Hugging Face's logo

Datasets:
stefan-it
/
autotrain-flair-hipe2022-fr-hmbert

Dataset card Files Community
stefan-it commited on
Commit
bb5bb1f
·
1 Parent(s): c2ea5e2

Upload folder using huggingface_hub

Browse files
Files changed (6) hide show
  1. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1/best-model.pt +3 -0
  2. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1/dev.tsv +0 -0
  3. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1/final-model.pt +3 -0
  4. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1/loss.tsv +11 -0
  5. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1/test.tsv +0 -0
  6. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1/training.log +242 -0
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1/best-model.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:edd31a499b3467cb798805345249f2d4279a6a673cfc9588ba8195d048dac32b
3
+ size 443334288
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1/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-lr5e-05-poolingfirst-layers-1-crfFalse-1/final-model.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:bd45925334141a5c3d3aa26f45604b2b95ef88650569a4108b605f5387c67c43
3
+ size 443334491
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1/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 09:51:15 0.0000 0.5450 0.1415 0.6883 0.7033 0.6958 0.5654
3
+ 2 09:53:52 0.0000 0.1214 0.1054 0.7681 0.8253 0.7957 0.6846
4
+ 3 09:56:30 0.0000 0.0742 0.1175 0.7708 0.8013 0.7857 0.6808
5
+ 4 09:59:05 0.0000 0.0500 0.1719 0.7744 0.7961 0.7851 0.6761
6
+ 5 10:01:43 0.0000 0.0349 0.1844 0.8096 0.8253 0.8174 0.7166
7
+ 6 10:04:22 0.0000 0.0277 0.1821 0.7916 0.8288 0.8097 0.7121
8
+ 7 10:07:00 0.0000 0.0194 0.2005 0.8182 0.8299 0.8240 0.7238
9
+ 8 10:09:39 0.0000 0.0121 0.2134 0.8153 0.8242 0.8197 0.7275
10
+ 9 10:12:17 0.0000 0.0087 0.2136 0.8242 0.8351 0.8296 0.7345
11
+ 10 10:14:57 0.0000 0.0053 0.2202 0.8227 0.8345 0.8285 0.7347
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1/test.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-lr5e-05-poolingfirst-layers-1-crfFalse-1/training.log ADDED
@@ -0,0 +1,242 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 2023-09-04 09:48:41,806 ----------------------------------------------------------------------------------------------------
2
+ 2023-09-04 09:48:41,807 Model: "SequenceTagger(
3
+ (embeddings): TransformerWordEmbeddings(
4
+ (model): BertModel(
5
+ (embeddings): BertEmbeddings(
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): BertEncoder(
13
+ (layer): ModuleList(
14
+ (0-11): 12 x BertLayer(
15
+ (attention): BertAttention(
16
+ (self): BertSelfAttention(
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): 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 09:48:41,807 ----------------------------------------------------------------------------------------------------
51
+ 2023-09-04 09:48:41,807 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 09:48:41,807 ----------------------------------------------------------------------------------------------------
54
+ 2023-09-04 09:48:41,807 Train: 5901 sentences
55
