Hugging Face's logo

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

Dataset card Files Community
stefan-it commited on
Commit
ac68c40
•
1 Parent(s): c311f19

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-5/best-model.pt +3 -0
  2. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5/dev.tsv +0 -0
  3. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5/final-model.pt +3 -0
  4. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5/loss.tsv +11 -0
  5. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5/test.tsv +0 -0
  6. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5/training.log +243 -0
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5/best-model.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:70e6e1d8bff502de23494473e798f4d8e881e61ae4b93b9cb6e83366c1b9ef0b
3
+ size 443334288
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-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-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5/final-model.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:eaa58dc1c013c2e5c5ebd592380678a1a9117372d3dd26d1a7d7db303ffaebc4
3
+ size 443334491
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5/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 18:03:56 0.0000 0.5185 0.1542 0.6483 0.7274 0.6856 0.5448
3
+ 2 18:06:54 0.0000 0.1295 0.1313 0.7428 0.8288 0.7834 0.6687
4
+ 3 18:09:53 0.0000 0.0799 0.1458 0.7866 0.8127 0.7994 0.6922
5
+ 4 18:12:52 0.0000 0.0566 0.1817 0.7930 0.8184 0.8055 0.7039
6
+ 5 18:15:52 0.0000 0.0407 0.1780 0.8160 0.8408 0.8282 0.7311
7
+ 6 18:18:51 0.0000 0.0278 0.2169 0.8130 0.8144 0.8137 0.7117
8
+ 7 18:21:49 0.0000 0.0196 0.2043 0.8297 0.8259 0.8278 0.7338
9
+ 8 18:24:47 0.0000 0.0125 0.2152 0.8184 0.8288 0.8236 0.7279
10
+ 9 18:27:45 0.0000 0.0095 0.2087 0.8175 0.8414 0.8292 0.7356
11
+ 10 18:30:44 0.0000 0.0065 0.2232 0.8205 0.8379 0.8291 0.7363
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5/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-5/training.log ADDED
@@ -0,0 +1,243 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 2023-09-04 18:01:02,696 ----------------------------------------------------------------------------------------------------
2
+ 2023-09-04 18:01:02,697 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 18:01:02,698 ----------------------------------------------------------------------------------------------------
51
+ 2023-09-04 18:01:02,698 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 18:01:02,698 ----------------------------------------------------------------------------------------------------
54
+ 2023-09-04 18:01:02,698 Train: 5901 sentences
55
+ 2023-09-04 18:01:02,698 (train_with_dev=False, train_with_test=False)
56
+ 2023-09-04 18:01:02,698 ----------------------------------------------------------------------------------------------------
57
+ 2023-09-04 18:01:02,698 Training Params:
58
+ 2023-09-04 18:01:02,698 - learning_rate: "3e-05"
59
+ 2023-09-04 18:01:02,698 - mini_batch_size: "4"
60
+ 2023-09-04 18:01:02,698 - max_epochs: "10"
61
+ 2023-09-04 18:01:02,698 - shuffle: "True"
62
