Hugging Face's logo

stefan-it
/
hmbench-hipe2020-de-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1

Token Classification
Flair
PyTorch
TensorBoard
German
sequence-tagger-model
Model card Files Metrics Community
stefan-it commited on
Commit
5cc41af
·
1 Parent(s): 45efb95

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.1697555451.4c6324b99746.1390.0 +3 -0
  5. test.tsv +0 -0
  6. training.log +243 -0
best-model.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:81b698efeab5c724d3a73a83c6a57fceeb29170f49c36e145092722bd0e02774
3
+ size 440966725
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 15:11:42 0.0000 0.8054 0.1782 0.6387 0.5473 0.5895 0.4253
3
+ 2 15:12:37 0.0000 0.1497 0.1211 0.6812 0.7568 0.7170 0.5793
4
+ 3 15:13:32 0.0000 0.0812 0.1568 0.7701 0.6654 0.7139 0.5632
5
+ 4 15:14:24 0.0000 0.0527 0.1432 0.7443 0.7873 0.7652 0.6402
6
+ 5 15:15:19 0.0000 0.0361 0.1796 0.7845 0.7514 0.7676 0.6390
7
+ 6 15:16:13 0.0000 0.0214 0.1912 0.7733 0.7920 0.7825 0.6557
8
+ 7 15:17:08 0.0000 0.0154 0.2020 0.7722 0.8108 0.7910 0.6677
9
+ 8 15:18:03 0.0000 0.0107 0.2056 0.7610 0.8092 0.7844 0.6601
10
+ 9 15:18:56 0.0000 0.0063 0.2127 0.7845 0.8084 0.7963 0.6771
11
+ 10 15:19:51 0.0000 0.0047 0.2154 0.7843 0.8100 0.7969 0.6771
runs/events.out.tfevents.1697555451.4c6324b99746.1390.0 ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:9d59fd41e1381b052835214191036659bba46f0d9729ed41a62cfbc2375044f5
3
+ size 253592
test.tsv ADDED
The diff for this file is too large to render. See raw diff
 
training.log ADDED
@@ -0,0 +1,243 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 2023-10-17 15:10:51,606 ----------------------------------------------------------------------------------------------------
2
+ 2023-10-17 15:10:51,608 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=21, bias=True)
44
+ (loss_function): CrossEntropyLoss()
45
+ )"
46
+ 2023-10-17 15:10:51,608 ----------------------------------------------------------------------------------------------------
47
+ 2023-10-17 15:10:51,608 MultiCorpus: 3575 train + 1235 dev + 1266 test sentences
48
+ - NER_HIPE_2022 Corpus: 3575 train + 1235 dev + 1266 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/hipe2020/de/with_doc_seperator
49
+ 2023-10-17 15:10:51,608 ----------------------------------------------------------------------------------------------------
50
+ 2023-10-17 15:10:51,608 Train: 3575 sentences
51
+ 2023-10-17 15:10:51,609 (train_with_dev=False, train_with_test=False)
52
+ 2023-10-17 15:10:51,609 ----------------------------------------------------------------------------------------------------
53
+ 2023-10-17 15:10:51,609 Training Params:
54
+ 2023-10-17 15:10:51,609 - learning_rate: "3e-05"
55
+ 2023-10-17 15:10:51,609 - mini_batch_size: "8"
56
+ 2023-10-17 15:10:51,609 - max_epochs: "10"
57
+ 2023-10-17 15:10:51,609 - shuffle: "True"
58
+ 2023-10-17 15:10:51,609 ----------------------------------------------------------------------------------------------------
59
+ 2023-10-17 15:10:51,609 Plugins:
60
+ 2023-10-17 15:10:51,609 - TensorboardLogger
61
+ 2023-10-17 15:10:51,609 - LinearScheduler | warmup_fraction: '0.1'
62
+ 2023-10-17 15:10:51,609 ----------------------------------------------------------------------------------------------------
63
+ 2023-10-17 15:10:51,609 Final evaluation on model from best epoch (best-model.pt)
64
+ 2023-10-17 15:10:51,610 - metric: "('micro avg', 'f1-score')"
65
+ 2023-10-17 15:10:51,610 ----------------------------------------------------------------------------------------------------
66
+ 2023-10-17 15:10:51,610 Computation:
67
+ 2023-10-17 15:10:51,610 - compute on device: cuda:0
68
+ 2023-10-17 15:10:51,610 - embedding storage: none
69
+ 2023-10-17 15:10:51,610 ----------------------------------------------------------------------------------------------------
70
+ 2023-10-17 15:10:51,610 Model training base path: "hmbench-hipe2020/de-hmteams/teams-base-historic-multilingual-discriminator-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1"
71
+ 2023-10-17 15:10:51,610 ----------------------------------------------------------------------------------------------------
72
+ 2023-10-17 15:10:51,610 ----------------------------------------------------------------------------------------------------
73
+ 2023-10-17 15:10:51,610 Logging anything other than scalars to TensorBoard is currently not supported.
