Hugging Face's logo

stefan-it
/
hmbench-newseye-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-2

Token Classification
Flair
PyTorch
TensorBoard
French
sequence-tagger-model
Model card Files Metrics Community
2
stefan-it commited on
Commit
23a9d2a
•
1 Parent(s): 1d7b0c1

Upload folder using huggingface_hub

Browse files
Files changed (7) hide show
  1. best-model.pt +3 -0
  2. dev.tsv +0 -0
  3. final-model.pt +3 -0
  4. loss.tsv +11 -0
  5. runs/events.out.tfevents.1697006363.de2e83fddbee.1120.7 +3 -0
  6. test.tsv +0 -0
  7. training.log +264 -0
best-model.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:88976c62f17c9a36badaab7a065a068a34cbf75c40f674436d3a25917c101f7b
3
+ size 870817519
dev.tsv ADDED
The diff for this file is too large to render. See raw diff
 
final-model.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:5f653dede83e8d80cc749aa2a4a103f4f5ca5237528de58a056374b4a2ef2150
3
+ size 870817636
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 06:48:48 0.0002 1.1242 0.2134 0.4597 0.4816 0.4704 0.3444
3
+ 2 06:57:53 0.0001 0.1394 0.1204 0.7062 0.7946 0.7478 0.6141
4
+ 3 07:07:36 0.0001 0.0763 0.1229 0.7583 0.7810 0.7694 0.6457
5
+ 4 07:17:11 0.0001 0.0533 0.1557 0.7759 0.8054 0.7904 0.6727
6
+ 5 07:26:59 0.0001 0.0393 0.1652 0.7936 0.8054 0.7995 0.6812
7
+ 6 07:36:42 0.0001 0.0290 0.1687 0.7791 0.8109 0.7947 0.6742
8
+ 7 07:46:32 0.0001 0.0201 0.1944 0.7844 0.8068 0.7954 0.6777
9
+ 8 07:56:17 0.0000 0.0167 0.1955 0.7965 0.8095 0.8030 0.6887
10
+ 9 08:06:04 0.0000 0.0104 0.2133 0.7850 0.8095 0.7971 0.6761
11
+ 10 08:15:53 0.0000 0.0078 0.2169 0.7893 0.8054 0.7973 0.6766
runs/events.out.tfevents.1697006363.de2e83fddbee.1120.7 ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:14b5a1e0eba00d19cad957cc506608f77dec5ba925b8ada8d2e9dc4d09792ee5
3
+ size 999862
test.tsv ADDED
The diff for this file is too large to render. See raw diff
 
training.log ADDED
@@ -0,0 +1,264 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 2023-10-11 06:39:23,992 ----------------------------------------------------------------------------------------------------
2
+ 2023-10-11 06:39:23,994 Model: "SequenceTagger(
3
+ (embeddings): ByT5Embeddings(
4
+ (model): T5EncoderModel(
5
+ (shared): Embedding(384, 1472)
6
+ (encoder): T5Stack(
7
+ (embed_tokens): Embedding(384, 1472)
8
+ (block): ModuleList(
9
+ (0): T5Block(
10
+ (layer): ModuleList(
11
+ (0): T5LayerSelfAttention(
12
+ (SelfAttention): T5Attention(
13
+ (q): Linear(in_features=1472, out_features=384, bias=False)
14
+ (k): Linear(in_features=1472, out_features=384, bias=False)
15
+ (v): Linear(in_features=1472, out_features=384, bias=False)
16
+ (o): Linear(in_features=384, out_features=1472, bias=False)
17
+ (relative_attention_bias): Embedding(32, 6)
18
+ )
19
+ (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
20
+ (dropout): Dropout(p=0.1, inplace=False)
21
+ )
22
+ (1): T5LayerFF(
23
+ (DenseReluDense): T5DenseGatedActDense(
24
+ (wi_0): Linear(in_features=1472, out_features=3584, bias=False)
25
+ (wi_1): Linear(in_features=1472, out_features=3584, bias=False)
26
+ (wo): Linear(in_features=3584, out_features=1472, bias=False)
27
+ (dropout): Dropout(p=0.1, inplace=False)
28
+ (act): NewGELUActivation()
29
+ )
30
+ (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
31
+ (dropout): Dropout(p=0.1, inplace=False)
32
+ )
33
+ )
34
+ )
35
+ (1-11): 11 x T5Block(
36
+ (layer): ModuleList(
37
+ (0): T5LayerSelfAttention(
38
+ (SelfAttention): T5Attention(
39
+ (q): Linear(in_features=1472, out_features=384, bias=False)
40