+ 2023-09-04 18:01:02,698 ----------------------------------------------------------------------------------------------------
63
+ 2023-09-04 18:01:02,698 Plugins:
64
+ 2023-09-04 18:01:02,698 - LinearScheduler | warmup_fraction: '0.1'
65
+ 2023-09-04 18:01:02,698 ----------------------------------------------------------------------------------------------------
66
+ 2023-09-04 18:01:02,698 Final evaluation on model from best epoch (best-model.pt)
67
+ 2023-09-04 18:01:02,699 - metric: "('micro avg', 'f1-score')"
68
+ 2023-09-04 18:01:02,699 ----------------------------------------------------------------------------------------------------
69
+ 2023-09-04 18:01:02,699 Computation:
70
+ 2023-09-04 18:01:02,699 - compute on device: cuda:0
71
+ 2023-09-04 18:01:02,699 - embedding storage: none
72
+ 2023-09-04 18:01:02,699 ----------------------------------------------------------------------------------------------------
73
+ 2023-09-04 18:01:02,699 Model training base path: "hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5"
74
+ 2023-09-04 18:01:02,699 ----------------------------------------------------------------------------------------------------
75
+ 2023-09-04 18:01:02,699 ----------------------------------------------------------------------------------------------------
76
+ 2023-09-04 18:01:18,094 epoch 1 - iter 147/1476 - loss 2.44376377 - time (sec): 15.39 - samples/sec: 1055.91 - lr: 0.000003 - momentum: 0.000000
77
+ 2023-09-04 18:01:33,845 epoch 1 - iter 294/1476 - loss 1.52151398 - time (sec): 31.15 - samples/sec: 1050.94 - lr: 0.000006 - momentum: 0.000000
78
+ 2023-09-04 18:01:49,471 epoch 1 - iter 441/1476 - loss 1.15461415 - time (sec): 46.77 - samples/sec: 1043.42 - lr: 0.000009 - momentum: 0.000000
79
+ 2023-09-04 18:02:05,099 epoch 1 - iter 588/1476 - loss 0.94850083 - time (sec): 62.40 - samples/sec: 1041.15 - lr: 0.000012 - momentum: 0.000000
80
+ 2023-09-04 18:02:21,896 epoch 1 - iter 735/1476 - loss 0.82532013 - time (sec): 79.20 - samples/sec: 1036.04 - lr: 0.000015 - momentum: 0.000000
81
+ 2023-09-04 18:02:36,710 epoch 1 - iter 882/1476 - loss 0.73671035 - time (sec): 94.01 - samples/sec: 1030.80 - lr: 0.000018 - momentum: 0.000000
82
+ 2023-09-04 18:02:53,060 epoch 1 - iter 1029/1476 - loss 0.66213797 - time (sec): 110.36 - samples/sec: 1036.86 - lr: 0.000021 - momentum: 0.000000
83
+ 2023-09-04 18:03:09,409 epoch 1 - iter 1176/1476 - loss 0.60019447 - time (sec): 126.71 - samples/sec: 1042.95 - lr: 0.000024 - momentum: 0.000000
84
+ 2023-09-04 18:03:24,876 epoch 1 - iter 1323/1476 - loss 0.55657173 - time (sec): 142.18 - samples/sec: 1045.22 - lr: 0.000027 - momentum: 0.000000
85
+ 2023-09-04 18:03:41,637 epoch 1 - iter 1470/1476 - loss 0.51947340 - time (sec): 158.94 - samples/sec: 1043.39 - lr: 0.000030 - momentum: 0.000000
86
+ 2023-09-04 18:03:42,206 ----------------------------------------------------------------------------------------------------
87
+ 2023-09-04 18:03:42,207 EPOCH 1 done: loss 0.5185 - lr: 0.000030
88
+ 2023-09-04 18:03:56,790 DEV : loss 0.15415577590465546 - f1-score (micro avg) 0.6856
89