74
+ 2023-10-17 15:10:56,808 epoch 1 - iter 44/447 - loss 3.46567148 - time (sec): 5.20 - samples/sec: 1711.38 - lr: 0.000003 - momentum: 0.000000
75
+ 2023-10-17 15:11:00,812 epoch 1 - iter 88/447 - loss 2.59735198 - time (sec): 9.20 - samples/sec: 1852.89 - lr: 0.000006 - momentum: 0.000000
76
+ 2023-10-17 15:11:04,862 epoch 1 - iter 132/447 - loss 1.95725490 - time (sec): 13.25 - samples/sec: 1922.63 - lr: 0.000009 - momentum: 0.000000
77
+ 2023-10-17 15:11:08,852 epoch 1 - iter 176/447 - loss 1.57353600 - time (sec): 17.24 - samples/sec: 2001.18 - lr: 0.000012 - momentum: 0.000000
78
+ 2023-10-17 15:11:13,098 epoch 1 - iter 220/447 - loss 1.32853362 - time (sec): 21.49 - samples/sec: 2016.42 - lr: 0.000015 - momentum: 0.000000
79
+ 2023-10-17 15:11:17,371 epoch 1 - iter 264/447 - loss 1.16250705 - time (sec): 25.76 - samples/sec: 2010.43 - lr: 0.000018 - momentum: 0.000000
80
+ 2023-10-17 15:11:21,615 epoch 1 - iter 308/447 - loss 1.04918854 - time (sec): 30.00 - samples/sec: 1997.94 - lr: 0.000021 - momentum: 0.000000
81
+ 2023-10-17 15:11:25,906 epoch 1 - iter 352/447 - loss 0.96192982 - time (sec): 34.29 - samples/sec: 1983.35 - lr: 0.000024 - momentum: 0.000000
82
+ 2023-10-17 15:11:30,459 epoch 1 - iter 396/447 - loss 0.87615452 - time (sec): 38.85 - samples/sec: 1991.18 - lr: 0.000027 - momentum: 0.000000
83
+ 2023-10-17 15:11:34,926 epoch 1 - iter 440/447 - loss 0.81319104 - time (sec): 43.31 - samples/sec: 1971.57 - lr: 0.000029 - momentum: 0.000000
84
+ 2023-10-17 15:11:35,605 ----------------------------------------------------------------------------------------------------
85
+ 2023-10-17 15:11:35,605 EPOCH 1 done: loss 0.8054 - lr: 0.000029
86
+ 2023-10-17 15:11:41,993 DEV : loss 0.17819160223007202 - f1-score (micro avg) 0.5895
87
+ 2023-10-17 15:11:42,045 saving best model
88
+ 2023-10-17 15:11:42,637 ----------------------------------------------------------------------------------------------------
89
+ 2023-10-17 15:11:46,828 epoch 2 - iter 44/447 - loss 0.21747760 - time (sec): 4.19 - samples/sec: 2038.97 - lr: 0.000030 - momentum: 0.000000
90
+ 2023-10-17 15:11:51,276 epoch 2 - iter 88/447 - loss 0.19904981 - time (sec): 8.64 - samples/sec: 1919.57 - lr: 0.000029 - momentum: 0.000000
91
+ 2023-10-17 15:11:55,382 epoch 2 - iter 132/447 - loss 0.19320002 - time (sec): 12.74 - samples/sec: 1949.21 - lr: 0.000029 - momentum: 0.000000
92
+ 2023-10-17 15:11:59,652 epoch 2 - iter 176/447 - loss 0.17629144 - time (sec): 17.01 - samples/sec: 1973.11 - lr: 0.000029 - momentum: 0.000000
93
+ 2023-10-17 15:12:03,802 epoch 2 - iter 220/447 - loss 0.16743674 - time (sec): 21.16 - samples/sec: 1979.03 - lr: 0.000028 - momentum: 0.000000
94
+ 2023-10-17 15:12:07,900 epoch 2 - iter 264/447 - loss 0.16450320 - time (sec): 25.26 - samples/sec: 2014.54 - lr: 0.000028 - momentum: 0.000000
95
+ 2023-10-17 15:12:12,274 epoch 2 - iter 308/447 - loss 0.16199472 - time (sec): 29.63 - samples/sec: 2010.23 - lr: 0.000028 - momentum: 0.000000