+ (k): Linear(in_features=1472, out_features=384, bias=False)
41
+ (v): Linear(in_features=1472, out_features=384, bias=False)
42
+ (o): Linear(in_features=384, out_features=1472, bias=False)
43
+ )
44
+ (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
45
+ (dropout): Dropout(p=0.1, inplace=False)
46
+ )
47
+ (1): T5LayerFF(
48
+ (DenseReluDense): T5DenseGatedActDense(
49
+ (wi_0): Linear(in_features=1472, out_features=3584, bias=False)
50
+ (wi_1): Linear(in_features=1472, out_features=3584, bias=False)
51
+ (wo): Linear(in_features=3584, out_features=1472, bias=False)
52
+ (dropout): Dropout(p=0.1, inplace=False)
53
+ (act): NewGELUActivation()
54
+ )
55
+ (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
56
+ (dropout): Dropout(p=0.1, inplace=False)
57
+ )
58
+ )
59
+ )
60
+ )
61
+ (final_layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
62
+ (dropout): Dropout(p=0.1, inplace=False)
63
+ )
64
+ )
65
+ )
66
+ (locked_dropout): LockedDropout(p=0.5)
67
+ (linear): Linear(in_features=1472, out_features=17, bias=True)
68
+ (loss_function): CrossEntropyLoss()
69
+ )"
70
+ 2023-10-11 06:39:23,994 ----------------------------------------------------------------------------------------------------
71
+ 2023-10-11 06:39:23,995 MultiCorpus: 7142 train + 698 dev + 2570 test sentences
72
+ - NER_HIPE_2022 Corpus: 7142 train + 698 dev + 2570 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/fr/with_doc_seperator
73
+ 2023-10-11 06:39:23,995 ----------------------------------------------------------------------------------------------------
74
+ 2023-10-11 06:39:23,995 Train: 7142 sentences
75
+ 2023-10-11 06:39:23,995 (train_with_dev=False, train_with_test=False)
76
+ 2023-10-11 06:39:23,995 ----------------------------------------------------------------------------------------------------
77
+ 2023-10-11 06:39:23,995 Training Params:
78
+ 2023-10-11 06:39:23,995 - learning_rate: "0.00016"
79
+ 2023-10-11 06:39:23,995 - mini_batch_size: "4"
80
+ 2023-10-11 06:39:23,995 - max_epochs: "10"
81
+ 2023-10-11 06:39:23,995 - shuffle: "True"
82
+ 2023-10-11 06:39:23,995 ----------------------------------------------------------------------------------------------------
83
+ 2023-10-11 06:39:23,995 Plugins:
84
+ 2023-10-11 06:39:23,995 - TensorboardLogger
85
+ 2023-10-11 06:39:23,995 - LinearScheduler | warmup_fraction: '0.1'
86
+ 2023-10-11 06:39:23,995 ----------------------------------------------------------------------------------------------------
87
+ 2023-10-11 06:39:23,996 Final evaluation on model from best epoch (best-model.pt)
88
+ 2023-10-11 06:39:23,996 - metric: "('micro avg', 'f1-score')"
89
+ 2023-10-11 06:39:23,996 ----------------------------------------------------------------------------------------------------
90
+ 2023-10-11 06:39:23,996 Computation:
91
+ 2023-10-11 06:39:23,996 - compute on device: cuda:0
92
+ 2023-10-11 06:39:23,996 - embedding storage: none
93
+ 2023-10-11 06:39:23,996 ----------------------------------------------------------------------------------------------------
94
+ 2023-10-11 06:39:23,996 Model training base path: "hmbench-newseye/fr-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-2"
95
+ 2023-10-11 06:39:23,996 ----------------------------------------------------------------------------------------------------
96
+ 2023-10-11 06:39:23,996 ----------------------------------------------------------------------------------------------------
97
+ 2023-10-11 06:39:23,996 Logging anything other than scalars to TensorBoard is currently not supported.
