4 - iter 5250/7500 - loss 0.29429266 - time (sec): 644.87 - samples/sec: 262.58 - lr: 0.000004 - momentum: 0.000000
131
+ 2023-11-16 01:45:36,284 epoch 4 - iter 6000/7500 - loss 0.29201070 - time (sec): 737.62 - samples/sec: 261.99 - lr: 0.000003 - momentum: 0.000000
132
+ 2023-11-16 01:47:11,437 epoch 4 - iter 6750/7500 - loss 0.29527772 - time (sec): 832.77 - samples/sec: 260.57 - lr: 0.000003 - momentum: 0.000000
133
+ 2023-11-16 01:48:47,245 epoch 4 - iter 7500/7500 - loss 0.29729031 - time (sec): 928.58 - samples/sec: 259.32 - lr: 0.000003 - momentum: 0.000000
134
+ 2023-11-16 01:48:47,248 ----------------------------------------------------------------------------------------------------
135
+ 2023-11-16 01:48:47,248 EPOCH 4 done: loss 0.2973 - lr: 0.000003
136
+ 2023-11-16 01:49:14,914 DEV : loss 0.3016064167022705 - f1-score (micro avg) 0.88
137
+ 2023-11-16 01:49:16,910 ----------------------------------------------------------------------------------------------------
138
+ 2023-11-16 01:50:49,496 epoch 5 - iter 750/7500 - loss 0.25377931 - time (sec): 92.58 - samples/sec: 262.37 - lr: 0.000003 - momentum: 0.000000
139
+ 2023-11-16 01:52:20,605 epoch 5 - iter 1500/7500 - loss 0.25135538 - time (sec): 183.69 - samples/sec: 265.72 - lr: 0.000003 - momentum: 0.000000
140
+ 2023-11-16 01:53:53,577 epoch 5 - iter 2250/7500 - loss 0.25580791 - time (sec): 276.66 - samples/sec: 262.18 - lr: 0.000003 - momentum: 0.000000
141
+ 2023-11-16 01:55:26,799 epoch 5 - iter 3000/7500 - loss 0.25964203 - time (sec): 369.89 - samples/sec: 260.86 - lr: 0.000003 - momentum: 0.000000
142
+ 2023-11-16 01:57:00,788 epoch 5 - iter 3750/7500 - loss 0.26132921 - time (sec): 463.87 - samples/sec: 259.14 - lr: 0.000003 - momentum: 0.000000
143
+ 2023-11-16 01:58:33,972 epoch 5 - iter 4500/7500 - loss 0.26036055 - time (sec): 557.06 - samples/sec: 258.72 - lr: 0.000003 - momentum: 0.000000
144
+ 2023-11-16 02:00:09,582 epoch 5 - iter 5250/7500 - loss 0.25878497 - time (sec): 652.67 - samples/sec: 257.81 - lr: 0.000003 - momentum: 0.000000
145
+ 2023-11-16 02:01:48,359 epoch 5 - iter 6000/7500 - loss 0.25604978 - time (sec): 751.45 - samples/sec: 256.54 - lr: 0.000003 - momentum: 0.000000
146
+ 2023-11-16 02:03:25,431 epoch 5 - iter 6750/7500 - loss 0.25651438 - time (sec): 848.52 - samples/sec: 255.47 - lr: 0.000003 - momentum: 0.000000
147
+ 2023-11-16 02:04:59,400 epoch 5 - iter 7500/7500 - loss 0.25488422 - time (sec): 942.49 - samples/sec: 255.49 - lr: 0.000003 - momentum: 0.000000
148
+ 2023-11-16 02:04:59,403 ----------------------------------------------------------------------------------------------------
149
+ 2023-11-16 02:04:59,403 EPOCH 5 done: loss 0.2549 - lr: 0.000003
150
+ 2023-11-16 02:05:26,452 DEV : loss 0.3108203411102295 - f1-score (micro avg) 0.8923
151
+ 2023-11-16 02:05:28,547 saving best model
152
+ 2023-11-16 02:05:31,087 ----------------------------------------------------------------------------------------------------
153
+ 2023-11-16 02:07:03,915 epoch 6 - iter 750/7500 - loss 0.20941918 - time (sec): 92.82 - samples/sec: 258.36 - lr: 0.000003 - momentum: 0.000000
154
+ 2023-11-16 02:08:34,259 epoch 6 - iter 1500/7500 - loss 0.20871668 - time (sec): 183.17 - samples/sec: 261.51 - lr: 0.000003 - momentum: 0.000000
155
+ 2023-11-16 02:10:06,286 epoch 6 - iter 2250/7500 - loss 0.21719166 - time (sec): 275.20 - samples/sec: 261.07 - lr: 0.000003 - momentum: 0.000000
