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autotrain-flair-hipe2022-de-hmbert

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  1. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2/best-model.pt +3 -0
  2. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2/dev.tsv +0 -0
  3. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2/final-model.pt +3 -0
  4. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2/loss.tsv +11 -0
  5. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2/test.tsv +0 -0
  6. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2/training.log +245 -0
hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2/best-model.pt ADDED
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hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2/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-lr3e-05-poolingfirst-layers-1-crfFalse-2/final-model.pt ADDED
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hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2/loss.tsv ADDED
@@ -0,0 +1,11 @@
 
 
 
 
 
 
 
 
 
 
 
 
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+ EPOCH TIMESTAMP LEARNING_RATE TRAIN_LOSS DEV_LOSS DEV_PRECISION DEV_RECALL DEV_F1 DEV_ACCURACY
2
+ 1 20:02:31 0.0000 0.6437 0.1913 0.6077 0.5582 0.5819 0.4212
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+ 2 20:04:18 0.0000 0.1622 0.1360 0.6709 0.7045 0.6873 0.5464
4
+ 3 20:06:06 0.0000 0.0917 0.1533 0.7178 0.7615 0.7390 0.6031
5
+ 4 20:07:52 0.0000 0.0560 0.1893 0.7704 0.7608 0.7655 0.6385
6
+ 5 20:09:37 0.0000 0.0365 0.2027 0.7247 0.7842 0.7533 0.6207
7
+ 6 20:11:22 0.0000 0.0256 0.2139 0.7532 0.7850 0.7688 0.6428
8
+ 7 20:13:10 0.0000 0.0158 0.2357 0.7797 0.7748 0.7773 0.6528
9
+ 8 20:14:59 0.0000 0.0117 0.2377 0.7677 0.7881 0.7778 0.6533
10
+ 9 20:16:47 0.0000 0.0093 0.2397 0.7661 0.7889 0.7773 0.6522
11
+ 10 20:18:34 0.0000 0.0059 0.2392 0.7810 0.7920 0.7865 0.6630
hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2/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-lr3e-05-poolingfirst-layers-1-crfFalse-2/training.log ADDED
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+ 2023-09-03 20:00:50,026 ----------------------------------------------------------------------------------------------------
2
+ 2023-09-03 20:00:50,027 Model: "SequenceTagger(
3
+ (embeddings): TransformerWordEmbeddings(
4
+ (model): BertModel(
5
+ (embeddings): BertEmbeddings(
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+ (word_embeddings): Embedding(32001, 768)
7
+ (position_embeddings): Embedding(512, 768)
8
+ (token_type_embeddings): Embedding(2, 768)
9
+ (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
10
+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ (encoder): BertEncoder(
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+ (layer): ModuleList(
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+ (0-11): 12 x BertLayer(
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+ (attention): BertAttention(
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+ (self): BertSelfAttention(
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+ (query): Linear(in_features=768, out_features=768, bias=True)
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(
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+ (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 20:00:50,027 ----------------------------------------------------------------------------------------------------
51
+ 2023-09-03 20:00:50,027 MultiCorpus: 3575 train + 1235 dev + 1266 test sentences