98
+ 2023-10-11 06:40:19,912 epoch 1 - iter 178/1786 - loss 2.82900261 - time (sec): 55.91 - samples/sec: 474.93 - lr: 0.000016 - momentum: 0.000000
99
+ 2023-10-11 06:41:14,029 epoch 1 - iter 356/1786 - loss 2.67404787 - time (sec): 110.03 - samples/sec: 470.57 - lr: 0.000032 - momentum: 0.000000
100
+ 2023-10-11 06:42:09,632 epoch 1 - iter 534/1786 - loss 2.38436083 - time (sec): 165.63 - samples/sec: 468.96 - lr: 0.000048 - momentum: 0.000000
101
+ 2023-10-11 06:43:05,404 epoch 1 - iter 712/1786 - loss 2.06699231 - time (sec): 221.41 - samples/sec: 464.97 - lr: 0.000064 - momentum: 0.000000
102
+ 2023-10-11 06:44:01,010 epoch 1 - iter 890/1786 - loss 1.80287645 - time (sec): 277.01 - samples/sec: 459.40 - lr: 0.000080 - momentum: 0.000000
103
+ 2023-10-11 06:44:56,410 epoch 1 - iter 1068/1786 - loss 1.59842649 - time (sec): 332.41 - samples/sec: 460.39 - lr: 0.000096 - momentum: 0.000000
104
+ 2023-10-11 06:45:50,950 epoch 1 - iter 1246/1786 - loss 1.43900727 - time (sec): 386.95 - samples/sec: 459.78 - lr: 0.000112 - momentum: 0.000000
105
+ 2023-10-11 06:46:43,346 epoch 1 - iter 1424/1786 - loss 1.31683155 - time (sec): 439.35 - samples/sec: 457.72 - lr: 0.000127 - momentum: 0.000000
106
+ 2023-10-11 06:47:34,731 epoch 1 - iter 1602/1786 - loss 1.21390367 - time (sec): 490.73 - samples/sec: 457.50 - lr: 0.000143 - momentum: 0.000000
107
+ 2023-10-11 06:48:27,379 epoch 1 - iter 1780/1786 - loss 1.12581674 - time (sec): 543.38 - samples/sec: 456.87 - lr: 0.000159 - momentum: 0.000000
108
+ 2023-10-11 06:48:28,795 ----------------------------------------------------------------------------------------------------
109
+ 2023-10-11 06:48:28,795 EPOCH 1 done: loss 1.1242 - lr: 0.000159
110
+ 2023-10-11 06:48:48,043 DEV : loss 0.21339952945709229 - f1-score (micro avg) 0.4704
111
+ 2023-10-11 06:48:48,073 saving best model
112
+ 2023-10-11 06:48:48,971 ----------------------------------------------------------------------------------------------------
113
+ 2023-10-11 06:49:40,643 epoch 2 - iter 178/1786 - loss 0.20730434 - time (sec): 51.67 - samples/sec: 486.40 - lr: 0.000158 - momentum: 0.000000
114
+ 2023-10-11 06:50:33,708 epoch 2 - iter 356/1786 - loss 0.19312469 - time (sec): 104.73 - samples/sec: 491.93 - lr: 0.000156 - momentum: 0.000000
115
+ 2023-10-11 06:51:25,317 epoch 2 - iter 534/1786 - loss 0.18172318 - time (sec): 156.34 - samples/sec: 488.33 - lr: 0.000155 - momentum: 0.000000
116
+ 2023-10-11 06:52:16,339 epoch 2 - iter 712/1786 - loss 0.17387846 - time (sec): 207.37 - samples/sec: 484.71 - lr: 0.000153 - momentum: 0.000000
117
+ 2023-10-11 06:53:07,904 epoch 2 - iter 890/1786 - loss 0.16698512 - time (sec): 258.93 - samples/sec: 481.09 - lr: 0.000151 - momentum: 0.000000
118
+ 2023-10-11 06:53:58,781 epoch 2 - iter 1068/1786 - loss 0.16051997 - time (sec): 309.81 - samples/sec: 479.18 - lr: 0.000149 - momentum: 0.000000
119
+ 2023-10-11 06:54:49,112 epoch 2 - iter 1246/1786 - loss 0.15405773 - time (sec): 360.14 - samples/sec: 477.91 - lr: 0.000148 - momentum: 0.000000
120
+ 2023-10-11 06:55:41,208 epoch 2 - iter 1424/1786 - loss 0.14923648 - time (sec): 412.23 - samples/sec: 478.67 - lr: 0.000146 - momentum: 0.000000
121
+ 2023-10-11 06:56:35,914 epoch 2 - iter 1602/1786 - loss 0.14482467 - time (sec): 466.94 - samples/sec: 478.44 - lr: 0.000144 - momentum: 0.000000
122
