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autotrain-flair-hipe2022-de-hmbert

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  1. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5/best-model.pt +3 -0
  2. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5/dev.tsv +0 -0
  3. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5/final-model.pt +3 -0
  4. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5/loss.tsv +11 -0
  5. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5/test.tsv +0 -0
  6. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5/training.log +245 -0
hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5/best-model.pt ADDED
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hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5/dev.tsv ADDED
The diff for this file is too large to render. See raw diff
 
hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5/final-model.pt ADDED
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hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5/loss.tsv ADDED
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+ EPOCH TIMESTAMP LEARNING_RATE TRAIN_LOSS DEV_LOSS DEV_PRECISION DEV_RECALL DEV_F1 DEV_ACCURACY
2
+ 1 23:16:00 0.0000 0.6484 0.1875 0.5873 0.6130 0.5998 0.4419
3
+ 2 23:17:27 0.0000 0.1527 0.1283 0.6730 0.7209 0.6961 0.5558
4
+ 3 23:18:57 0.0000 0.0818 0.1465 0.6777 0.7545 0.7140 0.5789
5
+ 4 23:20:29 0.0000 0.0502 0.1589 0.7093 0.7670 0.7370 0.6007
6
+ 5 23:22:02 0.0000 0.0329 0.2009 0.7903 0.7545 0.7720 0.6416
7
+ 6 23:23:34 0.0000 0.0237 0.1792 0.7589 0.7873 0.7728 0.6439
8
+ 7 23:25:06 0.0000 0.0152 0.2167 0.7867 0.7873 0.7870 0.6642
9
+ 8 23:26:34 0.0000 0.0092 0.2148 0.7793 0.7897 0.7845 0.6606
10
+ 9 23:28:01 0.0000 0.0064 0.2158 0.7795 0.8069 0.7929 0.6714
11
+ 10 23:29:29 0.0000 0.0048 0.2197 0.7679 0.8045 0.7858 0.6634
hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5/test.tsv ADDED
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hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5/training.log ADDED
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+ 2023-09-03 23:14:35,572 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 23:14:35,573 Model: "SequenceTagger(
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+ (embeddings): TransformerWordEmbeddings(
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+ (model): BertModel(
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+ (embeddings): BertEmbeddings(
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+ (word_embeddings): Embedding(32001, 768)
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+ (position_embeddings): Embedding(512, 768)
8
+ (token_type_embeddings): Embedding(2, 768)
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+ (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ (encoder): BertEncoder(
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+ (layer): ModuleList(
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+ (0-11): 12 x BertLayer(
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+ (attention): BertAttention(
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+ (self): BertSelfAttention(
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+ (query): Linear(in_features=768, out_features=768, bias=True)
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+ (key): Linear(in_features=768, out_features=768, bias=True)
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+ (value): Linear(in_features=768, out_features=768, bias=True)
20
+ (dropout): Dropout(p=0.1, inplace=False)
21
+ )
