Hugging Face's logo

stefan-it
/
hmbench-letemps-fr-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2

Token Classification
Flair
PyTorch
TensorBoard
French
sequence-tagger-model
Model card Files Metrics Community
stefan-it commited on
Commit
9b6507c
·
1 Parent(s): d61dc18

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.1697541186.4aef72135bc5.1113.4 +3 -0
  5. test.tsv +0 -0
  6. training.log +236 -0
best-model.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d38b77d25d77dabfbd2060514d83bd618e1ba550323d7fb418757478123175ce
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 11:16:57 0.0000 0.3077 0.1089 0.4840 0.6590 0.5581 0.3910
3
+ 2 11:20:53 0.0000 0.0996 0.1604 0.5471 0.8181 0.6557 0.4955
4
+ 3 11:24:47 0.0000 0.0773 0.1964 0.5633 0.7437 0.6410 0.4779
5
+ 4 11:28:37 0.0000 0.0571 0.2212 0.5709 0.7368 0.6434 0.4820
6
+ 5 11:32:29 0.0000 0.0394 0.2980 0.5502 0.8021 0.6527 0.4940
7
+ 6 11:36:27 0.0000 0.0291 0.3385 0.5511 0.7654 0.6408 0.4782
8
+ 7 11:40:18 0.0000 0.0216 0.3538 0.5514 0.7918 0.6501 0.4894
9
+ 8 11:44:21 0.0000 0.0130 0.3859 0.5507 0.8009 0.6527 0.4930
10
+ 9 11:48:19 0.0000 0.0083 0.4002 0.5684 0.7986 0.6641 0.5040
11
+ 10 11:52:15 0.0000 0.0053 0.4150 0.5612 0.8021 0.6604 0.4993
runs/events.out.tfevents.1697541186.4aef72135bc5.1113.4 ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:bf7fceff5327bb9e266861ee934b94fb84bdffe1ac368ec6111279059d382a36
3
+ size 2030580
test.tsv ADDED
The diff for this file is too large to render. See raw diff
 
training.log ADDED
@@ -0,0 +1,236 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 2023-10-17 11:13:06,040 ----------------------------------------------------------------------------------------------------
2
+ 2023-10-17 11:13:06,042 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 11:13:06,042 ----------------------------------------------------------------------------------------------------
47
+ 2023-10-17 11:13:06,042 MultiCorpus: 14465 train + 1392 dev + 2432 test sentences
48
+ - NER_HIPE_2022 Corpus: 14465 train + 1392 dev + 2432 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/letemps/fr/with_doc_seperator
49
+ 2023-10-17 11:13:06,042 ----------------------------------------------------------------------------------------------------
50
+ 2023-10-17 11:13:06,042 Train: 14465 sentences
51
+ 2023-10-17 11:13:06,042 (train_with_dev=False, train_with_test=False)
52
+ 2023-10-17 11:13:06,042 ----------------------------------------------------------------------------------------------------
53
+ 2023-10-17 11:13:06,042 Training Params:
54
+ 2023-10-17 11:13:06,042 - learning_rate: "3e-05"
55
+ 2023-10-17 11:13:06,042 - mini_batch_size: "4"
56
+ 2023-10-17 11:13:06,042 - max_epochs: "10"
57
+ 2023-10-17 11:13:06,042 - shuffle: "True"
58
+ 2023-10-17 11:13:06,042 ----------------------------------------------------------------------------------------------------
59
+ 2023-10-17 11:13:06,042 Plugins:
60
+ 2023-10-17 11:13:06,042 - TensorboardLogger
61
+ 2023-10-17 11:13:06,042 - LinearScheduler | warmup_fraction: '0.1'
62
+ 2023-10-17 11:13:06,042 ----------------------------------------------------------------------------------------------------
63
+ 2023-10-17 11:13:06,042 Final evaluation on model from best epoch (best-model.pt)
64
+ 2023-10-17 11:13:06,042 - metric: "('micro avg', 'f1-score')"
65
+ 2023-10-17 11:13:06,043 ----------------------------------------------------------------------------------------------------
66
+ 2023-10-17 11:13:06,043 Computation:
67
+ 2023-10-17 11:13:06,043 - compute on device: cuda:0
68
+ 2023-10-17 11:13:06,043 - embedding storage: none
69
+ 2023-10-17 11:13:06,043 ----------------------------------------------------------------------------------------------------
70
+ 2023-10-17 11:13:06,043 Model training base path: "hmbench-letemps/fr-hmteams/teams-base-historic-multilingual-discriminator-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2"
71
+ 2023-10-17 11:13:06,043 ----------------------------------------------------------------------------------------------------
72
+ 2023-10-17 11:13:06,043 ----------------------------------------------------------------------------------------------------
73
+ 2023-10-17 11:13:06,043 Logging anything other than scalars to TensorBoard is currently not supported.