+ 2023-10-11 06:57:29,822 epoch 2 - iter 1780/1786 - loss 0.13951975 - time (sec): 520.85 - samples/sec: 476.15 - lr: 0.000142 - momentum: 0.000000
123
+ 2023-10-11 06:57:31,487 ----------------------------------------------------------------------------------------------------
124
+ 2023-10-11 06:57:31,487 EPOCH 2 done: loss 0.1394 - lr: 0.000142
125
+ 2023-10-11 06:57:53,454 DEV : loss 0.12035670131444931 - f1-score (micro avg) 0.7478
126
+ 2023-10-11 06:57:53,485 saving best model
127
+ 2023-10-11 06:57:56,090 ----------------------------------------------------------------------------------------------------
128
+ 2023-10-11 06:58:49,895 epoch 3 - iter 178/1786 - loss 0.07648250 - time (sec): 53.80 - samples/sec: 480.38 - lr: 0.000140 - momentum: 0.000000
129
+ 2023-10-11 06:59:42,427 epoch 3 - iter 356/1786 - loss 0.07475891 - time (sec): 106.33 - samples/sec: 457.73 - lr: 0.000139 - momentum: 0.000000
130
+ 2023-10-11 07:00:36,639 epoch 3 - iter 534/1786 - loss 0.07760944 - time (sec): 160.54 - samples/sec: 461.01 - lr: 0.000137 - momentum: 0.000000
131
+ 2023-10-11 07:01:30,450 epoch 3 - iter 712/1786 - loss 0.07885752 - time (sec): 214.36 - samples/sec: 458.24 - lr: 0.000135 - momentum: 0.000000
132
+ 2023-10-11 07:02:24,329 epoch 3 - iter 890/1786 - loss 0.07553472 - time (sec): 268.23 - samples/sec: 458.26 - lr: 0.000133 - momentum: 0.000000
133
+ 2023-10-11 07:03:19,934 epoch 3 - iter 1068/1786 - loss 0.07803250 - time (sec): 323.84 - samples/sec: 457.48 - lr: 0.000132 - momentum: 0.000000
134
+ 2023-10-11 07:04:18,636 epoch 3 - iter 1246/1786 - loss 0.07541694 - time (sec): 382.54 - samples/sec: 456.93 - lr: 0.000130 - momentum: 0.000000
135
+ 2023-10-11 07:05:16,926 epoch 3 - iter 1424/1786 - loss 0.07566700 - time (sec): 440.83 - samples/sec: 452.91 - lr: 0.000128 - momentum: 0.000000
136
+ 2023-10-11 07:06:14,488 epoch 3 - iter 1602/1786 - loss 0.07521801 - time (sec): 498.39 - samples/sec: 449.09 - lr: 0.000126 - momentum: 0.000000
137
+ 2023-10-11 07:07:11,671 epoch 3 - iter 1780/1786 - loss 0.07599665 - time (sec): 555.58 - samples/sec: 446.70 - lr: 0.000125 - momentum: 0.000000
138
+ 2023-10-11 07:07:13,371 ----------------------------------------------------------------------------------------------------
139
+ 2023-10-11 07:07:13,371 EPOCH 3 done: loss 0.0763 - lr: 0.000125
140
+ 2023-10-11 07:07:36,874 DEV : loss 0.12293554842472076 - f1-score (micro avg) 0.7694
141
+ 2023-10-11 07:07:36,908 saving best model
142
+ 2023-10-11 07:07:39,619 ----------------------------------------------------------------------------------------------------
143
+ 2023-10-11 07:08:32,498 epoch 4 - iter 178/1786 - loss 0.06175889 - time (sec): 52.87 - samples/sec: 468.15 - lr: 0.000123 - momentum: 0.000000
144
+ 2023-10-11 07:09:25,152 epoch 4 - iter 356/1786 - loss 0.05291025 - time (sec): 105.53 - samples/sec: 453.32 - lr: 0.000121 - momentum: 0.000000
145
+ 2023-10-11 07:10:25,644 epoch 4 - iter 534/1786 - loss 0.05155940 - time (sec): 166.02 - samples/sec: 445.22 - lr: 0.000119 - momentum: 0.000000
146
+ 2023-10-11 07:11:24,877 epoch 4 - iter 712/1786 - loss 0.05289251 - time (sec): 225.25 - samples/sec: 446.72 - lr: 0.000117 - momentum: 0.000000
147
+ 2023-10-11 07:12:16,721 epoch 4 - iter 890/1786 - loss 0.05247741 - time (sec): 277.10 - samples/sec: 446.69 - lr: 0.000116 - momentum: 0.000000
148
+ 2023-10-11 07:13:08,568 epoch 4 - iter 1068/1786 - loss 0.05140588 - time (sec): 328.94 - samples/sec: 451.81 - lr: 0.000114 - momentum: 0.000000