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+ (output): BertSelfOutput(
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+ (dense): Linear(in_features=768, out_features=768, bias=True)
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+ (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
25
+ (dropout): Dropout(p=0.1, inplace=False)
26
+ )
27
+ )
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+ (intermediate): BertIntermediate(
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+ (dense): Linear(in_features=768, out_features=3072, bias=True)
30
+ (intermediate_act_fn): GELUActivation()
31
+ )
32
+ (output): BertOutput(
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+ (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
+ (pooler): BertPooler(
41
+ (dense): Linear(in_features=768, out_features=768, bias=True)
42
+ (activation): Tanh()
43
+ )
44
+ )
45
+ )
46
+ (locked_dropout): LockedDropout(p=0.5)
47
+ (linear): Linear(in_features=768, out_features=21, bias=True)
48
+ (loss_function): CrossEntropyLoss()
49
+ )"
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+ 2023-09-03 23:14:35,574 ----------------------------------------------------------------------------------------------------
51
+ 2023-09-03 23:14:35,574 MultiCorpus: 3575 train + 1235 dev + 1266 test sentences
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+ - NER_HIPE_2022 Corpus: 3575 train + 1235 dev + 1266 test sentences - /app/.flair/datasets/ner_hipe_2022/v2.1/hipe2020/de/with_doc_seperator
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+ 2023-09-03 23:14:35,574 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 23:14:35,574 Train: 3575 sentences
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+ 2023-09-03 23:14:35,574 (train_with_dev=False, train_with_test=False)
56
+ 2023-09-03 23:14:35,574 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 23:14:35,574 Training Params:
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+ 2023-09-03 23:14:35,574 - learning_rate: "5e-05"
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+ 2023-09-03 23:14:35,574 - mini_batch_size: "8"
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+ 2023-09-03 23:14:35,574 - max_epochs: "10"
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+ 2023-09-03 23:14:35,574 - shuffle: "True"
62
+ 2023-09-03 23:14:35,574 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 23:14:35,574 Plugins:
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+ 2023-09-03 23:14:35,574 - LinearScheduler | warmup_fraction: '0.1'
65
+ 2023-09-03 23:14:35,574 ----------------------------------------------------------------------------------------------------
66
+ 2023-09-03 23:14:35,574 Final evaluation on model from best epoch (best-model.pt)
67
+ 2023-09-03 23:14:35,574 - metric: "('micro avg', 'f1-score')"
68
+ 2023-09-03 23:14:35,574 ----------------------------------------------------------------------------------------------------
69
+ 2023-09-03 23:14:35,574 Computation:
70
+ 2023-09-03 23:14:35,575 - compute on device: cuda:0
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+ 2023-09-03 23:14:35,575 - embedding storage: none
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+ 2023-09-03 23:14:35,575 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 23:14:35,575 Model training base path: "hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5"
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+ 2023-09-03 23:14:35,575 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 23:14:35,575 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 23:14:45,199 epoch 1 - iter 44/447 - loss 2.74027356 - time (sec): 9.62 - samples/sec: 1018.36 - lr: 0.000005 - momentum: 0.000000
