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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-3/best-model.pt +3 -0
  2. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3/dev.tsv +0 -0
  3. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3/final-model.pt +3 -0
  4. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3/loss.tsv +11 -0
  5. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3/test.tsv +0 -0
  6. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3/training.log +244 -0
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3/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-3/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-3/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-3/loss.tsv ADDED
@@ -0,0 +1,11 @@
 
 
 
 
 
 
 
 
 
 
 
 
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+ EPOCH TIMESTAMP LEARNING_RATE TRAIN_LOSS DEV_LOSS DEV_PRECISION DEV_RECALL DEV_F1 DEV_ACCURACY
2
+ 1 13:14:25 0.0000 0.5892 0.1587 0.6165 0.6987 0.6550 0.5098
3
+ 2 13:17:03 0.0000 0.1282 0.1271 0.7215 0.8070 0.7618 0.6422
4
+ 3 13:19:41 0.0000 0.0701 0.1342 0.8198 0.8053 0.8125 0.7123
5
+ 4 13:22:20 0.0000 0.0480 0.1354 0.8137 0.8230 0.8183 0.7221
6
+ 5 13:24:59 0.0000 0.0338 0.1619 0.7995 0.8517 0.8247 0.7211
7
+ 6 13:27:36 0.0000 0.0241 0.1825 0.8040 0.8436 0.8234 0.7274
8
+ 7 13:30:13 0.0000 0.0162 0.1901 0.8071 0.8482 0.8271 0.7306
9
+ 8 13:32:51 0.0000 0.0122 0.1996 0.8367 0.8568 0.8466 0.7533
10
+ 9 13:35:30 0.0000 0.0105 0.2048 0.8151 0.8557 0.8349 0.7411
11
+ 10 13:38:07 0.0000 0.0072 0.2036 0.8169 0.8534 0.8347 0.7420
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3/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-3/training.log ADDED
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+ 2023-09-04 13:11:51,975 ----------------------------------------------------------------------------------------------------
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+ 2023-09-04 13:11:51,976 Model: "SequenceTagger(
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+ (embeddings): TransformerWordEmbeddings(
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+ (model): BertModel(
5
+ (embeddings): BertEmbeddings(
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+ (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)
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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(
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): 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 13:11:51,976 ----------------------------------------------------------------------------------------------------
51
+ 2023-09-04 13:11:51,976 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 13:11:51,977 ----------------------------------------------------------------------------------------------------
54
+ 2023-09-04 13:11:51,977 Train: 5901 sentences
55
+ 2023-09-04 13:11:51,977 (train_with_dev=False, train_with_test=False)
56
+ 2023-09-04 13:11:51,977 ----------------------------------------------------------------------------------------------------
57
+ 2023-09-04 13:11:51,977 Training Params:
58
+ 2023-09-04 13:11:51,977 - learning_rate: "3e-05"