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+ - 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 20:00:50,027 ----------------------------------------------------------------------------------------------------
54
+ 2023-09-03 20:00:50,027 Train: 3575 sentences
55
+ 2023-09-03 20:00:50,027 (train_with_dev=False, train_with_test=False)
56
+ 2023-09-03 20:00:50,028 ----------------------------------------------------------------------------------------------------
57
+ 2023-09-03 20:00:50,028 Training Params:
58
+ 2023-09-03 20:00:50,028 - learning_rate: "3e-05"
59
+ 2023-09-03 20:00:50,028 - mini_batch_size: "4"
60
+ 2023-09-03 20:00:50,028 - max_epochs: "10"
61
+ 2023-09-03 20:00:50,028 - shuffle: "True"
62
+ 2023-09-03 20:00:50,028 ----------------------------------------------------------------------------------------------------
63
+ 2023-09-03 20:00:50,028 Plugins:
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+ 2023-09-03 20:00:50,028 - LinearScheduler | warmup_fraction: '0.1'
65
+ 2023-09-03 20:00:50,028 ----------------------------------------------------------------------------------------------------
66
+ 2023-09-03 20:00:50,028 Final evaluation on model from best epoch (best-model.pt)
67
+ 2023-09-03 20:00:50,028 - metric: "('micro avg', 'f1-score')"
68
+ 2023-09-03 20:00:50,028 ----------------------------------------------------------------------------------------------------
69
+ 2023-09-03 20:00:50,028 Computation:
70
+ 2023-09-03 20:00:50,028 - compute on device: cuda:0
71
+ 2023-09-03 20:00:50,028 - embedding storage: none
72
+ 2023-09-03 20:00:50,028 ----------------------------------------------------------------------------------------------------
73
+ 2023-09-03 20:00:50,028 Model training base path: "hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2"
74
+ 2023-09-03 20:00:50,029 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 20:00:50,029 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 20:00:58,521 epoch 1 - iter 89/894 - loss 3.11851080 - time (sec): 8.49 - samples/sec: 943.73 - lr: 0.000003 - momentum: 0.000000
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+ 2023-09-03 20:01:07,144 epoch 1 - iter 178/894 - loss 2.08316776 - time (sec): 17.11 - samples/sec: 938.27 - lr: 0.000006 - momentum: 0.000000
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+ 2023-09-03 20:01:16,041 epoch 1 - iter 267/894 - loss 1.49514732 - time (sec): 26.01 - samples/sec: 958.60 - lr: 0.000009 - momentum: 0.000000
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+ 2023-09-03 20:01:24,740 epoch 1 - iter 356/894 - loss 1.23073638 - time (sec): 34.71 - samples/sec: 951.73 - lr: 0.000012 - momentum: 0.000000
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+ 2023-09-03 20:01:33,715 epoch 1 - iter 445/894 - loss 1.04071512 - time (sec): 43.69 - samples/sec: 958.86 - lr: 0.000015 - momentum: 0.000000
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+ 2023-09-03 20:01:43,916 epoch 1 - iter 534/894 - loss 0.90265949 - time (sec): 53.89 - samples/sec: 968.58 - lr: 0.000018 - momentum: 0.000000
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+ 2023-09-03 20:01:53,021 epoch 1 - iter 623/894 - loss 0.81910891 - time (sec): 62.99 - samples/sec: 960.05 - lr: 0.000021 - momentum: 0.000000
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+ 2023-09-03 20:02:02,012 epoch 1 - iter 712/894 - loss 0.74839847 - time (sec): 71.98 - samples/sec: 961.89 - lr: 0.000024 - momentum: 0.000000