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+ 2023-09-03 23:14:52,582 epoch 1 - iter 88/447 - loss 1.87722419 - time (sec): 17.01 - samples/sec: 1104.48 - lr: 0.000010 - momentum: 0.000000
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+ 2023-09-03 23:14:59,605 epoch 1 - iter 132/447 - loss 1.45785338 - time (sec): 24.03 - samples/sec: 1128.36 - lr: 0.000015 - momentum: 0.000000
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+ 2023-09-03 23:15:07,472 epoch 1 - iter 176/447 - loss 1.19701846 - time (sec): 31.90 - samples/sec: 1121.29 - lr: 0.000020 - momentum: 0.000000
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+ 2023-09-03 23:15:14,341 epoch 1 - iter 220/447 - loss 1.02548111 - time (sec): 38.77 - samples/sec: 1143.23 - lr: 0.000024 - momentum: 0.000000
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+ 2023-09-03 23:15:21,517 epoch 1 - iter 264/447 - loss 0.90487088 - time (sec): 45.94 - samples/sec: 1147.76 - lr: 0.000029 - momentum: 0.000000
82
+ 2023-09-03 23:15:28,612 epoch 1 - iter 308/447 - loss 0.81753032 - time (sec): 53.04 - samples/sec: 1150.69 - lr: 0.000034 - momentum: 0.000000
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+ 2023-09-03 23:15:35,520 epoch 1 - iter 352/447 - loss 0.74900207 - time (sec): 59.94 - samples/sec: 1156.46 - lr: 0.000039 - momentum: 0.000000
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+ 2023-09-03 23:15:42,004 epoch 1 - iter 396/447 - loss 0.69998245 - time (sec): 66.43 - samples/sec: 1161.49 - lr: 0.000044 - momentum: 0.000000
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+ 2023-09-03 23:15:49,092 epoch 1 - iter 440/447 - loss 0.65373957 - time (sec): 73.52 - samples/sec: 1163.14 - lr: 0.000049 - momentum: 0.000000
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+ 2023-09-03 23:15:50,073 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 23:15:50,073 EPOCH 1 done: loss 0.6484 - lr: 0.000049
88
+ 2023-09-03 23:16:00,166 DEV : loss 0.18753036856651306 - f1-score (micro avg) 0.5998
89
+ 2023-09-03 23:16:00,191 saving best model
90
+ 2023-09-03 23:16:00,626 ----------------------------------------------------------------------------------------------------
91
+ 2023-09-03 23:16:08,481 epoch 2 - iter 44/447 - loss 0.20721930 - time (sec): 7.85 - samples/sec: 1165.34 - lr: 0.000049 - momentum: 0.000000
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+ 2023-09-03 23:16:15,628 epoch 2 - iter 88/447 - loss 0.19132038 - time (sec): 15.00 - samples/sec: 1169.51 - lr: 0.000049 - momentum: 0.000000
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+ 2023-09-03 23:16:22,711 epoch 2 - iter 132/447 - loss 0.17549904 - time (sec): 22.08 - samples/sec: 1164.50 - lr: 0.000048 - momentum: 0.000000
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+ 2023-09-03 23:16:29,102 epoch 2 - iter 176/447 - loss 0.17575583 - time (sec): 28.47 - samples/sec: 1167.14 - lr: 0.000048 - momentum: 0.000000
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+ 2023-09-03 23:16:36,163 epoch 2 - iter 220/447 - loss 0.16833950 - time (sec): 35.54 - samples/sec: 1165.16 - lr: 0.000047 - momentum: 0.000000
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+ 2023-09-03 23:16:43,471 epoch 2 - iter 264/447 - loss 0.16541217 - time (sec): 42.84 - samples/sec: 1168.76 - lr: 0.000047 - momentum: 0.000000
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+ 2023-09-03 23:16:50,698 epoch 2 - iter 308/447 - loss 0.16461955 - time (sec): 50.07 - samples/sec: 1174.83 - lr: 0.000046 - momentum: 0.000000
