Hugging Face's logo

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

Dataset card Files Community
stefan-it commited on
Commit
105547b
•
1 Parent(s): 65f4243

Upload folder using huggingface_hub

Browse files
Files changed (6) hide show
  1. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5/best-model.pt +3 -0
  2. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5/dev.tsv +0 -0
  3. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5/final-model.pt +3 -0
  4. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5/loss.tsv +11 -0
  5. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5/test.tsv +0 -0
  6. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5/training.log +244 -0
hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-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 sha256:1e6017fa628468965a7147da5e6cb4b03a4fc4e3d31c8f13ef73ea4622d92694
3
+ size 443334288
hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5/dev.tsv ADDED
The diff for this file is too large to render. See raw diff
 
hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-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:f3f249c49f5c814b96aabf2e314eda5d413de422f779096a5e93dba4916ab4d6
3
+ size 443334491
hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-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 23:51:35 0.0000 0.5689 0.2134 0.5125 0.6083 0.5563 0.4052
3
+ 2 23:53:18 0.0000 0.1613 0.1757 0.7150 0.7099 0.7124 0.5761
4
+ 3 23:55:05 0.0000 0.0958 0.1885 0.7537 0.7514 0.7525 0.6257
5
+ 4 23:56:50 0.0000 0.0659 0.1929 0.7035 0.7717 0.7360 0.6089
6
+ 5 23:58:34 0.0000 0.0449 0.2485 0.7887 0.7295 0.7579 0.6228
7
+ 6 00:00:20 0.0000 0.0335 0.2256 0.7456 0.7701 0.7577 0.6278
8
+ 7 00:02:07 0.0000 0.0206 0.2645 0.7981 0.7295 0.7623 0.6296
9
+ 8 00:03:54 0.0000 0.0132 0.2514 0.7884 0.7662 0.7772 0.6529
10
+ 9 00:05:42 0.0000 0.0076 0.2370 0.7646 0.7873 0.7758 0.6505
11
+ 10 00:07:26 0.0000 0.0047 0.2501 0.7882 0.7858 0.7870 0.6642
hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5/test.tsv ADDED
The diff for this file is too large to render. See raw diff
 
hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5/training.log ADDED
@@ -0,0 +1,244 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 2023-09-03 23:49:54,879 ----------------------------------------------------------------------------------------------------
2
+ 2023-09-03 23:49:54,880 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
+ 2023-09-03 23:49:54,880 ----------------------------------------------------------------------------------------------------
51
+ 2023-09-03 23:49:54,880 MultiCorpus: 3575 train + 1235 dev + 1266 test sentences
52
+ - NER_HIPE_2022 Corpus: 3575 train + 1235 dev + 1266 test sentences - /app/.flair/datasets/ner_hipe_2022/v2.1/hipe2020/de/with_doc_seperator
53
+ 2023-09-03 23:49:54,880 ----------------------------------------------------------------------------------------------------
54
+ 2023-09-03 23:49:54,880 Train: 3575 sentences
55
+ 2023-09-03 23:49:54,880 (train_with_dev=False, train_with_test=False)
56
