Hugging Face's logo

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

Dataset card Files Community
stefan-it commited on
Commit
bd6f93e
•
1 Parent(s): bb5bb1f

Upload folder using huggingface_hub

Browse files
Files changed (6) hide show
  1. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1/best-model.pt +3 -0
  2. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1/dev.tsv +0 -0
  3. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1/final-model.pt +3 -0
  4. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1/loss.tsv +11 -0
  5. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1/test.tsv +0 -0
  6. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1/training.log +244 -0
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-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 sha256:91b2f34927ccad5da447ffa7c664d404906ccdbab5797fe6da83136ca7543571
3
+ size 443334288
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-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-bs4-wsFalse-e10-lr3e-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:d821d6fff205d57861f562139ab8db2b537ea59670a9bcb7e13a4d9de2375a8c
3
+ size 443334491
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-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 10:18:27 0.0000 0.5517 0.1486 0.6716 0.7543 0.7105 0.5861
3
+ 2 10:21:26 0.0000 0.1302 0.1537 0.7256 0.7829 0.7532 0.6397
4
+ 3 10:24:26 0.0000 0.0855 0.1584 0.7827 0.7984 0.7905 0.6881
5
+ 4 10:27:25 0.0000 0.0588 0.1862 0.7786 0.8299 0.8034 0.6960
6
+ 5 10:30:25 0.0000 0.0403 0.1622 0.8233 0.8379 0.8305 0.7330
7
+ 6 10:33:24 0.0000 0.0286 0.1850 0.8212 0.8236 0.8224 0.7212
8
+ 7 10:36:21 0.0000 0.0190 0.2055 0.8141 0.8305 0.8222 0.7218
9
+ 8 10:39:20 0.0000 0.0150 0.2135 0.8116 0.8436 0.8273 0.7299
10
+ 9 10:42:17 0.0000 0.0084 0.2232 0.8280 0.8408 0.8343 0.7347
11
+ 10 10:45:16 0.0000 0.0059 0.2212 0.8297 0.8396 0.8346 0.7367
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-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-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1/training.log ADDED
@@ -0,0 +1,244 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 2023-09-04 10:15:32,923 ----------------------------------------------------------------------------------------------------
2
+ 2023-09-04 10:15:32,924 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 10:15:32,924 ----------------------------------------------------------------------------------------------------
51
+ 2023-09-04 10:15:32,925 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 10:15:32,925 ----------------------------------------------------------------------------------------------------
54
+ 2023-09-04 10:15:32,925 Train: 5901 sentences
55
+ 2023-09-04 10:15:32,925 (train_with_dev=False, train_with_test=False)
56
+ 2023-09-04 10:15:32,925 ----------------------------------------------------------------------------------------------------
57
+ 2023-09-04 10:15:32,925 Training Params:
58
+ 2023-09-04 10:15:32,925 - learning_rate: "3e-05"
59
+ 2023-09-04 10:15:32,925 - mini_batch_size: "4"
60
+ 2023-09-04 10:15:32,925 - max_epochs: "10"