+ 2023-09-04 09:48:41,807 (train_with_dev=False, train_with_test=False)
56
+ 2023-09-04 09:48:41,807 ----------------------------------------------------------------------------------------------------
57
+ 2023-09-04 09:48:41,807 Training Params:
58
+ 2023-09-04 09:48:41,807 - learning_rate: "5e-05"
59
+ 2023-09-04 09:48:41,807 - mini_batch_size: "8"
60
+ 2023-09-04 09:48:41,807 - max_epochs: "10"
61
+ 2023-09-04 09:48:41,807 - shuffle: "True"
62
+ 2023-09-04 09:48:41,808 ----------------------------------------------------------------------------------------------------
63
+ 2023-09-04 09:48:41,808 Plugins:
64
+ 2023-09-04 09:48:41,808 - LinearScheduler | warmup_fraction: '0.1'
65
+ 2023-09-04 09:48:41,808 ----------------------------------------------------------------------------------------------------
66
+ 2023-09-04 09:48:41,808 Final evaluation on model from best epoch (best-model.pt)
67
+ 2023-09-04 09:48:41,808 - metric: "('micro avg', 'f1-score')"
68
+ 2023-09-04 09:48:41,808 ----------------------------------------------------------------------------------------------------
69
+ 2023-09-04 09:48:41,808 Computation:
70
+ 2023-09-04 09:48:41,808 - compute on device: cuda:0
71
+ 2023-09-04 09:48:41,808 - embedding storage: none
72
+ 2023-09-04 09:48:41,808 ----------------------------------------------------------------------------------------------------
73
+ 2023-09-04 09:48:41,808 Model training base path: "hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1"
74
+ 2023-09-04 09:48:41,808 ----------------------------------------------------------------------------------------------------
75
+ 2023-09-04 09:48:41,808 ----------------------------------------------------------------------------------------------------
76
+ 2023-09-04 09:48:55,323 epoch 1 - iter 73/738 - loss 2.75197739 - time (sec): 13.51 - samples/sec: 1234.03 - lr: 0.000005 - momentum: 0.000000
77
+ 2023-09-04 09:49:08,878 epoch 1 - iter 146/738 - loss 1.71690361 - time (sec): 27.07 - samples/sec: 1217.32 - lr: 0.000010 - momentum: 0.000000
78
+ 2023-09-04 09:49:25,640 epoch 1 - iter 219/738 - loss 1.24215990 - time (sec): 43.83 - samples/sec: 1183.07 - lr: 0.000015 - momentum: 0.000000
79
+ 2023-09-04 09:49:37,744 epoch 1 - iter 292/738 - loss 1.03165502 - time (sec): 55.94 - samples/sec: 1199.84 - lr: 0.000020 - momentum: 0.000000
80
+ 2023-09-04 09:49:50,959 epoch 1 - iter 365/738 - loss 0.89227501 - time (sec): 69.15 - samples/sec: 1201.16 - lr: 0.000025 - momentum: 0.000000
81
+ 2023-09-04 09:50:04,152 epoch 1 - iter 438/738 - loss 0.78997731 - time (sec): 82.34 - samples/sec: 1196.48 - lr: 0.000030 - momentum: 0.000000
82
+ 2023-09-04 09:50:16,277 epoch 1 - iter 511/738 - loss 0.71529128 - time (sec): 94.47 - samples/sec: 1203.79 - lr: 0.000035 - momentum: 0.000000
83
+ 2023-09-04 09:50:29,443 epoch 1 - iter 584/738 - loss 0.65413003 - time (sec): 107.63 - samples/sec: 1197.88 - lr: 0.000039 - momentum: 0.000000
84
+ 2023-09-04 09:50:46,164 epoch 1 - iter 657/738 - loss 0.59014415 - time (sec): 124.36 - samples/sec: 1191.93 - lr: 0.000044 - momentum: 0.000000
85