96
+ 2023-10-17 15:12:16,738 epoch 2 - iter 352/447 - loss 0.15732773 - time (sec): 34.10 - samples/sec: 2008.44 - lr: 0.000027 - momentum: 0.000000
97
+ 2023-10-17 15:12:21,021 epoch 2 - iter 396/447 - loss 0.15418639 - time (sec): 38.38 - samples/sec: 1989.87 - lr: 0.000027 - momentum: 0.000000
98
+ 2023-10-17 15:12:25,441 epoch 2 - iter 440/447 - loss 0.14935900 - time (sec): 42.80 - samples/sec: 1991.28 - lr: 0.000027 - momentum: 0.000000
99
+ 2023-10-17 15:12:26,065 ----------------------------------------------------------------------------------------------------
100
+ 2023-10-17 15:12:26,066 EPOCH 2 done: loss 0.1497 - lr: 0.000027
101
+ 2023-10-17 15:12:37,010 DEV : loss 0.121131531894207 - f1-score (micro avg) 0.717
102
+ 2023-10-17 15:12:37,066 saving best model
103
+ 2023-10-17 15:12:38,529 ----------------------------------------------------------------------------------------------------
104
+ 2023-10-17 15:12:43,063 epoch 3 - iter 44/447 - loss 0.07330655 - time (sec): 4.53 - samples/sec: 2087.82 - lr: 0.000026 - momentum: 0.000000
105
+ 2023-10-17 15:12:47,404 epoch 3 - iter 88/447 - loss 0.08406998 - time (sec): 8.87 - samples/sec: 2094.54 - lr: 0.000026 - momentum: 0.000000
106
+ 2023-10-17 15:12:51,445 epoch 3 - iter 132/447 - loss 0.08303447 - time (sec): 12.91 - samples/sec: 2060.80 - lr: 0.000026 - momentum: 0.000000
107
+ 2023-10-17 15:12:55,553 epoch 3 - iter 176/447 - loss 0.08452492 - time (sec): 17.02 - samples/sec: 2014.40 - lr: 0.000025 - momentum: 0.000000
108
+ 2023-10-17 15:12:59,615 epoch 3 - iter 220/447 - loss 0.07986201 - time (sec): 21.08 - samples/sec: 2019.64 - lr: 0.000025 - momentum: 0.000000
109
+ 2023-10-17 15:13:03,617 epoch 3 - iter 264/447 - loss 0.08080735 - time (sec): 25.08 - samples/sec: 2014.92 - lr: 0.000025 - momentum: 0.000000
110
+ 2023-10-17 15:13:07,691 epoch 3 - iter 308/447 - loss 0.08052641 - time (sec): 29.16 - samples/sec: 2012.25 - lr: 0.000024 - momentum: 0.000000
111
+ 2023-10-17 15:13:11,889 epoch 3 - iter 352/447 - loss 0.08174841 - time (sec): 33.36 - samples/sec: 2034.42 - lr: 0.000024 - momentum: 0.000000
112
+ 2023-10-17 15:13:16,242 epoch 3 - iter 396/447 - loss 0.08169126 - time (sec): 37.71 - samples/sec: 2030.32 - lr: 0.000024 - momentum: 0.000000
113
+ 2023-10-17 15:13:21,151 epoch 3 - iter 440/447 - loss 0.08007725 - time (sec): 42.62 - samples/sec: 2005.07 - lr: 0.000023 - momentum: 0.000000
114
+ 2023-10-17 15:13:21,853 ----------------------------------------------------------------------------------------------------
115
+ 2023-10-17 15:13:21,854 EPOCH 3 done: loss 0.0812 - lr: 0.000023
116
+ 2023-10-17 15:13:32,439 DEV : loss 0.15675058960914612 - f1-score (micro avg) 0.7139
117
+ 2023-10-17 15:13:32,498 ----------------------------------------------------------------------------------------------------
118