61
+ 2023-09-04 10:15:32,925 - shuffle: "True"
62
+ 2023-09-04 10:15:32,925 ----------------------------------------------------------------------------------------------------
63
+ 2023-09-04 10:15:32,925 Plugins:
64
+ 2023-09-04 10:15:32,925 - LinearScheduler | warmup_fraction: '0.1'
65
+ 2023-09-04 10:15:32,925 ----------------------------------------------------------------------------------------------------
66
+ 2023-09-04 10:15:32,925 Final evaluation on model from best epoch (best-model.pt)
67
+ 2023-09-04 10:15:32,925 - metric: "('micro avg', 'f1-score')"
68
+ 2023-09-04 10:15:32,926 ----------------------------------------------------------------------------------------------------
69
+ 2023-09-04 10:15:32,926 Computation:
70
+ 2023-09-04 10:15:32,926 - compute on device: cuda:0
71
+ 2023-09-04 10:15:32,926 - embedding storage: none
72
+ 2023-09-04 10:15:32,926 ----------------------------------------------------------------------------------------------------
73
+ 2023-09-04 10:15:32,926 Model training base path: "hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1"
74
+ 2023-09-04 10:15:32,926 ----------------------------------------------------------------------------------------------------
75
+ 2023-09-04 10:15:32,926 ----------------------------------------------------------------------------------------------------
76
+ 2023-09-04 10:15:48,783 epoch 1 - iter 147/1476 - loss 2.68170771 - time (sec): 15.86 - samples/sec: 1064.53 - lr: 0.000003 - momentum: 0.000000
77
+ 2023-09-04 10:16:04,522 epoch 1 - iter 294/1476 - loss 1.67034327 - time (sec): 31.59 - samples/sec: 1049.03 - lr: 0.000006 - momentum: 0.000000
78
+ 2023-09-04 10:16:22,329 epoch 1 - iter 441/1476 - loss 1.22451425 - time (sec): 49.40 - samples/sec: 1059.56 - lr: 0.000009 - momentum: 0.000000
79
+ 2023-09-04 10:16:37,229 epoch 1 - iter 588/1476 - loss 1.02423274 - time (sec): 64.30 - samples/sec: 1048.02 - lr: 0.000012 - momentum: 0.000000
80
+ 2023-09-04 10:16:52,864 epoch 1 - iter 735/1476 - loss 0.88773570 - time (sec): 79.94 - samples/sec: 1045.74 - lr: 0.000015 - momentum: 0.000000
81
+ 2023-09-04 10:17:08,447 epoch 1 - iter 882/1476 - loss 0.79176596 - time (sec): 95.52 - samples/sec: 1037.01 - lr: 0.000018 - momentum: 0.000000
82
+ 2023-09-04 10:17:23,931 epoch 1 - iter 1029/1476 - loss 0.71811676 - time (sec): 111.00 - samples/sec: 1030.07 - lr: 0.000021 - momentum: 0.000000
83
+ 2023-09-04 10:17:39,306 epoch 1 - iter 1176/1476 - loss 0.65662389 - time (sec): 126.38 - samples/sec: 1026.23 - lr: 0.000024 - momentum: 0.000000
84
+ 2023-09-04 10:17:57,254 epoch 1 - iter 1323/1476 - loss 0.59412693 - time (sec): 144.33 - samples/sec: 1033.32 - lr: 0.000027 - momentum: 0.000000
85
+ 2023-09-04 10:18:13,373 epoch 1 - iter 1470/1476 - loss 0.55325794 - time (sec): 160.45 - samples/sec: 1033.41 - lr: 0.000030 - momentum: 0.000000
86
+ 2023-09-04 10:18:13,961 ----------------------------------------------------------------------------------------------------
87
+ 2023-09-04 10:18:13,961 EPOCH 1 done: loss 0.5517 - lr: 0.000030
88
+ 2023-09-04 10:18:27,812 DEV : loss 0.14864952862262726 - f1-score (micro avg) 0.7105