59
+ 2023-09-04 13:11:51,977 - mini_batch_size: "8"
60
+ 2023-09-04 13:11:51,977 - max_epochs: "10"
61
+ 2023-09-04 13:11:51,977 - shuffle: "True"
62
+ 2023-09-04 13:11:51,977 ----------------------------------------------------------------------------------------------------
63
+ 2023-09-04 13:11:51,977 Plugins:
64
+ 2023-09-04 13:11:51,977 - LinearScheduler | warmup_fraction: '0.1'
65
+ 2023-09-04 13:11:51,977 ----------------------------------------------------------------------------------------------------
66
+ 2023-09-04 13:11:51,977 Final evaluation on model from best epoch (best-model.pt)
67
+ 2023-09-04 13:11:51,977 - metric: "('micro avg', 'f1-score')"
68
+ 2023-09-04 13:11:51,977 ----------------------------------------------------------------------------------------------------
69
+ 2023-09-04 13:11:51,977 Computation:
70
+ 2023-09-04 13:11:51,977 - compute on device: cuda:0
71
+ 2023-09-04 13:11:51,977 - embedding storage: none
72
+ 2023-09-04 13:11:51,977 ----------------------------------------------------------------------------------------------------
73
+ 2023-09-04 13:11:51,978 Model training base path: "hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3"
74
+ 2023-09-04 13:11:51,978 ----------------------------------------------------------------------------------------------------
75
+ 2023-09-04 13:11:51,978 ----------------------------------------------------------------------------------------------------
76
+ 2023-09-04 13:12:03,953 epoch 1 - iter 73/738 - loss 2.75602228 - time (sec): 11.97 - samples/sec: 1273.50 - lr: 0.000003 - momentum: 0.000000
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+ 2023-09-04 13:12:17,588 epoch 1 - iter 146/738 - loss 1.78975133 - time (sec): 25.61 - samples/sec: 1274.41 - lr: 0.000006 - momentum: 0.000000
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+ 2023-09-04 13:12:30,242 epoch 1 - iter 219/738 - loss 1.38693502 - time (sec): 38.26 - samples/sec: 1246.97 - lr: 0.000009 - momentum: 0.000000
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+ 2023-09-04 13:12:43,939 epoch 1 - iter 292/738 - loss 1.13179773 - time (sec): 51.96 - samples/sec: 1232.31 - lr: 0.000012 - momentum: 0.000000
80
+ 2023-09-04 13:12:57,996 epoch 1 - iter 365/738 - loss 0.96224049 - time (sec): 66.02 - samples/sec: 1228.92 - lr: 0.000015 - momentum: 0.000000
81
+ 2023-09-04 13:13:12,327 epoch 1 - iter 438/738 - loss 0.84747624 - time (sec): 80.35 - samples/sec: 1221.50 - lr: 0.000018 - momentum: 0.000000
82
+ 2023-09-04 13:13:27,827 epoch 1 - iter 511/738 - loss 0.75714596 - time (sec): 95.85 - samples/sec: 1207.80 - lr: 0.000021 - momentum: 0.000000
83
+ 2023-09-04 13:13:40,696 epoch 1 - iter 584/738 - loss 0.69586826 - time (sec): 108.72 - samples/sec: 1208.13 - lr: 0.000024 - momentum: 0.000000
84
+ 2023-09-04 13:13:56,760 epoch 1 - iter 657/738 - loss 0.63385064 - time (sec): 124.78 - samples/sec: 1196.11 - lr: 0.000027 - momentum: 0.000000
85
+ 2023-09-04 13:14:09,623 epoch 1 - iter 730/738 - loss 0.59220811 - time (sec): 137.64 - samples/sec: 1195.82 - lr: 0.000030 - momentum: 0.000000
86
+ 2023-09-04 13:14:11,411 ----------------------------------------------------------------------------------------------------