74
+ 2023-10-17 11:13:29,167 epoch 1 - iter 361/3617 - loss 1.78814431 - time (sec): 23.12 - samples/sec: 1638.45 - lr: 0.000003 - momentum: 0.000000
75
+ 2023-10-17 11:13:51,939 epoch 1 - iter 722/3617 - loss 1.02838317 - time (sec): 45.89 - samples/sec: 1619.95 - lr: 0.000006 - momentum: 0.000000
76
+ 2023-10-17 11:14:14,528 epoch 1 - iter 1083/3617 - loss 0.72995508 - time (sec): 68.48 - samples/sec: 1666.05 - lr: 0.000009 - momentum: 0.000000
77
+ 2023-10-17 11:14:36,884 epoch 1 - iter 1444/3617 - loss 0.58463242 - time (sec): 90.84 - samples/sec: 1681.06 - lr: 0.000012 - momentum: 0.000000
78
+ 2023-10-17 11:14:59,161 epoch 1 - iter 1805/3617 - loss 0.49786262 - time (sec): 113.12 - samples/sec: 1688.72 - lr: 0.000015 - momentum: 0.000000
79
+ 2023-10-17 11:15:21,931 epoch 1 - iter 2166/3617 - loss 0.43668822 - time (sec): 135.89 - samples/sec: 1681.37 - lr: 0.000018 - momentum: 0.000000
80
+ 2023-10-17 11:15:44,174 epoch 1 - iter 2527/3617 - loss 0.38974770 - time (sec): 158.13 - samples/sec: 1685.61 - lr: 0.000021 - momentum: 0.000000
81
+ 2023-10-17 11:16:06,276 epoch 1 - iter 2888/3617 - loss 0.35471982 - time (sec): 180.23 - samples/sec: 1697.15 - lr: 0.000024 - momentum: 0.000000
82
+ 2023-10-17 11:16:29,303 epoch 1 - iter 3249/3617 - loss 0.32794931 - time (sec): 203.26 - samples/sec: 1690.14 - lr: 0.000027 - momentum: 0.000000
83
+ 2023-10-17 11:16:51,321 epoch 1 - iter 3610/3617 - loss 0.30809550 - time (sec): 225.28 - samples/sec: 1683.37 - lr: 0.000030 - momentum: 0.000000
84
+ 2023-10-17 11:16:51,740 ----------------------------------------------------------------------------------------------------
85
+ 2023-10-17 11:16:51,740 EPOCH 1 done: loss 0.3077 - lr: 0.000030
86
+ 2023-10-17 11:16:57,177 DEV : loss 0.10886511206626892 - f1-score (micro avg) 0.5581
87
+ 2023-10-17 11:16:57,218 saving best model
88
+ 2023-10-17 11:16:57,714 ----------------------------------------------------------------------------------------------------
89
+ 2023-10-17 11:17:21,078 epoch 2 - iter 361/3617 - loss 0.09908194 - time (sec): 23.36 - samples/sec: 1665.79 - lr: 0.000030 - momentum: 0.000000
90
+ 2023-10-17 11:17:43,704 epoch 2 - iter 722/3617 - loss 0.09370839 - time (sec): 45.99 - samples/sec: 1678.56 - lr: 0.000029 - momentum: 0.000000
91
+ 2023-10-17 11:18:04,258 epoch 2 - iter 1083/3617 - loss 0.09669946 - time (sec): 66.54 - samples/sec: 1715.28 - lr: 0.000029 - momentum: 0.000000
92
+ 2023-10-17 11:18:27,278 epoch 2 - iter 1444/3617 - loss 0.09690415 - time (sec): 89.56 - samples/sec: 1693.33 - lr: 0.000029 - momentum: 0.000000
93
+ 2023-10-17 11:18:50,785 epoch 2 - iter 1805/3617 - loss 0.09640509 - time (sec): 113.07 - samples/sec: 1667.49 - lr: 0.000028 - momentum: 0.000000
94
+ 2023-10-17 11:19:13,316 epoch 2 - iter 2166/3617 - loss 0.09736145 - time (sec): 135.60 - samples/sec: 1673.97 - lr: 0.000028 - momentum: 0.000000
95