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+ 2023-09-03 23:16:58,058 epoch 2 - iter 352/447 - loss 0.16030829 - time (sec): 57.43 - samples/sec: 1172.46 - lr: 0.000046 - momentum: 0.000000
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+ 2023-09-03 23:17:05,981 epoch 2 - iter 396/447 - loss 0.15745044 - time (sec): 65.35 - samples/sec: 1168.81 - lr: 0.000045 - momentum: 0.000000
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+ 2023-09-03 23:17:13,576 epoch 2 - iter 440/447 - loss 0.15390948 - time (sec): 72.95 - samples/sec: 1165.36 - lr: 0.000045 - momentum: 0.000000
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+ 2023-09-03 23:17:14,816 ----------------------------------------------------------------------------------------------------
102
+ 2023-09-03 23:17:14,816 EPOCH 2 done: loss 0.1527 - lr: 0.000045
103
+ 2023-09-03 23:17:27,604 DEV : loss 0.12834911048412323 - f1-score (micro avg) 0.6961
104
+ 2023-09-03 23:17:27,629 saving best model
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+ 2023-09-03 23:17:29,658 ----------------------------------------------------------------------------------------------------
106
+ 2023-09-03 23:17:36,719 epoch 3 - iter 44/447 - loss 0.07584644 - time (sec): 7.06 - samples/sec: 1285.64 - lr: 0.000044 - momentum: 0.000000
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+ 2023-09-03 23:17:43,835 epoch 3 - iter 88/447 - loss 0.07784377 - time (sec): 14.18 - samples/sec: 1253.44 - lr: 0.000043 - momentum: 0.000000
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+ 2023-09-03 23:17:52,324 epoch 3 - iter 132/447 - loss 0.08102291 - time (sec): 22.66 - samples/sec: 1193.16 - lr: 0.000043 - momentum: 0.000000
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+ 2023-09-03 23:17:59,775 epoch 3 - iter 176/447 - loss 0.08617321 - time (sec): 30.12 - samples/sec: 1194.73 - lr: 0.000042 - momentum: 0.000000
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+ 2023-09-03 23:18:07,188 epoch 3 - iter 220/447 - loss 0.08415162 - time (sec): 37.53 - samples/sec: 1170.69 - lr: 0.000042 - momentum: 0.000000
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+ 2023-09-03 23:18:14,142 epoch 3 - iter 264/447 - loss 0.08537542 - time (sec): 44.48 - samples/sec: 1176.76 - lr: 0.000041 - momentum: 0.000000
112
+ 2023-09-03 23:18:21,330 epoch 3 - iter 308/447 - loss 0.08385874 - time (sec): 51.67 - samples/sec: 1162.87 - lr: 0.000041 - momentum: 0.000000
113
+ 2023-09-03 23:18:28,970 epoch 3 - iter 352/447 - loss 0.08411927 - time (sec): 59.31 - samples/sec: 1152.19 - lr: 0.000040 - momentum: 0.000000
114
+ 2023-09-03 23:18:36,107 epoch 3 - iter 396/447 - loss 0.08304946 - time (sec): 66.45 - samples/sec: 1160.88 - lr: 0.000040 - momentum: 0.000000
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+ 2023-09-03 23:18:43,031 epoch 3 - iter 440/447 - loss 0.08211976 - time (sec): 73.37 - samples/sec: 1161.95 - lr: 0.000039 - momentum: 0.000000
116
+ 2023-09-03 23:18:44,309 ----------------------------------------------------------------------------------------------------
117
+ 2023-09-03 23:18:44,310 EPOCH 3 done: loss 0.0818 - lr: 0.000039
118
+ 2023-09-03 23:18:57,145 DEV : loss 0.14649774134159088 - f1-score (micro avg) 0.714
119
+ 2023-09-03 23:18:57,170 saving best model
120
+ 2023-09-03 23:18:58,473 ----------------------------------------------------------------------------------------------------
121
+ 2023-09-03 23:19:06,526 epoch 4 - iter 44/447 - loss 0.05091241 - time (sec): 8.05 - samples/sec: 1138.41 - lr: 0.000038 - momentum: 0.000000
