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autotrain-flair-hipe2022-fr-hmbert

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  1. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5/best-model.pt +3 -0
  2. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5/dev.tsv +0 -0
  3. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5/final-model.pt +3 -0
  4. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5/loss.tsv +11 -0
  5. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5/test.tsv +0 -0
  6. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5/training.log +244 -0
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5/best-model.pt ADDED
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hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5/dev.tsv ADDED
The diff for this file is too large to render. See raw diff
 
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5/final-model.pt ADDED
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hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-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 17:08:00 0.0000 0.6063 0.1403 0.6580 0.7371 0.6953 0.5627
3
+ 2 17:10:38 0.0000 0.1286 0.1328 0.7214 0.8081 0.7623 0.6458
4
+ 3 17:13:15 0.0000 0.0699 0.1066 0.7916 0.8396 0.8149 0.7110
5
+ 4 17:15:54 0.0000 0.0470 0.1628 0.7760 0.8133 0.7942 0.6830
6
+ 5 17:18:33 0.0000 0.0330 0.1620 0.8086 0.8373 0.8227 0.7206
7
+ 6 17:21:13 0.0000 0.0254 0.1815 0.8013 0.8339 0.8173 0.7222
8
+ 7 17:23:50 0.0000 0.0189 0.1963 0.8328 0.8299 0.8313 0.7329
9
+ 8 17:26:29 0.0000 0.0144 0.1902 0.8086 0.8494 0.8285 0.7313
10
+ 9 17:29:07 0.0000 0.0090 0.1945 0.8279 0.8511 0.8393 0.7445
11
+ 10 17:31:45 0.0000 0.0074 0.1993 0.8304 0.8522 0.8412 0.7492
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5/test.tsv ADDED
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hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5/training.log ADDED
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+ 2023-09-04 17:05:25,751 ----------------------------------------------------------------------------------------------------
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+ 2023-09-04 17:05:25,752 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)
11
+ )
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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
+ )
22
+ (output): BertSelfOutput(
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+ (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): BertIntermediate(
29
+ (dense): Linear(in_features=768, out_features=3072, bias=True)
30
+ (intermediate_act_fn): GELUActivation()
31
+ )
32
+ (output): BertOutput(
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
+ (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
+ )"
50
+ 2023-09-04 17:05:25,752 ----------------------------------------------------------------------------------------------------
51
+ 2023-09-04 17:05:25,753 MultiCorpus: 5901 train + 1287 dev + 1505 test sentences
52
+ - NER_HIPE_2022 Corpus: 5901 train + 1287 dev + 1505 test sentences - /app/.flair/datasets/ner_hipe_2022/v2.1/hipe2020/fr/with_doc_seperator
53
+ 2023-09-04 17:05:25,753 ----------------------------------------------------------------------------------------------------