89
+ 2023-09-04 10:18:27,840 saving best model
90
+ 2023-09-04 10:18:28,311 ----------------------------------------------------------------------------------------------------
91
+ 2023-09-04 10:18:44,493 epoch 2 - iter 147/1476 - loss 0.16136429 - time (sec): 16.18 - samples/sec: 1050.48 - lr: 0.000030 - momentum: 0.000000
92
+ 2023-09-04 10:18:59,094 epoch 2 - iter 294/1476 - loss 0.14445262 - time (sec): 30.78 - samples/sec: 1015.31 - lr: 0.000029 - momentum: 0.000000
93
+ 2023-09-04 10:19:14,669 epoch 2 - iter 441/1476 - loss 0.14680848 - time (sec): 46.36 - samples/sec: 1008.31 - lr: 0.000029 - momentum: 0.000000
94
+ 2023-09-04 10:19:30,497 epoch 2 - iter 588/1476 - loss 0.14188641 - time (sec): 62.18 - samples/sec: 1014.22 - lr: 0.000029 - momentum: 0.000000
95
+ 2023-09-04 10:19:45,382 epoch 2 - iter 735/1476 - loss 0.13875752 - time (sec): 77.07 - samples/sec: 1016.89 - lr: 0.000028 - momentum: 0.000000
96
+ 2023-09-04 10:20:04,669 epoch 2 - iter 882/1476 - loss 0.14043434 - time (sec): 96.36 - samples/sec: 1040.10 - lr: 0.000028 - momentum: 0.000000
97
+ 2023-09-04 10:20:20,787 epoch 2 - iter 1029/1476 - loss 0.13703237 - time (sec): 112.47 - samples/sec: 1040.82 - lr: 0.000028 - momentum: 0.000000
98
+ 2023-09-04 10:20:36,765 epoch 2 - iter 1176/1476 - loss 0.13582667 - time (sec): 128.45 - samples/sec: 1036.82 - lr: 0.000027 - momentum: 0.000000
99
+ 2023-09-04 10:20:52,525 epoch 2 - iter 1323/1476 - loss 0.13342898 - time (sec): 144.21 - samples/sec: 1039.52 - lr: 0.000027 - momentum: 0.000000
100
+ 2023-09-04 10:21:07,635 epoch 2 - iter 1470/1476 - loss 0.13013555 - time (sec): 159.32 - samples/sec: 1041.20 - lr: 0.000027 - momentum: 0.000000
101
+ 2023-09-04 10:21:08,201 ----------------------------------------------------------------------------------------------------
102
+ 2023-09-04 10:21:08,201 EPOCH 2 done: loss 0.1302 - lr: 0.000027
103
+ 2023-09-04 10:21:26,704 DEV : loss 0.15365169942378998 - f1-score (micro avg) 0.7532
104
+ 2023-09-04 10:21:26,733 saving best model
105
+ 2023-09-04 10:21:28,097 ----------------------------------------------------------------------------------------------------
106
+ 2023-09-04 10:21:43,472 epoch 3 - iter 147/1476 - loss 0.08415968 - time (sec): 15.37 - samples/sec: 997.20 - lr: 0.000026 - momentum: 0.000000
107
+ 2023-09-04 10:21:59,372 epoch 3 - iter 294/1476 - loss 0.06821170 - time (sec): 31.27 - samples/sec: 1003.76 - lr: 0.000026 - momentum: 0.000000
108
+ 2023-09-04 10:22:15,145 epoch 3 - iter 441/1476 - loss 0.07765677 - time (sec): 47.05 - samples/sec: 1032.35 - lr: 0.000026 - momentum: 0.000000
109
+ 2023-09-04 10:22:31,543 epoch 3 - iter 588/1476 - loss 0.08017496 - time (sec): 63.44 - samples/sec: 1037.54 - lr: 0.000025 - momentum: 0.000000
110
+ 2023-09-04 10:22:48,214 epoch 3 - iter 735/1476 - loss 0.08741492 - time (sec): 80.11 - samples/sec: 1040.93 - lr: 0.000025 - momentum: 0.000000
111
+ 2023-09-04 10:23:03,382 epoch 3 - iter 882/1476 - loss 0.08847588 - time (sec): 95.28 - samples/sec: 1033.65 - lr: 0.000025 - momentum: 0.000000
112