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+ 2023-09-03 20:02:10,769 epoch 1 - iter 801/894 - loss 0.69749666 - time (sec): 80.74 - samples/sec: 956.43 - lr: 0.000027 - momentum: 0.000000
85
+ 2023-09-03 20:02:20,027 epoch 1 - iter 890/894 - loss 0.64658706 - time (sec): 90.00 - samples/sec: 956.03 - lr: 0.000030 - momentum: 0.000000
86
+ 2023-09-03 20:02:20,448 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 20:02:20,448 EPOCH 1 done: loss 0.6437 - lr: 0.000030
88
+ 2023-09-03 20:02:31,359 DEV : loss 0.19128236174583435 - f1-score (micro avg) 0.5819
89
+ 2023-09-03 20:02:31,389 saving best model
90
+ 2023-09-03 20:02:31,854 ----------------------------------------------------------------------------------------------------
91
+ 2023-09-03 20:02:40,979 epoch 2 - iter 89/894 - loss 0.22375762 - time (sec): 9.12 - samples/sec: 942.97 - lr: 0.000030 - momentum: 0.000000
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+ 2023-09-03 20:02:50,306 epoch 2 - iter 178/894 - loss 0.20586503 - time (sec): 18.45 - samples/sec: 923.13 - lr: 0.000029 - momentum: 0.000000
93
+ 2023-09-03 20:02:59,128 epoch 2 - iter 267/894 - loss 0.19298164 - time (sec): 27.27 - samples/sec: 924.96 - lr: 0.000029 - momentum: 0.000000
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+ 2023-09-03 20:03:08,378 epoch 2 - iter 356/894 - loss 0.18768150 - time (sec): 36.52 - samples/sec: 932.60 - lr: 0.000029 - momentum: 0.000000
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+ 2023-09-03 20:03:17,225 epoch 2 - iter 445/894 - loss 0.17916969 - time (sec): 45.37 - samples/sec: 929.52 - lr: 0.000028 - momentum: 0.000000
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+ 2023-09-03 20:03:26,962 epoch 2 - iter 534/894 - loss 0.17513122 - time (sec): 55.11 - samples/sec: 934.07 - lr: 0.000028 - momentum: 0.000000
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+ 2023-09-03 20:03:35,795 epoch 2 - iter 623/894 - loss 0.16785798 - time (sec): 63.94 - samples/sec: 935.39 - lr: 0.000028 - momentum: 0.000000
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+ 2023-09-03 20:03:45,615 epoch 2 - iter 712/894 - loss 0.16499300 - time (sec): 73.76 - samples/sec: 937.30 - lr: 0.000027 - momentum: 0.000000
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+ 2023-09-03 20:03:55,283 epoch 2 - iter 801/894 - loss 0.16353301 - time (sec): 83.43 - samples/sec: 933.55 - lr: 0.000027 - momentum: 0.000000
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+ 2023-09-03 20:04:04,298 epoch 2 - iter 890/894 - loss 0.16254544 - time (sec): 92.44 - samples/sec: 931.93 - lr: 0.000027 - momentum: 0.000000
101
+ 2023-09-03 20:04:04,696 ----------------------------------------------------------------------------------------------------
102
+ 2023-09-03 20:04:04,696 EPOCH 2 done: loss 0.1622 - lr: 0.000027
103
+ 2023-09-03 20:04:18,263 DEV : loss 0.13599510490894318 - f1-score (micro avg) 0.6873
104
+ 2023-09-03 20:04:18,290 saving best model
105
+ 2023-09-03 20:04:19,610 ----------------------------------------------------------------------------------------------------
106
+ 2023-09-03 20:04:29,061 epoch 3 - iter 89/894 - loss 0.08706196 - time (sec): 9.45 - samples/sec: 913.08 - lr: 0.000026 - momentum: 0.000000
107
+ 2023-09-03 20:04:38,966 epoch 3 - iter 178/894 - loss 0.07931150 - time (sec): 19.35 - samples/sec: 943.85 - lr: 0.000026 - momentum: 0.000000
108
+ 2023-09-03 20:04:48,571 epoch 3 - iter 267/894 - loss 0.08634813 - time (sec): 28.96 - samples/sec: 949.99 - lr: 0.000026 - momentum: 0.000000