87
+ 2023-09-04 13:14:11,412 EPOCH 1 done: loss 0.5892 - lr: 0.000030
88
+ 2023-09-04 13:14:25,706 DEV : loss 0.1587315797805786 - f1-score (micro avg) 0.655
89
+ 2023-09-04 13:14:25,738 saving best model
90
+ 2023-09-04 13:14:26,224 ----------------------------------------------------------------------------------------------------
91
+ 2023-09-04 13:14:39,513 epoch 2 - iter 73/738 - loss 0.14661112 - time (sec): 13.29 - samples/sec: 1235.59 - lr: 0.000030 - momentum: 0.000000
92
+ 2023-09-04 13:14:54,190 epoch 2 - iter 146/738 - loss 0.14644812 - time (sec): 27.96 - samples/sec: 1196.07 - lr: 0.000029 - momentum: 0.000000
93
+ 2023-09-04 13:15:09,943 epoch 2 - iter 219/738 - loss 0.14424972 - time (sec): 43.72 - samples/sec: 1185.36 - lr: 0.000029 - momentum: 0.000000
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+ 2023-09-04 13:15:22,687 epoch 2 - iter 292/738 - loss 0.13918826 - time (sec): 56.46 - samples/sec: 1192.27 - lr: 0.000029 - momentum: 0.000000
95
+ 2023-09-04 13:15:35,226 epoch 2 - iter 365/738 - loss 0.13897294 - time (sec): 69.00 - samples/sec: 1202.28 - lr: 0.000028 - momentum: 0.000000
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+ 2023-09-04 13:15:51,089 epoch 2 - iter 438/738 - loss 0.13695682 - time (sec): 84.86 - samples/sec: 1190.76 - lr: 0.000028 - momentum: 0.000000
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+ 2023-09-04 13:16:04,920 epoch 2 - iter 511/738 - loss 0.13451500 - time (sec): 98.70 - samples/sec: 1190.42 - lr: 0.000028 - momentum: 0.000000
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+ 2023-09-04 13:16:18,494 epoch 2 - iter 584/738 - loss 0.13136503 - time (sec): 112.27 - samples/sec: 1187.92 - lr: 0.000027 - momentum: 0.000000
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+ 2023-09-04 13:16:31,506 epoch 2 - iter 657/738 - loss 0.12943913 - time (sec): 125.28 - samples/sec: 1190.07 - lr: 0.000027 - momentum: 0.000000
100
+ 2023-09-04 13:16:44,391 epoch 2 - iter 730/738 - loss 0.12776621 - time (sec): 138.17 - samples/sec: 1194.73 - lr: 0.000027 - momentum: 0.000000
101
+ 2023-09-04 13:16:45,552 ----------------------------------------------------------------------------------------------------
102
+ 2023-09-04 13:16:45,552 EPOCH 2 done: loss 0.1282 - lr: 0.000027
103
+ 2023-09-04 13:17:03,310 DEV : loss 0.12707410752773285 - f1-score (micro avg) 0.7618
104
+ 2023-09-04 13:17:03,352 saving best model
105
+ 2023-09-04 13:17:04,706 ----------------------------------------------------------------------------------------------------
106
+ 2023-09-04 13:17:16,977 epoch 3 - iter 73/738 - loss 0.05888291 - time (sec): 12.27 - samples/sec: 1236.67 - lr: 0.000026 - momentum: 0.000000
107
+ 2023-09-04 13:17:30,274 epoch 3 - iter 146/738 - loss 0.06740253 - time (sec): 25.57 - samples/sec: 1244.96 - lr: 0.000026 - momentum: 0.000000
108
+ 2023-09-04 13:17:43,955 epoch 3 - iter 219/738 - loss 0.06793000 - time (sec): 39.25 - samples/sec: 1228.69 - lr: 0.000026 - momentum: 0.000000
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+ 2023-09-04 13:17:59,973 epoch 3 - iter 292/738 - loss 0.06543958 - time (sec): 55.27 - samples/sec: 1220.52 - lr: 0.000025 - momentum: 0.000000