+ 2023-09-04 09:51:00,700 epoch 1 - iter 730/738 - loss 0.54914187 - time (sec): 138.89 - samples/sec: 1187.13 - lr: 0.000049 - momentum: 0.000000
86
+ 2023-09-04 09:51:01,859 ----------------------------------------------------------------------------------------------------
87
+ 2023-09-04 09:51:01,860 EPOCH 1 done: loss 0.5450 - lr: 0.000049
88
+ 2023-09-04 09:51:15,759 DEV : loss 0.14151449501514435 - f1-score (micro avg) 0.6958
89
+ 2023-09-04 09:51:15,788 saving best model
90
+ 2023-09-04 09:51:16,259 ----------------------------------------------------------------------------------------------------
91
+ 2023-09-04 09:51:30,850 epoch 2 - iter 73/738 - loss 0.14862480 - time (sec): 14.59 - samples/sec: 1159.75 - lr: 0.000049 - momentum: 0.000000
92
+ 2023-09-04 09:51:42,425 epoch 2 - iter 146/738 - loss 0.13979206 - time (sec): 26.16 - samples/sec: 1188.23 - lr: 0.000049 - momentum: 0.000000
93
+ 2023-09-04 09:51:55,705 epoch 2 - iter 219/738 - loss 0.14009677 - time (sec): 39.45 - samples/sec: 1176.65 - lr: 0.000048 - momentum: 0.000000
94
+ 2023-09-04 09:52:09,411 epoch 2 - iter 292/738 - loss 0.13853738 - time (sec): 53.15 - samples/sec: 1178.96 - lr: 0.000048 - momentum: 0.000000
95
+ 2023-09-04 09:52:21,181 epoch 2 - iter 365/738 - loss 0.13365282 - time (sec): 64.92 - samples/sec: 1201.10 - lr: 0.000047 - momentum: 0.000000
96
+ 2023-09-04 09:52:39,994 epoch 2 - iter 438/738 - loss 0.13313182 - time (sec): 83.73 - samples/sec: 1191.57 - lr: 0.000047 - momentum: 0.000000
97
+ 2023-09-04 09:52:53,782 epoch 2 - iter 511/738 - loss 0.12927067 - time (sec): 97.52 - samples/sec: 1190.12 - lr: 0.000046 - momentum: 0.000000
98
+ 2023-09-04 09:53:08,020 epoch 2 - iter 584/738 - loss 0.12799258 - time (sec): 111.76 - samples/sec: 1185.29 - lr: 0.000046 - momentum: 0.000000
99
+ 2023-09-04 09:53:21,559 epoch 2 - iter 657/738 - loss 0.12438234 - time (sec): 125.30 - samples/sec: 1189.34 - lr: 0.000045 - momentum: 0.000000
100
+ 2023-09-04 09:53:33,885 epoch 2 - iter 730/738 - loss 0.12130248 - time (sec): 137.63 - samples/sec: 1196.95 - lr: 0.000045 - momentum: 0.000000
101
+ 2023-09-04 09:53:35,167 ----------------------------------------------------------------------------------------------------
102
+ 2023-09-04 09:53:35,167 EPOCH 2 done: loss 0.1214 - lr: 0.000045
103
+ 2023-09-04 09:53:52,836 DEV : loss 0.10535410046577454 - f1-score (micro avg) 0.7957
104
+ 2023-09-04 09:53:52,869 saving best model
105
+ 2023-09-04 09:53:54,224 ----------------------------------------------------------------------------------------------------
106
+ 2023-09-04 09:54:06,884 epoch 3 - iter 73/738 - loss 0.07855581 - time (sec): 12.66 - samples/sec: 1200.70 - lr: 0.000044 - momentum: 0.000000
107
+ 2023-09-04 09:54:19,648 epoch 3 - iter 146/738 - loss 0.06968428 - time (sec): 25.42 - samples/sec: 1227.51 - lr: 0.000043 - momentum: 0.000000
108
+ 2023-09-04 09:54:32,962 epoch 3 - iter 219/738 - loss 0.07333591 - time (sec): 38.74 - samples/sec: 1247.50 - lr: 0.000043 - momentum: 0.000000
109
+ 2023-09-04 09:54:47,464 epoch 3 - iter 292/738 - loss 0.07202760 - time (sec): 53.24 - samples/sec: 1228.86 - lr: 0.000042 - momentum: 0.000000
110