156
+ 2023-11-16 02:11:38,611 epoch 6 - iter 3000/7500 - loss 0.22345226 - time (sec): 367.52 - samples/sec: 260.76 - lr: 0.000003 - momentum: 0.000000
157
+ 2023-11-16 02:13:13,015 epoch 6 - iter 3750/7500 - loss 0.21948790 - time (sec): 461.92 - samples/sec: 260.22 - lr: 0.000003 - momentum: 0.000000
158
+ 2023-11-16 02:14:48,651 epoch 6 - iter 4500/7500 - loss 0.22195698 - time (sec): 557.56 - samples/sec: 257.83 - lr: 0.000002 - momentum: 0.000000
159
+ 2023-11-16 02:16:23,096 epoch 6 - iter 5250/7500 - loss 0.22241666 - time (sec): 652.01 - samples/sec: 257.73 - lr: 0.000002 - momentum: 0.000000
160
+ 2023-11-16 02:17:57,154 epoch 6 - iter 6000/7500 - loss 0.22130923 - time (sec): 746.06 - samples/sec: 258.53 - lr: 0.000002 - momentum: 0.000000
161
+ 2023-11-16 02:19:29,542 epoch 6 - iter 6750/7500 - loss 0.21994780 - time (sec): 838.45 - samples/sec: 258.60 - lr: 0.000002 - momentum: 0.000000
162
+ 2023-11-16 02:21:01,165 epoch 6 - iter 7500/7500 - loss 0.21770578 - time (sec): 930.07 - samples/sec: 258.90 - lr: 0.000002 - momentum: 0.000000
163
+ 2023-11-16 02:21:01,168 ----------------------------------------------------------------------------------------------------
164
+ 2023-11-16 02:21:01,168 EPOCH 6 done: loss 0.2177 - lr: 0.000002
165
+ 2023-11-16 02:21:28,850 DEV : loss 0.31180956959724426 - f1-score (micro avg) 0.8955
166
+ 2023-11-16 02:21:31,381 saving best model
167
+ 2023-11-16 02:21:34,381 ----------------------------------------------------------------------------------------------------
168
+ 2023-11-16 02:23:08,670 epoch 7 - iter 750/7500 - loss 0.17025570 - time (sec): 94.28 - samples/sec: 253.94 - lr: 0.000002 - momentum: 0.000000
169
+ 2023-11-16 02:24:44,294 epoch 7 - iter 1500/7500 - loss 0.18032455 - time (sec): 189.91 - samples/sec: 253.38 - lr: 0.000002 - momentum: 0.000000
170
+ 2023-11-16 02:26:19,207 epoch 7 - iter 2250/7500 - loss 0.18368583 - time (sec): 284.82 - samples/sec: 253.98 - lr: 0.000002 - momentum: 0.000000
171
+ 2023-11-16 02:27:52,445 epoch 7 - iter 3000/7500 - loss 0.18638293 - time (sec): 378.06 - samples/sec: 254.49 - lr: 0.000002 - momentum: 0.000000
172
+ 2023-11-16 02:29:25,782 epoch 7 - iter 3750/7500 - loss 0.18144838 - time (sec): 471.40 - samples/sec: 255.30 - lr: 0.000002 - momentum: 0.000000
173
+ 2023-11-16 02:30:59,415 epoch 7 - iter 4500/7500 - loss 0.18697815 - time (sec): 565.03 - samples/sec: 255.93 - lr: 0.000002 - momentum: 0.000000
174
+ 2023-11-16 02:32:31,970 epoch 7 - iter 5250/7500 - loss 0.18690520 - time (sec): 657.58 - samples/sec: 256.63 - lr: 0.000002 - momentum: 0.000000
175
+ 2023-11-16 02:34:05,459 epoch 7 - iter 6000/7500 - loss 0.18389577 - time (sec): 751.07 - samples/sec: 256.38 - lr: 0.000002 - momentum: 0.000000
176
+ 2023-11-16 02:35:40,741 epoch 7 - iter 6750/7500 - loss 0.18345948 - time (sec): 846.36 - samples/sec: 255.76 - lr: 0.000002 - momentum: 0.000000
177
+ 2023-11-16 02:37:17,970 epoch 7 - iter 7500/7500 - loss 0.18350743 - time (sec): 943.58 - samples/sec: 255.19 - lr: 0.000002 - momentum: 0.000000
178
+ 2023-11-16 02:37:17,972 ----------------------------------------------------------------------------------------------------
179
+ 2023-11-16 02:37:17,972 EPOCH 7 done: loss 0.1835 - lr: 0.000002
180
+ 2023-11-16 02:37:45,548 DEV : loss 0.31052064895629883 - f1-score (micro avg) 0.901
181
+ 2023-11-16 02:37:47,535 saving best model