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+ 2023-09-04 17:05:25,753 Train: 5901 sentences
55
+ 2023-09-04 17:05:25,753 (train_with_dev=False, train_with_test=False)
56
+ 2023-09-04 17:05:25,753 ----------------------------------------------------------------------------------------------------
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+ 2023-09-04 17:05:25,753 Training Params:
58
+ 2023-09-04 17:05:25,753 - learning_rate: "3e-05"
59
+ 2023-09-04 17:05:25,753 - mini_batch_size: "8"
60
+ 2023-09-04 17:05:25,753 - max_epochs: "10"
61
+ 2023-09-04 17:05:25,753 - shuffle: "True"
62
+ 2023-09-04 17:05:25,753 ----------------------------------------------------------------------------------------------------
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+ 2023-09-04 17:05:25,753 Plugins:
64
+ 2023-09-04 17:05:25,753 - LinearScheduler | warmup_fraction: '0.1'
65
+ 2023-09-04 17:05:25,753 ----------------------------------------------------------------------------------------------------
66
+ 2023-09-04 17:05:25,753 Final evaluation on model from best epoch (best-model.pt)
67
+ 2023-09-04 17:05:25,753 - metric: "('micro avg', 'f1-score')"
68
+ 2023-09-04 17:05:25,754 ----------------------------------------------------------------------------------------------------
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+ 2023-09-04 17:05:25,754 Computation:
70
+ 2023-09-04 17:05:25,754 - compute on device: cuda:0
71
+ 2023-09-04 17:05:25,754 - embedding storage: none
72
+ 2023-09-04 17:05:25,754 ----------------------------------------------------------------------------------------------------
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+ 2023-09-04 17:05:25,754 Model training base path: "hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5"
74
+ 2023-09-04 17:05:25,754 ----------------------------------------------------------------------------------------------------
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+ 2023-09-04 17:05:25,754 ----------------------------------------------------------------------------------------------------
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+ 2023-09-04 17:05:38,776 epoch 1 - iter 73/738 - loss 2.79947477 - time (sec): 13.02 - samples/sec: 1241.10 - lr: 0.000003 - momentum: 0.000000
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+ 2023-09-04 17:05:52,157 epoch 1 - iter 146/738 - loss 1.85938896 - time (sec): 26.40 - samples/sec: 1230.73 - lr: 0.000006 - momentum: 0.000000
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+ 2023-09-04 17:06:05,878 epoch 1 - iter 219/738 - loss 1.41174525 - time (sec): 40.12 - samples/sec: 1211.31 - lr: 0.000009 - momentum: 0.000000
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+ 2023-09-04 17:06:19,158 epoch 1 - iter 292/738 - loss 1.15342819 - time (sec): 53.40 - samples/sec: 1208.32 - lr: 0.000012 - momentum: 0.000000
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+ 2023-09-04 17:06:34,261 epoch 1 - iter 365/738 - loss 0.99186943 - time (sec): 68.51 - samples/sec: 1185.04 - lr: 0.000015 - momentum: 0.000000
81
+ 2023-09-04 17:06:47,195 epoch 1 - iter 438/738 - loss 0.88137840 - time (sec): 81.44 - samples/sec: 1183.75 - lr: 0.000018 - momentum: 0.000000
82
+ 2023-09-04 17:07:01,562 epoch 1 - iter 511/738 - loss 0.78731674 - time (sec): 95.81 - samples/sec: 1187.65 - lr: 0.000021 - momentum: 0.000000