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+ 2023-09-03 20:04:58,032 epoch 3 - iter 356/894 - loss 0.08190028 - time (sec): 38.42 - samples/sec: 949.48 - lr: 0.000025 - momentum: 0.000000
110
+ 2023-09-03 20:05:07,602 epoch 3 - iter 445/894 - loss 0.08993317 - time (sec): 47.99 - samples/sec: 947.59 - lr: 0.000025 - momentum: 0.000000
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+ 2023-09-03 20:05:16,437 epoch 3 - iter 534/894 - loss 0.09358812 - time (sec): 56.83 - samples/sec: 935.41 - lr: 0.000025 - momentum: 0.000000
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+ 2023-09-03 20:05:25,291 epoch 3 - iter 623/894 - loss 0.09173455 - time (sec): 65.68 - samples/sec: 936.92 - lr: 0.000024 - momentum: 0.000000
113
+ 2023-09-03 20:05:34,095 epoch 3 - iter 712/894 - loss 0.09180758 - time (sec): 74.48 - samples/sec: 933.17 - lr: 0.000024 - momentum: 0.000000
114
+ 2023-09-03 20:05:43,327 epoch 3 - iter 801/894 - loss 0.09299434 - time (sec): 83.71 - samples/sec: 930.35 - lr: 0.000024 - momentum: 0.000000
115
+ 2023-09-03 20:05:52,294 epoch 3 - iter 890/894 - loss 0.09212889 - time (sec): 92.68 - samples/sec: 929.32 - lr: 0.000023 - momentum: 0.000000
116
+ 2023-09-03 20:05:52,755 ----------------------------------------------------------------------------------------------------
117
+ 2023-09-03 20:05:52,755 EPOCH 3 done: loss 0.0917 - lr: 0.000023
118
+ 2023-09-03 20:06:06,532 DEV : loss 0.1533261090517044 - f1-score (micro avg) 0.739
119
+ 2023-09-03 20:06:06,558 saving best model
120
+ 2023-09-03 20:06:07,888 ----------------------------------------------------------------------------------------------------
121
+ 2023-09-03 20:06:16,539 epoch 4 - iter 89/894 - loss 0.05568495 - time (sec): 8.65 - samples/sec: 881.27 - lr: 0.000023 - momentum: 0.000000
122
+ 2023-09-03 20:06:26,449 epoch 4 - iter 178/894 - loss 0.05265981 - time (sec): 18.56 - samples/sec: 914.74 - lr: 0.000023 - momentum: 0.000000
123
+ 2023-09-03 20:06:35,557 epoch 4 - iter 267/894 - loss 0.06012918 - time (sec): 27.67 - samples/sec: 914.68 - lr: 0.000022 - momentum: 0.000000
124
+ 2023-09-03 20:06:44,626 epoch 4 - iter 356/894 - loss 0.06063329 - time (sec): 36.74 - samples/sec: 924.20 - lr: 0.000022 - momentum: 0.000000
125
+ 2023-09-03 20:06:53,287 epoch 4 - iter 445/894 - loss 0.06192230 - time (sec): 45.40 - samples/sec: 915.79 - lr: 0.000022 - momentum: 0.000000
126
+ 2023-09-03 20:07:03,572 epoch 4 - iter 534/894 - loss 0.05840812 - time (sec): 55.68 - samples/sec: 932.72 - lr: 0.000021 - momentum: 0.000000
127
+ 2023-09-03 20:07:12,832 epoch 4 - iter 623/894 - loss 0.05596147 - time (sec): 64.94 - samples/sec: 931.43 - lr: 0.000021 - momentum: 0.000000
128
+ 2023-09-03 20:07:21,523 epoch 4 - iter 712/894 - loss 0.05656731 - time (sec): 73.63 - samples/sec: 931.66 - lr: 0.000021 - momentum: 0.000000
129
+ 2023-09-03 20:07:30,668 epoch 4 - iter 801/894 - loss 0.05657801 - time (sec): 82.78 - samples/sec: 939.65 - lr: 0.000020 - momentum: 0.000000
130
+ 2023-09-03 20:07:39,471 epoch 4 - iter 890/894 - loss 0.05608795 - time (sec): 91.58 - samples/sec: 941.83 - lr: 0.000020 - momentum: 0.000000
131
+ 2023-09-03 20:07:39,842 ----------------------------------------------------------------------------------------------------
132
+ 2023-09-03 20:07:39,842 EPOCH 4 done: loss 0.0560 - lr: 0.000020
133
+ 2023-09-03 20:07:52,581 DEV : loss 0.18926754593849182 - f1-score (micro avg) 0.7655
134
+ 2023-09-03 20:07:52,608 saving best model
135
+ 2023-09-03 20:07:53,942 ----------------------------------------------------------------------------------------------------