110
+ 2023-09-04 13:18:14,484 epoch 3 - iter 365/738 - loss 0.06924468 - time (sec): 69.78 - samples/sec: 1201.50 - lr: 0.000025 - momentum: 0.000000
111
+ 2023-09-04 13:18:27,108 epoch 3 - iter 438/738 - loss 0.06805337 - time (sec): 82.40 - samples/sec: 1207.20 - lr: 0.000025 - momentum: 0.000000
112
+ 2023-09-04 13:18:42,057 epoch 3 - iter 511/738 - loss 0.07060851 - time (sec): 97.35 - samples/sec: 1199.40 - lr: 0.000024 - momentum: 0.000000
113
+ 2023-09-04 13:18:55,060 epoch 3 - iter 584/738 - loss 0.06855825 - time (sec): 110.35 - samples/sec: 1201.96 - lr: 0.000024 - momentum: 0.000000
114
+ 2023-09-04 13:19:09,159 epoch 3 - iter 657/738 - loss 0.07127784 - time (sec): 124.45 - samples/sec: 1196.62 - lr: 0.000024 - momentum: 0.000000
115
+ 2023-09-04 13:19:22,578 epoch 3 - iter 730/738 - loss 0.07001988 - time (sec): 137.87 - samples/sec: 1193.91 - lr: 0.000023 - momentum: 0.000000
116
+ 2023-09-04 13:19:24,071 ----------------------------------------------------------------------------------------------------
117
+ 2023-09-04 13:19:24,071 EPOCH 3 done: loss 0.0701 - lr: 0.000023
118
+ 2023-09-04 13:19:41,811 DEV : loss 0.13421885669231415 - f1-score (micro avg) 0.8125
119
+ 2023-09-04 13:19:41,840 saving best model
120
+ 2023-09-04 13:19:43,182 ----------------------------------------------------------------------------------------------------
121
+ 2023-09-04 13:19:57,692 epoch 4 - iter 73/738 - loss 0.05014642 - time (sec): 14.51 - samples/sec: 1191.67 - lr: 0.000023 - momentum: 0.000000
122
+ 2023-09-04 13:20:11,003 epoch 4 - iter 146/738 - loss 0.04616425 - time (sec): 27.82 - samples/sec: 1209.40 - lr: 0.000023 - momentum: 0.000000
123
+ 2023-09-04 13:20:25,225 epoch 4 - iter 219/738 - loss 0.04529066 - time (sec): 42.04 - samples/sec: 1208.84 - lr: 0.000022 - momentum: 0.000000
124
+ 2023-09-04 13:20:38,127 epoch 4 - iter 292/738 - loss 0.04577257 - time (sec): 54.94 - samples/sec: 1212.18 - lr: 0.000022 - momentum: 0.000000
125
+ 2023-09-04 13:20:50,019 epoch 4 - iter 365/738 - loss 0.04568850 - time (sec): 66.84 - samples/sec: 1219.99 - lr: 0.000022 - momentum: 0.000000
126
+ 2023-09-04 13:21:04,952 epoch 4 - iter 438/738 - loss 0.04500584 - time (sec): 81.77 - samples/sec: 1206.64 - lr: 0.000021 - momentum: 0.000000
127
+ 2023-09-04 13:21:21,135 epoch 4 - iter 511/738 - loss 0.04786348 - time (sec): 97.95 - samples/sec: 1200.02 - lr: 0.000021 - momentum: 0.000000
128
+ 2023-09-04 13:21:33,905 epoch 4 - iter 584/738 - loss 0.04748241 - time (sec): 110.72 - samples/sec: 1198.21 - lr: 0.000021 - momentum: 0.000000
129
+ 2023-09-04 13:21:47,311 epoch 4 - iter 657/738 - loss 0.04592685 - time (sec): 124.13 - samples/sec: 1195.38 - lr: 0.000020 - momentum: 0.000000
130
+ 2023-09-04 13:22:00,502 epoch 4 - iter 730/738 - loss 0.04810729 - time (sec): 137.32 - samples/sec: 1199.11 - lr: 0.000020 - momentum: 0.000000
131
+ 2023-09-04 13:22:02,199 ----------------------------------------------------------------------------------------------------
132
+ 2023-09-04 13:22:02,199 EPOCH 4 done: loss 0.0480 - lr: 0.000020
133