83
+ 2023-09-04 17:07:15,895 epoch 1 - iter 584/738 - loss 0.71236636 - time (sec): 110.14 - samples/sec: 1191.99 - lr: 0.000024 - momentum: 0.000000
84
+ 2023-09-04 17:07:29,507 epoch 1 - iter 657/738 - loss 0.65776613 - time (sec): 123.75 - samples/sec: 1192.68 - lr: 0.000027 - momentum: 0.000000
85
+ 2023-09-04 17:07:44,366 epoch 1 - iter 730/738 - loss 0.61047558 - time (sec): 138.61 - samples/sec: 1188.00 - lr: 0.000030 - momentum: 0.000000
86
+ 2023-09-04 17:07:45,975 ----------------------------------------------------------------------------------------------------
87
+ 2023-09-04 17:07:45,975 EPOCH 1 done: loss 0.6063 - lr: 0.000030
88
+ 2023-09-04 17:07:59,999 DEV : loss 0.14032933115959167 - f1-score (micro avg) 0.6953
89
+ 2023-09-04 17:08:00,027 saving best model
90
+ 2023-09-04 17:08:00,521 ----------------------------------------------------------------------------------------------------
91
+ 2023-09-04 17:08:15,648 epoch 2 - iter 73/738 - loss 0.14356789 - time (sec): 15.13 - samples/sec: 1103.28 - lr: 0.000030 - momentum: 0.000000
92
+ 2023-09-04 17:08:29,005 epoch 2 - iter 146/738 - loss 0.13851315 - time (sec): 28.48 - samples/sec: 1159.12 - lr: 0.000029 - momentum: 0.000000
93
+ 2023-09-04 17:08:43,305 epoch 2 - iter 219/738 - loss 0.14079137 - time (sec): 42.78 - samples/sec: 1161.95 - lr: 0.000029 - momentum: 0.000000
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+ 2023-09-04 17:08:55,881 epoch 2 - iter 292/738 - loss 0.13574339 - time (sec): 55.36 - samples/sec: 1176.72 - lr: 0.000029 - momentum: 0.000000
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+ 2023-09-04 17:09:10,231 epoch 2 - iter 365/738 - loss 0.13329483 - time (sec): 69.71 - samples/sec: 1192.92 - lr: 0.000028 - momentum: 0.000000
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+ 2023-09-04 17:09:28,757 epoch 2 - iter 438/738 - loss 0.13555888 - time (sec): 88.23 - samples/sec: 1177.17 - lr: 0.000028 - momentum: 0.000000
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+ 2023-09-04 17:09:41,022 epoch 2 - iter 511/738 - loss 0.13260413 - time (sec): 100.50 - samples/sec: 1184.54 - lr: 0.000028 - momentum: 0.000000
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+ 2023-09-04 17:09:55,486 epoch 2 - iter 584/738 - loss 0.13231785 - time (sec): 114.96 - samples/sec: 1182.57 - lr: 0.000027 - momentum: 0.000000
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+ 2023-09-04 17:10:06,836 epoch 2 - iter 657/738 - loss 0.13038074 - time (sec): 126.31 - samples/sec: 1190.15 - lr: 0.000027 - momentum: 0.000000
100
+ 2023-09-04 17:10:19,322 epoch 2 - iter 730/738 - loss 0.12905878 - time (sec): 138.80 - samples/sec: 1188.62 - lr: 0.000027 - momentum: 0.000000
101
+ 2023-09-04 17:10:20,357 ----------------------------------------------------------------------------------------------------
102
+ 2023-09-04 17:10:20,358 EPOCH 2 done: loss 0.1286 - lr: 0.000027
103
+ 2023-09-04 17:10:38,110 DEV : loss 0.13280732929706573 - f1-score (micro avg) 0.7623
104
+ 2023-09-04 17:10:38,138 saving best model
105
+ 2023-09-04 17:10:39,470 ----------------------------------------------------------------------------------------------------
106