+ 2023-10-17 15:13:36,551 epoch 4 - iter 44/447 - loss 0.05960453 - time (sec): 4.05 - samples/sec: 2032.10 - lr: 0.000023 - momentum: 0.000000
119
+ 2023-10-17 15:13:40,410 epoch 4 - iter 88/447 - loss 0.05853170 - time (sec): 7.91 - samples/sec: 2075.57 - lr: 0.000023 - momentum: 0.000000
120
+ 2023-10-17 15:13:44,159 epoch 4 - iter 132/447 - loss 0.05869183 - time (sec): 11.66 - samples/sec: 2053.57 - lr: 0.000022 - momentum: 0.000000
121
+ 2023-10-17 15:13:48,179 epoch 4 - iter 176/447 - loss 0.05390845 - time (sec): 15.68 - samples/sec: 2080.79 - lr: 0.000022 - momentum: 0.000000
122
+ 2023-10-17 15:13:52,777 epoch 4 - iter 220/447 - loss 0.05440630 - time (sec): 20.28 - samples/sec: 2117.44 - lr: 0.000022 - momentum: 0.000000
123
+ 2023-10-17 15:13:56,642 epoch 4 - iter 264/447 - loss 0.05213091 - time (sec): 24.14 - samples/sec: 2122.45 - lr: 0.000021 - momentum: 0.000000
124
+ 2023-10-17 15:14:00,645 epoch 4 - iter 308/447 - loss 0.05379991 - time (sec): 28.14 - samples/sec: 2127.14 - lr: 0.000021 - momentum: 0.000000
125
+ 2023-10-17 15:14:04,887 epoch 4 - iter 352/447 - loss 0.05183150 - time (sec): 32.39 - samples/sec: 2119.64 - lr: 0.000021 - momentum: 0.000000
126
+ 2023-10-17 15:14:09,178 epoch 4 - iter 396/447 - loss 0.05387250 - time (sec): 36.68 - samples/sec: 2101.13 - lr: 0.000020 - momentum: 0.000000
127
+ 2023-10-17 15:14:13,147 epoch 4 - iter 440/447 - loss 0.05274153 - time (sec): 40.65 - samples/sec: 2095.95 - lr: 0.000020 - momentum: 0.000000
128
+ 2023-10-17 15:14:13,788 ----------------------------------------------------------------------------------------------------
129
+ 2023-10-17 15:14:13,789 EPOCH 4 done: loss 0.0527 - lr: 0.000020
130
+ 2023-10-17 15:14:24,686 DEV : loss 0.1432371437549591 - f1-score (micro avg) 0.7652
131
+ 2023-10-17 15:14:24,741 saving best model
132
+ 2023-10-17 15:14:26,201 ----------------------------------------------------------------------------------------------------
133
+ 2023-10-17 15:14:30,362 epoch 5 - iter 44/447 - loss 0.03425498 - time (sec): 4.15 - samples/sec: 2131.34 - lr: 0.000020 - momentum: 0.000000
134
+ 2023-10-17 15:14:34,382 epoch 5 - iter 88/447 - loss 0.04218378 - time (sec): 8.17 - samples/sec: 2117.81 - lr: 0.000019 - momentum: 0.000000
135
+ 2023-10-17 15:14:38,597 epoch 5 - iter 132/447 - loss 0.03552931 - time (sec): 12.39 - samples/sec: 2128.68 - lr: 0.000019 - momentum: 0.000000
136
+ 2023-10-17 15:14:42,572 epoch 5 - iter 176/447 - loss 0.03486655 - time (sec): 16.36 - samples/sec: 2084.69 - lr: 0.000019 - momentum: 0.000000
137
+ 2023-10-17 15:14:46,916 epoch 5 - iter 220/447 - loss 0.03783913 - time (sec): 20.71 - samples/sec: 2092.19 - lr: 0.000018 - momentum: 0.000000
138
+ 2023-10-17 15:14:50,784 epoch 5 - iter 264/447 - loss 0.03764549 - time (sec): 24.58 - samples/sec: 2092.49 - lr: 0.000018 - momentum: 0.000000
139
+ 2023-10-17 15:14:54,875 epoch 5 - iter 308/447 - loss 0.03645640 - time (sec): 28.67 - samples/sec: 2087.45 - lr: 0.000018 - momentum: 0.000000