+ 2023-09-04 18:03:56,837 saving best model
90
+ 2023-09-04 18:03:57,328 ----------------------------------------------------------------------------------------------------
91
+ 2023-09-04 18:04:13,436 epoch 2 - iter 147/1476 - loss 0.13762875 - time (sec): 16.11 - samples/sec: 1041.34 - lr: 0.000030 - momentum: 0.000000
92
+ 2023-09-04 18:04:29,294 epoch 2 - iter 294/1476 - loss 0.14049549 - time (sec): 31.96 - samples/sec: 1038.45 - lr: 0.000029 - momentum: 0.000000
93
+ 2023-09-04 18:04:45,489 epoch 2 - iter 441/1476 - loss 0.13824014 - time (sec): 48.16 - samples/sec: 1037.93 - lr: 0.000029 - momentum: 0.000000
94
+ 2023-09-04 18:05:00,555 epoch 2 - iter 588/1476 - loss 0.13296353 - time (sec): 63.23 - samples/sec: 1035.21 - lr: 0.000029 - momentum: 0.000000
95
+ 2023-09-04 18:05:16,929 epoch 2 - iter 735/1476 - loss 0.12947844 - time (sec): 79.60 - samples/sec: 1052.53 - lr: 0.000028 - momentum: 0.000000
96
+ 2023-09-04 18:05:35,682 epoch 2 - iter 882/1476 - loss 0.13359823 - time (sec): 98.35 - samples/sec: 1063.30 - lr: 0.000028 - momentum: 0.000000
97
+ 2023-09-04 18:05:50,461 epoch 2 - iter 1029/1476 - loss 0.13133894 - time (sec): 113.13 - samples/sec: 1058.87 - lr: 0.000028 - momentum: 0.000000
98
+ 2023-09-04 18:06:06,506 epoch 2 - iter 1176/1476 - loss 0.13058551 - time (sec): 129.18 - samples/sec: 1059.23 - lr: 0.000027 - momentum: 0.000000
99
+ 2023-09-04 18:06:20,816 epoch 2 - iter 1323/1476 - loss 0.13091216 - time (sec): 143.49 - samples/sec: 1053.59 - lr: 0.000027 - momentum: 0.000000
100
+ 2023-09-04 18:06:35,935 epoch 2 - iter 1470/1476 - loss 0.12972873 - time (sec): 158.61 - samples/sec: 1046.77 - lr: 0.000027 - momentum: 0.000000
101
+ 2023-09-04 18:06:36,455 ----------------------------------------------------------------------------------------------------
102
+ 2023-09-04 18:06:36,455 EPOCH 2 done: loss 0.1295 - lr: 0.000027
103
+ 2023-09-04 18:06:54,283 DEV : loss 0.13132880628108978 - f1-score (micro avg) 0.7834
104
+ 2023-09-04 18:06:54,312 saving best model
105
+ 2023-09-04 18:06:55,664 ----------------------------------------------------------------------------------------------------
106
+ 2023-09-04 18:07:12,339 epoch 3 - iter 147/1476 - loss 0.06202239 - time (sec): 16.67 - samples/sec: 1111.70 - lr: 0.000026 - momentum: 0.000000
107
+ 2023-09-04 18:07:28,604 epoch 3 - iter 294/1476 - loss 0.06649857 - time (sec): 32.94 - samples/sec: 1070.17 - lr: 0.000026 - momentum: 0.000000
108
+ 2023-09-04 18:07:44,511 epoch 3 - iter 441/1476 - loss 0.06984059 - time (sec): 48.85 - samples/sec: 1065.42 - lr: 0.000026 - momentum: 0.000000
109
+ 2023-09-04 18:08:01,651 epoch 3 - iter 588/1476 - loss 0.07783875 - time (sec): 65.99 - samples/sec: 1064.77 - lr: 0.000025 - momentum: 0.000000
110
+ 2023-09-04 18:08:16,965 epoch 3 - iter 735/1476 - loss 0.07932757 - time (sec): 81.30 - samples/sec: 1054.93 - lr: 0.000025 - momentum: 0.000000
111
+ 2023-09-04 18:08:32,668 epoch 3 - iter 882/1476 - loss 0.07615306 - time (sec): 97.00 - samples/sec: 1049.99 - lr: 0.000025 - momentum: 0.000000
112