+ 2023-09-04 17:10:54,138 epoch 3 - iter 73/738 - loss 0.06806240 - time (sec): 14.67 - samples/sec: 1260.66 - lr: 0.000026 - momentum: 0.000000
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+ 2023-09-04 17:11:08,490 epoch 3 - iter 146/738 - loss 0.06502561 - time (sec): 29.02 - samples/sec: 1207.92 - lr: 0.000026 - momentum: 0.000000
108
+ 2023-09-04 17:11:21,862 epoch 3 - iter 219/738 - loss 0.06791553 - time (sec): 42.39 - samples/sec: 1221.08 - lr: 0.000026 - momentum: 0.000000
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+ 2023-09-04 17:11:37,918 epoch 3 - iter 292/738 - loss 0.07414308 - time (sec): 58.45 - samples/sec: 1196.90 - lr: 0.000025 - momentum: 0.000000
110
+ 2023-09-04 17:11:50,589 epoch 3 - iter 365/738 - loss 0.07518110 - time (sec): 71.12 - samples/sec: 1197.27 - lr: 0.000025 - momentum: 0.000000
111
+ 2023-09-04 17:12:04,515 epoch 3 - iter 438/738 - loss 0.07255404 - time (sec): 85.04 - samples/sec: 1190.92 - lr: 0.000025 - momentum: 0.000000
112
+ 2023-09-04 17:12:17,200 epoch 3 - iter 511/738 - loss 0.07205114 - time (sec): 97.73 - samples/sec: 1195.33 - lr: 0.000024 - momentum: 0.000000
113
+ 2023-09-04 17:12:30,368 epoch 3 - iter 584/738 - loss 0.07076641 - time (sec): 110.90 - samples/sec: 1197.15 - lr: 0.000024 - momentum: 0.000000
114
+ 2023-09-04 17:12:43,912 epoch 3 - iter 657/738 - loss 0.06991155 - time (sec): 124.44 - samples/sec: 1195.31 - lr: 0.000024 - momentum: 0.000000
115
+ 2023-09-04 17:12:56,870 epoch 3 - iter 730/738 - loss 0.06945430 - time (sec): 137.40 - samples/sec: 1200.20 - lr: 0.000023 - momentum: 0.000000
116
+ 2023-09-04 17:12:58,099 ----------------------------------------------------------------------------------------------------
117
+ 2023-09-04 17:12:58,099 EPOCH 3 done: loss 0.0699 - lr: 0.000023
118
+ 2023-09-04 17:13:15,894 DEV : loss 0.10660892724990845 - f1-score (micro avg) 0.8149
119
+ 2023-09-04 17:13:15,923 saving best model
120
+ 2023-09-04 17:13:17,273 ----------------------------------------------------------------------------------------------------
121
+ 2023-09-04 17:13:30,735 epoch 4 - iter 73/738 - loss 0.04211501 - time (sec): 13.46 - samples/sec: 1186.10 - lr: 0.000023 - momentum: 0.000000
122
+ 2023-09-04 17:13:47,152 epoch 4 - iter 146/738 - loss 0.05217893 - time (sec): 29.88 - samples/sec: 1187.82 - lr: 0.000023 - momentum: 0.000000
123
+ 2023-09-04 17:14:01,230 epoch 4 - iter 219/738 - loss 0.05192011 - time (sec): 43.96 - samples/sec: 1170.82 - lr: 0.000022 - momentum: 0.000000
124
+ 2023-09-04 17:14:13,520 epoch 4 - iter 292/738 - loss 0.05199982 - time (sec): 56.25 - samples/sec: 1174.31 - lr: 0.000022 - momentum: 0.000000
125
+ 2023-09-04 17:14:27,693 epoch 4 - iter 365/738 - loss 0.04904077 - time (sec): 70.42 - samples/sec: 1175.60 - lr: 0.000022 - momentum: 0.000000
126
+ 2023-09-04 17:14:40,612 epoch 4 - iter 438/738 - loss 0.04879644 - time (sec): 83.34 - samples/sec: 1186.45 - lr: 0.000021 - momentum: 0.000000
127
+ 2023-09-04 17:14:53,285 epoch 4 - iter 511/738 - loss 0.04726162 - time (sec): 96.01 - samples/sec: 1186.82 - lr: 0.000021 - momentum: 0.000000
128
+ 2023-09-04 17:15:05,939 epoch 4 - iter 584/738 - loss 0.04724198 - time (sec): 108.66 - samples/sec: 1193.64 - lr: 0.000021 - momentum: 0.000000
129
+ 2023-09-04 17:15:19,601 epoch 4 - iter 657/738 - loss 0.04765169 - time (sec): 122.33 - samples/sec: 1190.20 - lr: 0.000020 - momentum: 0.000000