+ 2023-09-04 13:22:19,999 DEV : loss 0.13538895547389984 - f1-score (micro avg) 0.8183
134
+ 2023-09-04 13:22:20,028 saving best model
135
+ 2023-09-04 13:22:21,380 ----------------------------------------------------------------------------------------------------
136
+ 2023-09-04 13:22:36,051 epoch 5 - iter 73/738 - loss 0.04671179 - time (sec): 14.67 - samples/sec: 1233.47 - lr: 0.000020 - momentum: 0.000000
137
+ 2023-09-04 13:22:49,208 epoch 5 - iter 146/738 - loss 0.03446949 - time (sec): 27.83 - samples/sec: 1221.96 - lr: 0.000019 - momentum: 0.000000
138
+ 2023-09-04 13:23:04,474 epoch 5 - iter 219/738 - loss 0.03659458 - time (sec): 43.09 - samples/sec: 1192.53 - lr: 0.000019 - momentum: 0.000000
139
+ 2023-09-04 13:23:17,370 epoch 5 - iter 292/738 - loss 0.03531527 - time (sec): 55.99 - samples/sec: 1192.59 - lr: 0.000019 - momentum: 0.000000
140
+ 2023-09-04 13:23:29,921 epoch 5 - iter 365/738 - loss 0.03436687 - time (sec): 68.54 - samples/sec: 1200.03 - lr: 0.000018 - momentum: 0.000000
141
+ 2023-09-04 13:23:43,300 epoch 5 - iter 438/738 - loss 0.03255600 - time (sec): 81.92 - samples/sec: 1193.43 - lr: 0.000018 - momentum: 0.000000
142
+ 2023-09-04 13:23:57,599 epoch 5 - iter 511/738 - loss 0.03506066 - time (sec): 96.22 - samples/sec: 1187.46 - lr: 0.000018 - momentum: 0.000000
143
+ 2023-09-04 13:24:11,271 epoch 5 - iter 584/738 - loss 0.03440222 - time (sec): 109.89 - samples/sec: 1183.17 - lr: 0.000017 - momentum: 0.000000
144
+ 2023-09-04 13:24:23,715 epoch 5 - iter 657/738 - loss 0.03342787 - time (sec): 122.33 - samples/sec: 1189.46 - lr: 0.000017 - momentum: 0.000000
145
+ 2023-09-04 13:24:39,828 epoch 5 - iter 730/738 - loss 0.03369684 - time (sec): 138.45 - samples/sec: 1189.46 - lr: 0.000017 - momentum: 0.000000
146
+ 2023-09-04 13:24:41,217 ----------------------------------------------------------------------------------------------------
147
+ 2023-09-04 13:24:41,217 EPOCH 5 done: loss 0.0338 - lr: 0.000017
148
+ 2023-09-04 13:24:58,989 DEV : loss 0.1618889570236206 - f1-score (micro avg) 0.8247
149
+ 2023-09-04 13:24:59,017 saving best model
150
+ 2023-09-04 13:25:00,709 ----------------------------------------------------------------------------------------------------
151
+ 2023-09-04 13:25:15,781 epoch 6 - iter 73/738 - loss 0.02828833 - time (sec): 15.07 - samples/sec: 1198.38 - lr: 0.000016 - momentum: 0.000000
152
+ 2023-09-04 13:25:30,703 epoch 6 - iter 146/738 - loss 0.02403498 - time (sec): 29.99 - samples/sec: 1181.15 - lr: 0.000016 - momentum: 0.000000
153
+ 2023-09-04 13:25:46,210 epoch 6 - iter 219/738 - loss 0.02668753 - time (sec): 45.50 - samples/sec: 1181.33 - lr: 0.000016 - momentum: 0.000000
154
+ 2023-09-04 13:25:59,184 epoch 6 - iter 292/738 - loss 0.02491381 - time (sec): 58.47 - samples/sec: 1191.98 - lr: 0.000015 - momentum: 0.000000
155
+ 2023-09-04 13:26:12,291 epoch 6 - iter 365/738 - loss 0.02476714 - time (sec): 71.58 - samples/sec: 1196.85 - lr: 0.000015 - momentum: 0.000000
156
+ 2023-09-04 13:26:26,470 epoch 6 - iter 438/738 - loss 0.02513810 - time (sec): 85.76 - samples/sec: 1189.15 - lr: 0.000015 - momentum: 0.000000
157