+ 2023-09-04 10:23:19,219 epoch 3 - iter 1029/1476 - loss 0.08740812 - time (sec): 111.12 - samples/sec: 1038.44 - lr: 0.000024 - momentum: 0.000000
113
+ 2023-09-04 10:23:35,735 epoch 3 - iter 1176/1476 - loss 0.08776189 - time (sec): 127.64 - samples/sec: 1035.46 - lr: 0.000024 - momentum: 0.000000
114
+ 2023-09-04 10:23:51,499 epoch 3 - iter 1323/1476 - loss 0.08771237 - time (sec): 143.40 - samples/sec: 1037.06 - lr: 0.000024 - momentum: 0.000000
115
+ 2023-09-04 10:24:08,395 epoch 3 - iter 1470/1476 - loss 0.08546308 - time (sec): 160.30 - samples/sec: 1035.22 - lr: 0.000023 - momentum: 0.000000
116
+ 2023-09-04 10:24:08,944 ----------------------------------------------------------------------------------------------------
117
+ 2023-09-04 10:24:08,945 EPOCH 3 done: loss 0.0855 - lr: 0.000023
118
+ 2023-09-04 10:24:26,679 DEV : loss 0.15837229788303375 - f1-score (micro avg) 0.7905
119
+ 2023-09-04 10:24:26,708 saving best model
120
+ 2023-09-04 10:24:28,052 ----------------------------------------------------------------------------------------------------
121
+ 2023-09-04 10:24:43,380 epoch 4 - iter 147/1476 - loss 0.05396197 - time (sec): 15.33 - samples/sec: 1033.71 - lr: 0.000023 - momentum: 0.000000
122
+ 2023-09-04 10:24:59,780 epoch 4 - iter 294/1476 - loss 0.05569312 - time (sec): 31.73 - samples/sec: 1058.11 - lr: 0.000023 - momentum: 0.000000
123
+ 2023-09-04 10:25:17,275 epoch 4 - iter 441/1476 - loss 0.05709274 - time (sec): 49.22 - samples/sec: 1069.00 - lr: 0.000022 - momentum: 0.000000
124
+ 2023-09-04 10:25:32,432 epoch 4 - iter 588/1476 - loss 0.05511440 - time (sec): 64.38 - samples/sec: 1053.12 - lr: 0.000022 - momentum: 0.000000
125
+ 2023-09-04 10:25:47,716 epoch 4 - iter 735/1476 - loss 0.05505667 - time (sec): 79.66 - samples/sec: 1047.04 - lr: 0.000022 - momentum: 0.000000
126
+ 2023-09-04 10:26:02,544 epoch 4 - iter 882/1476 - loss 0.05392414 - time (sec): 94.49 - samples/sec: 1037.44 - lr: 0.000021 - momentum: 0.000000
127
+ 2023-09-04 10:26:19,211 epoch 4 - iter 1029/1476 - loss 0.05429164 - time (sec): 111.16 - samples/sec: 1044.26 - lr: 0.000021 - momentum: 0.000000
128
+ 2023-09-04 10:26:34,538 epoch 4 - iter 1176/1476 - loss 0.05456421 - time (sec): 126.48 - samples/sec: 1037.19 - lr: 0.000021 - momentum: 0.000000
129
+ 2023-09-04 10:26:51,620 epoch 4 - iter 1323/1476 - loss 0.05768971 - time (sec): 143.57 - samples/sec: 1038.71 - lr: 0.000020 - momentum: 0.000000
130
+ 2023-09-04 10:27:07,587 epoch 4 - iter 1470/1476 - loss 0.05893092 - time (sec): 159.53 - samples/sec: 1039.45 - lr: 0.000020 - momentum: 0.000000
131
+ 2023-09-04 10:27:08,132 ----------------------------------------------------------------------------------------------------
132
+ 2023-09-04 10:27:08,133 EPOCH 4 done: loss 0.0588 - lr: 0.000020
133
+ 2023-09-04 10:27:25,851 DEV : loss 0.18615181744098663 - f1-score (micro avg) 0.8034
134
+ 2023-09-04 10:27:25,880 saving best model
135
+ 2023-09-04 10:27:27,230 ----------------------------------------------------------------------------------------------------
136