+ 2023-09-03 23:49:54,880 ----------------------------------------------------------------------------------------------------
57
+ 2023-09-03 23:49:54,880 Training Params:
58
+ 2023-09-03 23:49:54,880 - learning_rate: "5e-05"
59
+ 2023-09-03 23:49:54,880 - mini_batch_size: "4"
60
+ 2023-09-03 23:49:54,881 - max_epochs: "10"
61
+ 2023-09-03 23:49:54,881 - shuffle: "True"
62
+ 2023-09-03 23:49:54,881 ----------------------------------------------------------------------------------------------------
63
+ 2023-09-03 23:49:54,881 Plugins:
64
+ 2023-09-03 23:49:54,881 - LinearScheduler | warmup_fraction: '0.1'
65
+ 2023-09-03 23:49:54,881 ----------------------------------------------------------------------------------------------------
66
+ 2023-09-03 23:49:54,881 Final evaluation on model from best epoch (best-model.pt)
67
+ 2023-09-03 23:49:54,881 - metric: "('micro avg', 'f1-score')"
68
+ 2023-09-03 23:49:54,881 ----------------------------------------------------------------------------------------------------
69
+ 2023-09-03 23:49:54,881 Computation:
70
+ 2023-09-03 23:49:54,881 - compute on device: cuda:0
71
+ 2023-09-03 23:49:54,881 - embedding storage: none
72
+ 2023-09-03 23:49:54,881 ----------------------------------------------------------------------------------------------------
73
+ 2023-09-03 23:49:54,881 Model training base path: "hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5"
74
+ 2023-09-03 23:49:54,881 ----------------------------------------------------------------------------------------------------
75
+ 2023-09-03 23:49:54,881 ----------------------------------------------------------------------------------------------------
76
+ 2023-09-03 23:50:04,981 epoch 1 - iter 89/894 - loss 2.49911709 - time (sec): 10.10 - samples/sec: 976.80 - lr: 0.000005 - momentum: 0.000000
77
+ 2023-09-03 23:50:13,927 epoch 1 - iter 178/894 - loss 1.57130390 - time (sec): 19.04 - samples/sec: 1000.81 - lr: 0.000010 - momentum: 0.000000
78
+ 2023-09-03 23:50:22,685 epoch 1 - iter 267/894 - loss 1.23056165 - time (sec): 27.80 - samples/sec: 985.66 - lr: 0.000015 - momentum: 0.000000
79
+ 2023-09-03 23:50:31,925 epoch 1 - iter 356/894 - loss 1.01016053 - time (sec): 37.04 - samples/sec: 977.51 - lr: 0.000020 - momentum: 0.000000
80
+ 2023-09-03 23:50:40,637 epoch 1 - iter 445/894 - loss 0.87202120 - time (sec): 45.75 - samples/sec: 981.00 - lr: 0.000025 - momentum: 0.000000
81
+ 2023-09-03 23:50:49,474 epoch 1 - iter 534/894 - loss 0.77262181 - time (sec): 54.59 - samples/sec: 975.76 - lr: 0.000030 - momentum: 0.000000
82
+ 2023-09-03 23:50:58,367 epoch 1 - iter 623/894 - loss 0.70232218 - time (sec): 63.49 - samples/sec: 973.94 - lr: 0.000035 - momentum: 0.000000
83
+ 2023-09-03 23:51:07,041 epoch 1 - iter 712/894 - loss 0.64770928 - time (sec): 72.16 - samples/sec: 969.83 - lr: 0.000040 - momentum: 0.000000
84
+ 2023-09-03 23:51:15,673 epoch 1 - iter 801/894 - loss 0.60661190 - time (sec): 80.79 - samples/sec: 965.01 - lr: 0.000045 - momentum: 0.000000
85
+ 2023-09-03 23:51:24,436 epoch 1 - iter 890/894 - loss 0.57035929 - time (sec): 89.55 - samples/sec: 962.80 - lr: 0.000050 - momentum: 0.000000
86