122
+ 2023-09-03 23:19:13,514 epoch 4 - iter 88/447 - loss 0.05229174 - time (sec): 15.04 - samples/sec: 1151.21 - lr: 0.000038 - momentum: 0.000000
123
+ 2023-09-03 23:19:22,457 epoch 4 - iter 132/447 - loss 0.05207457 - time (sec): 23.98 - samples/sec: 1140.96 - lr: 0.000037 - momentum: 0.000000
124
+ 2023-09-03 23:19:29,639 epoch 4 - iter 176/447 - loss 0.05082370 - time (sec): 31.17 - samples/sec: 1133.83 - lr: 0.000037 - momentum: 0.000000
125
+ 2023-09-03 23:19:37,127 epoch 4 - iter 220/447 - loss 0.05109694 - time (sec): 38.65 - samples/sec: 1129.54 - lr: 0.000036 - momentum: 0.000000
126
+ 2023-09-03 23:19:45,757 epoch 4 - iter 264/447 - loss 0.05073943 - time (sec): 47.28 - samples/sec: 1121.79 - lr: 0.000036 - momentum: 0.000000
127
+ 2023-09-03 23:19:53,028 epoch 4 - iter 308/447 - loss 0.05066311 - time (sec): 54.55 - samples/sec: 1121.80 - lr: 0.000035 - momentum: 0.000000
128
+ 2023-09-03 23:20:00,126 epoch 4 - iter 352/447 - loss 0.05110715 - time (sec): 61.65 - samples/sec: 1120.72 - lr: 0.000035 - momentum: 0.000000
129
+ 2023-09-03 23:20:07,197 epoch 4 - iter 396/447 - loss 0.05133488 - time (sec): 68.72 - samples/sec: 1120.50 - lr: 0.000034 - momentum: 0.000000
130
+ 2023-09-03 23:20:14,776 epoch 4 - iter 440/447 - loss 0.04979905 - time (sec): 76.30 - samples/sec: 1117.81 - lr: 0.000033 - momentum: 0.000000
131
+ 2023-09-03 23:20:15,903 ----------------------------------------------------------------------------------------------------
132
+ 2023-09-03 23:20:15,903 EPOCH 4 done: loss 0.0502 - lr: 0.000033
133
+ 2023-09-03 23:20:29,278 DEV : loss 0.15886610746383667 - f1-score (micro avg) 0.737
134
+ 2023-09-03 23:20:29,304 saving best model
135
+ 2023-09-03 23:20:30,602 ----------------------------------------------------------------------------------------------------
136
+ 2023-09-03 23:20:38,512 epoch 5 - iter 44/447 - loss 0.02798718 - time (sec): 7.91 - samples/sec: 1089.87 - lr: 0.000033 - momentum: 0.000000
137
+ 2023-09-03 23:20:45,702 epoch 5 - iter 88/447 - loss 0.03166076 - time (sec): 15.10 - samples/sec: 1115.69 - lr: 0.000032 - momentum: 0.000000
138
+ 2023-09-03 23:20:52,964 epoch 5 - iter 132/447 - loss 0.02864004 - time (sec): 22.36 - samples/sec: 1111.83 - lr: 0.000032 - momentum: 0.000000
139
+ 2023-09-03 23:21:01,452 epoch 5 - iter 176/447 - loss 0.03483057 - time (sec): 30.85 - samples/sec: 1114.31 - lr: 0.000031 - momentum: 0.000000
140
+ 2023-09-03 23:21:09,585 epoch 5 - iter 220/447 - loss 0.03360977 - time (sec): 38.98 - samples/sec: 1113.29 - lr: 0.000031 - momentum: 0.000000
141
+ 2023-09-03 23:21:18,095 epoch 5 - iter 264/447 - loss 0.03291349 - time (sec): 47.49 - samples/sec: 1101.75 - lr: 0.000030 - momentum: 0.000000
142
+ 2023-09-03 23:21:25,230 epoch 5 - iter 308/447 - loss 0.03309030 - time (sec): 54.63 - samples/sec: 1106.32 - lr: 0.000030 - momentum: 0.000000
143
+ 2023-09-03 23:21:33,070 epoch 5 - iter 352/447 - loss 0.03345743 - time (sec): 62.47 - samples/sec: 1106.09 - lr: 0.000029 - momentum: 0.000000
144
+ 2023-09-03 23:21:40,268 epoch 5 - iter 396/447 - loss 0.03305828 - time (sec): 69.67 - samples/sec: 1105.55 - lr: 0.000028 - momentum: 0.000000
145
+ 2023-09-03 23:21:47,859 epoch 5 - iter 440/447 - loss 0.03301473 - time (sec): 77.26 - samples/sec: 1103.18 - lr: 0.000028 - momentum: 0.000000
146
+ 2023-09-03 23:21:49,024 ----------------------------------------------------------------------------------------------------
147
+ 2023-09-03 23:21:49,024 EPOCH 5 done: loss 0.0329 - lr: 0.000028
148