182
+ 2023-11-16 02:37:49,958 ----------------------------------------------------------------------------------------------------
183
+ 2023-11-16 02:39:25,797 epoch 8 - iter 750/7500 - loss 0.14917987 - time (sec): 95.84 - samples/sec: 255.82 - lr: 0.000002 - momentum: 0.000000
184
+ 2023-11-16 02:40:58,825 epoch 8 - iter 1500/7500 - loss 0.16554104 - time (sec): 188.86 - samples/sec: 254.74 - lr: 0.000002 - momentum: 0.000000
185
+ 2023-11-16 02:42:33,026 epoch 8 - iter 2250/7500 - loss 0.16246413 - time (sec): 283.06 - samples/sec: 254.01 - lr: 0.000002 - momentum: 0.000000
186
+ 2023-11-16 02:44:05,013 epoch 8 - iter 3000/7500 - loss 0.15793136 - time (sec): 375.05 - samples/sec: 254.92 - lr: 0.000001 - momentum: 0.000000
187
+ 2023-11-16 02:45:37,970 epoch 8 - iter 3750/7500 - loss 0.15705842 - time (sec): 468.01 - samples/sec: 255.77 - lr: 0.000001 - momentum: 0.000000
188
+ 2023-11-16 02:47:09,872 epoch 8 - iter 4500/7500 - loss 0.15757577 - time (sec): 559.91 - samples/sec: 256.37 - lr: 0.000001 - momentum: 0.000000
189
+ 2023-11-16 02:48:43,951 epoch 8 - iter 5250/7500 - loss 0.15530409 - time (sec): 653.99 - samples/sec: 256.53 - lr: 0.000001 - momentum: 0.000000
190
+ 2023-11-16 02:50:15,610 epoch 8 - iter 6000/7500 - loss 0.15633332 - time (sec): 745.65 - samples/sec: 258.01 - lr: 0.000001 - momentum: 0.000000
191
+ 2023-11-16 02:51:49,707 epoch 8 - iter 6750/7500 - loss 0.15781340 - time (sec): 839.75 - samples/sec: 258.51 - lr: 0.000001 - momentum: 0.000000
192
+ 2023-11-16 02:53:22,758 epoch 8 - iter 7500/7500 - loss 0.15738201 - time (sec): 932.80 - samples/sec: 258.14 - lr: 0.000001 - momentum: 0.000000
193
+ 2023-11-16 02:53:22,761 ----------------------------------------------------------------------------------------------------
194
+ 2023-11-16 02:53:22,761 EPOCH 8 done: loss 0.1574 - lr: 0.000001
195
+ 2023-11-16 02:53:49,642 DEV : loss 0.31349387764930725 - f1-score (micro avg) 0.9012
196
+ 2023-11-16 02:53:51,537 saving best model
197
+ 2023-11-16 02:53:53,925 ----------------------------------------------------------------------------------------------------
198
+ 2023-11-16 02:55:30,620 epoch 9 - iter 750/7500 - loss 0.12454614 - time (sec): 96.69 - samples/sec: 248.06 - lr: 0.000001 - momentum: 0.000000
199
+ 2023-11-16 02:57:02,449 epoch 9 - iter 1500/7500 - loss 0.12492215 - time (sec): 188.52 - samples/sec: 254.80 - lr: 0.000001 - momentum: 0.000000
200
+ 2023-11-16 02:58:34,812 epoch 9 - iter 2250/7500 - loss 0.13002767 - time (sec): 280.88 - samples/sec: 257.65 - lr: 0.000001 - momentum: 0.000000
201
+ 2023-11-16 03:00:06,592 epoch 9 - iter 3000/7500 - loss 0.12936109 - time (sec): 372.66 - samples/sec: 259.06 - lr: 0.000001 - momentum: 0.000000
202
+ 2023-11-16 03:01:36,302 epoch 9 - iter 3750/7500 - loss 0.12949282 - time (sec): 462.37 - samples/sec: 260.59 - lr: 0.000001 - momentum: 0.000000
203
+ 2023-11-16 03:03:09,938 epoch 9 - iter 4500/7500 - loss 0.13100535 - time (sec): 556.01 - samples/sec: 259.28 - lr: 0.000001 - momentum: 0.000000
204
+ 2023-11-16 03:04:42,142 epoch 9 - iter 5250/7500 - loss 0.13241958 - time (sec): 648.21 - samples/sec: 259.22 - lr: 0.000001 - momentum: 0.000000
205
+ 2023-11-16 03:06:15,744 epoch 9 - iter 6000/7500 - loss 0.13197722 - time (sec): 741.81 - samples/sec: 260.12 - lr: 0.000001 - momentum: 0.000000
206