+ 2023-09-03 23:51:24,824 ----------------------------------------------------------------------------------------------------
87
+ 2023-09-03 23:51:24,825 EPOCH 1 done: loss 0.5689 - lr: 0.000050
88
+ 2023-09-03 23:51:35,155 DEV : loss 0.21339794993400574 - f1-score (micro avg) 0.5563
89
+ 2023-09-03 23:51:35,181 saving best model
90
+ 2023-09-03 23:51:35,640 ----------------------------------------------------------------------------------------------------
91
+ 2023-09-03 23:51:45,039 epoch 2 - iter 89/894 - loss 0.21013502 - time (sec): 9.40 - samples/sec: 983.60 - lr: 0.000049 - momentum: 0.000000
92
+ 2023-09-03 23:51:53,889 epoch 2 - iter 178/894 - loss 0.19672128 - time (sec): 18.25 - samples/sec: 972.22 - lr: 0.000049 - momentum: 0.000000
93
+ 2023-09-03 23:52:02,679 epoch 2 - iter 267/894 - loss 0.17839080 - time (sec): 27.04 - samples/sec: 959.74 - lr: 0.000048 - momentum: 0.000000
94
+ 2023-09-03 23:52:11,167 epoch 2 - iter 356/894 - loss 0.18098041 - time (sec): 35.53 - samples/sec: 944.57 - lr: 0.000048 - momentum: 0.000000
95
+ 2023-09-03 23:52:20,010 epoch 2 - iter 445/894 - loss 0.17633854 - time (sec): 44.37 - samples/sec: 949.23 - lr: 0.000047 - momentum: 0.000000
96
+ 2023-09-03 23:52:28,894 epoch 2 - iter 534/894 - loss 0.17821529 - time (sec): 53.25 - samples/sec: 951.78 - lr: 0.000047 - momentum: 0.000000
97
+ 2023-09-03 23:52:37,787 epoch 2 - iter 623/894 - loss 0.17431678 - time (sec): 62.15 - samples/sec: 955.35 - lr: 0.000046 - momentum: 0.000000
98
+ 2023-09-03 23:52:46,852 epoch 2 - iter 712/894 - loss 0.16974298 - time (sec): 71.21 - samples/sec: 957.06 - lr: 0.000046 - momentum: 0.000000
99
+ 2023-09-03 23:52:56,273 epoch 2 - iter 801/894 - loss 0.16497164 - time (sec): 80.63 - samples/sec: 956.42 - lr: 0.000045 - momentum: 0.000000
100
+ 2023-09-03 23:53:05,538 epoch 2 - iter 890/894 - loss 0.16110279 - time (sec): 89.90 - samples/sec: 956.93 - lr: 0.000044 - momentum: 0.000000
101
+ 2023-09-03 23:53:05,973 ----------------------------------------------------------------------------------------------------
102
+ 2023-09-03 23:53:05,974 EPOCH 2 done: loss 0.1613 - lr: 0.000044
103
+ 2023-09-03 23:53:18,846 DEV : loss 0.17570480704307556 - f1-score (micro avg) 0.7124
104
+ 2023-09-03 23:53:18,871 saving best model
105
+ 2023-09-03 23:53:20,189 ----------------------------------------------------------------------------------------------------
106
+ 2023-09-03 23:53:29,124 epoch 3 - iter 89/894 - loss 0.08462301 - time (sec): 8.93 - samples/sec: 1028.89 - lr: 0.000044 - momentum: 0.000000
107
+ 2023-09-03 23:53:38,056 epoch 3 - iter 178/894 - loss 0.09200761 - time (sec): 17.87 - samples/sec: 1003.61 - lr: 0.000043 - momentum: 0.000000
108
+ 2023-09-03 23:53:47,952 epoch 3 - iter 267/894 - loss 0.09128881 - time (sec): 27.76 - samples/sec: 982.32 - lr: 0.000043 - momentum: 0.000000
109
+ 2023-09-03 23:53:57,309 epoch 3 - iter 356/894 - loss 0.09166396 - time (sec): 37.12 - samples/sec: 977.85 - lr: 0.000042 - momentum: 0.000000
110
+ 2023-09-03 23:54:06,475 epoch 3 - iter 445/894 - loss 0.09180486 - time (sec): 46.28 - samples/sec: 959.82 - lr: 0.000042 - momentum: 0.000000