+ 2023-10-17 11:19:36,497 epoch 2 - iter 2527/3617 - loss 0.09724732 - time (sec): 158.78 - samples/sec: 1674.03 - lr: 0.000028 - momentum: 0.000000
96
+ 2023-10-17 11:19:59,357 epoch 2 - iter 2888/3617 - loss 0.09753849 - time (sec): 181.64 - samples/sec: 1671.08 - lr: 0.000027 - momentum: 0.000000
97
+ 2023-10-17 11:20:21,693 epoch 2 - iter 3249/3617 - loss 0.09835920 - time (sec): 203.98 - samples/sec: 1678.98 - lr: 0.000027 - momentum: 0.000000
98
+ 2023-10-17 11:20:44,900 epoch 2 - iter 3610/3617 - loss 0.09956665 - time (sec): 227.18 - samples/sec: 1669.58 - lr: 0.000027 - momentum: 0.000000
99
+ 2023-10-17 11:20:45,322 ----------------------------------------------------------------------------------------------------
100
+ 2023-10-17 11:20:45,323 EPOCH 2 done: loss 0.0996 - lr: 0.000027
101
+ 2023-10-17 11:20:53,342 DEV : loss 0.16037893295288086 - f1-score (micro avg) 0.6557
102
+ 2023-10-17 11:20:53,398 saving best model
103
+ 2023-10-17 11:20:54,092 ----------------------------------------------------------------------------------------------------
104
+ 2023-10-17 11:21:17,522 epoch 3 - iter 361/3617 - loss 0.07564517 - time (sec): 23.43 - samples/sec: 1569.42 - lr: 0.000026 - momentum: 0.000000
105
+ 2023-10-17 11:21:40,082 epoch 3 - iter 722/3617 - loss 0.07332119 - time (sec): 45.99 - samples/sec: 1636.38 - lr: 0.000026 - momentum: 0.000000
106
+ 2023-10-17 11:22:02,267 epoch 3 - iter 1083/3617 - loss 0.07222144 - time (sec): 68.17 - samples/sec: 1661.66 - lr: 0.000026 - momentum: 0.000000
107
+ 2023-10-17 11:22:25,386 epoch 3 - iter 1444/3617 - loss 0.07619382 - time (sec): 91.29 - samples/sec: 1655.98 - lr: 0.000025 - momentum: 0.000000
108
+ 2023-10-17 11:22:48,049 epoch 3 - iter 1805/3617 - loss 0.07401567 - time (sec): 113.95 - samples/sec: 1657.30 - lr: 0.000025 - momentum: 0.000000
109
+ 2023-10-17 11:23:11,429 epoch 3 - iter 2166/3617 - loss 0.07403687 - time (sec): 137.33 - samples/sec: 1661.13 - lr: 0.000025 - momentum: 0.000000
110
+ 2023-10-17 11:23:34,867 epoch 3 - iter 2527/3617 - loss 0.07637370 - time (sec): 160.77 - samples/sec: 1646.45 - lr: 0.000024 - momentum: 0.000000
111
+ 2023-10-17 11:23:56,997 epoch 3 - iter 2888/3617 - loss 0.07722497 - time (sec): 182.90 - samples/sec: 1656.67 - lr: 0.000024 - momentum: 0.000000
112
+ 2023-10-17 11:24:18,777 epoch 3 - iter 3249/3617 - loss 0.07737109 - time (sec): 204.68 - samples/sec: 1671.08 - lr: 0.000024 - momentum: 0.000000
113
+ 2023-10-17 11:24:40,574 epoch 3 - iter 3610/3617 - loss 0.07738280 - time (sec): 226.48 - samples/sec: 1674.38 - lr: 0.000023 - momentum: 0.000000
114
+ 2023-10-17 11:24:41,006 ----------------------------------------------------------------------------------------------------
115
+ 2023-10-17 11:24:41,007 EPOCH 3 done: loss 0.0773 - lr: 0.000023
116
+ 2023-10-17 11:24:47,385 DEV : loss 0.19637347757816315 - f1-score (micro avg) 0.641
117