+ 2023-09-04 10:27:42,404 epoch 5 - iter 147/1476 - loss 0.03932232 - time (sec): 15.17 - samples/sec: 1014.93 - lr: 0.000020 - momentum: 0.000000
137
+ 2023-09-04 10:27:58,100 epoch 5 - iter 294/1476 - loss 0.03663678 - time (sec): 30.87 - samples/sec: 1019.48 - lr: 0.000019 - momentum: 0.000000
138
+ 2023-09-04 10:28:14,301 epoch 5 - iter 441/1476 - loss 0.03830042 - time (sec): 47.07 - samples/sec: 1037.06 - lr: 0.000019 - momentum: 0.000000
139
+ 2023-09-04 10:28:30,270 epoch 5 - iter 588/1476 - loss 0.03739481 - time (sec): 63.04 - samples/sec: 1025.18 - lr: 0.000019 - momentum: 0.000000
140
+ 2023-09-04 10:28:46,600 epoch 5 - iter 735/1476 - loss 0.03739315 - time (sec): 79.37 - samples/sec: 1035.66 - lr: 0.000018 - momentum: 0.000000
141
+ 2023-09-04 10:29:02,629 epoch 5 - iter 882/1476 - loss 0.03919707 - time (sec): 95.40 - samples/sec: 1037.36 - lr: 0.000018 - momentum: 0.000000
142
+ 2023-09-04 10:29:17,969 epoch 5 - iter 1029/1476 - loss 0.03993012 - time (sec): 110.74 - samples/sec: 1034.01 - lr: 0.000018 - momentum: 0.000000
143
+ 2023-09-04 10:29:35,054 epoch 5 - iter 1176/1476 - loss 0.03988301 - time (sec): 127.82 - samples/sec: 1039.41 - lr: 0.000017 - momentum: 0.000000
144
+ 2023-09-04 10:29:51,019 epoch 5 - iter 1323/1476 - loss 0.03946526 - time (sec): 143.79 - samples/sec: 1038.11 - lr: 0.000017 - momentum: 0.000000
145
+ 2023-09-04 10:30:06,767 epoch 5 - iter 1470/1476 - loss 0.04013420 - time (sec): 159.53 - samples/sec: 1039.96 - lr: 0.000017 - momentum: 0.000000
146
+ 2023-09-04 10:30:07,330 ----------------------------------------------------------------------------------------------------
147
+ 2023-09-04 10:30:07,331 EPOCH 5 done: loss 0.0403 - lr: 0.000017
148
+ 2023-09-04 10:30:24,997 DEV : loss 0.16224519908428192 - f1-score (micro avg) 0.8305
149
+ 2023-09-04 10:30:25,026 saving best model
150
+ 2023-09-04 10:30:26,354 ----------------------------------------------------------------------------------------------------
151
+ 2023-09-04 10:30:41,433 epoch 6 - iter 147/1476 - loss 0.03058216 - time (sec): 15.08 - samples/sec: 997.39 - lr: 0.000016 - momentum: 0.000000
152
+ 2023-09-04 10:30:57,010 epoch 6 - iter 294/1476 - loss 0.02298688 - time (sec): 30.65 - samples/sec: 1000.48 - lr: 0.000016 - momentum: 0.000000
153
+ 2023-09-04 10:31:14,210 epoch 6 - iter 441/1476 - loss 0.02484566 - time (sec): 47.85 - samples/sec: 1030.62 - lr: 0.000016 - momentum: 0.000000
154
+ 2023-09-04 10:31:30,363 epoch 6 - iter 588/1476 - loss 0.02832007 - time (sec): 64.01 - samples/sec: 1026.78 - lr: 0.000015 - momentum: 0.000000
155
+ 2023-09-04 10:31:45,999 epoch 6 - iter 735/1476 - loss 0.02976912 - time (sec): 79.64 - samples/sec: 1031.86 - lr: 0.000015 - momentum: 0.000000
156
+ 2023-09-04 10:32:02,220 epoch 6 - iter 882/1476 - loss 0.02842896 - time (sec): 95.86 - samples/sec: 1039.56 - lr: 0.000015 - momentum: 0.000000
157
+ 2023-09-04 10:32:17,402 epoch 6 - iter 1029/1476 - loss 0.02820504 - time (sec): 111.05 - samples/sec: 1033.85 - lr: 0.000014 - momentum: 0.000000
158
+ 2023-09-04 10:32:33,101 epoch 6 - iter 1176/1476 - loss 0.02817965 - time (sec): 126.75 - samples/sec: 1034.00 - lr: 0.000014 - momentum: 0.000000