136
+ 2023-09-03 20:08:02,799 epoch 5 - iter 89/894 - loss 0.05861320 - time (sec): 8.86 - samples/sec: 918.71 - lr: 0.000020 - momentum: 0.000000
137
+ 2023-09-03 20:08:11,444 epoch 5 - iter 178/894 - loss 0.04567469 - time (sec): 17.50 - samples/sec: 916.80 - lr: 0.000019 - momentum: 0.000000
138
+ 2023-09-03 20:08:20,508 epoch 5 - iter 267/894 - loss 0.04306743 - time (sec): 26.56 - samples/sec: 931.58 - lr: 0.000019 - momentum: 0.000000
139
+ 2023-09-03 20:08:30,195 epoch 5 - iter 356/894 - loss 0.04255572 - time (sec): 36.25 - samples/sec: 940.70 - lr: 0.000019 - momentum: 0.000000
140
+ 2023-09-03 20:08:39,048 epoch 5 - iter 445/894 - loss 0.03995375 - time (sec): 45.10 - samples/sec: 953.32 - lr: 0.000018 - momentum: 0.000000
141
+ 2023-09-03 20:08:47,607 epoch 5 - iter 534/894 - loss 0.03930735 - time (sec): 53.66 - samples/sec: 958.07 - lr: 0.000018 - momentum: 0.000000
142
+ 2023-09-03 20:08:56,976 epoch 5 - iter 623/894 - loss 0.03752091 - time (sec): 63.03 - samples/sec: 959.33 - lr: 0.000018 - momentum: 0.000000
143
+ 2023-09-03 20:09:06,688 epoch 5 - iter 712/894 - loss 0.03698791 - time (sec): 72.74 - samples/sec: 959.71 - lr: 0.000017 - momentum: 0.000000
144
+ 2023-09-03 20:09:15,510 epoch 5 - iter 801/894 - loss 0.03588804 - time (sec): 81.57 - samples/sec: 961.66 - lr: 0.000017 - momentum: 0.000000
145
+ 2023-09-03 20:09:24,037 epoch 5 - iter 890/894 - loss 0.03634887 - time (sec): 90.09 - samples/sec: 956.82 - lr: 0.000017 - momentum: 0.000000
146
+ 2023-09-03 20:09:24,392 ----------------------------------------------------------------------------------------------------
147
+ 2023-09-03 20:09:24,393 EPOCH 5 done: loss 0.0365 - lr: 0.000017
148
+ 2023-09-03 20:09:37,245 DEV : loss 0.20267988741397858 - f1-score (micro avg) 0.7533
149
+ 2023-09-03 20:09:37,271 ----------------------------------------------------------------------------------------------------
150
+ 2023-09-03 20:09:46,219 epoch 6 - iter 89/894 - loss 0.03095029 - time (sec): 8.95 - samples/sec: 970.06 - lr: 0.000016 - momentum: 0.000000
151
+ 2023-09-03 20:09:54,982 epoch 6 - iter 178/894 - loss 0.02722403 - time (sec): 17.71 - samples/sec: 956.34 - lr: 0.000016 - momentum: 0.000000
152
+ 2023-09-03 20:10:03,718 epoch 6 - iter 267/894 - loss 0.02513090 - time (sec): 26.45 - samples/sec: 948.52 - lr: 0.000016 - momentum: 0.000000
153
+ 2023-09-03 20:10:12,687 epoch 6 - iter 356/894 - loss 0.02334869 - time (sec): 35.41 - samples/sec: 952.61 - lr: 0.000015 - momentum: 0.000000
154
+ 2023-09-03 20:10:21,633 epoch 6 - iter 445/894 - loss 0.02369827 - time (sec): 44.36 - samples/sec: 946.44 - lr: 0.000015 - momentum: 0.000000
155
+ 2023-09-03 20:10:30,370 epoch 6 - iter 534/894 - loss 0.02301427 - time (sec): 53.10 - samples/sec: 949.91 - lr: 0.000015 - momentum: 0.000000
156
+ 2023-09-03 20:10:39,144 epoch 6 - iter 623/894 - loss 0.02362530 - time (sec): 61.87 - samples/sec: 946.65 - lr: 0.000014 - momentum: 0.000000
157
+ 2023-09-03 20:10:48,318 epoch 6 - iter 712/894 - loss 0.02570622 - time (sec): 71.05 - samples/sec: 942.81 - lr: 0.000014 - momentum: 0.000000
158
+ 2023-09-03 20:10:58,080 epoch 6 - iter 801/894 - loss 0.02593093 - time (sec): 80.81 - samples/sec: 939.92 - lr: 0.000014 - momentum: 0.000000
159
+ 2023-09-03 20:11:08,282 epoch 6 - iter 890/894 - loss 0.02550216 - time (sec): 91.01 - samples/sec: 944.89 - lr: 0.000013 - momentum: 0.000000
160
+ 2023-09-03 20:11:08,787 ----------------------------------------------------------------------------------------------------