+ 2023-10-17 11:24:47,429 ----------------------------------------------------------------------------------------------------
118
+ 2023-10-17 11:25:07,646 epoch 4 - iter 361/3617 - loss 0.05411431 - time (sec): 20.22 - samples/sec: 1910.72 - lr: 0.000023 - momentum: 0.000000
119
+ 2023-10-17 11:25:31,466 epoch 4 - iter 722/3617 - loss 0.05682246 - time (sec): 44.03 - samples/sec: 1731.33 - lr: 0.000023 - momentum: 0.000000
120
+ 2023-10-17 11:25:53,516 epoch 4 - iter 1083/3617 - loss 0.05717665 - time (sec): 66.09 - samples/sec: 1747.56 - lr: 0.000022 - momentum: 0.000000
121
+ 2023-10-17 11:26:15,688 epoch 4 - iter 1444/3617 - loss 0.05618963 - time (sec): 88.26 - samples/sec: 1736.28 - lr: 0.000022 - momentum: 0.000000
122
+ 2023-10-17 11:26:38,155 epoch 4 - iter 1805/3617 - loss 0.05712367 - time (sec): 110.72 - samples/sec: 1724.46 - lr: 0.000022 - momentum: 0.000000
123
+ 2023-10-17 11:27:00,949 epoch 4 - iter 2166/3617 - loss 0.05692867 - time (sec): 133.52 - samples/sec: 1712.86 - lr: 0.000021 - momentum: 0.000000
124
+ 2023-10-17 11:27:23,467 epoch 4 - iter 2527/3617 - loss 0.05681678 - time (sec): 156.04 - samples/sec: 1709.15 - lr: 0.000021 - momentum: 0.000000
125
+ 2023-10-17 11:27:46,046 epoch 4 - iter 2888/3617 - loss 0.05638457 - time (sec): 178.62 - samples/sec: 1709.53 - lr: 0.000021 - momentum: 0.000000
126
+ 2023-10-17 11:28:08,185 epoch 4 - iter 3249/3617 - loss 0.05669655 - time (sec): 200.75 - samples/sec: 1707.91 - lr: 0.000020 - momentum: 0.000000
127
+ 2023-10-17 11:28:29,984 epoch 4 - iter 3610/3617 - loss 0.05711627 - time (sec): 222.55 - samples/sec: 1704.86 - lr: 0.000020 - momentum: 0.000000
128
+ 2023-10-17 11:28:30,384 ----------------------------------------------------------------------------------------------------
129
+ 2023-10-17 11:28:30,384 EPOCH 4 done: loss 0.0571 - lr: 0.000020
130
+ 2023-10-17 11:28:37,543 DEV : loss 0.22119659185409546 - f1-score (micro avg) 0.6434
131
+ 2023-10-17 11:28:37,584 ----------------------------------------------------------------------------------------------------
132
+ 2023-10-17 11:28:58,907 epoch 5 - iter 361/3617 - loss 0.02823394 - time (sec): 21.32 - samples/sec: 1779.84 - lr: 0.000020 - momentum: 0.000000
133
+ 2023-10-17 11:29:20,770 epoch 5 - iter 722/3617 - loss 0.03377243 - time (sec): 43.18 - samples/sec: 1773.25 - lr: 0.000019 - momentum: 0.000000
134
+ 2023-10-17 11:29:43,045 epoch 5 - iter 1083/3617 - loss 0.03634121 - time (sec): 65.46 - samples/sec: 1746.02 - lr: 0.000019 - momentum: 0.000000
135
+ 2023-10-17 11:30:05,310 epoch 5 - iter 1444/3617 - loss 0.03734307 - time (sec): 87.72 - samples/sec: 1736.47 - lr: 0.000019 - momentum: 0.000000
136
+ 2023-10-17 11:30:28,900 epoch 5 - iter 1805/3617 - loss 0.03666498 - time (sec): 111.31 - samples/sec: 1721.54 - lr: 0.000018 - momentum: 0.000000
137
+ 2023-10-17 11:30:51,224 epoch 5 - iter 2166/3617 - loss 0.03718897 - time (sec): 133.64 - samples/sec: 1727.19 - lr: 0.000018 - momentum: 0.000000