+ 2023-09-04 13:26:40,199 epoch 6 - iter 511/738 - loss 0.02481393 - time (sec): 99.49 - samples/sec: 1188.50 - lr: 0.000014 - momentum: 0.000000
158
+ 2023-09-04 13:26:52,644 epoch 6 - iter 584/738 - loss 0.02428163 - time (sec): 111.93 - samples/sec: 1191.01 - lr: 0.000014 - momentum: 0.000000
159
+ 2023-09-04 13:27:05,379 epoch 6 - iter 657/738 - loss 0.02398217 - time (sec): 124.67 - samples/sec: 1193.36 - lr: 0.000014 - momentum: 0.000000
160
+ 2023-09-04 13:27:17,979 epoch 6 - iter 730/738 - loss 0.02425206 - time (sec): 137.27 - samples/sec: 1198.33 - lr: 0.000013 - momentum: 0.000000
161
+ 2023-09-04 13:27:19,256 ----------------------------------------------------------------------------------------------------
162
+ 2023-09-04 13:27:19,256 EPOCH 6 done: loss 0.0241 - lr: 0.000013
163
+ 2023-09-04 13:27:36,813 DEV : loss 0.1825319528579712 - f1-score (micro avg) 0.8234
164
+ 2023-09-04 13:27:36,842 ----------------------------------------------------------------------------------------------------
165
+ 2023-09-04 13:27:48,635 epoch 7 - iter 73/738 - loss 0.00914290 - time (sec): 11.79 - samples/sec: 1304.15 - lr: 0.000013 - momentum: 0.000000
166
+ 2023-09-04 13:28:03,792 epoch 7 - iter 146/738 - loss 0.01069055 - time (sec): 26.95 - samples/sec: 1224.80 - lr: 0.000013 - momentum: 0.000000
167
+ 2023-09-04 13:28:15,680 epoch 7 - iter 219/738 - loss 0.01521915 - time (sec): 38.84 - samples/sec: 1230.65 - lr: 0.000012 - momentum: 0.000000
168
+ 2023-09-04 13:28:31,130 epoch 7 - iter 292/738 - loss 0.01541385 - time (sec): 54.29 - samples/sec: 1200.87 - lr: 0.000012 - momentum: 0.000000
169
+ 2023-09-04 13:28:43,444 epoch 7 - iter 365/738 - loss 0.01508426 - time (sec): 66.60 - samples/sec: 1211.95 - lr: 0.000012 - momentum: 0.000000
170
+ 2023-09-04 13:28:55,691 epoch 7 - iter 438/738 - loss 0.01526207 - time (sec): 78.85 - samples/sec: 1215.71 - lr: 0.000011 - momentum: 0.000000
171
+ 2023-09-04 13:29:13,342 epoch 7 - iter 511/738 - loss 0.01443015 - time (sec): 96.50 - samples/sec: 1198.49 - lr: 0.000011 - momentum: 0.000000
172
+ 2023-09-04 13:29:27,917 epoch 7 - iter 584/738 - loss 0.01549634 - time (sec): 111.07 - samples/sec: 1196.09 - lr: 0.000011 - momentum: 0.000000
173
+ 2023-09-04 13:29:42,011 epoch 7 - iter 657/738 - loss 0.01657500 - time (sec): 125.17 - samples/sec: 1193.80 - lr: 0.000010 - momentum: 0.000000
174
+ 2023-09-04 13:29:54,812 epoch 7 - iter 730/738 - loss 0.01625377 - time (sec): 137.97 - samples/sec: 1195.47 - lr: 0.000010 - momentum: 0.000000
175
+ 2023-09-04 13:29:55,998 ----------------------------------------------------------------------------------------------------
176
+ 2023-09-04 13:29:55,998 EPOCH 7 done: loss 0.0162 - lr: 0.000010
177
+ 2023-09-04 13:30:13,528 DEV : loss 0.19014191627502441 - f1-score (micro avg) 0.8271
178
+ 2023-09-04 13:30:13,559 saving best model
179
+ 2023-09-04 13:30:14,932 ----------------------------------------------------------------------------------------------------
180
+ 2023-09-04 13:30:28,527 epoch 8 - iter 73/738 - loss 0.01108812 - time (sec): 13.59 - samples/sec: 1234.53 - lr: 0.000010 - momentum: 0.000000
181
+ 2023-09-04 13:30:43,067 epoch 8 - iter 146/738 - loss 0.01146445 - time (sec): 28.13 - samples/sec: 1216.69 - lr: 0.000009 - momentum: 0.000000