140
+ 2023-10-17 15:14:58,976 epoch 5 - iter 352/447 - loss 0.03547440 - time (sec): 32.77 - samples/sec: 2087.15 - lr: 0.000017 - momentum: 0.000000
141
+ 2023-10-17 15:15:02,988 epoch 5 - iter 396/447 - loss 0.03520422 - time (sec): 36.78 - samples/sec: 2085.61 - lr: 0.000017 - momentum: 0.000000
142
+ 2023-10-17 15:15:07,284 epoch 5 - iter 440/447 - loss 0.03609226 - time (sec): 41.07 - samples/sec: 2079.54 - lr: 0.000017 - momentum: 0.000000
143
+ 2023-10-17 15:15:07,916 ----------------------------------------------------------------------------------------------------
144
+ 2023-10-17 15:15:07,916 EPOCH 5 done: loss 0.0361 - lr: 0.000017
145
+ 2023-10-17 15:15:19,049 DEV : loss 0.17960673570632935 - f1-score (micro avg) 0.7676
146
+ 2023-10-17 15:15:19,119 saving best model
147
+ 2023-10-17 15:15:19,772 ----------------------------------------------------------------------------------------------------
148
+ 2023-10-17 15:15:23,817 epoch 6 - iter 44/447 - loss 0.01947292 - time (sec): 4.04 - samples/sec: 2052.75 - lr: 0.000016 - momentum: 0.000000
149
+ 2023-10-17 15:15:28,029 epoch 6 - iter 88/447 - loss 0.02262571 - time (sec): 8.25 - samples/sec: 2033.87 - lr: 0.000016 - momentum: 0.000000
150
+ 2023-10-17 15:15:32,196 epoch 6 - iter 132/447 - loss 0.02552581 - time (sec): 12.42 - samples/sec: 2048.82 - lr: 0.000016 - momentum: 0.000000
151
+ 2023-10-17 15:15:36,588 epoch 6 - iter 176/447 - loss 0.02416652 - time (sec): 16.81 - samples/sec: 2033.48 - lr: 0.000015 - momentum: 0.000000
152
+ 2023-10-17 15:15:41,376 epoch 6 - iter 220/447 - loss 0.02289846 - time (sec): 21.60 - samples/sec: 1992.25 - lr: 0.000015 - momentum: 0.000000
153
+ 2023-10-17 15:15:45,381 epoch 6 - iter 264/447 - loss 0.02205499 - time (sec): 25.61 - samples/sec: 1982.77 - lr: 0.000015 - momentum: 0.000000
154
+ 2023-10-17 15:15:49,840 epoch 6 - iter 308/447 - loss 0.02142818 - time (sec): 30.06 - samples/sec: 2011.66 - lr: 0.000014 - momentum: 0.000000
155
+ 2023-10-17 15:15:53,927 epoch 6 - iter 352/447 - loss 0.02163330 - time (sec): 34.15 - samples/sec: 2018.37 - lr: 0.000014 - momentum: 0.000000
156
+ 2023-10-17 15:15:58,088 epoch 6 - iter 396/447 - loss 0.02092477 - time (sec): 38.31 - samples/sec: 2018.93 - lr: 0.000014 - momentum: 0.000000
157
+ 2023-10-17 15:16:02,149 epoch 6 - iter 440/447 - loss 0.02118031 - time (sec): 42.37 - samples/sec: 2012.21 - lr: 0.000013 - momentum: 0.000000
158
+ 2023-10-17 15:16:02,784 ----------------------------------------------------------------------------------------------------
159
+ 2023-10-17 15:16:02,784 EPOCH 6 done: loss 0.0214 - lr: 0.000013
160
+ 2023-10-17 15:16:13,256 DEV : loss 0.19122734665870667 - f1-score (micro avg) 0.7825
161
+ 2023-10-17 15:16:13,317 saving best model
162
+ 2023-10-17 15:16:14,798 ----------------------------------------------------------------------------------------------------