159
+ 2023-09-04 10:32:50,323 epoch 6 - iter 1323/1476 - loss 0.02870199 - time (sec): 143.97 - samples/sec: 1042.87 - lr: 0.000014 - momentum: 0.000000
160
+ 2023-09-04 10:33:05,811 epoch 6 - iter 1470/1476 - loss 0.02862665 - time (sec): 159.46 - samples/sec: 1040.37 - lr: 0.000013 - momentum: 0.000000
161
+ 2023-09-04 10:33:06,364 ----------------------------------------------------------------------------------------------------
162
+ 2023-09-04 10:33:06,364 EPOCH 6 done: loss 0.0286 - lr: 0.000013
163
+ 2023-09-04 10:33:24,035 DEV : loss 0.18499363958835602 - f1-score (micro avg) 0.8224
164
+ 2023-09-04 10:33:24,064 ----------------------------------------------------------------------------------------------------
165
+ 2023-09-04 10:33:39,126 epoch 7 - iter 147/1476 - loss 0.01971207 - time (sec): 15.06 - samples/sec: 1018.33 - lr: 0.000013 - momentum: 0.000000
166
+ 2023-09-04 10:33:56,242 epoch 7 - iter 294/1476 - loss 0.02127106 - time (sec): 32.18 - samples/sec: 1053.78 - lr: 0.000013 - momentum: 0.000000
167
+ 2023-09-04 10:34:11,004 epoch 7 - iter 441/1476 - loss 0.01914688 - time (sec): 46.94 - samples/sec: 1046.62 - lr: 0.000012 - momentum: 0.000000
168
+ 2023-09-04 10:34:27,402 epoch 7 - iter 588/1476 - loss 0.02003916 - time (sec): 63.34 - samples/sec: 1034.90 - lr: 0.000012 - momentum: 0.000000
169
+ 2023-09-04 10:34:43,237 epoch 7 - iter 735/1476 - loss 0.01896599 - time (sec): 79.17 - samples/sec: 1038.52 - lr: 0.000012 - momentum: 0.000000
170
+ 2023-09-04 10:34:59,759 epoch 7 - iter 882/1476 - loss 0.01947820 - time (sec): 95.69 - samples/sec: 1051.54 - lr: 0.000011 - momentum: 0.000000
171
+ 2023-09-04 10:35:16,402 epoch 7 - iter 1029/1476 - loss 0.01953019 - time (sec): 112.34 - samples/sec: 1053.86 - lr: 0.000011 - momentum: 0.000000
172
+ 2023-09-04 10:35:32,814 epoch 7 - iter 1176/1476 - loss 0.01913403 - time (sec): 128.75 - samples/sec: 1047.64 - lr: 0.000011 - momentum: 0.000000
173
+ 2023-09-04 10:35:47,989 epoch 7 - iter 1323/1476 - loss 0.01935599 - time (sec): 143.92 - samples/sec: 1041.00 - lr: 0.000010 - momentum: 0.000000
174
+ 2023-09-04 10:36:03,567 epoch 7 - iter 1470/1476 - loss 0.01905400 - time (sec): 159.50 - samples/sec: 1040.00 - lr: 0.000010 - momentum: 0.000000
175
+ 2023-09-04 10:36:04,131 ----------------------------------------------------------------------------------------------------
176
+ 2023-09-04 10:36:04,132 EPOCH 7 done: loss 0.0190 - lr: 0.000010
177
+ 2023-09-04 10:36:21,817 DEV : loss 0.20547997951507568 - f1-score (micro avg) 0.8222
178
+ 2023-09-04 10:36:21,846 ----------------------------------------------------------------------------------------------------
179
+ 2023-09-04 10:36:37,343 epoch 8 - iter 147/1476 - loss 0.00927443 - time (sec): 15.50 - samples/sec: 1053.86 - lr: 0.000010 - momentum: 0.000000
180
+ 2023-09-04 10:36:52,803 epoch 8 - iter 294/1476 - loss 0.01443232 - time (sec): 30.96 - samples/sec: 1038.01 - lr: 0.000009 - momentum: 0.000000
181
+ 2023-09-04 10:37:09,709 epoch 8 - iter 441/1476 - loss 0.01406090 - time (sec): 47.86 - samples/sec: 1042.94 - lr: 0.000009 - momentum: 0.000000