+ 2023-09-04 18:08:48,048 epoch 3 - iter 1029/1476 - loss 0.07522442 - time (sec): 112.38 - samples/sec: 1045.01 - lr: 0.000024 - momentum: 0.000000
113
+ 2023-09-04 18:09:03,784 epoch 3 - iter 1176/1476 - loss 0.07465154 - time (sec): 128.12 - samples/sec: 1043.48 - lr: 0.000024 - momentum: 0.000000
114
+ 2023-09-04 18:09:19,691 epoch 3 - iter 1323/1476 - loss 0.07700065 - time (sec): 144.03 - samples/sec: 1041.14 - lr: 0.000024 - momentum: 0.000000
115
+ 2023-09-04 18:09:34,988 epoch 3 - iter 1470/1476 - loss 0.07972797 - time (sec): 159.32 - samples/sec: 1041.18 - lr: 0.000023 - momentum: 0.000000
116
+ 2023-09-04 18:09:35,518 ----------------------------------------------------------------------------------------------------
117
+ 2023-09-04 18:09:35,518 EPOCH 3 done: loss 0.0799 - lr: 0.000023
118
+ 2023-09-04 18:09:53,055 DEV : loss 0.14578530192375183 - f1-score (micro avg) 0.7994
119
+ 2023-09-04 18:09:53,083 saving best model
120
+ 2023-09-04 18:09:54,422 ----------------------------------------------------------------------------------------------------
121
+ 2023-09-04 18:10:10,140 epoch 4 - iter 147/1476 - loss 0.05746252 - time (sec): 15.72 - samples/sec: 1026.05 - lr: 0.000023 - momentum: 0.000000
122
+ 2023-09-04 18:10:27,722 epoch 4 - iter 294/1476 - loss 0.06102346 - time (sec): 33.30 - samples/sec: 1071.19 - lr: 0.000023 - momentum: 0.000000
123
+ 2023-09-04 18:10:43,854 epoch 4 - iter 441/1476 - loss 0.05967365 - time (sec): 49.43 - samples/sec: 1047.86 - lr: 0.000022 - momentum: 0.000000
124
+ 2023-09-04 18:10:58,746 epoch 4 - iter 588/1476 - loss 0.06001754 - time (sec): 64.32 - samples/sec: 1030.36 - lr: 0.000022 - momentum: 0.000000
125
+ 2023-09-04 18:11:15,070 epoch 4 - iter 735/1476 - loss 0.05838120 - time (sec): 80.65 - samples/sec: 1034.71 - lr: 0.000022 - momentum: 0.000000
126
+ 2023-09-04 18:11:30,504 epoch 4 - iter 882/1476 - loss 0.05932716 - time (sec): 96.08 - samples/sec: 1036.16 - lr: 0.000021 - momentum: 0.000000
127
+ 2023-09-04 18:11:45,730 epoch 4 - iter 1029/1476 - loss 0.05890917 - time (sec): 111.31 - samples/sec: 1029.82 - lr: 0.000021 - momentum: 0.000000
128
+ 2023-09-04 18:12:01,064 epoch 4 - iter 1176/1476 - loss 0.05796255 - time (sec): 126.64 - samples/sec: 1031.35 - lr: 0.000021 - momentum: 0.000000
129
+ 2023-09-04 18:12:17,046 epoch 4 - iter 1323/1476 - loss 0.05817651 - time (sec): 142.62 - samples/sec: 1029.12 - lr: 0.000020 - momentum: 0.000000
130
+ 2023-09-04 18:12:34,542 epoch 4 - iter 1470/1476 - loss 0.05645201 - time (sec): 160.12 - samples/sec: 1035.95 - lr: 0.000020 - momentum: 0.000000
131
+ 2023-09-04 18:12:35,105 ----------------------------------------------------------------------------------------------------
132
+ 2023-09-04 18:12:35,105 EPOCH 4 done: loss 0.0566 - lr: 0.000020
133
+ 2023-09-04 18:12:52,812 DEV : loss 0.18173334002494812 - f1-score (micro avg) 0.8055
134
+ 2023-09-04 18:12:52,842 saving best model
135
+ 2023-09-04 18:12:54,191 ----------------------------------------------------------------------------------------------------
136
+ 2023-09-04 18:13:09,997 epoch 5 - iter 147/1476 - loss 0.05138894 - time (sec): 15.80 - samples/sec: 1064.24 - lr: 0.000020 - momentum: 0.000000