+ 2023-09-04 09:55:01,990 epoch 3 - iter 365/738 - loss 0.07890980 - time (sec): 67.77 - samples/sec: 1223.74 - lr: 0.000042 - momentum: 0.000000
111
+ 2023-09-04 09:55:14,515 epoch 3 - iter 438/738 - loss 0.07835108 - time (sec): 80.29 - samples/sec: 1218.91 - lr: 0.000041 - momentum: 0.000000
112
+ 2023-09-04 09:55:27,666 epoch 3 - iter 511/738 - loss 0.07800211 - time (sec): 93.44 - samples/sec: 1223.59 - lr: 0.000041 - momentum: 0.000000
113
+ 2023-09-04 09:55:42,958 epoch 3 - iter 584/738 - loss 0.07666708 - time (sec): 108.73 - samples/sec: 1207.73 - lr: 0.000040 - momentum: 0.000000
114
+ 2023-09-04 09:55:55,990 epoch 3 - iter 657/738 - loss 0.07624028 - time (sec): 121.76 - samples/sec: 1213.35 - lr: 0.000040 - momentum: 0.000000
115
+ 2023-09-04 09:56:11,329 epoch 3 - iter 730/738 - loss 0.07460535 - time (sec): 137.10 - samples/sec: 1202.33 - lr: 0.000039 - momentum: 0.000000
116
+ 2023-09-04 09:56:12,473 ----------------------------------------------------------------------------------------------------
117
+ 2023-09-04 09:56:12,474 EPOCH 3 done: loss 0.0742 - lr: 0.000039
118
+ 2023-09-04 09:56:30,049 DEV : loss 0.11746015399694443 - f1-score (micro avg) 0.7857
119
+ 2023-09-04 09:56:30,078 ----------------------------------------------------------------------------------------------------
120
+ 2023-09-04 09:56:43,003 epoch 4 - iter 73/738 - loss 0.04851575 - time (sec): 12.92 - samples/sec: 1219.08 - lr: 0.000038 - momentum: 0.000000
121
+ 2023-09-04 09:56:57,468 epoch 4 - iter 146/738 - loss 0.04840274 - time (sec): 27.39 - samples/sec: 1220.42 - lr: 0.000038 - momentum: 0.000000
122
+ 2023-09-04 09:57:13,264 epoch 4 - iter 219/738 - loss 0.04877993 - time (sec): 43.18 - samples/sec: 1205.68 - lr: 0.000037 - momentum: 0.000000
123
+ 2023-09-04 09:57:26,103 epoch 4 - iter 292/738 - loss 0.04772389 - time (sec): 56.02 - samples/sec: 1203.41 - lr: 0.000037 - momentum: 0.000000
124
+ 2023-09-04 09:57:38,885 epoch 4 - iter 365/738 - loss 0.04841152 - time (sec): 68.81 - samples/sec: 1205.67 - lr: 0.000036 - momentum: 0.000000
125
+ 2023-09-04 09:57:50,700 epoch 4 - iter 438/738 - loss 0.04752652 - time (sec): 80.62 - samples/sec: 1209.44 - lr: 0.000036 - momentum: 0.000000
126
+ 2023-09-04 09:58:05,429 epoch 4 - iter 511/738 - loss 0.04914308 - time (sec): 95.35 - samples/sec: 1207.20 - lr: 0.000035 - momentum: 0.000000
127
+ 2023-09-04 09:58:18,443 epoch 4 - iter 584/738 - loss 0.04873240 - time (sec): 108.36 - samples/sec: 1203.42 - lr: 0.000035 - momentum: 0.000000
128
+ 2023-09-04 09:58:33,878 epoch 4 - iter 657/738 - loss 0.04980558 - time (sec): 123.80 - samples/sec: 1197.95 - lr: 0.000034 - momentum: 0.000000
129
+ 2023-09-04 09:58:47,182 epoch 4 - iter 730/738 - loss 0.05037312 - time (sec): 137.10 - samples/sec: 1203.05 - lr: 0.000033 - momentum: 0.000000
130
+ 2023-09-04 09:58:48,324 ----------------------------------------------------------------------------------------------------
131
+ 2023-09-04 09:58:48,324 EPOCH 4 done: loss 0.0500 - lr: 0.000033
132
+ 2023-09-04 09:59:05,958 DEV : loss 0.1719038486480713 - f1-score (micro avg) 0.7851
133