+ 2023-09-03 23:22:02,093 DEV : loss 0.20093011856079102 - f1-score (micro avg) 0.772
149
+ 2023-09-03 23:22:02,119 saving best model
150
+ 2023-09-03 23:22:03,419 ----------------------------------------------------------------------------------------------------
151
+ 2023-09-03 23:22:10,295 epoch 6 - iter 44/447 - loss 0.02157301 - time (sec): 6.88 - samples/sec: 1202.28 - lr: 0.000027 - momentum: 0.000000
152
+ 2023-09-03 23:22:18,104 epoch 6 - iter 88/447 - loss 0.02366163 - time (sec): 14.68 - samples/sec: 1135.67 - lr: 0.000027 - momentum: 0.000000
153
+ 2023-09-03 23:22:28,060 epoch 6 - iter 132/447 - loss 0.02274858 - time (sec): 24.64 - samples/sec: 1090.36 - lr: 0.000026 - momentum: 0.000000
154
+ 2023-09-03 23:22:36,009 epoch 6 - iter 176/447 - loss 0.02139094 - time (sec): 32.59 - samples/sec: 1089.63 - lr: 0.000026 - momentum: 0.000000
155
+ 2023-09-03 23:22:43,874 epoch 6 - iter 220/447 - loss 0.02016554 - time (sec): 40.45 - samples/sec: 1106.69 - lr: 0.000025 - momentum: 0.000000
156
+ 2023-09-03 23:22:51,573 epoch 6 - iter 264/447 - loss 0.02201676 - time (sec): 48.15 - samples/sec: 1100.90 - lr: 0.000025 - momentum: 0.000000
157
+ 2023-09-03 23:22:58,894 epoch 6 - iter 308/447 - loss 0.02216916 - time (sec): 55.47 - samples/sec: 1098.26 - lr: 0.000024 - momentum: 0.000000
158
+ 2023-09-03 23:23:05,812 epoch 6 - iter 352/447 - loss 0.02338615 - time (sec): 62.39 - samples/sec: 1109.14 - lr: 0.000023 - momentum: 0.000000
159
+ 2023-09-03 23:23:13,509 epoch 6 - iter 396/447 - loss 0.02291749 - time (sec): 70.09 - samples/sec: 1107.97 - lr: 0.000023 - momentum: 0.000000
160
+ 2023-09-03 23:23:20,421 epoch 6 - iter 440/447 - loss 0.02388491 - time (sec): 77.00 - samples/sec: 1106.28 - lr: 0.000022 - momentum: 0.000000
161
+ 2023-09-03 23:23:21,594 ----------------------------------------------------------------------------------------------------
162
+ 2023-09-03 23:23:21,594 EPOCH 6 done: loss 0.0237 - lr: 0.000022
163
+ 2023-09-03 23:23:34,546 DEV : loss 0.17919400334358215 - f1-score (micro avg) 0.7728
164
+ 2023-09-03 23:23:34,572 saving best model
165
+ 2023-09-03 23:23:35,863 ----------------------------------------------------------------------------------------------------
166
+ 2023-09-03 23:23:42,589 epoch 7 - iter 44/447 - loss 0.01691354 - time (sec): 6.72 - samples/sec: 1144.38 - lr: 0.000022 - momentum: 0.000000
167
+ 2023-09-03 23:23:49,518 epoch 7 - iter 88/447 - loss 0.01382308 - time (sec): 13.65 - samples/sec: 1132.85 - lr: 0.000021 - momentum: 0.000000
168
+ 2023-09-03 23:23:58,353 epoch 7 - iter 132/447 - loss 0.01318139 - time (sec): 22.49 - samples/sec: 1113.40 - lr: 0.000021 - momentum: 0.000000
169
+ 2023-09-03 23:24:05,808 epoch 7 - iter 176/447 - loss 0.01247074 - time (sec): 29.94 - samples/sec: 1125.72 - lr: 0.000020 - momentum: 0.000000
170
+ 2023-09-03 23:24:13,915 epoch 7 - iter 220/447 - loss 0.01456098 - time (sec): 38.05 - samples/sec: 1116.90 - lr: 0.000020 - momentum: 0.000000
171
+ 2023-09-03 23:24:21,966 epoch 7 - iter 264/447 - loss 0.01548619 - time (sec): 46.10 - samples/sec: 1101.42 - lr: 0.000019 - momentum: 0.000000
172
+ 2023-09-03 23:24:29,278 epoch 7 - iter 308/447 - loss 0.01562612 - time (sec): 53.41 - samples/sec: 1105.73 - lr: 0.000018 - momentum: 0.000000
173
+ 2023-09-03 23:24:38,364 epoch 7 - iter 352/447 - loss 0.01625171 - time (sec): 62.50 - samples/sec: 1089.21 - lr: 0.000018 - momentum: 0.000000
174
+ 2023-09-03 23:24:45,434 epoch 7 - iter 396/447 - loss 0.01565909 - time (sec): 69.57 - samples/sec: 1100.32 - lr: 0.000017 - momentum: 0.000000
175