163
+ 2023-10-17 15:16:19,322 epoch 7 - iter 44/447 - loss 0.01563041 - time (sec): 4.52 - samples/sec: 2112.02 - lr: 0.000013 - momentum: 0.000000
164
+ 2023-10-17 15:16:23,326 epoch 7 - iter 88/447 - loss 0.01611607 - time (sec): 8.52 - samples/sec: 2092.41 - lr: 0.000013 - momentum: 0.000000
165
+ 2023-10-17 15:16:27,501 epoch 7 - iter 132/447 - loss 0.01689545 - time (sec): 12.70 - samples/sec: 2094.16 - lr: 0.000012 - momentum: 0.000000
166
+ 2023-10-17 15:16:32,005 epoch 7 - iter 176/447 - loss 0.01747896 - time (sec): 17.20 - samples/sec: 2094.26 - lr: 0.000012 - momentum: 0.000000
167
+ 2023-10-17 15:16:36,046 epoch 7 - iter 220/447 - loss 0.01716074 - time (sec): 21.24 - samples/sec: 2096.32 - lr: 0.000012 - momentum: 0.000000
168
+ 2023-10-17 15:16:40,073 epoch 7 - iter 264/447 - loss 0.01541518 - time (sec): 25.27 - samples/sec: 2078.44 - lr: 0.000011 - momentum: 0.000000
169
+ 2023-10-17 15:16:44,301 epoch 7 - iter 308/447 - loss 0.01645470 - time (sec): 29.50 - samples/sec: 2057.27 - lr: 0.000011 - momentum: 0.000000
170
+ 2023-10-17 15:16:48,361 epoch 7 - iter 352/447 - loss 0.01683787 - time (sec): 33.56 - samples/sec: 2062.46 - lr: 0.000011 - momentum: 0.000000
171
+ 2023-10-17 15:16:52,388 epoch 7 - iter 396/447 - loss 0.01616763 - time (sec): 37.58 - samples/sec: 2062.57 - lr: 0.000010 - momentum: 0.000000
172
+ 2023-10-17 15:16:56,416 epoch 7 - iter 440/447 - loss 0.01561036 - time (sec): 41.61 - samples/sec: 2048.57 - lr: 0.000010 - momentum: 0.000000
173
+ 2023-10-17 15:16:57,057 ----------------------------------------------------------------------------------------------------
174
+ 2023-10-17 15:16:57,058 EPOCH 7 done: loss 0.0154 - lr: 0.000010
175
+ 2023-10-17 15:17:07,982 DEV : loss 0.20203104615211487 - f1-score (micro avg) 0.791
176
+ 2023-10-17 15:17:08,037 saving best model
177
+ 2023-10-17 15:17:09,651 ----------------------------------------------------------------------------------------------------
178
+ 2023-10-17 15:17:14,128 epoch 8 - iter 44/447 - loss 0.00477927 - time (sec): 4.48 - samples/sec: 1823.23 - lr: 0.000010 - momentum: 0.000000
179
+ 2023-10-17 15:17:18,206 epoch 8 - iter 88/447 - loss 0.01009409 - time (sec): 8.55 - samples/sec: 1909.09 - lr: 0.000009 - momentum: 0.000000
180
+ 2023-10-17 15:17:22,409 epoch 8 - iter 132/447 - loss 0.01037176 - time (sec): 12.76 - samples/sec: 1898.29 - lr: 0.000009 - momentum: 0.000000
181
+ 2023-10-17 15:17:26,995 epoch 8 - iter 176/447 - loss 0.00910357 - time (sec): 17.34 - samples/sec: 1899.72 - lr: 0.000009 - momentum: 0.000000
182
+ 2023-10-17 15:17:31,092 epoch 8 - iter 220/447 - loss 0.00883076 - time (sec): 21.44 - samples/sec: 1947.85 - lr: 0.000008 - momentum: 0.000000
183
+ 2023-10-17 15:17:35,078 epoch 8 - iter 264/447 - loss 0.00841667 - time (sec): 25.42 - samples/sec: 1967.05 - lr: 0.000008 - momentum: 0.000000
184