130
+ 2023-09-04 17:15:35,654 epoch 4 - iter 730/738 - loss 0.04691897 - time (sec): 138.38 - samples/sec: 1190.42 - lr: 0.000020 - momentum: 0.000000
131
+ 2023-09-04 17:15:36,883 ----------------------------------------------------------------------------------------------------
132
+ 2023-09-04 17:15:36,883 EPOCH 4 done: loss 0.0470 - lr: 0.000020
133
+ 2023-09-04 17:15:54,816 DEV : loss 0.1627804934978485 - f1-score (micro avg) 0.7942
134
+ 2023-09-04 17:15:54,846 ----------------------------------------------------------------------------------------------------
135
+ 2023-09-04 17:16:08,151 epoch 5 - iter 73/738 - loss 0.04390553 - time (sec): 13.30 - samples/sec: 1257.47 - lr: 0.000020 - momentum: 0.000000
136
+ 2023-09-04 17:16:20,536 epoch 5 - iter 146/738 - loss 0.03994896 - time (sec): 25.69 - samples/sec: 1219.31 - lr: 0.000019 - momentum: 0.000000
137
+ 2023-09-04 17:16:34,713 epoch 5 - iter 219/738 - loss 0.03412480 - time (sec): 39.87 - samples/sec: 1203.06 - lr: 0.000019 - momentum: 0.000000
138
+ 2023-09-04 17:16:48,741 epoch 5 - iter 292/738 - loss 0.03160942 - time (sec): 53.89 - samples/sec: 1195.55 - lr: 0.000019 - momentum: 0.000000
139
+ 2023-09-04 17:17:03,353 epoch 5 - iter 365/738 - loss 0.03042788 - time (sec): 68.51 - samples/sec: 1189.82 - lr: 0.000018 - momentum: 0.000000
140
+ 2023-09-04 17:17:17,963 epoch 5 - iter 438/738 - loss 0.03062164 - time (sec): 83.12 - samples/sec: 1182.32 - lr: 0.000018 - momentum: 0.000000
141
+ 2023-09-04 17:17:32,140 epoch 5 - iter 511/738 - loss 0.03165621 - time (sec): 97.29 - samples/sec: 1180.48 - lr: 0.000018 - momentum: 0.000000
142
+ 2023-09-04 17:17:47,195 epoch 5 - iter 584/738 - loss 0.03216632 - time (sec): 112.35 - samples/sec: 1173.68 - lr: 0.000017 - momentum: 0.000000
143
+ 2023-09-04 17:18:01,364 epoch 5 - iter 657/738 - loss 0.03288178 - time (sec): 126.52 - samples/sec: 1175.57 - lr: 0.000017 - momentum: 0.000000
144
+ 2023-09-04 17:18:13,959 epoch 5 - iter 730/738 - loss 0.03326441 - time (sec): 139.11 - samples/sec: 1183.24 - lr: 0.000017 - momentum: 0.000000
145
+ 2023-09-04 17:18:15,739 ----------------------------------------------------------------------------------------------------
146
+ 2023-09-04 17:18:15,739 EPOCH 5 done: loss 0.0330 - lr: 0.000017
147
+ 2023-09-04 17:18:33,545 DEV : loss 0.16203893721103668 - f1-score (micro avg) 0.8227
148
+ 2023-09-04 17:18:33,578 saving best model
149
+ 2023-09-04 17:18:35,644 ----------------------------------------------------------------------------------------------------
150
+ 2023-09-04 17:18:49,846 epoch 6 - iter 73/738 - loss 0.02957209 - time (sec): 14.20 - samples/sec: 1194.69 - lr: 0.000016 - momentum: 0.000000
151
+ 2023-09-04 17:19:02,829 epoch 6 - iter 146/738 - loss 0.02604171 - time (sec): 27.18 - samples/sec: 1186.45 - lr: 0.000016 - momentum: 0.000000
152
+ 2023-09-04 17:19:16,880 epoch 6 - iter 219/738 - loss 0.02445018 - time (sec): 41.23 - samples/sec: 1173.40 - lr: 0.000016 - momentum: 0.000000
153
+ 2023-09-04 17:19:30,735 epoch 6 - iter 292/738 - loss 0.02570378 - time (sec): 55.09 - samples/sec: 1172.42 - lr: 0.000015 - momentum: 0.000000
154
+ 2023-09-04 17:19:44,117 epoch 6 - iter 365/738 - loss 0.02654966 - time (sec): 68.47 - samples/sec: 1170.66 - lr: 0.000015 - momentum: 0.000000