149
+ 2023-10-11 07:14:02,792 epoch 4 - iter 1246/1786 - loss 0.05313146 - time (sec): 383.17 - samples/sec: 455.46 - lr: 0.000112 - momentum: 0.000000
150
+ 2023-10-11 07:14:58,620 epoch 4 - iter 1424/1786 - loss 0.05344596 - time (sec): 439.00 - samples/sec: 452.79 - lr: 0.000110 - momentum: 0.000000
151
+ 2023-10-11 07:15:55,417 epoch 4 - iter 1602/1786 - loss 0.05307124 - time (sec): 495.79 - samples/sec: 450.00 - lr: 0.000109 - momentum: 0.000000
152
+ 2023-10-11 07:16:48,635 epoch 4 - iter 1780/1786 - loss 0.05340878 - time (sec): 549.01 - samples/sec: 452.21 - lr: 0.000107 - momentum: 0.000000
153
+ 2023-10-11 07:16:50,081 ----------------------------------------------------------------------------------------------------
154
+ 2023-10-11 07:16:50,081 EPOCH 4 done: loss 0.0533 - lr: 0.000107
155
+ 2023-10-11 07:17:11,531 DEV : loss 0.15573516488075256 - f1-score (micro avg) 0.7904
156
+ 2023-10-11 07:17:11,562 saving best model
157
+ 2023-10-11 07:17:14,160 ----------------------------------------------------------------------------------------------------
158
+ 2023-10-11 07:18:09,045 epoch 5 - iter 178/1786 - loss 0.04583648 - time (sec): 54.87 - samples/sec: 453.51 - lr: 0.000105 - momentum: 0.000000
159
+ 2023-10-11 07:19:01,699 epoch 5 - iter 356/1786 - loss 0.04299453 - time (sec): 107.53 - samples/sec: 442.06 - lr: 0.000103 - momentum: 0.000000
160
+ 2023-10-11 07:19:53,208 epoch 5 - iter 534/1786 - loss 0.04267254 - time (sec): 159.04 - samples/sec: 455.11 - lr: 0.000101 - momentum: 0.000000
161
+ 2023-10-11 07:20:50,338 epoch 5 - iter 712/1786 - loss 0.04135181 - time (sec): 216.17 - samples/sec: 450.26 - lr: 0.000100 - momentum: 0.000000
162
+ 2023-10-11 07:21:51,130 epoch 5 - iter 890/1786 - loss 0.04091209 - time (sec): 276.96 - samples/sec: 437.86 - lr: 0.000098 - momentum: 0.000000
163
+ 2023-10-11 07:22:51,933 epoch 5 - iter 1068/1786 - loss 0.03936224 - time (sec): 337.76 - samples/sec: 433.05 - lr: 0.000096 - momentum: 0.000000
164
+ 2023-10-11 07:23:51,852 epoch 5 - iter 1246/1786 - loss 0.03988643 - time (sec): 397.68 - samples/sec: 434.38 - lr: 0.000094 - momentum: 0.000000
165
+ 2023-10-11 07:24:47,391 epoch 5 - iter 1424/1786 - loss 0.04034676 - time (sec): 453.22 - samples/sec: 436.01 - lr: 0.000093 - momentum: 0.000000
166
+ 2023-10-11 07:25:43,025 epoch 5 - iter 1602/1786 - loss 0.03940605 - time (sec): 508.85 - samples/sec: 436.78 - lr: 0.000091 - momentum: 0.000000
167
+ 2023-10-11 07:26:37,195 epoch 5 - iter 1780/1786 - loss 0.03943045 - time (sec): 563.02 - samples/sec: 440.61 - lr: 0.000089 - momentum: 0.000000
168
+ 2023-10-11 07:26:38,788 ----------------------------------------------------------------------------------------------------
169
+ 2023-10-11 07:26:38,788 EPOCH 5 done: loss 0.0393 - lr: 0.000089
170
+ 2023-10-11 07:26:59,794 DEV : loss 0.16520686447620392 - f1-score (micro avg) 0.7995
171
+ 2023-10-11 07:26:59,824 saving best model
172
+ 2023-10-11 07:27:02,424 ----------------------------------------------------------------------------------------------------
173
+ 2023-10-11 07:27:57,899 epoch 6 - iter 178/1786 - loss 0.03094883 - time (sec): 55.47 - samples/sec: 446.32 - lr: 0.000087 - momentum: 0.000000
174
+ 2023-10-11 07:28:53,808 epoch 6 - iter 356/1786 - loss 0.03037107 - time (sec): 111.38 - samples/sec: 447.38 - lr: 0.000085 - momentum: 0.000000
175