111
+ 2023-09-03 23:54:15,322 epoch 3 - iter 534/894 - loss 0.09122519 - time (sec): 55.13 - samples/sec: 957.66 - lr: 0.000041 - momentum: 0.000000
112
+ 2023-09-03 23:54:24,286 epoch 3 - iter 623/894 - loss 0.08994307 - time (sec): 64.10 - samples/sec: 947.34 - lr: 0.000041 - momentum: 0.000000
113
+ 2023-09-03 23:54:33,689 epoch 3 - iter 712/894 - loss 0.09217948 - time (sec): 73.50 - samples/sec: 942.36 - lr: 0.000040 - momentum: 0.000000
114
+ 2023-09-03 23:54:42,584 epoch 3 - iter 801/894 - loss 0.09505426 - time (sec): 82.39 - samples/sec: 946.60 - lr: 0.000039 - momentum: 0.000000
115
+ 2023-09-03 23:54:51,535 epoch 3 - iter 890/894 - loss 0.09536157 - time (sec): 91.35 - samples/sec: 943.84 - lr: 0.000039 - momentum: 0.000000
116
+ 2023-09-03 23:54:51,936 ----------------------------------------------------------------------------------------------------
117
+ 2023-09-03 23:54:51,936 EPOCH 3 done: loss 0.0958 - lr: 0.000039
118
+ 2023-09-03 23:55:05,314 DEV : loss 0.1884852945804596 - f1-score (micro avg) 0.7525
119
+ 2023-09-03 23:55:05,340 saving best model
120
+ 2023-09-03 23:55:06,641 ----------------------------------------------------------------------------------------------------
121
+ 2023-09-03 23:55:16,142 epoch 4 - iter 89/894 - loss 0.05240251 - time (sec): 9.50 - samples/sec: 978.39 - lr: 0.000038 - momentum: 0.000000
122
+ 2023-09-03 23:55:24,952 epoch 4 - iter 178/894 - loss 0.06355295 - time (sec): 18.31 - samples/sec: 955.45 - lr: 0.000038 - momentum: 0.000000
123
+ 2023-09-03 23:55:35,123 epoch 4 - iter 267/894 - loss 0.06155621 - time (sec): 28.48 - samples/sec: 970.58 - lr: 0.000037 - momentum: 0.000000
124
+ 2023-09-03 23:55:43,882 epoch 4 - iter 356/894 - loss 0.05929480 - time (sec): 37.24 - samples/sec: 958.58 - lr: 0.000037 - momentum: 0.000000
125
+ 2023-09-03 23:55:52,734 epoch 4 - iter 445/894 - loss 0.06054846 - time (sec): 46.09 - samples/sec: 957.84 - lr: 0.000036 - momentum: 0.000000
126
+ 2023-09-03 23:56:02,199 epoch 4 - iter 534/894 - loss 0.06317822 - time (sec): 55.56 - samples/sec: 963.18 - lr: 0.000036 - momentum: 0.000000
127
+ 2023-09-03 23:56:10,996 epoch 4 - iter 623/894 - loss 0.06282027 - time (sec): 64.35 - samples/sec: 958.49 - lr: 0.000035 - momentum: 0.000000
128
+ 2023-09-03 23:56:19,732 epoch 4 - iter 712/894 - loss 0.06250668 - time (sec): 73.09 - samples/sec: 956.15 - lr: 0.000034 - momentum: 0.000000
129
+ 2023-09-03 23:56:28,582 epoch 4 - iter 801/894 - loss 0.06526141 - time (sec): 81.94 - samples/sec: 952.00 - lr: 0.000034 - momentum: 0.000000
130
+ 2023-09-03 23:56:37,545 epoch 4 - iter 890/894 - loss 0.06588491 - time (sec): 90.90 - samples/sec: 948.55 - lr: 0.000033 - momentum: 0.000000
131
+ 2023-09-03 23:56:37,897 ----------------------------------------------------------------------------------------------------
132
+ 2023-09-03 23:56:37,897 EPOCH 4 done: loss 0.0659 - lr: 0.000033
133
+ 2023-09-03 23:56:50,710 DEV : loss 0.19290471076965332 - f1-score (micro avg) 0.736
134
+ 2023-09-03 23:56:50,737 ----------------------------------------------------------------------------------------------------