138
+ 2023-10-17 11:31:13,996 epoch 5 - iter 2527/3617 - loss 0.03914152 - time (sec): 156.41 - samples/sec: 1708.55 - lr: 0.000018 - momentum: 0.000000
139
+ 2023-10-17 11:31:36,302 epoch 5 - iter 2888/3617 - loss 0.03939405 - time (sec): 178.72 - samples/sec: 1693.34 - lr: 0.000017 - momentum: 0.000000
140
+ 2023-10-17 11:31:58,970 epoch 5 - iter 3249/3617 - loss 0.03829028 - time (sec): 201.38 - samples/sec: 1694.76 - lr: 0.000017 - momentum: 0.000000
141
+ 2023-10-17 11:32:22,708 epoch 5 - iter 3610/3617 - loss 0.03943816 - time (sec): 225.12 - samples/sec: 1683.96 - lr: 0.000017 - momentum: 0.000000
142
+ 2023-10-17 11:32:23,170 ----------------------------------------------------------------------------------------------------
143
+ 2023-10-17 11:32:23,170 EPOCH 5 done: loss 0.0394 - lr: 0.000017
144
+ 2023-10-17 11:32:29,478 DEV : loss 0.29799073934555054 - f1-score (micro avg) 0.6527
145
+ 2023-10-17 11:32:29,521 ----------------------------------------------------------------------------------------------------
146
+ 2023-10-17 11:32:52,384 epoch 6 - iter 361/3617 - loss 0.02573007 - time (sec): 22.86 - samples/sec: 1637.30 - lr: 0.000016 - momentum: 0.000000
147
+ 2023-10-17 11:33:15,775 epoch 6 - iter 722/3617 - loss 0.02511481 - time (sec): 46.25 - samples/sec: 1615.72 - lr: 0.000016 - momentum: 0.000000
148
+ 2023-10-17 11:33:38,654 epoch 6 - iter 1083/3617 - loss 0.02682181 - time (sec): 69.13 - samples/sec: 1631.61 - lr: 0.000016 - momentum: 0.000000
149
+ 2023-10-17 11:34:02,271 epoch 6 - iter 1444/3617 - loss 0.02846690 - time (sec): 92.75 - samples/sec: 1630.95 - lr: 0.000015 - momentum: 0.000000
150
+ 2023-10-17 11:34:25,914 epoch 6 - iter 1805/3617 - loss 0.02838906 - time (sec): 116.39 - samples/sec: 1631.91 - lr: 0.000015 - momentum: 0.000000
151
+ 2023-10-17 11:34:49,761 epoch 6 - iter 2166/3617 - loss 0.02877434 - time (sec): 140.24 - samples/sec: 1626.20 - lr: 0.000015 - momentum: 0.000000
152
+ 2023-10-17 11:35:13,140 epoch 6 - iter 2527/3617 - loss 0.02838507 - time (sec): 163.62 - samples/sec: 1627.41 - lr: 0.000014 - momentum: 0.000000
153
+ 2023-10-17 11:35:35,438 epoch 6 - iter 2888/3617 - loss 0.02895275 - time (sec): 185.92 - samples/sec: 1635.29 - lr: 0.000014 - momentum: 0.000000
154
+ 2023-10-17 11:35:58,001 epoch 6 - iter 3249/3617 - loss 0.02881247 - time (sec): 208.48 - samples/sec: 1636.41 - lr: 0.000014 - momentum: 0.000000
155
+ 2023-10-17 11:36:21,047 epoch 6 - iter 3610/3617 - loss 0.02904294 - time (sec): 231.52 - samples/sec: 1638.64 - lr: 0.000013 - momentum: 0.000000
156
+ 2023-10-17 11:36:21,475 ----------------------------------------------------------------------------------------------------
157
+ 2023-10-17 11:36:21,475 EPOCH 6 done: loss 0.0291 - lr: 0.000013
158
+ 2023-10-17 11:36:27,876 DEV : loss 0.3385041356086731 - f1-score (micro avg) 0.6408
159