161
+ 2023-09-03 20:11:08,787 EPOCH 6 done: loss 0.0256 - lr: 0.000013
162
+ 2023-09-03 20:11:22,117 DEV : loss 0.21390819549560547 - f1-score (micro avg) 0.7688
163
+ 2023-09-03 20:11:22,144 saving best model
164
+ 2023-09-03 20:11:23,484 ----------------------------------------------------------------------------------------------------
165
+ 2023-09-03 20:11:32,539 epoch 7 - iter 89/894 - loss 0.02358594 - time (sec): 9.05 - samples/sec: 959.67 - lr: 0.000013 - momentum: 0.000000
166
+ 2023-09-03 20:11:41,614 epoch 7 - iter 178/894 - loss 0.02018242 - time (sec): 18.13 - samples/sec: 951.83 - lr: 0.000013 - momentum: 0.000000
167
+ 2023-09-03 20:11:50,578 epoch 7 - iter 267/894 - loss 0.01855891 - time (sec): 27.09 - samples/sec: 970.83 - lr: 0.000012 - momentum: 0.000000
168
+ 2023-09-03 20:12:00,093 epoch 7 - iter 356/894 - loss 0.01796381 - time (sec): 36.61 - samples/sec: 958.74 - lr: 0.000012 - momentum: 0.000000
169
+ 2023-09-03 20:12:09,251 epoch 7 - iter 445/894 - loss 0.01543157 - time (sec): 45.77 - samples/sec: 944.04 - lr: 0.000012 - momentum: 0.000000
170
+ 2023-09-03 20:12:18,564 epoch 7 - iter 534/894 - loss 0.01541795 - time (sec): 55.08 - samples/sec: 940.59 - lr: 0.000011 - momentum: 0.000000
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+ 2023-09-03 20:12:27,674 epoch 7 - iter 623/894 - loss 0.01583522 - time (sec): 64.19 - samples/sec: 935.79 - lr: 0.000011 - momentum: 0.000000
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+ 2023-09-03 20:12:36,956 epoch 7 - iter 712/894 - loss 0.01637177 - time (sec): 73.47 - samples/sec: 931.78 - lr: 0.000011 - momentum: 0.000000
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+ 2023-09-03 20:12:45,869 epoch 7 - iter 801/894 - loss 0.01638907 - time (sec): 82.38 - samples/sec: 925.24 - lr: 0.000010 - momentum: 0.000000
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+ 2023-09-03 20:12:56,566 epoch 7 - iter 890/894 - loss 0.01589087 - time (sec): 93.08 - samples/sec: 924.56 - lr: 0.000010 - momentum: 0.000000
175
+ 2023-09-03 20:12:57,013 ----------------------------------------------------------------------------------------------------
176
+ 2023-09-03 20:12:57,014 EPOCH 7 done: loss 0.0158 - lr: 0.000010
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+ 2023-09-03 20:13:10,562 DEV : loss 0.2357018142938614 - f1-score (micro avg) 0.7773
178
+ 2023-09-03 20:13:10,590 saving best model
179
+ 2023-09-03 20:13:11,916 ----------------------------------------------------------------------------------------------------
180
+ 2023-09-03 20:13:20,796 epoch 8 - iter 89/894 - loss 0.01168538 - time (sec): 8.88 - samples/sec: 945.49 - lr: 0.000010 - momentum: 0.000000
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+ 2023-09-03 20:13:31,458 epoch 8 - iter 178/894 - loss 0.01122629 - time (sec): 19.54 - samples/sec: 925.43 - lr: 0.000009 - momentum: 0.000000
182
+ 2023-09-03 20:13:40,609 epoch 8 - iter 267/894 - loss 0.01210043 - time (sec): 28.69 - samples/sec: 919.98 - lr: 0.000009 - momentum: 0.000000
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+ 2023-09-03 20:13:49,786 epoch 8 - iter 356/894 - loss 0.01094018 - time (sec): 37.87 - samples/sec: 924.94 - lr: 0.000009 - momentum: 0.000000
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+ 2023-09-03 20:13:58,653 epoch 8 - iter 445/894 - loss 0.01076425 - time (sec): 46.74 - samples/sec: 915.81 - lr: 0.000008 - momentum: 0.000000
185