137
+ 2023-09-04 18:13:24,951 epoch 5 - iter 294/1476 - loss 0.04721485 - time (sec): 30.76 - samples/sec: 1027.23 - lr: 0.000019 - momentum: 0.000000
138
+ 2023-09-04 18:13:41,035 epoch 5 - iter 441/1476 - loss 0.04141632 - time (sec): 46.84 - samples/sec: 1030.23 - lr: 0.000019 - momentum: 0.000000
139
+ 2023-09-04 18:13:56,918 epoch 5 - iter 588/1476 - loss 0.03965347 - time (sec): 62.73 - samples/sec: 1034.00 - lr: 0.000019 - momentum: 0.000000
140
+ 2023-09-04 18:14:13,476 epoch 5 - iter 735/1476 - loss 0.04115344 - time (sec): 79.28 - samples/sec: 1035.68 - lr: 0.000018 - momentum: 0.000000
141
+ 2023-09-04 18:14:29,425 epoch 5 - iter 882/1476 - loss 0.04010953 - time (sec): 95.23 - samples/sec: 1038.19 - lr: 0.000018 - momentum: 0.000000
142
+ 2023-09-04 18:14:46,066 epoch 5 - iter 1029/1476 - loss 0.03987999 - time (sec): 111.87 - samples/sec: 1037.38 - lr: 0.000018 - momentum: 0.000000
143
+ 2023-09-04 18:15:02,228 epoch 5 - iter 1176/1476 - loss 0.04089865 - time (sec): 128.04 - samples/sec: 1036.12 - lr: 0.000017 - momentum: 0.000000
144
+ 2023-09-04 18:15:18,305 epoch 5 - iter 1323/1476 - loss 0.04058762 - time (sec): 144.11 - samples/sec: 1038.65 - lr: 0.000017 - momentum: 0.000000
145
+ 2023-09-04 18:15:33,710 epoch 5 - iter 1470/1476 - loss 0.04089062 - time (sec): 159.52 - samples/sec: 1039.41 - lr: 0.000017 - momentum: 0.000000
146
+ 2023-09-04 18:15:34,350 ----------------------------------------------------------------------------------------------------
147
+ 2023-09-04 18:15:34,351 EPOCH 5 done: loss 0.0407 - lr: 0.000017
148
+ 2023-09-04 18:15:52,045 DEV : loss 0.17798171937465668 - f1-score (micro avg) 0.8282
149
+ 2023-09-04 18:15:52,073 saving best model
150
+ 2023-09-04 18:15:53,401 ----------------------------------------------------------------------------------------------------
151
+ 2023-09-04 18:16:09,370 epoch 6 - iter 147/1476 - loss 0.03077746 - time (sec): 15.97 - samples/sec: 1068.35 - lr: 0.000016 - momentum: 0.000000
152
+ 2023-09-04 18:16:24,765 epoch 6 - iter 294/1476 - loss 0.02870391 - time (sec): 31.36 - samples/sec: 1035.23 - lr: 0.000016 - momentum: 0.000000
153
+ 2023-09-04 18:16:40,709 epoch 6 - iter 441/1476 - loss 0.02709286 - time (sec): 47.31 - samples/sec: 1033.40 - lr: 0.000016 - momentum: 0.000000
154
+ 2023-09-04 18:16:56,794 epoch 6 - iter 588/1476 - loss 0.02749007 - time (sec): 63.39 - samples/sec: 1030.11 - lr: 0.000015 - momentum: 0.000000
155
+ 2023-09-04 18:17:12,164 epoch 6 - iter 735/1476 - loss 0.02642923 - time (sec): 78.76 - samples/sec: 1024.79 - lr: 0.000015 - momentum: 0.000000
156
+ 2023-09-04 18:17:27,358 epoch 6 - iter 882/1476 - loss 0.02612535 - time (sec): 93.96 - samples/sec: 1022.85 - lr: 0.000015 - momentum: 0.000000
157
+ 2023-09-04 18:17:43,902 epoch 6 - iter 1029/1476 - loss 0.02662349 - time (sec): 110.50 - samples/sec: 1029.41 - lr: 0.000014 - momentum: 0.000000
158
+ 2023-09-04 18:17:59,949 epoch 6 - iter 1176/1476 - loss 0.02660086 - time (sec): 126.55 - samples/sec: 1029.02 - lr: 0.000014 - momentum: 0.000000
159