+ 2023-10-17 15:17:39,106 epoch 8 - iter 308/447 - loss 0.00937271 - time (sec): 29.45 - samples/sec: 1973.97 - lr: 0.000008 - momentum: 0.000000
185
+ 2023-10-17 15:17:43,218 epoch 8 - iter 352/447 - loss 0.01054465 - time (sec): 33.56 - samples/sec: 1977.60 - lr: 0.000007 - momentum: 0.000000
186
+ 2023-10-17 15:17:47,869 epoch 8 - iter 396/447 - loss 0.01105462 - time (sec): 38.22 - samples/sec: 1983.69 - lr: 0.000007 - momentum: 0.000000
187
+ 2023-10-17 15:17:52,348 epoch 8 - iter 440/447 - loss 0.01082183 - time (sec): 42.69 - samples/sec: 1996.49 - lr: 0.000007 - momentum: 0.000000
188
+ 2023-10-17 15:17:52,961 ----------------------------------------------------------------------------------------------------
189
+ 2023-10-17 15:17:52,962 EPOCH 8 done: loss 0.0107 - lr: 0.000007
190
+ 2023-10-17 15:18:03,854 DEV : loss 0.20561201870441437 - f1-score (micro avg) 0.7844
191
+ 2023-10-17 15:18:03,914 ----------------------------------------------------------------------------------------------------
192
+ 2023-10-17 15:18:08,009 epoch 9 - iter 44/447 - loss 0.00258760 - time (sec): 4.09 - samples/sec: 1856.78 - lr: 0.000006 - momentum: 0.000000
193
+ 2023-10-17 15:18:12,008 epoch 9 - iter 88/447 - loss 0.00437519 - time (sec): 8.09 - samples/sec: 1977.19 - lr: 0.000006 - momentum: 0.000000
194
+ 2023-10-17 15:18:16,094 epoch 9 - iter 132/447 - loss 0.00474581 - time (sec): 12.18 - samples/sec: 1996.66 - lr: 0.000006 - momentum: 0.000000
195
+ 2023-10-17 15:18:20,052 epoch 9 - iter 176/447 - loss 0.00461352 - time (sec): 16.14 - samples/sec: 2023.33 - lr: 0.000005 - momentum: 0.000000
196
+ 2023-10-17 15:18:24,813 epoch 9 - iter 220/447 - loss 0.00511861 - time (sec): 20.90 - samples/sec: 2058.16 - lr: 0.000005 - momentum: 0.000000
197
+ 2023-10-17 15:18:28,845 epoch 9 - iter 264/447 - loss 0.00514456 - time (sec): 24.93 - samples/sec: 2081.35 - lr: 0.000005 - momentum: 0.000000
198
+ 2023-10-17 15:18:32,841 epoch 9 - iter 308/447 - loss 0.00607397 - time (sec): 28.93 - samples/sec: 2074.45 - lr: 0.000004 - momentum: 0.000000
199
+ 2023-10-17 15:18:37,079 epoch 9 - iter 352/447 - loss 0.00657719 - time (sec): 33.16 - samples/sec: 2052.89 - lr: 0.000004 - momentum: 0.000000
200
+ 2023-10-17 15:18:41,085 epoch 9 - iter 396/447 - loss 0.00653218 - time (sec): 37.17 - samples/sec: 2061.49 - lr: 0.000004 - momentum: 0.000000
201
+ 2023-10-17 15:18:45,228 epoch 9 - iter 440/447 - loss 0.00638953 - time (sec): 41.31 - samples/sec: 2055.17 - lr: 0.000003 - momentum: 0.000000
202
+ 2023-10-17 15:18:45,868 ----------------------------------------------------------------------------------------------------
203
+ 2023-10-17 15:18:45,868 EPOCH 9 done: loss 0.0063 - lr: 0.000003
204
+ 2023-10-17 15:18:56,866 DEV : loss 0.21265345811843872 - f1-score (micro avg) 0.7963
205
+ 2023-10-17 15:18:56,921 saving best model
206
+ 2023-10-17 15:18:58,425 ----------------------------------------------------------------------------------------------------