+ 2023-09-04 09:59:05,986 ----------------------------------------------------------------------------------------------------
134
+ 2023-09-04 09:59:18,460 epoch 5 - iter 73/738 - loss 0.04447295 - time (sec): 12.47 - samples/sec: 1231.82 - lr: 0.000033 - momentum: 0.000000
135
+ 2023-09-04 09:59:31,845 epoch 5 - iter 146/738 - loss 0.03577802 - time (sec): 25.86 - samples/sec: 1208.85 - lr: 0.000032 - momentum: 0.000000
136
+ 2023-09-04 09:59:46,449 epoch 5 - iter 219/738 - loss 0.03944701 - time (sec): 40.46 - samples/sec: 1200.83 - lr: 0.000032 - momentum: 0.000000
137
+ 2023-09-04 09:59:59,621 epoch 5 - iter 292/738 - loss 0.03601240 - time (sec): 53.63 - samples/sec: 1192.56 - lr: 0.000031 - momentum: 0.000000
138
+ 2023-09-04 10:00:14,788 epoch 5 - iter 365/738 - loss 0.03579331 - time (sec): 68.80 - samples/sec: 1183.78 - lr: 0.000031 - momentum: 0.000000
139
+ 2023-09-04 10:00:29,387 epoch 5 - iter 438/738 - loss 0.03637083 - time (sec): 83.40 - samples/sec: 1179.77 - lr: 0.000030 - momentum: 0.000000
140
+ 2023-09-04 10:00:42,287 epoch 5 - iter 511/738 - loss 0.03562360 - time (sec): 96.30 - samples/sec: 1182.45 - lr: 0.000030 - momentum: 0.000000
141
+ 2023-09-04 10:00:55,760 epoch 5 - iter 584/738 - loss 0.03438591 - time (sec): 109.77 - samples/sec: 1183.62 - lr: 0.000029 - momentum: 0.000000
142
+ 2023-09-04 10:01:11,351 epoch 5 - iter 657/738 - loss 0.03423439 - time (sec): 125.36 - samples/sec: 1183.68 - lr: 0.000028 - momentum: 0.000000
143
+ 2023-09-04 10:01:24,682 epoch 5 - iter 730/738 - loss 0.03464056 - time (sec): 138.69 - samples/sec: 1189.68 - lr: 0.000028 - momentum: 0.000000
144
+ 2023-09-04 10:01:25,840 ----------------------------------------------------------------------------------------------------
145
+ 2023-09-04 10:01:25,840 EPOCH 5 done: loss 0.0349 - lr: 0.000028
146
+ 2023-09-04 10:01:43,481 DEV : loss 0.184407576918602 - f1-score (micro avg) 0.8174
147
+ 2023-09-04 10:01:43,509 saving best model
148
+ 2023-09-04 10:01:44,863 ----------------------------------------------------------------------------------------------------
149
+ 2023-09-04 10:01:57,320 epoch 6 - iter 73/738 - loss 0.02983421 - time (sec): 12.46 - samples/sec: 1199.48 - lr: 0.000027 - momentum: 0.000000
150
+ 2023-09-04 10:02:10,036 epoch 6 - iter 146/738 - loss 0.03152107 - time (sec): 25.17 - samples/sec: 1205.22 - lr: 0.000027 - momentum: 0.000000
151
+ 2023-09-04 10:02:26,749 epoch 6 - iter 219/738 - loss 0.02842564 - time (sec): 41.88 - samples/sec: 1173.64 - lr: 0.000026 - momentum: 0.000000
152
+ 2023-09-04 10:02:40,769 epoch 6 - iter 292/738 - loss 0.02926616 - time (sec): 55.90 - samples/sec: 1163.95 - lr: 0.000026 - momentum: 0.000000
153
+ 2023-09-04 10:02:53,744 epoch 6 - iter 365/738 - loss 0.02867305 - time (sec): 68.88 - samples/sec: 1175.89 - lr: 0.000025 - momentum: 0.000000
154
+ 2023-09-04 10:03:08,923 epoch 6 - iter 438/738 - loss 0.02825542 - time (sec): 84.06 - samples/sec: 1178.22 - lr: 0.000025 - momentum: 0.000000
155
+ 2023-09-04 10:03:21,352 epoch 6 - iter 511/738 - loss 0.02890450 - time (sec): 96.49 - samples/sec: 1183.24 - lr: 0.000024 - momentum: 0.000000
156