+ 2023-11-16 03:07:52,365 epoch 9 - iter 6750/7500 - loss 0.13101789 - time (sec): 838.44 - samples/sec: 258.40 - lr: 0.000001 - momentum: 0.000000
207
+ 2023-11-16 03:09:30,882 epoch 9 - iter 7500/7500 - loss 0.13192127 - time (sec): 936.95 - samples/sec: 257.00 - lr: 0.000001 - momentum: 0.000000
208
+ 2023-11-16 03:09:30,885 ----------------------------------------------------------------------------------------------------
209
+ 2023-11-16 03:09:30,886 EPOCH 9 done: loss 0.1319 - lr: 0.000001
210
+ 2023-11-16 03:09:58,661 DEV : loss 0.3276961147785187 - f1-score (micro avg) 0.9002
211
+ 2023-11-16 03:10:01,082 ----------------------------------------------------------------------------------------------------
212
+ 2023-11-16 03:11:36,851 epoch 10 - iter 750/7500 - loss 0.10811317 - time (sec): 95.77 - samples/sec: 255.65 - lr: 0.000001 - momentum: 0.000000
213
+ 2023-11-16 03:13:11,188 epoch 10 - iter 1500/7500 - loss 0.10879497 - time (sec): 190.10 - samples/sec: 251.97 - lr: 0.000000 - momentum: 0.000000
214
+ 2023-11-16 03:14:44,600 epoch 10 - iter 2250/7500 - loss 0.11179486 - time (sec): 283.52 - samples/sec: 253.42 - lr: 0.000000 - momentum: 0.000000
215
+ 2023-11-16 03:16:16,891 epoch 10 - iter 3000/7500 - loss 0.11110255 - time (sec): 375.81 - samples/sec: 256.07 - lr: 0.000000 - momentum: 0.000000
216
+ 2023-11-16 03:17:51,280 epoch 10 - iter 3750/7500 - loss 0.11617599 - time (sec): 470.20 - samples/sec: 254.90 - lr: 0.000000 - momentum: 0.000000
217
+ 2023-11-16 03:19:22,531 epoch 10 - iter 4500/7500 - loss 0.11661813 - time (sec): 561.45 - samples/sec: 256.54 - lr: 0.000000 - momentum: 0.000000
218
+ 2023-11-16 03:20:53,037 epoch 10 - iter 5250/7500 - loss 0.11803804 - time (sec): 651.95 - samples/sec: 257.89 - lr: 0.000000 - momentum: 0.000000
219
+ 2023-11-16 03:22:24,015 epoch 10 - iter 6000/7500 - loss 0.11722958 - time (sec): 742.93 - samples/sec: 258.60 - lr: 0.000000 - momentum: 0.000000
220
+ 2023-11-16 03:23:55,236 epoch 10 - iter 6750/7500 - loss 0.11710786 - time (sec): 834.15 - samples/sec: 260.07 - lr: 0.000000 - momentum: 0.000000
221
+ 2023-11-16 03:25:24,728 epoch 10 - iter 7500/7500 - loss 0.11716487 - time (sec): 923.64 - samples/sec: 260.70 - lr: 0.000000 - momentum: 0.000000
222
+ 2023-11-16 03:25:24,731 ----------------------------------------------------------------------------------------------------
223
+ 2023-11-16 03:25:24,731 EPOCH 10 done: loss 0.1172 - lr: 0.000000
224
+ 2023-11-16 03:25:51,758 DEV : loss 0.32983964681625366 - f1-score (micro avg) 0.9006
225
+ 2023-11-16 03:25:55,590 ----------------------------------------------------------------------------------------------------
226
+ 2023-11-16 03:25:55,592 Loading model from best epoch ...
227
+ 2023-11-16 03:26:03,736 SequenceTagger predicts: Dictionary with 13 tags: O, S-LOC, B-LOC, E-LOC, I-LOC, S-ORG, B-ORG, E-ORG, I-ORG, S-PER, B-PER, E-PER, I-PER
228
+ 2023-11-16 03:26:31,850
229
+ Results:
230
+ - F-score (micro) 0.9027
231
+ - F-score (macro) 0.9014
232
+ - Accuracy 0.8521
233
+
234
+ By class:
235
+ precision recall f1-score support
236
+
237
+ LOC 0.9036 0.9141 0.9088 5288
238
+ PER 0.9238 0.9427 0.9332 3962
239
+ ORG 0.8593 0.8650 0.8622 3807
240
+
241
+ micro avg 0.8969 0.9085 0.9027 13057
242
+ macro avg 0.8956 0.9073 0.9014 13057
243
+ weighted avg 0.8968 0.9085 0.9026 13057
244
+
245
+ 2023-11-16 03:26:31,850 ----------------------------------------------------------------------------------------------------