182
+ 2023-09-04 10:37:25,575 epoch 8 - iter 588/1476 - loss 0.01412371 - time (sec): 63.73 - samples/sec: 1034.28 - lr: 0.000009 - momentum: 0.000000
183
+ 2023-09-04 10:37:41,102 epoch 8 - iter 735/1476 - loss 0.01528631 - time (sec): 79.25 - samples/sec: 1032.64 - lr: 0.000008 - momentum: 0.000000
184
+ 2023-09-04 10:37:57,054 epoch 8 - iter 882/1476 - loss 0.01570723 - time (sec): 95.21 - samples/sec: 1026.50 - lr: 0.000008 - momentum: 0.000000
185
+ 2023-09-04 10:38:12,755 epoch 8 - iter 1029/1476 - loss 0.01513567 - time (sec): 110.91 - samples/sec: 1024.38 - lr: 0.000008 - momentum: 0.000000
186
+ 2023-09-04 10:38:29,935 epoch 8 - iter 1176/1476 - loss 0.01578666 - time (sec): 128.09 - samples/sec: 1028.83 - lr: 0.000007 - momentum: 0.000000
187
+ 2023-09-04 10:38:45,899 epoch 8 - iter 1323/1476 - loss 0.01470102 - time (sec): 144.05 - samples/sec: 1029.98 - lr: 0.000007 - momentum: 0.000000
188
+ 2023-09-04 10:39:02,230 epoch 8 - iter 1470/1476 - loss 0.01506677 - time (sec): 160.38 - samples/sec: 1034.73 - lr: 0.000007 - momentum: 0.000000
189
+ 2023-09-04 10:39:02,768 ----------------------------------------------------------------------------------------------------
190
+ 2023-09-04 10:39:02,769 EPOCH 8 done: loss 0.0150 - lr: 0.000007
191
+ 2023-09-04 10:39:20,422 DEV : loss 0.2134592980146408 - f1-score (micro avg) 0.8273
192
+ 2023-09-04 10:39:20,451 ----------------------------------------------------------------------------------------------------
193
+ 2023-09-04 10:39:36,679 epoch 9 - iter 147/1476 - loss 0.01104624 - time (sec): 16.23 - samples/sec: 1072.17 - lr: 0.000006 - momentum: 0.000000
194
+ 2023-09-04 10:39:52,339 epoch 9 - iter 294/1476 - loss 0.00872244 - time (sec): 31.89 - samples/sec: 1060.98 - lr: 0.000006 - momentum: 0.000000
195
+ 2023-09-04 10:40:07,971 epoch 9 - iter 441/1476 - loss 0.01029263 - time (sec): 47.52 - samples/sec: 1042.53 - lr: 0.000006 - momentum: 0.000000
196
+ 2023-09-04 10:40:23,878 epoch 9 - iter 588/1476 - loss 0.00956266 - time (sec): 63.43 - samples/sec: 1039.02 - lr: 0.000005 - momentum: 0.000000
197
+ 2023-09-04 10:40:39,647 epoch 9 - iter 735/1476 - loss 0.00884433 - time (sec): 79.20 - samples/sec: 1036.17 - lr: 0.000005 - momentum: 0.000000
198
+ 2023-09-04 10:40:54,897 epoch 9 - iter 882/1476 - loss 0.00898675 - time (sec): 94.45 - samples/sec: 1032.79 - lr: 0.000005 - momentum: 0.000000
199
+ 2023-09-04 10:41:10,985 epoch 9 - iter 1029/1476 - loss 0.00837164 - time (sec): 110.53 - samples/sec: 1040.01 - lr: 0.000004 - momentum: 0.000000
200
+ 2023-09-04 10:41:27,844 epoch 9 - iter 1176/1476 - loss 0.00815663 - time (sec): 127.39 - samples/sec: 1038.64 - lr: 0.000004 - momentum: 0.000000
201
+ 2023-09-04 10:41:43,609 epoch 9 - iter 1323/1476 - loss 0.00873377 - time (sec): 143.16 - samples/sec: 1040.38 - lr: 0.000004 - momentum: 0.000000
202
+ 2023-09-04 10:41:59,651 epoch 9 - iter 1470/1476 - loss 0.00840249 - time (sec): 159.20 - samples/sec: 1042.41 - lr: 0.000003 - momentum: 0.000000
203
+ 2023-09-04 10:42:00,183 ----------------------------------------------------------------------------------------------------