155
+ 2023-09-04 17:19:56,314 epoch 6 - iter 438/738 - loss 0.02575595 - time (sec): 80.67 - samples/sec: 1181.73 - lr: 0.000015 - momentum: 0.000000
156
+ 2023-09-04 17:20:11,322 epoch 6 - iter 511/738 - loss 0.02571190 - time (sec): 95.68 - samples/sec: 1181.81 - lr: 0.000014 - momentum: 0.000000
157
+ 2023-09-04 17:20:24,989 epoch 6 - iter 584/738 - loss 0.02564663 - time (sec): 109.34 - samples/sec: 1182.99 - lr: 0.000014 - momentum: 0.000000
158
+ 2023-09-04 17:20:39,162 epoch 6 - iter 657/738 - loss 0.02646083 - time (sec): 123.52 - samples/sec: 1180.71 - lr: 0.000014 - momentum: 0.000000
159
+ 2023-09-04 17:20:53,350 epoch 6 - iter 730/738 - loss 0.02546313 - time (sec): 137.71 - samples/sec: 1191.68 - lr: 0.000013 - momentum: 0.000000
160
+ 2023-09-04 17:20:55,497 ----------------------------------------------------------------------------------------------------
161
+ 2023-09-04 17:20:55,497 EPOCH 6 done: loss 0.0254 - lr: 0.000013
162
+ 2023-09-04 17:21:13,397 DEV : loss 0.18152987957000732 - f1-score (micro avg) 0.8173
163
+ 2023-09-04 17:21:13,426 ----------------------------------------------------------------------------------------------------
164
+ 2023-09-04 17:21:27,269 epoch 7 - iter 73/738 - loss 0.02416734 - time (sec): 13.84 - samples/sec: 1253.04 - lr: 0.000013 - momentum: 0.000000
165
+ 2023-09-04 17:21:44,635 epoch 7 - iter 146/738 - loss 0.02179280 - time (sec): 31.21 - samples/sec: 1145.86 - lr: 0.000013 - momentum: 0.000000
166
+ 2023-09-04 17:21:59,425 epoch 7 - iter 219/738 - loss 0.02002199 - time (sec): 46.00 - samples/sec: 1151.72 - lr: 0.000012 - momentum: 0.000000
167
+ 2023-09-04 17:22:14,202 epoch 7 - iter 292/738 - loss 0.02148718 - time (sec): 60.77 - samples/sec: 1168.55 - lr: 0.000012 - momentum: 0.000000
168
+ 2023-09-04 17:22:25,591 epoch 7 - iter 365/738 - loss 0.02035128 - time (sec): 72.16 - samples/sec: 1190.31 - lr: 0.000012 - momentum: 0.000000
169
+ 2023-09-04 17:22:39,441 epoch 7 - iter 438/738 - loss 0.01976529 - time (sec): 86.01 - samples/sec: 1190.69 - lr: 0.000011 - momentum: 0.000000
170
+ 2023-09-04 17:22:52,656 epoch 7 - iter 511/738 - loss 0.01906580 - time (sec): 99.23 - samples/sec: 1190.42 - lr: 0.000011 - momentum: 0.000000
171
+ 2023-09-04 17:23:05,413 epoch 7 - iter 584/738 - loss 0.01840833 - time (sec): 111.99 - samples/sec: 1192.81 - lr: 0.000011 - momentum: 0.000000
172
+ 2023-09-04 17:23:18,403 epoch 7 - iter 657/738 - loss 0.01877719 - time (sec): 124.98 - samples/sec: 1195.22 - lr: 0.000010 - momentum: 0.000000
173
+ 2023-09-04 17:23:31,117 epoch 7 - iter 730/738 - loss 0.01892299 - time (sec): 137.69 - samples/sec: 1195.41 - lr: 0.000010 - momentum: 0.000000
174
+ 2023-09-04 17:23:32,472 ----------------------------------------------------------------------------------------------------
175
+ 2023-09-04 17:23:32,472 EPOCH 7 done: loss 0.0189 - lr: 0.000010
176
+ 2023-09-04 17:23:50,228 DEV : loss 0.19626368582248688 - f1-score (micro avg) 0.8313
177
+ 2023-09-04 17:23:50,258 saving best model
178
+ 2023-09-04 17:23:51,614 ----------------------------------------------------------------------------------------------------
179
+ 2023-09-04 17:24:05,491 epoch 8 - iter 73/738 - loss 0.01534096 - time (sec): 13.87 - samples/sec: 1270.44 - lr: 0.000010 - momentum: 0.000000