182
+ 2023-09-04 13:30:58,044 epoch 8 - iter 219/738 - loss 0.01584998 - time (sec): 43.11 - samples/sec: 1214.91 - lr: 0.000009 - momentum: 0.000000
183
+ 2023-09-04 13:31:12,736 epoch 8 - iter 292/738 - loss 0.01561122 - time (sec): 57.80 - samples/sec: 1195.87 - lr: 0.000009 - momentum: 0.000000
184
+ 2023-09-04 13:31:26,522 epoch 8 - iter 365/738 - loss 0.01502835 - time (sec): 71.59 - samples/sec: 1193.29 - lr: 0.000008 - momentum: 0.000000
185
+ 2023-09-04 13:31:40,747 epoch 8 - iter 438/738 - loss 0.01318786 - time (sec): 85.81 - samples/sec: 1193.34 - lr: 0.000008 - momentum: 0.000000
186
+ 2023-09-04 13:31:53,389 epoch 8 - iter 511/738 - loss 0.01283994 - time (sec): 98.46 - samples/sec: 1199.78 - lr: 0.000008 - momentum: 0.000000
187
+ 2023-09-04 13:32:05,728 epoch 8 - iter 584/738 - loss 0.01185692 - time (sec): 110.79 - samples/sec: 1197.11 - lr: 0.000007 - momentum: 0.000000
188
+ 2023-09-04 13:32:17,932 epoch 8 - iter 657/738 - loss 0.01228898 - time (sec): 123.00 - samples/sec: 1202.88 - lr: 0.000007 - momentum: 0.000000
189
+ 2023-09-04 13:32:31,626 epoch 8 - iter 730/738 - loss 0.01226298 - time (sec): 136.69 - samples/sec: 1202.13 - lr: 0.000007 - momentum: 0.000000
190
+ 2023-09-04 13:32:33,521 ----------------------------------------------------------------------------------------------------
191
+ 2023-09-04 13:32:33,522 EPOCH 8 done: loss 0.0122 - lr: 0.000007
192
+ 2023-09-04 13:32:51,210 DEV : loss 0.19962483644485474 - f1-score (micro avg) 0.8466
193
+ 2023-09-04 13:32:51,239 saving best model
194
+ 2023-09-04 13:32:52,602 ----------------------------------------------------------------------------------------------------
195
+ 2023-09-04 13:33:08,735 epoch 9 - iter 73/738 - loss 0.01247255 - time (sec): 16.13 - samples/sec: 1107.03 - lr: 0.000006 - momentum: 0.000000
196
+ 2023-09-04 13:33:23,042 epoch 9 - iter 146/738 - loss 0.00811625 - time (sec): 30.44 - samples/sec: 1144.30 - lr: 0.000006 - momentum: 0.000000
197
+ 2023-09-04 13:33:36,126 epoch 9 - iter 219/738 - loss 0.00728959 - time (sec): 43.52 - samples/sec: 1155.49 - lr: 0.000006 - momentum: 0.000000
198
+ 2023-09-04 13:33:49,468 epoch 9 - iter 292/738 - loss 0.00705434 - time (sec): 56.86 - samples/sec: 1172.72 - lr: 0.000005 - momentum: 0.000000
199
+ 2023-09-04 13:34:01,292 epoch 9 - iter 365/738 - loss 0.00947955 - time (sec): 68.69 - samples/sec: 1182.24 - lr: 0.000005 - momentum: 0.000000
200
+ 2023-09-04 13:34:15,930 epoch 9 - iter 438/738 - loss 0.01064723 - time (sec): 83.33 - samples/sec: 1177.40 - lr: 0.000005 - momentum: 0.000000
201
+ 2023-09-04 13:34:30,665 epoch 9 - iter 511/738 - loss 0.01062089 - time (sec): 98.06 - samples/sec: 1181.74 - lr: 0.000004 - momentum: 0.000000
202
+ 2023-09-04 13:34:43,192 epoch 9 - iter 584/738 - loss 0.01063647 - time (sec): 110.59 - samples/sec: 1194.00 - lr: 0.000004 - momentum: 0.000000
203
+ 2023-09-04 13:34:56,263 epoch 9 - iter 657/738 - loss 0.01080669 - time (sec): 123.66 - samples/sec: 1199.00 - lr: 0.000004 - momentum: 0.000000
204
+ 2023-09-04 13:35:10,415 epoch 9 - iter 730/738 - loss 0.01055278 - time (sec): 137.81 - samples/sec: 1194.07 - lr: 0.000003 - momentum: 0.000000