204
+ 2023-09-04 10:42:00,184 EPOCH 9 done: loss 0.0084 - lr: 0.000003
205
+ 2023-09-04 10:42:17,894 DEV : loss 0.22319921851158142 - f1-score (micro avg) 0.8343
206
+ 2023-09-04 10:42:17,924 saving best model
207
+ 2023-09-04 10:42:19,293 ----------------------------------------------------------------------------------------------------
208
+ 2023-09-04 10:42:34,885 epoch 10 - iter 147/1476 - loss 0.00964131 - time (sec): 15.59 - samples/sec: 1035.70 - lr: 0.000003 - momentum: 0.000000
209
+ 2023-09-04 10:42:52,863 epoch 10 - iter 294/1476 - loss 0.00616879 - time (sec): 33.57 - samples/sec: 1062.37 - lr: 0.000003 - momentum: 0.000000
210
+ 2023-09-04 10:43:08,744 epoch 10 - iter 441/1476 - loss 0.00580351 - time (sec): 49.45 - samples/sec: 1045.47 - lr: 0.000002 - momentum: 0.000000
211
+ 2023-09-04 10:43:23,800 epoch 10 - iter 588/1476 - loss 0.00507810 - time (sec): 64.51 - samples/sec: 1044.13 - lr: 0.000002 - momentum: 0.000000
212
+ 2023-09-04 10:43:38,573 epoch 10 - iter 735/1476 - loss 0.00440984 - time (sec): 79.28 - samples/sec: 1043.39 - lr: 0.000002 - momentum: 0.000000
213
+ 2023-09-04 10:43:53,641 epoch 10 - iter 882/1476 - loss 0.00502582 - time (sec): 94.35 - samples/sec: 1038.56 - lr: 0.000001 - momentum: 0.000000
214
+ 2023-09-04 10:44:10,066 epoch 10 - iter 1029/1476 - loss 0.00502751 - time (sec): 110.77 - samples/sec: 1041.61 - lr: 0.000001 - momentum: 0.000000
215
+ 2023-09-04 10:44:26,110 epoch 10 - iter 1176/1476 - loss 0.00582801 - time (sec): 126.82 - samples/sec: 1039.77 - lr: 0.000001 - momentum: 0.000000
216
+ 2023-09-04 10:44:41,795 epoch 10 - iter 1323/1476 - loss 0.00550940 - time (sec): 142.50 - samples/sec: 1037.73 - lr: 0.000000 - momentum: 0.000000
217
+ 2023-09-04 10:44:58,748 epoch 10 - iter 1470/1476 - loss 0.00586537 - time (sec): 159.45 - samples/sec: 1041.35 - lr: 0.000000 - momentum: 0.000000
218
+ 2023-09-04 10:44:59,258 ----------------------------------------------------------------------------------------------------
219
+ 2023-09-04 10:44:59,258 EPOCH 10 done: loss 0.0059 - lr: 0.000000
220
+ 2023-09-04 10:45:16,958 DEV : loss 0.22122718393802643 - f1-score (micro avg) 0.8346
221
+ 2023-09-04 10:45:16,991 saving best model
222
+ 2023-09-04 10:45:18,844 ----------------------------------------------------------------------------------------------------
223
+ 2023-09-04 10:45:18,845 Loading model from best epoch ...
224
+ 2023-09-04 10:45:20,664 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 10:45:35,338
226
+ Results:
227
+ - F-score (micro) 0.7978
228
+ - F-score (macro) 0.6844
229
+ - Accuracy 0.6878
230
+
231
+ By class:
232
+ precision recall f1-score support
233
+
234
+ loc 0.8781 0.8730 0.8755 858
235
+ pers 0.7500 0.8045 0.7763 537
236
+ org 0.5522 0.5606 0.5564 132
237
+ time 0.4921 0.5741 0.5299 54
238
+ prod 0.7143 0.6557 0.6838 61
239
+
240
+ micro avg 0.7883 0.8076 0.7978 1642
241
+ macro avg 0.6773 0.6936 0.6844 1642
242
+ weighted avg 0.7912 0.8076 0.7989 1642
243
+
244
+ 2023-09-04 10:45:35,338 ----------------------------------------------------------------------------------------------------