+ 2023-09-04 10:03:34,690 epoch 6 - iter 584/738 - loss 0.02820225 - time (sec): 109.83 - samples/sec: 1184.97 - lr: 0.000023 - momentum: 0.000000
157
+ 2023-09-04 10:03:50,609 epoch 6 - iter 657/738 - loss 0.02811722 - time (sec): 125.74 - samples/sec: 1185.64 - lr: 0.000023 - momentum: 0.000000
158
+ 2023-09-04 10:04:03,820 epoch 6 - iter 730/738 - loss 0.02759351 - time (sec): 138.95 - samples/sec: 1186.25 - lr: 0.000022 - momentum: 0.000000
159
+ 2023-09-04 10:04:05,039 ----------------------------------------------------------------------------------------------------
160
+ 2023-09-04 10:04:05,040 EPOCH 6 done: loss 0.0277 - lr: 0.000022
161
+ 2023-09-04 10:04:22,770 DEV : loss 0.1820913851261139 - f1-score (micro avg) 0.8097
162
+ 2023-09-04 10:04:22,799 ----------------------------------------------------------------------------------------------------
163
+ 2023-09-04 10:04:35,318 epoch 7 - iter 73/738 - loss 0.01693471 - time (sec): 12.52 - samples/sec: 1214.41 - lr: 0.000022 - momentum: 0.000000
164
+ 2023-09-04 10:04:50,746 epoch 7 - iter 146/738 - loss 0.01623046 - time (sec): 27.95 - samples/sec: 1203.49 - lr: 0.000021 - momentum: 0.000000
165
+ 2023-09-04 10:05:02,509 epoch 7 - iter 219/738 - loss 0.01647041 - time (sec): 39.71 - samples/sec: 1226.50 - lr: 0.000021 - momentum: 0.000000
166
+ 2023-09-04 10:05:17,074 epoch 7 - iter 292/738 - loss 0.01673835 - time (sec): 54.27 - samples/sec: 1193.56 - lr: 0.000020 - momentum: 0.000000
167
+ 2023-09-04 10:05:31,647 epoch 7 - iter 365/738 - loss 0.01753294 - time (sec): 68.85 - samples/sec: 1187.98 - lr: 0.000020 - momentum: 0.000000
168
+ 2023-09-04 10:05:45,873 epoch 7 - iter 438/738 - loss 0.02112900 - time (sec): 83.07 - samples/sec: 1201.80 - lr: 0.000019 - momentum: 0.000000
169
+ 2023-09-04 10:06:00,695 epoch 7 - iter 511/738 - loss 0.02069520 - time (sec): 97.90 - samples/sec: 1198.92 - lr: 0.000018 - momentum: 0.000000
170
+ 2023-09-04 10:06:15,580 epoch 7 - iter 584/738 - loss 0.01949050 - time (sec): 112.78 - samples/sec: 1189.95 - lr: 0.000018 - momentum: 0.000000
171
+ 2023-09-04 10:06:28,145 epoch 7 - iter 657/738 - loss 0.01954963 - time (sec): 125.35 - samples/sec: 1188.39 - lr: 0.000017 - momentum: 0.000000
172
+ 2023-09-04 10:06:41,228 epoch 7 - iter 730/738 - loss 0.01924009 - time (sec): 138.43 - samples/sec: 1188.63 - lr: 0.000017 - momentum: 0.000000
173
+ 2023-09-04 10:06:42,518 ----------------------------------------------------------------------------------------------------
174
+ 2023-09-04 10:06:42,518 EPOCH 7 done: loss 0.0194 - lr: 0.000017
175
+ 2023-09-04 10:07:00,249 DEV : loss 0.20051106810569763 - f1-score (micro avg) 0.824
176
+ 2023-09-04 10:07:00,286 saving best model
177
+ 2023-09-04 10:07:01,635 ----------------------------------------------------------------------------------------------------
178
+ 2023-09-04 10:07:14,878 epoch 8 - iter 73/738 - loss 0.00489601 - time (sec): 13.24 - samples/sec: 1220.47 - lr: 0.000016 - momentum: 0.000000
179
+ 2023-09-04 10:07:27,834 epoch 8 - iter 146/738 - loss 0.00962196 - time (sec): 26.20 - samples/sec: 1218.92 - lr: 0.000016 - momentum: 0.000000
180