180
+ 2023-09-04 17:24:18,087 epoch 8 - iter 146/738 - loss 0.01255125 - time (sec): 26.47 - samples/sec: 1240.77 - lr: 0.000009 - momentum: 0.000000
181
+ 2023-09-04 17:24:33,547 epoch 8 - iter 219/738 - loss 0.01428924 - time (sec): 41.93 - samples/sec: 1230.57 - lr: 0.000009 - momentum: 0.000000
182
+ 2023-09-04 17:24:47,671 epoch 8 - iter 292/738 - loss 0.01416082 - time (sec): 56.05 - samples/sec: 1195.15 - lr: 0.000009 - momentum: 0.000000
183
+ 2023-09-04 17:24:59,280 epoch 8 - iter 365/738 - loss 0.01570083 - time (sec): 67.66 - samples/sec: 1203.39 - lr: 0.000008 - momentum: 0.000000
184
+ 2023-09-04 17:25:12,833 epoch 8 - iter 438/738 - loss 0.01512316 - time (sec): 81.22 - samples/sec: 1197.69 - lr: 0.000008 - momentum: 0.000000
185
+ 2023-09-04 17:25:27,061 epoch 8 - iter 511/738 - loss 0.01512551 - time (sec): 95.45 - samples/sec: 1202.02 - lr: 0.000008 - momentum: 0.000000
186
+ 2023-09-04 17:25:40,956 epoch 8 - iter 584/738 - loss 0.01461940 - time (sec): 109.34 - samples/sec: 1197.41 - lr: 0.000007 - momentum: 0.000000
187
+ 2023-09-04 17:25:54,972 epoch 8 - iter 657/738 - loss 0.01505426 - time (sec): 123.36 - samples/sec: 1192.80 - lr: 0.000007 - momentum: 0.000000
188
+ 2023-09-04 17:26:09,352 epoch 8 - iter 730/738 - loss 0.01455100 - time (sec): 137.74 - samples/sec: 1191.20 - lr: 0.000007 - momentum: 0.000000
189
+ 2023-09-04 17:26:11,397 ----------------------------------------------------------------------------------------------------
190
+ 2023-09-04 17:26:11,397 EPOCH 8 done: loss 0.0144 - lr: 0.000007
191
+ 2023-09-04 17:26:29,167 DEV : loss 0.1902199536561966 - f1-score (micro avg) 0.8285
192
+ 2023-09-04 17:26:29,196 ----------------------------------------------------------------------------------------------------
193
+ 2023-09-04 17:26:42,681 epoch 9 - iter 73/738 - loss 0.01365241 - time (sec): 13.48 - samples/sec: 1184.35 - lr: 0.000006 - momentum: 0.000000
194
+ 2023-09-04 17:26:55,970 epoch 9 - iter 146/738 - loss 0.01227763 - time (sec): 26.77 - samples/sec: 1200.85 - lr: 0.000006 - momentum: 0.000000
195
+ 2023-09-04 17:27:07,868 epoch 9 - iter 219/738 - loss 0.01017123 - time (sec): 38.67 - samples/sec: 1212.02 - lr: 0.000006 - momentum: 0.000000
196
+ 2023-09-04 17:27:23,028 epoch 9 - iter 292/738 - loss 0.00963736 - time (sec): 53.83 - samples/sec: 1184.85 - lr: 0.000005 - momentum: 0.000000
197
+ 2023-09-04 17:27:35,982 epoch 9 - iter 365/738 - loss 0.00973205 - time (sec): 66.78 - samples/sec: 1187.16 - lr: 0.000005 - momentum: 0.000000
198
+ 2023-09-04 17:27:50,061 epoch 9 - iter 438/738 - loss 0.00867506 - time (sec): 80.86 - samples/sec: 1180.32 - lr: 0.000005 - momentum: 0.000000
199
+ 2023-09-04 17:28:04,505 epoch 9 - iter 511/738 - loss 0.00896850 - time (sec): 95.31 - samples/sec: 1186.53 - lr: 0.000004 - momentum: 0.000000
200
+ 2023-09-04 17:28:20,307 epoch 9 - iter 584/738 - loss 0.00934014 - time (sec): 111.11 - samples/sec: 1183.19 - lr: 0.000004 - momentum: 0.000000
201
+ 2023-09-04 17:28:33,109 epoch 9 - iter 657/738 - loss 0.00881533 - time (sec): 123.91 - samples/sec: 1184.76 - lr: 0.000004 - momentum: 0.000000
202
+ 2023-09-04 17:28:46,582 epoch 9 - iter 730/738 - loss 0.00916508 - time (sec): 137.39 - samples/sec: 1191.24 - lr: 0.000003 - momentum: 0.000000