+ 2023-09-04 18:18:16,060 epoch 6 - iter 1323/1476 - loss 0.02752760 - time (sec): 142.66 - samples/sec: 1028.15 - lr: 0.000014 - momentum: 0.000000
160
+ 2023-09-04 18:18:32,511 epoch 6 - iter 1470/1476 - loss 0.02784187 - time (sec): 159.11 - samples/sec: 1037.65 - lr: 0.000013 - momentum: 0.000000
161
+ 2023-09-04 18:18:33,705 ----------------------------------------------------------------------------------------------------
162
+ 2023-09-04 18:18:33,706 EPOCH 6 done: loss 0.0278 - lr: 0.000013
163
+ 2023-09-04 18:18:51,348 DEV : loss 0.2169143557548523 - f1-score (micro avg) 0.8137
164
+ 2023-09-04 18:18:51,377 ----------------------------------------------------------------------------------------------------
165
+ 2023-09-04 18:19:07,437 epoch 7 - iter 147/1476 - loss 0.02004925 - time (sec): 16.06 - samples/sec: 1087.97 - lr: 0.000013 - momentum: 0.000000
166
+ 2023-09-04 18:19:25,106 epoch 7 - iter 294/1476 - loss 0.01928499 - time (sec): 33.73 - samples/sec: 1067.37 - lr: 0.000013 - momentum: 0.000000
167
+ 2023-09-04 18:19:41,853 epoch 7 - iter 441/1476 - loss 0.01863859 - time (sec): 50.47 - samples/sec: 1059.56 - lr: 0.000012 - momentum: 0.000000
168
+ 2023-09-04 18:19:58,276 epoch 7 - iter 588/1476 - loss 0.02152433 - time (sec): 66.90 - samples/sec: 1068.83 - lr: 0.000012 - momentum: 0.000000
169
+ 2023-09-04 18:20:12,850 epoch 7 - iter 735/1476 - loss 0.02091597 - time (sec): 81.47 - samples/sec: 1061.81 - lr: 0.000012 - momentum: 0.000000
170
+ 2023-09-04 18:20:29,285 epoch 7 - iter 882/1476 - loss 0.02099112 - time (sec): 97.91 - samples/sec: 1056.78 - lr: 0.000011 - momentum: 0.000000
171
+ 2023-09-04 18:20:44,364 epoch 7 - iter 1029/1476 - loss 0.02043229 - time (sec): 112.99 - samples/sec: 1051.36 - lr: 0.000011 - momentum: 0.000000
172
+ 2023-09-04 18:20:59,863 epoch 7 - iter 1176/1476 - loss 0.01948168 - time (sec): 128.48 - samples/sec: 1046.27 - lr: 0.000011 - momentum: 0.000000
173
+ 2023-09-04 18:21:15,257 epoch 7 - iter 1323/1476 - loss 0.02009420 - time (sec): 143.88 - samples/sec: 1043.76 - lr: 0.000010 - momentum: 0.000000
174
+ 2023-09-04 18:21:30,772 epoch 7 - iter 1470/1476 - loss 0.01960339 - time (sec): 159.39 - samples/sec: 1040.65 - lr: 0.000010 - momentum: 0.000000
175
+ 2023-09-04 18:21:31,414 ----------------------------------------------------------------------------------------------------
176
+ 2023-09-04 18:21:31,415 EPOCH 7 done: loss 0.0196 - lr: 0.000010
177
+ 2023-09-04 18:21:49,585 DEV : loss 0.20429323613643646 - f1-score (micro avg) 0.8278
178
+ 2023-09-04 18:21:49,614 ----------------------------------------------------------------------------------------------------
179
+ 2023-09-04 18:22:05,738 epoch 8 - iter 147/1476 - loss 0.01227334 - time (sec): 16.12 - samples/sec: 1097.99 - lr: 0.000010 - momentum: 0.000000
180
+ 2023-09-04 18:22:20,978 epoch 8 - iter 294/1476 - loss 0.00862639 - time (sec): 31.36 - samples/sec: 1054.47 - lr: 0.000009 - momentum: 0.000000
181
+ 2023-09-04 18:22:38,095 epoch 8 - iter 441/1476 - loss 0.01267560 - time (sec): 48.48 - samples/sec: 1069.95 - lr: 0.000009 - momentum: 0.000000