135
+ 2023-09-03 23:56:59,941 epoch 5 - iter 89/894 - loss 0.04378803 - time (sec): 9.20 - samples/sec: 947.86 - lr: 0.000033 - momentum: 0.000000
136
+ 2023-09-03 23:57:08,667 epoch 5 - iter 178/894 - loss 0.04322444 - time (sec): 17.93 - samples/sec: 950.03 - lr: 0.000032 - momentum: 0.000000
137
+ 2023-09-03 23:57:18,178 epoch 5 - iter 267/894 - loss 0.04077992 - time (sec): 27.44 - samples/sec: 956.30 - lr: 0.000032 - momentum: 0.000000
138
+ 2023-09-03 23:57:26,888 epoch 5 - iter 356/894 - loss 0.04680300 - time (sec): 36.15 - samples/sec: 962.42 - lr: 0.000031 - momentum: 0.000000
139
+ 2023-09-03 23:57:36,155 epoch 5 - iter 445/894 - loss 0.04586860 - time (sec): 45.42 - samples/sec: 970.46 - lr: 0.000031 - momentum: 0.000000
140
+ 2023-09-03 23:57:45,336 epoch 5 - iter 534/894 - loss 0.04603960 - time (sec): 54.60 - samples/sec: 966.08 - lr: 0.000030 - momentum: 0.000000
141
+ 2023-09-03 23:57:54,047 epoch 5 - iter 623/894 - loss 0.04461011 - time (sec): 63.31 - samples/sec: 964.60 - lr: 0.000029 - momentum: 0.000000
142
+ 2023-09-03 23:58:03,327 epoch 5 - iter 712/894 - loss 0.04685309 - time (sec): 72.59 - samples/sec: 962.53 - lr: 0.000029 - momentum: 0.000000
143
+ 2023-09-03 23:58:11,862 epoch 5 - iter 801/894 - loss 0.04718910 - time (sec): 81.12 - samples/sec: 958.65 - lr: 0.000028 - momentum: 0.000000
144
+ 2023-09-03 23:58:20,812 epoch 5 - iter 890/894 - loss 0.04511495 - time (sec): 90.07 - samples/sec: 956.23 - lr: 0.000028 - momentum: 0.000000
145
+ 2023-09-03 23:58:21,238 ----------------------------------------------------------------------------------------------------
146
+ 2023-09-03 23:58:21,238 EPOCH 5 done: loss 0.0449 - lr: 0.000028
147
+ 2023-09-03 23:58:34,157 DEV : loss 0.24845477938652039 - f1-score (micro avg) 0.7579
148
+ 2023-09-03 23:58:34,183 saving best model
149
+ 2023-09-03 23:58:35,858 ----------------------------------------------------------------------------------------------------
150
+ 2023-09-03 23:58:44,506 epoch 6 - iter 89/894 - loss 0.03461256 - time (sec): 8.65 - samples/sec: 967.39 - lr: 0.000027 - momentum: 0.000000
151
+ 2023-09-03 23:58:53,620 epoch 6 - iter 178/894 - loss 0.04111758 - time (sec): 17.76 - samples/sec: 944.26 - lr: 0.000027 - momentum: 0.000000
152
+ 2023-09-03 23:59:03,951 epoch 6 - iter 267/894 - loss 0.03468304 - time (sec): 28.09 - samples/sec: 965.34 - lr: 0.000026 - momentum: 0.000000
153
+ 2023-09-03 23:59:13,037 epoch 6 - iter 356/894 - loss 0.03392497 - time (sec): 37.18 - samples/sec: 963.85 - lr: 0.000026 - momentum: 0.000000
154
+ 2023-09-03 23:59:22,668 epoch 6 - iter 445/894 - loss 0.02951774 - time (sec): 46.81 - samples/sec: 974.74 - lr: 0.000025 - momentum: 0.000000
155
+ 2023-09-03 23:59:31,337 epoch 6 - iter 534/894 - loss 0.03254347 - time (sec): 55.48 - samples/sec: 965.46 - lr: 0.000024 - momentum: 0.000000
156
+ 2023-09-03 23:59:40,296 epoch 6 - iter 623/894 - loss 0.03212717 - time (sec): 64.44 - samples/sec: 955.74 - lr: 0.000024 - momentum: 0.000000
157
+ 2023-09-03 23:59:48,991 epoch 6 - iter 712/894 - loss 0.03343075 - time (sec): 73.13 - samples/sec: 956.38 - lr: 0.000023 - momentum: 0.000000