+ 2023-10-11 07:29:48,736 epoch 6 - iter 534/1786 - loss 0.02899602 - time (sec): 166.31 - samples/sec: 447.72 - lr: 0.000084 - momentum: 0.000000
176
+ 2023-10-11 07:30:44,570 epoch 6 - iter 712/1786 - loss 0.02900632 - time (sec): 222.14 - samples/sec: 446.71 - lr: 0.000082 - momentum: 0.000000
177
+ 2023-10-11 07:31:40,780 epoch 6 - iter 890/1786 - loss 0.02906074 - time (sec): 278.35 - samples/sec: 446.06 - lr: 0.000080 - momentum: 0.000000
178
+ 2023-10-11 07:32:38,978 epoch 6 - iter 1068/1786 - loss 0.02883507 - time (sec): 336.55 - samples/sec: 446.30 - lr: 0.000078 - momentum: 0.000000
179
+ 2023-10-11 07:33:33,658 epoch 6 - iter 1246/1786 - loss 0.02715296 - time (sec): 391.23 - samples/sec: 445.77 - lr: 0.000077 - momentum: 0.000000
180
+ 2023-10-11 07:34:29,044 epoch 6 - iter 1424/1786 - loss 0.02742899 - time (sec): 446.62 - samples/sec: 446.73 - lr: 0.000075 - momentum: 0.000000
181
+ 2023-10-11 07:35:22,481 epoch 6 - iter 1602/1786 - loss 0.02809173 - time (sec): 500.05 - samples/sec: 450.53 - lr: 0.000073 - momentum: 0.000000
182
+ 2023-10-11 07:36:15,937 epoch 6 - iter 1780/1786 - loss 0.02903797 - time (sec): 553.51 - samples/sec: 448.58 - lr: 0.000071 - momentum: 0.000000
183
+ 2023-10-11 07:36:17,437 ----------------------------------------------------------------------------------------------------
184
+ 2023-10-11 07:36:17,438 EPOCH 6 done: loss 0.0290 - lr: 0.000071
185
+ 2023-10-11 07:36:42,730 DEV : loss 0.16866964101791382 - f1-score (micro avg) 0.7947
186
+ 2023-10-11 07:36:42,764 ----------------------------------------------------------------------------------------------------
187
+ 2023-10-11 07:37:41,091 epoch 7 - iter 178/1786 - loss 0.02176058 - time (sec): 58.33 - samples/sec: 469.08 - lr: 0.000069 - momentum: 0.000000
188
+ 2023-10-11 07:38:39,578 epoch 7 - iter 356/1786 - loss 0.02292926 - time (sec): 116.81 - samples/sec: 445.15 - lr: 0.000068 - momentum: 0.000000
189
+ 2023-10-11 07:39:34,898 epoch 7 - iter 534/1786 - loss 0.02247289 - time (sec): 172.13 - samples/sec: 439.81 - lr: 0.000066 - momentum: 0.000000
190
+ 2023-10-11 07:40:31,803 epoch 7 - iter 712/1786 - loss 0.02241562 - time (sec): 229.04 - samples/sec: 436.51 - lr: 0.000064 - momentum: 0.000000
191
+ 2023-10-11 07:41:27,757 epoch 7 - iter 890/1786 - loss 0.02220387 - time (sec): 284.99 - samples/sec: 436.33 - lr: 0.000062 - momentum: 0.000000
192
+ 2023-10-11 07:42:23,342 epoch 7 - iter 1068/1786 - loss 0.02137649 - time (sec): 340.58 - samples/sec: 440.01 - lr: 0.000061 - momentum: 0.000000
193
+ 2023-10-11 07:43:18,915 epoch 7 - iter 1246/1786 - loss 0.02099827 - time (sec): 396.15 - samples/sec: 438.43 - lr: 0.000059 - momentum: 0.000000
194
+ 2023-10-11 07:44:18,289 epoch 7 - iter 1424/1786 - loss 0.02038066 - time (sec): 455.52 - samples/sec: 439.21 - lr: 0.000057 - momentum: 0.000000
195
+ 2023-10-11 07:45:16,495 epoch 7 - iter 1602/1786 - loss 0.01985291 - time (sec): 513.73 - samples/sec: 437.28 - lr: 0.000055 - momentum: 0.000000
196
+ 2023-10-11 07:46:09,692 epoch 7 - iter 1780/1786 - loss 0.02003687 - time (sec): 566.93 - samples/sec: 437.53 - lr: 0.000053 - momentum: 0.000000
197
+ 2023-10-11 07:46:11,314 ----------------------------------------------------------------------------------------------------
198
+ 2023-10-11 07:46:11,314 EPOCH 7 done: loss 0.0201 - lr: 0.000053
199
+ 2023-10-11 07:46:32,834 DEV : loss 0.19440826773643494 - f1-score (micro avg) 0.7954
200