+ 2023-10-17 11:36:27,920 ----------------------------------------------------------------------------------------------------
160
+ 2023-10-17 11:36:50,238 epoch 7 - iter 361/3617 - loss 0.01432701 - time (sec): 22.32 - samples/sec: 1700.15 - lr: 0.000013 - momentum: 0.000000
161
+ 2023-10-17 11:37:10,807 epoch 7 - iter 722/3617 - loss 0.01555267 - time (sec): 42.88 - samples/sec: 1756.46 - lr: 0.000013 - momentum: 0.000000
162
+ 2023-10-17 11:37:34,793 epoch 7 - iter 1083/3617 - loss 0.01777501 - time (sec): 66.87 - samples/sec: 1692.09 - lr: 0.000012 - momentum: 0.000000
163
+ 2023-10-17 11:37:56,843 epoch 7 - iter 1444/3617 - loss 0.02027093 - time (sec): 88.92 - samples/sec: 1699.03 - lr: 0.000012 - momentum: 0.000000
164
+ 2023-10-17 11:38:19,092 epoch 7 - iter 1805/3617 - loss 0.01980969 - time (sec): 111.17 - samples/sec: 1702.08 - lr: 0.000012 - momentum: 0.000000
165
+ 2023-10-17 11:38:41,205 epoch 7 - iter 2166/3617 - loss 0.02022525 - time (sec): 133.28 - samples/sec: 1702.67 - lr: 0.000011 - momentum: 0.000000
166
+ 2023-10-17 11:39:04,065 epoch 7 - iter 2527/3617 - loss 0.02165812 - time (sec): 156.14 - samples/sec: 1700.48 - lr: 0.000011 - momentum: 0.000000
167
+ 2023-10-17 11:39:27,133 epoch 7 - iter 2888/3617 - loss 0.02180513 - time (sec): 179.21 - samples/sec: 1692.36 - lr: 0.000011 - momentum: 0.000000
168
+ 2023-10-17 11:39:49,393 epoch 7 - iter 3249/3617 - loss 0.02148273 - time (sec): 201.47 - samples/sec: 1697.80 - lr: 0.000010 - momentum: 0.000000
169
+ 2023-10-17 11:40:11,734 epoch 7 - iter 3610/3617 - loss 0.02149257 - time (sec): 223.81 - samples/sec: 1694.80 - lr: 0.000010 - momentum: 0.000000
170
+ 2023-10-17 11:40:12,185 ----------------------------------------------------------------------------------------------------
171
+ 2023-10-17 11:40:12,186 EPOCH 7 done: loss 0.0216 - lr: 0.000010
172
+ 2023-10-17 11:40:18,549 DEV : loss 0.3538167476654053 - f1-score (micro avg) 0.6501
173
+ 2023-10-17 11:40:18,591 ----------------------------------------------------------------------------------------------------
174
+ 2023-10-17 11:40:40,700 epoch 8 - iter 361/3617 - loss 0.01192797 - time (sec): 22.11 - samples/sec: 1705.35 - lr: 0.000010 - momentum: 0.000000
175
+ 2023-10-17 11:41:03,756 epoch 8 - iter 722/3617 - loss 0.01213621 - time (sec): 45.16 - samples/sec: 1658.34 - lr: 0.000009 - momentum: 0.000000
176
+ 2023-10-17 11:41:27,927 epoch 8 - iter 1083/3617 - loss 0.01286573 - time (sec): 69.33 - samples/sec: 1624.59 - lr: 0.000009 - momentum: 0.000000
177
+ 2023-10-17 11:41:51,754 epoch 8 - iter 1444/3617 - loss 0.01313113 - time (sec): 93.16 - samples/sec: 1629.06 - lr: 0.000009 - momentum: 0.000000
178
+ 2023-10-17 11:42:16,517 epoch 8 - iter 1805/3617 - loss 0.01306628 - time (sec): 117.92 - samples/sec: 1601.35 - lr: 0.000008 - momentum: 0.000000
179
+ 2023-10-17 11:42:41,872 epoch 8 - iter 2166/3617 - loss 0.01391368 - time (sec): 143.28 - samples/sec: 1578.71 - lr: 0.000008 - momentum: 0.000000