+ 2023-09-03 23:24:52,939 epoch 7 - iter 440/447 - loss 0.01533494 - time (sec): 77.07 - samples/sec: 1105.24 - lr: 0.000017 - momentum: 0.000000
176
+ 2023-09-03 23:24:54,128 ----------------------------------------------------------------------------------------------------
177
+ 2023-09-03 23:24:54,129 EPOCH 7 done: loss 0.0152 - lr: 0.000017
178
+ 2023-09-03 23:25:06,545 DEV : loss 0.21673361957073212 - f1-score (micro avg) 0.787
179
+ 2023-09-03 23:25:06,571 saving best model
180
+ 2023-09-03 23:25:07,898 ----------------------------------------------------------------------------------------------------
181
+ 2023-09-03 23:25:15,045 epoch 8 - iter 44/447 - loss 0.02005421 - time (sec): 7.15 - samples/sec: 1165.99 - lr: 0.000016 - momentum: 0.000000
182
+ 2023-09-03 23:25:22,139 epoch 8 - iter 88/447 - loss 0.01232935 - time (sec): 14.24 - samples/sec: 1163.54 - lr: 0.000016 - momentum: 0.000000
183
+ 2023-09-03 23:25:29,910 epoch 8 - iter 132/447 - loss 0.01028182 - time (sec): 22.01 - samples/sec: 1173.54 - lr: 0.000015 - momentum: 0.000000
184
+ 2023-09-03 23:25:37,950 epoch 8 - iter 176/447 - loss 0.00912799 - time (sec): 30.05 - samples/sec: 1168.29 - lr: 0.000015 - momentum: 0.000000
185
+ 2023-09-03 23:25:45,774 epoch 8 - iter 220/447 - loss 0.00932027 - time (sec): 37.88 - samples/sec: 1152.47 - lr: 0.000014 - momentum: 0.000000
186
+ 2023-09-03 23:25:52,169 epoch 8 - iter 264/447 - loss 0.00902241 - time (sec): 44.27 - samples/sec: 1169.04 - lr: 0.000013 - momentum: 0.000000
187
+ 2023-09-03 23:25:59,304 epoch 8 - iter 308/447 - loss 0.00946560 - time (sec): 51.40 - samples/sec: 1168.74 - lr: 0.000013 - momentum: 0.000000
188
+ 2023-09-03 23:26:06,031 epoch 8 - iter 352/447 - loss 0.00926488 - time (sec): 58.13 - samples/sec: 1177.19 - lr: 0.000012 - momentum: 0.000000
189
+ 2023-09-03 23:26:13,024 epoch 8 - iter 396/447 - loss 0.00911860 - time (sec): 65.13 - samples/sec: 1178.30 - lr: 0.000012 - momentum: 0.000000
190
+ 2023-09-03 23:26:20,730 epoch 8 - iter 440/447 - loss 0.00910991 - time (sec): 72.83 - samples/sec: 1172.11 - lr: 0.000011 - momentum: 0.000000
191
+ 2023-09-03 23:26:21,657 ----------------------------------------------------------------------------------------------------
192
+ 2023-09-03 23:26:21,658 EPOCH 8 done: loss 0.0092 - lr: 0.000011
193
+ 2023-09-03 23:26:34,175 DEV : loss 0.2148038148880005 - f1-score (micro avg) 0.7845
194
+ 2023-09-03 23:26:34,201 ----------------------------------------------------------------------------------------------------
195
+ 2023-09-03 23:26:41,022 epoch 9 - iter 44/447 - loss 0.01047279 - time (sec): 6.82 - samples/sec: 1199.38 - lr: 0.000011 - momentum: 0.000000
196
+ 2023-09-03 23:26:49,312 epoch 9 - iter 88/447 - loss 0.00774178 - time (sec): 15.11 - samples/sec: 1157.91 - lr: 0.000010 - momentum: 0.000000
197
+ 2023-09-03 23:26:57,286 epoch 9 - iter 132/447 - loss 0.00617666 - time (sec): 23.08 - samples/sec: 1151.82 - lr: 0.000010 - momentum: 0.000000
198
+ 2023-09-03 23:27:04,572 epoch 9 - iter 176/447 - loss 0.00678688 - time (sec): 30.37 - samples/sec: 1154.02 - lr: 0.000009 - momentum: 0.000000
199
+ 2023-09-03 23:27:12,024 epoch 9 - iter 220/447 - loss 0.00619083 - time (sec): 37.82 - samples/sec: 1141.96 - lr: 0.000008 - momentum: 0.000000
200
+ 2023-09-03 23:27:19,053 epoch 9 - iter 264/447 - loss 0.00569243 - time (sec): 44.85 - samples/sec: 1146.93 - lr: 0.000008 - momentum: 0.000000
201
+ 2023-09-03 23:27:25,882 epoch 9 - iter 308/447 - loss 0.00663431 - time (sec): 51.68 - samples/sec: 1160.01 - lr: 0.000007 - momentum: 0.000000
202