205
+ 2023-09-04 13:35:12,137 ----------------------------------------------------------------------------------------------------
206
+ 2023-09-04 13:35:12,137 EPOCH 9 done: loss 0.0105 - lr: 0.000003
207
+ 2023-09-04 13:35:29,981 DEV : loss 0.20482125878334045 - f1-score (micro avg) 0.8349
208
+ 2023-09-04 13:35:30,010 ----------------------------------------------------------------------------------------------------
209
+ 2023-09-04 13:35:44,528 epoch 10 - iter 73/738 - loss 0.00718317 - time (sec): 14.52 - samples/sec: 1234.09 - lr: 0.000003 - momentum: 0.000000
210
+ 2023-09-04 13:35:56,824 epoch 10 - iter 146/738 - loss 0.00740199 - time (sec): 26.81 - samples/sec: 1227.37 - lr: 0.000003 - momentum: 0.000000
211
+ 2023-09-04 13:36:11,371 epoch 10 - iter 219/738 - loss 0.00534209 - time (sec): 41.36 - samples/sec: 1207.24 - lr: 0.000002 - momentum: 0.000000
212
+ 2023-09-04 13:36:24,891 epoch 10 - iter 292/738 - loss 0.00518679 - time (sec): 54.88 - samples/sec: 1189.48 - lr: 0.000002 - momentum: 0.000000
213
+ 2023-09-04 13:36:40,128 epoch 10 - iter 365/738 - loss 0.00715314 - time (sec): 70.12 - samples/sec: 1197.55 - lr: 0.000002 - momentum: 0.000000
214
+ 2023-09-04 13:36:53,702 epoch 10 - iter 438/738 - loss 0.00673732 - time (sec): 83.69 - samples/sec: 1195.61 - lr: 0.000001 - momentum: 0.000000
215
+ 2023-09-04 13:37:07,468 epoch 10 - iter 511/738 - loss 0.00696276 - time (sec): 97.46 - samples/sec: 1196.19 - lr: 0.000001 - momentum: 0.000000
216
+ 2023-09-04 13:37:21,101 epoch 10 - iter 584/738 - loss 0.00713198 - time (sec): 111.09 - samples/sec: 1199.97 - lr: 0.000001 - momentum: 0.000000
217
+ 2023-09-04 13:37:33,771 epoch 10 - iter 657/738 - loss 0.00680166 - time (sec): 123.76 - samples/sec: 1204.49 - lr: 0.000000 - momentum: 0.000000
218
+ 2023-09-04 13:37:48,728 epoch 10 - iter 730/738 - loss 0.00710272 - time (sec): 138.72 - samples/sec: 1190.21 - lr: 0.000000 - momentum: 0.000000
219
+ 2023-09-04 13:37:49,854 ----------------------------------------------------------------------------------------------------
220
+ 2023-09-04 13:37:49,855 EPOCH 10 done: loss 0.0072 - lr: 0.000000
221
+ 2023-09-04 13:38:07,696 DEV : loss 0.20363713800907135 - f1-score (micro avg) 0.8347
222
+ 2023-09-04 13:38:08,211 ----------------------------------------------------------------------------------------------------
223
+ 2023-09-04 13:38:08,212 Loading model from best epoch ...
224
+ 2023-09-04 13:38:10,073 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 13:38:25,478
226
+ Results:
227
+ - F-score (micro) 0.7973
228
+ - F-score (macro) 0.6802
229
+ - Accuracy 0.6864
230
+
231
+ By class:
232
+ precision recall f1-score support
233
+
234
+ loc 0.8719 0.8730 0.8725 858
235
+ pers 0.7474 0.8156 0.7801 537
236
+ org 0.5769 0.5682 0.5725 132
237
+ prod 0.6418 0.7049 0.6719 61
238
+ time 0.4615 0.5556 0.5042 54
239
+
240
+ micro avg 0.7821 0.8130 0.7973 1642
241
+ macro avg 0.6599 0.7035 0.6802 1642
242
+ weighted avg 0.7855 0.8130 0.7986 1642
243
+
244
+ 2023-09-04 13:38:25,478 ----------------------------------------------------------------------------------------------------