203
+ 2023-09-04 17:28:49,264 ----------------------------------------------------------------------------------------------------
204
+ 2023-09-04 17:28:49,264 EPOCH 9 done: loss 0.0090 - lr: 0.000003
205
+ 2023-09-04 17:29:07,088 DEV : loss 0.19453999400138855 - f1-score (micro avg) 0.8393
206
+ 2023-09-04 17:29:07,117 saving best model
207
+ 2023-09-04 17:29:08,499 ----------------------------------------------------------------------------------------------------
208
+ 2023-09-04 17:29:20,809 epoch 10 - iter 73/738 - loss 0.00176209 - time (sec): 12.31 - samples/sec: 1233.49 - lr: 0.000003 - momentum: 0.000000
209
+ 2023-09-04 17:29:35,751 epoch 10 - iter 146/738 - loss 0.00467834 - time (sec): 27.25 - samples/sec: 1191.62 - lr: 0.000003 - momentum: 0.000000
210
+ 2023-09-04 17:29:50,939 epoch 10 - iter 219/738 - loss 0.00511860 - time (sec): 42.44 - samples/sec: 1160.63 - lr: 0.000002 - momentum: 0.000000
211
+ 2023-09-04 17:30:05,636 epoch 10 - iter 292/738 - loss 0.00537182 - time (sec): 57.14 - samples/sec: 1161.72 - lr: 0.000002 - momentum: 0.000000
212
+ 2023-09-04 17:30:20,785 epoch 10 - iter 365/738 - loss 0.00554034 - time (sec): 72.28 - samples/sec: 1165.42 - lr: 0.000002 - momentum: 0.000000
213
+ 2023-09-04 17:30:33,186 epoch 10 - iter 438/738 - loss 0.00597059 - time (sec): 84.69 - samples/sec: 1178.41 - lr: 0.000001 - momentum: 0.000000
214
+ 2023-09-04 17:30:45,324 epoch 10 - iter 511/738 - loss 0.00746141 - time (sec): 96.82 - samples/sec: 1189.04 - lr: 0.000001 - momentum: 0.000000
215
+ 2023-09-04 17:30:59,646 epoch 10 - iter 584/738 - loss 0.00734769 - time (sec): 111.15 - samples/sec: 1183.33 - lr: 0.000001 - momentum: 0.000000
216
+ 2023-09-04 17:31:14,105 epoch 10 - iter 657/738 - loss 0.00748926 - time (sec): 125.60 - samples/sec: 1188.68 - lr: 0.000000 - momentum: 0.000000
217
+ 2023-09-04 17:31:26,568 epoch 10 - iter 730/738 - loss 0.00742395 - time (sec): 138.07 - samples/sec: 1193.87 - lr: 0.000000 - momentum: 0.000000
218
+ 2023-09-04 17:31:27,760 ----------------------------------------------------------------------------------------------------
219
+ 2023-09-04 17:31:27,760 EPOCH 10 done: loss 0.0074 - lr: 0.000000
220
+ 2023-09-04 17:31:45,856 DEV : loss 0.19926118850708008 - f1-score (micro avg) 0.8412
221
+ 2023-09-04 17:31:45,886 saving best model
222
+ 2023-09-04 17:31:48,133 ----------------------------------------------------------------------------------------------------
223
+ 2023-09-04 17:31:48,135 Loading model from best epoch ...
224
+ 2023-09-04 17:31:50,019 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-time, B-time, E-time, I-time, S-prod, B-prod, E-prod, I-prod
225
+ 2023-09-04 17:32:04,887
226
+ Results:
227
+ - F-score (micro) 0.8043
228
+ - F-score (macro) 0.6894
229
+ - Accuracy 0.6952
230
+
231
+ By class:
232
+ precision recall f1-score support
233
+
234
+ loc 0.8685 0.8928 0.8805 858
235
+ pers 0.7614 0.8082 0.7841 537
236
+ org 0.5639 0.5682 0.5660 132
237
+ prod 0.6333 0.6230 0.6281 61
238
+ time 0.5385 0.6481 0.5882 54
239
+
240
+ micro avg 0.7883 0.8210 0.8043 1642
241
+ macro avg 0.6731 0.7080 0.6894 1642
242
+ weighted avg 0.7894 0.8210 0.8047 1642
243
+
244
+ 2023-09-04 17:32:04,887 ----------------------------------------------------------------------------------------------------