+ 2023-09-04 10:07:42,083 epoch 8 - iter 219/738 - loss 0.01101891 - time (sec): 40.45 - samples/sec: 1226.67 - lr: 0.000015 - momentum: 0.000000
181
+ 2023-09-04 10:07:56,130 epoch 8 - iter 292/738 - loss 0.01223130 - time (sec): 54.49 - samples/sec: 1199.62 - lr: 0.000015 - momentum: 0.000000
182
+ 2023-09-04 10:08:09,189 epoch 8 - iter 365/738 - loss 0.01324665 - time (sec): 67.55 - samples/sec: 1198.89 - lr: 0.000014 - momentum: 0.000000
183
+ 2023-09-04 10:08:23,887 epoch 8 - iter 438/738 - loss 0.01338890 - time (sec): 82.25 - samples/sec: 1182.25 - lr: 0.000013 - momentum: 0.000000
184
+ 2023-09-04 10:08:36,707 epoch 8 - iter 511/738 - loss 0.01230530 - time (sec): 95.07 - samples/sec: 1183.87 - lr: 0.000013 - momentum: 0.000000
185
+ 2023-09-04 10:08:52,837 epoch 8 - iter 584/738 - loss 0.01326889 - time (sec): 111.20 - samples/sec: 1177.42 - lr: 0.000012 - momentum: 0.000000
186
+ 2023-09-04 10:09:05,829 epoch 8 - iter 657/738 - loss 0.01245615 - time (sec): 124.19 - samples/sec: 1182.40 - lr: 0.000012 - momentum: 0.000000
187
+ 2023-09-04 10:09:20,904 epoch 8 - iter 730/738 - loss 0.01184970 - time (sec): 139.27 - samples/sec: 1183.92 - lr: 0.000011 - momentum: 0.000000
188
+ 2023-09-04 10:09:22,110 ----------------------------------------------------------------------------------------------------
189
+ 2023-09-04 10:09:22,110 EPOCH 8 done: loss 0.0121 - lr: 0.000011
190
+ 2023-09-04 10:09:39,810 DEV : loss 0.21338051557540894 - f1-score (micro avg) 0.8197
191
+ 2023-09-04 10:09:39,839 ----------------------------------------------------------------------------------------------------
192
+ 2023-09-04 10:09:53,855 epoch 9 - iter 73/738 - loss 0.01125750 - time (sec): 14.01 - samples/sec: 1236.13 - lr: 0.000011 - momentum: 0.000000
193
+ 2023-09-04 10:10:07,313 epoch 9 - iter 146/738 - loss 0.00755546 - time (sec): 27.47 - samples/sec: 1224.42 - lr: 0.000010 - momentum: 0.000000
194
+ 2023-09-04 10:10:21,034 epoch 9 - iter 219/738 - loss 0.00996792 - time (sec): 41.19 - samples/sec: 1198.70 - lr: 0.000010 - momentum: 0.000000
195
+ 2023-09-04 10:10:34,945 epoch 9 - iter 292/738 - loss 0.00946332 - time (sec): 55.10 - samples/sec: 1187.77 - lr: 0.000009 - momentum: 0.000000
196
+ 2023-09-04 10:10:48,831 epoch 9 - iter 365/738 - loss 0.01008565 - time (sec): 68.99 - samples/sec: 1182.32 - lr: 0.000008 - momentum: 0.000000
197
+ 2023-09-04 10:11:01,584 epoch 9 - iter 438/738 - loss 0.00942640 - time (sec): 81.74 - samples/sec: 1186.19 - lr: 0.000008 - momentum: 0.000000
198
+ 2023-09-04 10:11:15,094 epoch 9 - iter 511/738 - loss 0.00887169 - time (sec): 95.25 - samples/sec: 1198.57 - lr: 0.000007 - momentum: 0.000000
199
+ 2023-09-04 10:11:30,473 epoch 9 - iter 584/738 - loss 0.00878542 - time (sec): 110.63 - samples/sec: 1187.27 - lr: 0.000007 - momentum: 0.000000
200
+ 2023-09-04 10:11:44,404 epoch 9 - iter 657/738 - loss 0.00874953 - time (sec): 124.56 - samples/sec: 1187.52 - lr: 0.000006 - momentum: 0.000000
201
+ 2023-09-04 10:11:58,025 epoch 9 - iter 730/738 - loss 0.00877126 - time (sec): 138.18 - samples/sec: 1189.25 - lr: 0.000006 - momentum: 0.000000
202