+ 2023-09-03 23:27:32,934 epoch 9 - iter 352/447 - loss 0.00671540 - time (sec): 58.73 - samples/sec: 1167.91 - lr: 0.000007 - momentum: 0.000000
203
+ 2023-09-03 23:27:40,474 epoch 9 - iter 396/447 - loss 0.00662211 - time (sec): 66.27 - samples/sec: 1161.44 - lr: 0.000006 - momentum: 0.000000
204
+ 2023-09-03 23:27:47,742 epoch 9 - iter 440/447 - loss 0.00644648 - time (sec): 73.54 - samples/sec: 1159.92 - lr: 0.000006 - momentum: 0.000000
205
+ 2023-09-03 23:27:48,819 ----------------------------------------------------------------------------------------------------
206
+ 2023-09-03 23:27:48,819 EPOCH 9 done: loss 0.0064 - lr: 0.000006
207
+ 2023-09-03 23:28:01,440 DEV : loss 0.2157980501651764 - f1-score (micro avg) 0.7929
208
+ 2023-09-03 23:28:01,466 saving best model
209
+ 2023-09-03 23:28:02,793 ----------------------------------------------------------------------------------------------------
210
+ 2023-09-03 23:28:09,836 epoch 10 - iter 44/447 - loss 0.00335420 - time (sec): 7.04 - samples/sec: 1212.02 - lr: 0.000005 - momentum: 0.000000
211
+ 2023-09-03 23:28:16,761 epoch 10 - iter 88/447 - loss 0.00423038 - time (sec): 13.97 - samples/sec: 1163.21 - lr: 0.000005 - momentum: 0.000000
212
+ 2023-09-03 23:28:24,165 epoch 10 - iter 132/447 - loss 0.00350644 - time (sec): 21.37 - samples/sec: 1161.68 - lr: 0.000004 - momentum: 0.000000
213
+ 2023-09-03 23:28:30,606 epoch 10 - iter 176/447 - loss 0.00413289 - time (sec): 27.81 - samples/sec: 1176.30 - lr: 0.000003 - momentum: 0.000000
214
+ 2023-09-03 23:28:39,362 epoch 10 - iter 220/447 - loss 0.00442341 - time (sec): 36.57 - samples/sec: 1159.54 - lr: 0.000003 - momentum: 0.000000
215
+ 2023-09-03 23:28:47,931 epoch 10 - iter 264/447 - loss 0.00439773 - time (sec): 45.14 - samples/sec: 1143.54 - lr: 0.000002 - momentum: 0.000000
216
+ 2023-09-03 23:28:54,693 epoch 10 - iter 308/447 - loss 0.00407270 - time (sec): 51.90 - samples/sec: 1153.20 - lr: 0.000002 - momentum: 0.000000
217
+ 2023-09-03 23:29:01,343 epoch 10 - iter 352/447 - loss 0.00363053 - time (sec): 58.55 - samples/sec: 1153.29 - lr: 0.000001 - momentum: 0.000000
218
+ 2023-09-03 23:29:09,168 epoch 10 - iter 396/447 - loss 0.00409451 - time (sec): 66.37 - samples/sec: 1159.80 - lr: 0.000001 - momentum: 0.000000
219
+ 2023-09-03 23:29:16,158 epoch 10 - iter 440/447 - loss 0.00438619 - time (sec): 73.36 - samples/sec: 1163.41 - lr: 0.000000 - momentum: 0.000000
220
+ 2023-09-03 23:29:17,213 ----------------------------------------------------------------------------------------------------
221
+ 2023-09-03 23:29:17,213 EPOCH 10 done: loss 0.0048 - lr: 0.000000
222
+ 2023-09-03 23:29:29,906 DEV : loss 0.21965348720550537 - f1-score (micro avg) 0.7858
223
+ 2023-09-03 23:29:30,376 ----------------------------------------------------------------------------------------------------
224
+ 2023-09-03 23:29:30,377 Loading model from best epoch ...
225
+ 2023-09-03 23:29:32,121 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
226
+ 2023-09-03 23:29:42,059
227
+ Results:
228
+ - F-score (micro) 0.7452
229
+ - F-score (macro) 0.6835
230
+ - Accuracy 0.6147
231
+
232
+ By class:
233
+ precision recall f1-score support
234
+
235
+ loc 0.8295 0.8406 0.8350 596
236
+ pers 0.6506 0.7718 0.7060 333
237
+ org 0.5000 0.4545 0.4762 132
238
+ prod 0.6909 0.5758 0.6281 66
239
+ time 0.7500 0.7959 0.7723 49
240
+
241
+ micro avg 0.7300 0.7611 0.7452 1176
242
+ macro avg 0.6842 0.6877 0.6835 1176
243
+ weighted avg 0.7308 0.7611 0.7440 1176
244
+
245
+ 2023-09-03 23:29:42,059 ----------------------------------------------------------------------------------------------------