180
+ 2023-10-17 11:43:06,573 epoch 8 - iter 2527/3617 - loss 0.01391572 - time (sec): 167.98 - samples/sec: 1569.84 - lr: 0.000008 - momentum: 0.000000
181
+ 2023-10-17 11:43:30,349 epoch 8 - iter 2888/3617 - loss 0.01336401 - time (sec): 191.76 - samples/sec: 1576.08 - lr: 0.000007 - momentum: 0.000000
182
+ 2023-10-17 11:43:51,479 epoch 8 - iter 3249/3617 - loss 0.01320290 - time (sec): 212.89 - samples/sec: 1603.34 - lr: 0.000007 - momentum: 0.000000
183
+ 2023-10-17 11:44:14,085 epoch 8 - iter 3610/3617 - loss 0.01301754 - time (sec): 235.49 - samples/sec: 1610.19 - lr: 0.000007 - momentum: 0.000000
184
+ 2023-10-17 11:44:14,508 ----------------------------------------------------------------------------------------------------
185
+ 2023-10-17 11:44:14,508 EPOCH 8 done: loss 0.0130 - lr: 0.000007
186
+ 2023-10-17 11:44:20,974 DEV : loss 0.3859119117259979 - f1-score (micro avg) 0.6527
187
+ 2023-10-17 11:44:21,026 ----------------------------------------------------------------------------------------------------
188
+ 2023-10-17 11:44:46,418 epoch 9 - iter 361/3617 - loss 0.00859029 - time (sec): 25.39 - samples/sec: 1498.46 - lr: 0.000006 - momentum: 0.000000
189
+ 2023-10-17 11:45:09,053 epoch 9 - iter 722/3617 - loss 0.00920184 - time (sec): 48.02 - samples/sec: 1550.20 - lr: 0.000006 - momentum: 0.000000
190
+ 2023-10-17 11:45:31,985 epoch 9 - iter 1083/3617 - loss 0.00936040 - time (sec): 70.96 - samples/sec: 1569.14 - lr: 0.000006 - momentum: 0.000000
191
+ 2023-10-17 11:45:55,056 epoch 9 - iter 1444/3617 - loss 0.00967607 - time (sec): 94.03 - samples/sec: 1585.73 - lr: 0.000005 - momentum: 0.000000
192
+ 2023-10-17 11:46:18,172 epoch 9 - iter 1805/3617 - loss 0.00907964 - time (sec): 117.14 - samples/sec: 1605.14 - lr: 0.000005 - momentum: 0.000000
193
+ 2023-10-17 11:46:41,668 epoch 9 - iter 2166/3617 - loss 0.00893534 - time (sec): 140.64 - samples/sec: 1607.77 - lr: 0.000005 - momentum: 0.000000
194
+ 2023-10-17 11:47:04,925 epoch 9 - iter 2527/3617 - loss 0.00849365 - time (sec): 163.90 - samples/sec: 1612.69 - lr: 0.000004 - momentum: 0.000000
195
+ 2023-10-17 11:47:28,072 epoch 9 - iter 2888/3617 - loss 0.00835917 - time (sec): 187.04 - samples/sec: 1618.16 - lr: 0.000004 - momentum: 0.000000
196
+ 2023-10-17 11:47:51,051 epoch 9 - iter 3249/3617 - loss 0.00834899 - time (sec): 210.02 - samples/sec: 1619.02 - lr: 0.000004 - momentum: 0.000000
197
+ 2023-10-17 11:48:12,830 epoch 9 - iter 3610/3617 - loss 0.00831963 - time (sec): 231.80 - samples/sec: 1635.73 - lr: 0.000003 - momentum: 0.000000
198
+ 2023-10-17 11:48:13,250 ----------------------------------------------------------------------------------------------------
199
+ 2023-10-17 11:48:13,251 EPOCH 9 done: loss 0.0083 - lr: 0.000003
200
+ 2023-10-17 11:48:19,769 DEV : loss 0.4002314805984497 - f1-score (micro avg) 0.6641
201
+ 2023-10-17 11:48:19,813 saving best model