+ 2023-10-11 07:46:32,868 ----------------------------------------------------------------------------------------------------
201
+ 2023-10-11 07:47:26,349 epoch 8 - iter 178/1786 - loss 0.02118880 - time (sec): 53.48 - samples/sec: 450.21 - lr: 0.000052 - momentum: 0.000000
202
+ 2023-10-11 07:48:21,828 epoch 8 - iter 356/1786 - loss 0.01874784 - time (sec): 108.96 - samples/sec: 445.93 - lr: 0.000050 - momentum: 0.000000
203
+ 2023-10-11 07:49:16,307 epoch 8 - iter 534/1786 - loss 0.01722056 - time (sec): 163.44 - samples/sec: 450.09 - lr: 0.000048 - momentum: 0.000000
204
+ 2023-10-11 07:50:13,820 epoch 8 - iter 712/1786 - loss 0.01921550 - time (sec): 220.95 - samples/sec: 449.54 - lr: 0.000046 - momentum: 0.000000
205
+ 2023-10-11 07:51:10,663 epoch 8 - iter 890/1786 - loss 0.01831347 - time (sec): 277.79 - samples/sec: 444.64 - lr: 0.000044 - momentum: 0.000000
206
+ 2023-10-11 07:52:09,449 epoch 8 - iter 1068/1786 - loss 0.01900935 - time (sec): 336.58 - samples/sec: 440.68 - lr: 0.000043 - momentum: 0.000000
207
+ 2023-10-11 07:53:04,351 epoch 8 - iter 1246/1786 - loss 0.01828550 - time (sec): 391.48 - samples/sec: 441.25 - lr: 0.000041 - momentum: 0.000000
208
+ 2023-10-11 07:54:00,162 epoch 8 - iter 1424/1786 - loss 0.01722443 - time (sec): 447.29 - samples/sec: 443.60 - lr: 0.000039 - momentum: 0.000000
209
+ 2023-10-11 07:54:56,327 epoch 8 - iter 1602/1786 - loss 0.01696995 - time (sec): 503.46 - samples/sec: 442.39 - lr: 0.000037 - momentum: 0.000000
210
+ 2023-10-11 07:55:53,224 epoch 8 - iter 1780/1786 - loss 0.01668282 - time (sec): 560.35 - samples/sec: 442.21 - lr: 0.000036 - momentum: 0.000000
211
+ 2023-10-11 07:55:55,029 ----------------------------------------------------------------------------------------------------
212
+ 2023-10-11 07:55:55,030 EPOCH 8 done: loss 0.0167 - lr: 0.000036
213
+ 2023-10-11 07:56:17,077 DEV : loss 0.1955002099275589 - f1-score (micro avg) 0.803
214
+ 2023-10-11 07:56:17,108 saving best model
215
+ 2023-10-11 07:56:19,773 ----------------------------------------------------------------------------------------------------
216
+ 2023-10-11 07:57:19,182 epoch 9 - iter 178/1786 - loss 0.00753763 - time (sec): 59.40 - samples/sec: 434.60 - lr: 0.000034 - momentum: 0.000000
217
+ 2023-10-11 07:58:14,298 epoch 9 - iter 356/1786 - loss 0.01214607 - time (sec): 114.52 - samples/sec: 438.67 - lr: 0.000032 - momentum: 0.000000
218
+ 2023-10-11 07:59:07,909 epoch 9 - iter 534/1786 - loss 0.00983533 - time (sec): 168.13 - samples/sec: 445.20 - lr: 0.000030 - momentum: 0.000000
219
+ 2023-10-11 08:00:01,122 epoch 9 - iter 712/1786 - loss 0.01002213 - time (sec): 221.34 - samples/sec: 448.23 - lr: 0.000028 - momentum: 0.000000
220
+ 2023-10-11 08:00:56,745 epoch 9 - iter 890/1786 - loss 0.00888437 - time (sec): 276.97 - samples/sec: 447.41 - lr: 0.000027 - momentum: 0.000000
221
+ 2023-10-11 08:01:52,410 epoch 9 - iter 1068/1786 - loss 0.00895876 - time (sec): 332.63 - samples/sec: 446.60 - lr: 0.000025 - momentum: 0.000000
222
+ 2023-10-11 08:02:51,663 epoch 9 - iter 1246/1786 - loss 0.00900417 - time (sec): 391.89 - samples/sec: 437.63 - lr: 0.000023 - momentum: 0.000000
223
+ 2023-10-11 08:03:51,512 epoch 9 - iter 1424/1786 - loss 0.01017587 - time (sec): 451.73 - samples/sec: 436.07 - lr: 0.000021 - momentum: 0.000000
224
+ 2023-10-11 08:04:46,799 epoch 9 - iter 1602/1786 - loss 0.01074771 - time (sec): 507.02 - samples/sec: 440.42 - lr: 0.000020 - momentum: 0.000000