182
+ 2023-09-04 18:22:53,899 epoch 8 - iter 588/1476 - loss 0.01122963 - time (sec): 64.28 - samples/sec: 1049.34 - lr: 0.000009 - momentum: 0.000000
183
+ 2023-09-04 18:23:08,476 epoch 8 - iter 735/1476 - loss 0.01313610 - time (sec): 78.86 - samples/sec: 1037.64 - lr: 0.000008 - momentum: 0.000000
184
+ 2023-09-04 18:23:25,524 epoch 8 - iter 882/1476 - loss 0.01420155 - time (sec): 95.91 - samples/sec: 1040.54 - lr: 0.000008 - momentum: 0.000000
185
+ 2023-09-04 18:23:41,088 epoch 8 - iter 1029/1476 - loss 0.01313444 - time (sec): 111.47 - samples/sec: 1040.35 - lr: 0.000008 - momentum: 0.000000
186
+ 2023-09-04 18:23:56,612 epoch 8 - iter 1176/1476 - loss 0.01285575 - time (sec): 127.00 - samples/sec: 1037.89 - lr: 0.000007 - momentum: 0.000000
187
+ 2023-09-04 18:24:12,668 epoch 8 - iter 1323/1476 - loss 0.01287226 - time (sec): 143.05 - samples/sec: 1036.18 - lr: 0.000007 - momentum: 0.000000
188
+ 2023-09-04 18:24:29,072 epoch 8 - iter 1470/1476 - loss 0.01249485 - time (sec): 159.46 - samples/sec: 1040.11 - lr: 0.000007 - momentum: 0.000000
189
+ 2023-09-04 18:24:29,617 ----------------------------------------------------------------------------------------------------
190
+ 2023-09-04 18:24:29,617 EPOCH 8 done: loss 0.0125 - lr: 0.000007
191
+ 2023-09-04 18:24:47,345 DEV : loss 0.21515436470508575 - f1-score (micro avg) 0.8236
192
+ 2023-09-04 18:24:47,374 ----------------------------------------------------------------------------------------------------
193
+ 2023-09-04 18:25:03,113 epoch 9 - iter 147/1476 - loss 0.01194312 - time (sec): 15.74 - samples/sec: 1021.80 - lr: 0.000006 - momentum: 0.000000
194
+ 2023-09-04 18:25:18,626 epoch 9 - iter 294/1476 - loss 0.01113643 - time (sec): 31.25 - samples/sec: 1034.37 - lr: 0.000006 - momentum: 0.000000
195
+ 2023-09-04 18:25:33,931 epoch 9 - iter 441/1476 - loss 0.00892407 - time (sec): 46.56 - samples/sec: 1010.97 - lr: 0.000006 - momentum: 0.000000
196
+ 2023-09-04 18:25:50,615 epoch 9 - iter 588/1476 - loss 0.01054983 - time (sec): 63.24 - samples/sec: 1015.46 - lr: 0.000005 - momentum: 0.000000
197
+ 2023-09-04 18:26:05,989 epoch 9 - iter 735/1476 - loss 0.01020447 - time (sec): 78.61 - samples/sec: 1014.85 - lr: 0.000005 - momentum: 0.000000
198
+ 2023-09-04 18:26:21,846 epoch 9 - iter 882/1476 - loss 0.00970575 - time (sec): 94.47 - samples/sec: 1016.08 - lr: 0.000005 - momentum: 0.000000
199
+ 2023-09-04 18:26:38,280 epoch 9 - iter 1029/1476 - loss 0.00972550 - time (sec): 110.90 - samples/sec: 1026.61 - lr: 0.000004 - momentum: 0.000000
200
+ 2023-09-04 18:26:55,525 epoch 9 - iter 1176/1476 - loss 0.01059971 - time (sec): 128.15 - samples/sec: 1031.92 - lr: 0.000004 - momentum: 0.000000
201
+ 2023-09-04 18:27:10,728 epoch 9 - iter 1323/1476 - loss 0.01001943 - time (sec): 143.35 - samples/sec: 1029.52 - lr: 0.000004 - momentum: 0.000000
202
+ 2023-09-04 18:27:26,756 epoch 9 - iter 1470/1476 - loss 0.00955175 - time (sec): 159.38 - samples/sec: 1035.45 - lr: 0.000003 - momentum: 0.000000
203
+ 2023-09-04 18:27:27,865 ----------------------------------------------------------------------------------------------------