202
+ 2023-10-17 11:48:20,421 ----------------------------------------------------------------------------------------------------
203
+ 2023-10-17 11:48:42,618 epoch 10 - iter 361/3617 - loss 0.00672252 - time (sec): 22.20 - samples/sec: 1651.90 - lr: 0.000003 - momentum: 0.000000
204
+ 2023-10-17 11:49:05,425 epoch 10 - iter 722/3617 - loss 0.00611528 - time (sec): 45.00 - samples/sec: 1679.01 - lr: 0.000003 - momentum: 0.000000
205
+ 2023-10-17 11:49:28,674 epoch 10 - iter 1083/3617 - loss 0.00518175 - time (sec): 68.25 - samples/sec: 1637.70 - lr: 0.000002 - momentum: 0.000000
206
+ 2023-10-17 11:49:51,468 epoch 10 - iter 1444/3617 - loss 0.00501517 - time (sec): 91.04 - samples/sec: 1652.46 - lr: 0.000002 - momentum: 0.000000
207
+ 2023-10-17 11:50:13,801 epoch 10 - iter 1805/3617 - loss 0.00526015 - time (sec): 113.38 - samples/sec: 1658.65 - lr: 0.000002 - momentum: 0.000000
208
+ 2023-10-17 11:50:33,920 epoch 10 - iter 2166/3617 - loss 0.00556562 - time (sec): 133.50 - samples/sec: 1695.49 - lr: 0.000001 - momentum: 0.000000
209
+ 2023-10-17 11:50:57,467 epoch 10 - iter 2527/3617 - loss 0.00515816 - time (sec): 157.04 - samples/sec: 1674.52 - lr: 0.000001 - momentum: 0.000000
210
+ 2023-10-17 11:51:21,410 epoch 10 - iter 2888/3617 - loss 0.00538365 - time (sec): 180.99 - samples/sec: 1670.23 - lr: 0.000001 - momentum: 0.000000
211
+ 2023-10-17 11:51:44,574 epoch 10 - iter 3249/3617 - loss 0.00546926 - time (sec): 204.15 - samples/sec: 1673.63 - lr: 0.000000 - momentum: 0.000000
212
+ 2023-10-17 11:52:07,470 epoch 10 - iter 3610/3617 - loss 0.00532840 - time (sec): 227.05 - samples/sec: 1670.42 - lr: 0.000000 - momentum: 0.000000
213
+ 2023-10-17 11:52:07,907 ----------------------------------------------------------------------------------------------------
214
+ 2023-10-17 11:52:07,908 EPOCH 10 done: loss 0.0053 - lr: 0.000000
215
+ 2023-10-17 11:52:15,079 DEV : loss 0.4149819612503052 - f1-score (micro avg) 0.6604
216
+ 2023-10-17 11:52:15,631 ----------------------------------------------------------------------------------------------------
217
+ 2023-10-17 11:52:15,632 Loading model from best epoch ...
218
+ 2023-10-17 11:52:17,415 SequenceTagger predicts: Dictionary with 13 tags: O, S-loc, B-loc, E-loc, I-loc, S-pers, B-pers, E-pers, I-pers, S-org, B-org, E-org, I-org
219
+ 2023-10-17 11:52:25,444
220
+ Results:
221
+ - F-score (micro) 0.6525
222
+ - F-score (macro) 0.5043
223
+ - Accuracy 0.498
224
+
225
+ By class:
226
+ precision recall f1-score support
227
+
228
+ loc 0.6511 0.7800 0.7098 591
229
+ pers 0.5768 0.7367 0.6470 357
230
+ org 0.1774 0.1392 0.1560 79
231
+
232
+ micro avg 0.5995 0.7157 0.6525 1027
233
+ macro avg 0.4684 0.5520 0.5043 1027
234
+ weighted avg 0.5888 0.7157 0.6454 1027
235
+
236
+ 2023-10-17 11:52:25,444 ----------------------------------------------------------------------------------------------------