225
+ 2023-10-11 08:05:40,266 epoch 9 - iter 1780/1786 - loss 0.01046865 - time (sec): 560.49 - samples/sec: 442.27 - lr: 0.000018 - momentum: 0.000000
226
+ 2023-10-11 08:05:42,023 ----------------------------------------------------------------------------------------------------
227
+ 2023-10-11 08:05:42,023 EPOCH 9 done: loss 0.0104 - lr: 0.000018
228
+ 2023-10-11 08:06:04,094 DEV : loss 0.21326804161071777 - f1-score (micro avg) 0.7971
229
+ 2023-10-11 08:06:04,125 ----------------------------------------------------------------------------------------------------
230
+ 2023-10-11 08:06:58,019 epoch 10 - iter 178/1786 - loss 0.00672035 - time (sec): 53.89 - samples/sec: 436.00 - lr: 0.000016 - momentum: 0.000000
231
+ 2023-10-11 08:07:53,539 epoch 10 - iter 356/1786 - loss 0.00687932 - time (sec): 109.41 - samples/sec: 441.96 - lr: 0.000014 - momentum: 0.000000
232
+ 2023-10-11 08:08:52,210 epoch 10 - iter 534/1786 - loss 0.00685882 - time (sec): 168.08 - samples/sec: 439.86 - lr: 0.000012 - momentum: 0.000000
233
+ 2023-10-11 08:09:49,775 epoch 10 - iter 712/1786 - loss 0.00638074 - time (sec): 225.65 - samples/sec: 433.50 - lr: 0.000011 - momentum: 0.000000
234
+ 2023-10-11 08:10:46,408 epoch 10 - iter 890/1786 - loss 0.00750964 - time (sec): 282.28 - samples/sec: 439.14 - lr: 0.000009 - momentum: 0.000000
235
+ 2023-10-11 08:11:42,983 epoch 10 - iter 1068/1786 - loss 0.00846706 - time (sec): 338.86 - samples/sec: 444.69 - lr: 0.000007 - momentum: 0.000000
236
+ 2023-10-11 08:12:37,467 epoch 10 - iter 1246/1786 - loss 0.00856117 - time (sec): 393.34 - samples/sec: 441.99 - lr: 0.000005 - momentum: 0.000000
237
+ 2023-10-11 08:13:35,598 epoch 10 - iter 1424/1786 - loss 0.00826761 - time (sec): 451.47 - samples/sec: 441.54 - lr: 0.000004 - momentum: 0.000000
238
+ 2023-10-11 08:14:36,646 epoch 10 - iter 1602/1786 - loss 0.00793073 - time (sec): 512.52 - samples/sec: 436.65 - lr: 0.000002 - momentum: 0.000000
239
+ 2023-10-11 08:15:31,095 epoch 10 - iter 1780/1786 - loss 0.00780604 - time (sec): 566.97 - samples/sec: 437.59 - lr: 0.000000 - momentum: 0.000000
240
+ 2023-10-11 08:15:32,665 ----------------------------------------------------------------------------------------------------
241
+ 2023-10-11 08:15:32,666 EPOCH 10 done: loss 0.0078 - lr: 0.000000
242
+ 2023-10-11 08:15:53,506 DEV : loss 0.21688954532146454 - f1-score (micro avg) 0.7973
243
+ 2023-10-11 08:15:54,599 ----------------------------------------------------------------------------------------------------
244
+ 2023-10-11 08:15:54,601 Loading model from best epoch ...
245
+ 2023-10-11 08:15:58,969 SequenceTagger predicts: Dictionary with 17 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, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd
246
+ 2023-10-11 08:17:06,782
247
+ Results:
248
+ - F-score (micro) 0.7048
249
+ - F-score (macro) 0.6244
250
+ - Accuracy 0.5644
251
+
252
+ By class:
253
+ precision recall f1-score support
254
+
255
+ LOC 0.7416 0.7078 0.7243 1095
256
+ PER 0.7849 0.7826 0.7838 1012
257
+ ORG 0.4219 0.5826 0.4894 357
258
+ HumanProd 0.4000 0.6667 0.5000 33
259
+
260
+ micro avg 0.6906 0.7197 0.7048 2497
261
+ macro avg 0.5871 0.6849 0.6244 2497
262
+ weighted avg 0.7090 0.7197 0.7119 2497
263
+
264
+ 2023-10-11 08:17:06,782 ----------------------------------------------------------------------------------------------------