158
+ 2023-09-03 23:59:58,106 epoch 6 - iter 801/894 - loss 0.03338150 - time (sec): 82.25 - samples/sec: 953.99 - lr: 0.000023 - momentum: 0.000000
159
+ 2023-09-04 00:00:06,974 epoch 6 - iter 890/894 - loss 0.03361345 - time (sec): 91.11 - samples/sec: 945.75 - lr: 0.000022 - momentum: 0.000000
160
+ 2023-09-04 00:00:07,349 ----------------------------------------------------------------------------------------------------
161
+ 2023-09-04 00:00:07,349 EPOCH 6 done: loss 0.0335 - lr: 0.000022
162
+ 2023-09-04 00:00:20,688 DEV : loss 0.22561180591583252 - f1-score (micro avg) 0.7577
163
+ 2023-09-04 00:00:20,714 ----------------------------------------------------------------------------------------------------
164
+ 2023-09-04 00:00:29,364 epoch 7 - iter 89/894 - loss 0.01961733 - time (sec): 8.65 - samples/sec: 903.13 - lr: 0.000022 - momentum: 0.000000
165
+ 2023-09-04 00:00:38,037 epoch 7 - iter 178/894 - loss 0.01877410 - time (sec): 17.32 - samples/sec: 903.83 - lr: 0.000021 - momentum: 0.000000
166
+ 2023-09-04 00:00:48,023 epoch 7 - iter 267/894 - loss 0.01714216 - time (sec): 27.31 - samples/sec: 928.99 - lr: 0.000021 - momentum: 0.000000
167
+ 2023-09-04 00:00:57,190 epoch 7 - iter 356/894 - loss 0.01836201 - time (sec): 36.48 - samples/sec: 935.60 - lr: 0.000020 - momentum: 0.000000
168
+ 2023-09-04 00:01:06,729 epoch 7 - iter 445/894 - loss 0.01842745 - time (sec): 46.01 - samples/sec: 936.81 - lr: 0.000019 - momentum: 0.000000
169
+ 2023-09-04 00:01:16,078 epoch 7 - iter 534/894 - loss 0.01773843 - time (sec): 55.36 - samples/sec: 927.27 - lr: 0.000019 - momentum: 0.000000
170
+ 2023-09-04 00:01:25,086 epoch 7 - iter 623/894 - loss 0.01894952 - time (sec): 64.37 - samples/sec: 925.66 - lr: 0.000018 - momentum: 0.000000
171
+ 2023-09-04 00:01:35,228 epoch 7 - iter 712/894 - loss 0.01879554 - time (sec): 74.51 - samples/sec: 923.87 - lr: 0.000018 - momentum: 0.000000
172
+ 2023-09-04 00:01:44,223 epoch 7 - iter 801/894 - loss 0.02041142 - time (sec): 83.51 - samples/sec: 927.18 - lr: 0.000017 - momentum: 0.000000
173
+ 2023-09-04 00:01:53,559 epoch 7 - iter 890/894 - loss 0.02062909 - time (sec): 92.84 - samples/sec: 928.10 - lr: 0.000017 - momentum: 0.000000
174
+ 2023-09-04 00:01:53,956 ----------------------------------------------------------------------------------------------------
175
+ 2023-09-04 00:01:53,956 EPOCH 7 done: loss 0.0206 - lr: 0.000017
176
+ 2023-09-04 00:02:07,430 DEV : loss 0.26449108123779297 - f1-score (micro avg) 0.7623
177
+ 2023-09-04 00:02:07,456 saving best model
178
+ 2023-09-04 00:02:08,766 ----------------------------------------------------------------------------------------------------
179
+ 2023-09-04 00:02:17,962 epoch 8 - iter 89/894 - loss 0.02340993 - time (sec): 9.19 - samples/sec: 919.70 - lr: 0.000016 - momentum: 0.000000
180
+ 2023-09-04 00:02:27,060 epoch 8 - iter 178/894 - loss 0.01671841 - time (sec): 18.29 - samples/sec: 910.12 - lr: 0.000016 - momentum: 0.000000
181
+ 2023-09-04 00:02:36,846 epoch 8 - iter 267/894 - loss 0.01347799 - time (sec): 28.08 - samples/sec: 928.68 - lr: 0.000015 - momentum: 0.000000