207
+ 2023-10-17 15:19:02,563 epoch 10 - iter 44/447 - loss 0.00272302 - time (sec): 4.13 - samples/sec: 2064.17 - lr: 0.000003 - momentum: 0.000000
208
+ 2023-10-17 15:19:06,593 epoch 10 - iter 88/447 - loss 0.00325765 - time (sec): 8.16 - samples/sec: 2021.55 - lr: 0.000003 - momentum: 0.000000
209
+ 2023-10-17 15:19:11,047 epoch 10 - iter 132/447 - loss 0.00362277 - time (sec): 12.61 - samples/sec: 2084.14 - lr: 0.000002 - momentum: 0.000000
210
+ 2023-10-17 15:19:15,000 epoch 10 - iter 176/447 - loss 0.00528389 - time (sec): 16.57 - samples/sec: 2062.23 - lr: 0.000002 - momentum: 0.000000
211
+ 2023-10-17 15:19:19,112 epoch 10 - iter 220/447 - loss 0.00509353 - time (sec): 20.68 - samples/sec: 2078.14 - lr: 0.000002 - momentum: 0.000000
212
+ 2023-10-17 15:19:23,347 epoch 10 - iter 264/447 - loss 0.00465258 - time (sec): 24.91 - samples/sec: 2077.33 - lr: 0.000001 - momentum: 0.000000
213
+ 2023-10-17 15:19:27,449 epoch 10 - iter 308/447 - loss 0.00478916 - time (sec): 29.01 - samples/sec: 2067.22 - lr: 0.000001 - momentum: 0.000000
214
+ 2023-10-17 15:19:31,720 epoch 10 - iter 352/447 - loss 0.00507025 - time (sec): 33.29 - samples/sec: 2067.93 - lr: 0.000001 - momentum: 0.000000
215
+ 2023-10-17 15:19:35,681 epoch 10 - iter 396/447 - loss 0.00469527 - time (sec): 37.25 - samples/sec: 2054.64 - lr: 0.000000 - momentum: 0.000000
216
+ 2023-10-17 15:19:39,828 epoch 10 - iter 440/447 - loss 0.00471393 - time (sec): 41.39 - samples/sec: 2062.13 - lr: 0.000000 - momentum: 0.000000
217
+ 2023-10-17 15:19:40,443 ----------------------------------------------------------------------------------------------------
218
+ 2023-10-17 15:19:40,444 EPOCH 10 done: loss 0.0047 - lr: 0.000000
219
+ 2023-10-17 15:19:51,679 DEV : loss 0.21538716554641724 - f1-score (micro avg) 0.7969
220
+ 2023-10-17 15:19:51,731 saving best model
221
+ 2023-10-17 15:19:53,070 ----------------------------------------------------------------------------------------------------
222
+ 2023-10-17 15:19:53,072 Loading model from best epoch ...
223
+ 2023-10-17 15:19:55,890 SequenceTagger predicts: Dictionary with 21 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, S-prod, B-prod, E-prod, I-prod, S-time, B-time, E-time, I-time
224
+ 2023-10-17 15:20:01,461
225
+ Results:
226
+ - F-score (micro) 0.7606
227
+ - F-score (macro) 0.6789
228
+ - Accuracy 0.6344
229
+
230
+ By class:
231
+ precision recall f1-score support
232
+
233
+ loc 0.8482 0.8624 0.8552 596
234
+ pers 0.6778 0.7898 0.7295 333
235
+ org 0.5143 0.5455 0.5294 132
236
+ prod 0.6000 0.5000 0.5455 66
237
+ time 0.7347 0.7347 0.7347 49
238
+
239
+ micro avg 0.7415 0.7806 0.7606 1176
240
+ macro avg 0.6750 0.6865 0.6789 1176
241
+ weighted avg 0.7438 0.7806 0.7607 1176
242
+
243
+ 2023-10-17 15:20:01,461 ----------------------------------------------------------------------------------------------------