+ 2023-09-04 10:11:59,937 ----------------------------------------------------------------------------------------------------
203
+ 2023-09-04 10:11:59,938 EPOCH 9 done: loss 0.0087 - lr: 0.000006
204
+ 2023-09-04 10:12:17,752 DEV : loss 0.2136041671037674 - f1-score (micro avg) 0.8296
205
+ 2023-09-04 10:12:17,781 saving best model
206
+ 2023-09-04 10:12:19,123 ----------------------------------------------------------------------------------------------------
207
+ 2023-09-04 10:12:32,712 epoch 10 - iter 73/738 - loss 0.00731223 - time (sec): 13.59 - samples/sec: 1186.40 - lr: 0.000005 - momentum: 0.000000
208
+ 2023-09-04 10:12:49,371 epoch 10 - iter 146/738 - loss 0.00553627 - time (sec): 30.25 - samples/sec: 1170.99 - lr: 0.000004 - momentum: 0.000000
209
+ 2023-09-04 10:13:03,632 epoch 10 - iter 219/738 - loss 0.00517112 - time (sec): 44.51 - samples/sec: 1154.78 - lr: 0.000004 - momentum: 0.000000
210
+ 2023-09-04 10:13:15,666 epoch 10 - iter 292/738 - loss 0.00504998 - time (sec): 56.54 - samples/sec: 1181.42 - lr: 0.000003 - momentum: 0.000000
211
+ 2023-09-04 10:13:27,683 epoch 10 - iter 365/738 - loss 0.00436551 - time (sec): 68.56 - samples/sec: 1199.43 - lr: 0.000003 - momentum: 0.000000
212
+ 2023-09-04 10:13:40,098 epoch 10 - iter 438/738 - loss 0.00476034 - time (sec): 80.97 - samples/sec: 1202.85 - lr: 0.000002 - momentum: 0.000000
213
+ 2023-09-04 10:13:54,814 epoch 10 - iter 511/738 - loss 0.00494000 - time (sec): 95.69 - samples/sec: 1200.66 - lr: 0.000002 - momentum: 0.000000
214
+ 2023-09-04 10:14:08,660 epoch 10 - iter 584/738 - loss 0.00569954 - time (sec): 109.54 - samples/sec: 1195.92 - lr: 0.000001 - momentum: 0.000000
215
+ 2023-09-04 10:14:22,306 epoch 10 - iter 657/738 - loss 0.00574333 - time (sec): 123.18 - samples/sec: 1193.07 - lr: 0.000001 - momentum: 0.000000
216
+ 2023-09-04 10:14:38,335 epoch 10 - iter 730/738 - loss 0.00530863 - time (sec): 139.21 - samples/sec: 1185.27 - lr: 0.000000 - momentum: 0.000000
217
+ 2023-09-04 10:14:39,414 ----------------------------------------------------------------------------------------------------
218
+ 2023-09-04 10:14:39,414 EPOCH 10 done: loss 0.0053 - lr: 0.000000
219
+ 2023-09-04 10:14:57,026 DEV : loss 0.22019889950752258 - f1-score (micro avg) 0.8285
220
+ 2023-09-04 10:14:57,535 ----------------------------------------------------------------------------------------------------
221
+ 2023-09-04 10:14:57,536 Loading model from best epoch ...
222
+ 2023-09-04 10:14:59,490 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
223
+ 2023-09-04 10:15:14,183
224
+ Results:
225
+ - F-score (micro) 0.801
226
+ - F-score (macro) 0.6802
227
+ - Accuracy 0.6889
228
+
229
+ By class:
230
+ precision recall f1-score support
231
+
232
+ loc 0.8721 0.8741 0.8731 858
233
+ pers 0.7655 0.8268 0.7950 537
234
+ org 0.5429 0.5758 0.5588 132
235
+ time 0.4328 0.5370 0.4793 54
236
+ prod 0.7193 0.6721 0.6949 61
237
+
238
+ micro avg 0.7864 0.8161 0.8010 1642
239
+ macro avg 0.6665 0.6972 0.6802 1642
240
+ weighted avg 0.7906 0.8161 0.8027 1642
241
+
242
+ 2023-09-04 10:15:14,183 ----------------------------------------------------------------------------------------------------