182
+ 2023-09-04 00:02:46,608 epoch 8 - iter 356/894 - loss 0.01326122 - time (sec): 37.84 - samples/sec: 937.04 - lr: 0.000014 - momentum: 0.000000
183
+ 2023-09-04 00:02:56,078 epoch 8 - iter 445/894 - loss 0.01356914 - time (sec): 47.31 - samples/sec: 928.07 - lr: 0.000014 - momentum: 0.000000
184
+ 2023-09-04 00:03:04,925 epoch 8 - iter 534/894 - loss 0.01398169 - time (sec): 56.16 - samples/sec: 932.27 - lr: 0.000013 - momentum: 0.000000
185
+ 2023-09-04 00:03:13,953 epoch 8 - iter 623/894 - loss 0.01316480 - time (sec): 65.19 - samples/sec: 933.74 - lr: 0.000013 - momentum: 0.000000
186
+ 2023-09-04 00:03:22,829 epoch 8 - iter 712/894 - loss 0.01294454 - time (sec): 74.06 - samples/sec: 935.70 - lr: 0.000012 - momentum: 0.000000
187
+ 2023-09-04 00:03:31,798 epoch 8 - iter 801/894 - loss 0.01246025 - time (sec): 83.03 - samples/sec: 934.22 - lr: 0.000012 - momentum: 0.000000
188
+ 2023-09-04 00:03:41,099 epoch 8 - iter 890/894 - loss 0.01314571 - time (sec): 92.33 - samples/sec: 933.60 - lr: 0.000011 - momentum: 0.000000
189
+ 2023-09-04 00:03:41,474 ----------------------------------------------------------------------------------------------------
190
+ 2023-09-04 00:03:41,475 EPOCH 8 done: loss 0.0132 - lr: 0.000011
191
+ 2023-09-04 00:03:54,887 DEV : loss 0.25144723057746887 - f1-score (micro avg) 0.7772
192
+ 2023-09-04 00:03:54,913 saving best model
193
+ 2023-09-04 00:03:56,227 ----------------------------------------------------------------------------------------------------
194
+ 2023-09-04 00:04:05,079 epoch 9 - iter 89/894 - loss 0.00654807 - time (sec): 8.85 - samples/sec: 931.88 - lr: 0.000011 - momentum: 0.000000
195
+ 2023-09-04 00:04:15,182 epoch 9 - iter 178/894 - loss 0.00633478 - time (sec): 18.95 - samples/sec: 936.98 - lr: 0.000010 - momentum: 0.000000
196
+ 2023-09-04 00:04:24,866 epoch 9 - iter 267/894 - loss 0.00584558 - time (sec): 28.64 - samples/sec: 937.01 - lr: 0.000009 - momentum: 0.000000
197
+ 2023-09-04 00:04:33,996 epoch 9 - iter 356/894 - loss 0.00572325 - time (sec): 37.77 - samples/sec: 935.42 - lr: 0.000009 - momentum: 0.000000
198
+ 2023-09-04 00:04:43,263 epoch 9 - iter 445/894 - loss 0.00628639 - time (sec): 47.03 - samples/sec: 925.03 - lr: 0.000008 - momentum: 0.000000
199
+ 2023-09-04 00:04:52,248 epoch 9 - iter 534/894 - loss 0.00685272 - time (sec): 56.02 - samples/sec: 929.84 - lr: 0.000008 - momentum: 0.000000
200
+ 2023-09-04 00:05:01,361 epoch 9 - iter 623/894 - loss 0.00725272 - time (sec): 65.13 - samples/sec: 933.09 - lr: 0.000007 - momentum: 0.000000
201
+ 2023-09-04 00:05:10,188 epoch 9 - iter 712/894 - loss 0.00717432 - time (sec): 73.96 - samples/sec: 937.89 - lr: 0.000007 - momentum: 0.000000
202
+ 2023-09-04 00:05:19,431 epoch 9 - iter 801/894 - loss 0.00798669 - time (sec): 83.20 - samples/sec: 932.87 - lr: 0.000006 - momentum: 0.000000
203
+ 2023-09-04 00:05:28,748 epoch 9 - iter 890/894 - loss 0.00759713 - time (sec): 92.52 - samples/sec: 931.77 - lr: 0.000006 - momentum: 0.000000
204
+ 2023-09-04 00:05:29,121 ----------------------------------------------------------------------------------------------------