204
+ 2023-09-04 18:27:27,866 EPOCH 9 done: loss 0.0095 - lr: 0.000003
205
+ 2023-09-04 18:27:45,558 DEV : loss 0.20868642628192902 - f1-score (micro avg) 0.8292
206
+ 2023-09-04 18:27:45,587 saving best model
207
+ 2023-09-04 18:27:46,955 ----------------------------------------------------------------------------------------------------
208
+ 2023-09-04 18:28:02,137 epoch 10 - iter 147/1476 - loss 0.00101060 - time (sec): 15.18 - samples/sec: 1010.93 - lr: 0.000003 - momentum: 0.000000
209
+ 2023-09-04 18:28:18,974 epoch 10 - iter 294/1476 - loss 0.00390884 - time (sec): 32.02 - samples/sec: 1028.47 - lr: 0.000003 - momentum: 0.000000
210
+ 2023-09-04 18:28:35,317 epoch 10 - iter 441/1476 - loss 0.00447299 - time (sec): 48.36 - samples/sec: 1024.21 - lr: 0.000002 - momentum: 0.000000
211
+ 2023-09-04 18:28:51,711 epoch 10 - iter 588/1476 - loss 0.00417455 - time (sec): 64.75 - samples/sec: 1030.00 - lr: 0.000002 - momentum: 0.000000
212
+ 2023-09-04 18:29:08,401 epoch 10 - iter 735/1476 - loss 0.00470003 - time (sec): 81.44 - samples/sec: 1039.68 - lr: 0.000002 - momentum: 0.000000
213
+ 2023-09-04 18:29:23,342 epoch 10 - iter 882/1476 - loss 0.00479490 - time (sec): 96.38 - samples/sec: 1041.02 - lr: 0.000001 - momentum: 0.000000
214
+ 2023-09-04 18:29:38,135 epoch 10 - iter 1029/1476 - loss 0.00653311 - time (sec): 111.18 - samples/sec: 1041.64 - lr: 0.000001 - momentum: 0.000000
215
+ 2023-09-04 18:29:54,398 epoch 10 - iter 1176/1476 - loss 0.00644572 - time (sec): 127.44 - samples/sec: 1038.01 - lr: 0.000001 - momentum: 0.000000
216
+ 2023-09-04 18:30:10,831 epoch 10 - iter 1323/1476 - loss 0.00614733 - time (sec): 143.87 - samples/sec: 1044.88 - lr: 0.000000 - momentum: 0.000000
217
+ 2023-09-04 18:30:26,129 epoch 10 - iter 1470/1476 - loss 0.00651115 - time (sec): 159.17 - samples/sec: 1041.46 - lr: 0.000000 - momentum: 0.000000
218
+ 2023-09-04 18:30:26,732 ----------------------------------------------------------------------------------------------------
219
+ 2023-09-04 18:30:26,732 EPOCH 10 done: loss 0.0065 - lr: 0.000000
220
+ 2023-09-04 18:30:44,728 DEV : loss 0.22322718799114227 - f1-score (micro avg) 0.8291
221
+ 2023-09-04 18:30:45,239 ----------------------------------------------------------------------------------------------------
222
+ 2023-09-04 18:30:45,241 Loading model from best epoch ...
223
+ 2023-09-04 18:30:47,124 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
224
+ 2023-09-04 18:31:01,874
225
+ Results:
226
+ - F-score (micro) 0.7899
227
+ - F-score (macro) 0.6984
228
+ - Accuracy 0.6764
229
+
230
+ By class:
231
+ precision recall f1-score support
232
+
233
+ loc 0.8319 0.8765 0.8536 858
234
+ pers 0.7709 0.7896 0.7801 537
235
+ org 0.5034 0.5606 0.5305 132
236
+ time 0.5645 0.6481 0.6034 54
237
+ prod 0.7636 0.6885 0.7241 61
238
+
239
+ micro avg 0.7724 0.8082 0.7899 1642
240
+ macro avg 0.6869 0.7127 0.6984 1642
241
+ weighted avg 0.7742 0.8082 0.7905 1642
242
+
243
+ 2023-09-04 18:31:01,875 ----------------------------------------------------------------------------------------------------