205
+ 2023-09-04 00:05:29,121 EPOCH 9 done: loss 0.0076 - lr: 0.000006
206
+ 2023-09-04 00:05:42,505 DEV : loss 0.23698772490024567 - f1-score (micro avg) 0.7758
207
+ 2023-09-04 00:05:42,531 ----------------------------------------------------------------------------------------------------
208
+ 2023-09-04 00:05:51,565 epoch 10 - iter 89/894 - loss 0.00608742 - time (sec): 9.03 - samples/sec: 948.67 - lr: 0.000005 - momentum: 0.000000
209
+ 2023-09-04 00:06:00,798 epoch 10 - iter 178/894 - loss 0.00378861 - time (sec): 18.27 - samples/sec: 901.22 - lr: 0.000004 - momentum: 0.000000
210
+ 2023-09-04 00:06:09,852 epoch 10 - iter 267/894 - loss 0.00294312 - time (sec): 27.32 - samples/sec: 915.22 - lr: 0.000004 - momentum: 0.000000
211
+ 2023-09-04 00:06:18,459 epoch 10 - iter 356/894 - loss 0.00437083 - time (sec): 35.93 - samples/sec: 922.33 - lr: 0.000003 - momentum: 0.000000
212
+ 2023-09-04 00:06:28,337 epoch 10 - iter 445/894 - loss 0.00431928 - time (sec): 45.81 - samples/sec: 935.46 - lr: 0.000003 - momentum: 0.000000
213
+ 2023-09-04 00:06:38,166 epoch 10 - iter 534/894 - loss 0.00438719 - time (sec): 55.63 - samples/sec: 937.43 - lr: 0.000002 - momentum: 0.000000
214
+ 2023-09-04 00:06:46,951 epoch 10 - iter 623/894 - loss 0.00506611 - time (sec): 64.42 - samples/sec: 939.36 - lr: 0.000002 - momentum: 0.000000
215
+ 2023-09-04 00:06:55,653 epoch 10 - iter 712/894 - loss 0.00464180 - time (sec): 73.12 - samples/sec: 937.43 - lr: 0.000001 - momentum: 0.000000
216
+ 2023-09-04 00:07:04,794 epoch 10 - iter 801/894 - loss 0.00441981 - time (sec): 82.26 - samples/sec: 946.94 - lr: 0.000001 - momentum: 0.000000
217
+ 2023-09-04 00:07:13,556 epoch 10 - iter 890/894 - loss 0.00469970 - time (sec): 91.02 - samples/sec: 946.89 - lr: 0.000000 - momentum: 0.000000
218
+ 2023-09-04 00:07:13,911 ----------------------------------------------------------------------------------------------------
219
+ 2023-09-04 00:07:13,912 EPOCH 10 done: loss 0.0047 - lr: 0.000000
220
+ 2023-09-04 00:07:26,810 DEV : loss 0.25010257959365845 - f1-score (micro avg) 0.787
221
+ 2023-09-04 00:07:26,838 saving best model
222
+ 2023-09-04 00:07:28,625 ----------------------------------------------------------------------------------------------------
223
+ 2023-09-04 00:07:28,626 Loading model from best epoch ...
224
+ 2023-09-04 00:07:30,386 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-prod, B-prod, E-prod, I-prod, S-time, B-time, E-time, I-time
225
+ 2023-09-04 00:07:40,273
226
+ Results:
227
+ - F-score (micro) 0.7355
228
+ - F-score (macro) 0.6486
229
+ - Accuracy 0.5999
230
+
231
+ By class:
232
+ precision recall f1-score support
233
+
234
+ loc 0.8333 0.8389 0.8361 596
235
+ pers 0.6585 0.7237 0.6896 333
236
+ org 0.5273 0.4394 0.4793 132
237
+ prod 0.6087 0.4242 0.5000 66
238
+ time 0.7037 0.7755 0.7379 49
239
+
240
+ micro avg 0.7355 0.7355 0.7355 1176
241
+ macro avg 0.6663 0.6404 0.6486 1176
242
+ weighted avg 0.7315 0.7355 0.7316 1176
243
+
244
+ 2023-09-04 00:07:40,273 ----------------------------------------------------------------------------------------------------