+ 2023-09-03 20:14:08,387 epoch 8 - iter 534/894 - loss 0.01045307 - time (sec): 56.47 - samples/sec: 916.66 - lr: 0.000008 - momentum: 0.000000
186
+ 2023-09-03 20:14:17,535 epoch 8 - iter 623/894 - loss 0.01021745 - time (sec): 65.62 - samples/sec: 923.75 - lr: 0.000008 - momentum: 0.000000
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+ 2023-09-03 20:14:26,697 epoch 8 - iter 712/894 - loss 0.01172481 - time (sec): 74.78 - samples/sec: 921.17 - lr: 0.000007 - momentum: 0.000000
188
+ 2023-09-03 20:14:35,900 epoch 8 - iter 801/894 - loss 0.01208878 - time (sec): 83.98 - samples/sec: 922.74 - lr: 0.000007 - momentum: 0.000000
189
+ 2023-09-03 20:14:45,183 epoch 8 - iter 890/894 - loss 0.01172491 - time (sec): 93.27 - samples/sec: 924.22 - lr: 0.000007 - momentum: 0.000000
190
+ 2023-09-03 20:14:45,566 ----------------------------------------------------------------------------------------------------
191
+ 2023-09-03 20:14:45,566 EPOCH 8 done: loss 0.0117 - lr: 0.000007
192
+ 2023-09-03 20:14:59,155 DEV : loss 0.2376643270254135 - f1-score (micro avg) 0.7778
193
+ 2023-09-03 20:14:59,181 saving best model
194
+ 2023-09-03 20:15:00,503 ----------------------------------------------------------------------------------------------------
195
+ 2023-09-03 20:15:09,698 epoch 9 - iter 89/894 - loss 0.00658409 - time (sec): 9.19 - samples/sec: 941.04 - lr: 0.000006 - momentum: 0.000000
196
+ 2023-09-03 20:15:18,535 epoch 9 - iter 178/894 - loss 0.00659844 - time (sec): 18.03 - samples/sec: 945.85 - lr: 0.000006 - momentum: 0.000000
197
+ 2023-09-03 20:15:27,665 epoch 9 - iter 267/894 - loss 0.00935523 - time (sec): 27.16 - samples/sec: 932.87 - lr: 0.000006 - momentum: 0.000000
198
+ 2023-09-03 20:15:36,752 epoch 9 - iter 356/894 - loss 0.00888493 - time (sec): 36.25 - samples/sec: 939.41 - lr: 0.000005 - momentum: 0.000000
199
+ 2023-09-03 20:15:47,156 epoch 9 - iter 445/894 - loss 0.00905866 - time (sec): 46.65 - samples/sec: 939.24 - lr: 0.000005 - momentum: 0.000000
200
+ 2023-09-03 20:15:56,230 epoch 9 - iter 534/894 - loss 0.00892902 - time (sec): 55.73 - samples/sec: 937.12 - lr: 0.000005 - momentum: 0.000000
201
+ 2023-09-03 20:16:05,440 epoch 9 - iter 623/894 - loss 0.00921909 - time (sec): 64.94 - samples/sec: 932.93 - lr: 0.000004 - momentum: 0.000000
202
+ 2023-09-03 20:16:14,937 epoch 9 - iter 712/894 - loss 0.00903307 - time (sec): 74.43 - samples/sec: 933.31 - lr: 0.000004 - momentum: 0.000000
203
+ 2023-09-03 20:16:23,744 epoch 9 - iter 801/894 - loss 0.00922263 - time (sec): 83.24 - samples/sec: 931.39 - lr: 0.000004 - momentum: 0.000000
204
+ 2023-09-03 20:16:33,239 epoch 9 - iter 890/894 - loss 0.00914557 - time (sec): 92.74 - samples/sec: 929.37 - lr: 0.000003 - momentum: 0.000000
205
+ 2023-09-03 20:16:33,641 ----------------------------------------------------------------------------------------------------
206
+ 2023-09-03 20:16:33,642 EPOCH 9 done: loss 0.0093 - lr: 0.000003
207
+ 2023-09-03 20:16:47,247 DEV : loss 0.2397419661283493 - f1-score (micro avg) 0.7773
208
+ 2023-09-03 20:16:47,274 ----------------------------------------------------------------------------------------------------
209
+ 2023-09-03 20:16:56,973 epoch 10 - iter 89/894 - loss 0.00098514 - time (sec): 9.70 - samples/sec: 953.14 - lr: 0.000003 - momentum: 0.000000
210
+ 2023-09-03 20:17:06,027 epoch 10 - iter 178/894 - loss 0.00223991 - time (sec): 18.75 - samples/sec: 924.58 - lr: 0.000003 - momentum: 0.000000
211
+ 2023-09-03 20:17:15,258 epoch 10 - iter 267/894 - loss 0.00432898 - time (sec): 27.98 - samples/sec: 920.50 - lr: 0.000002 - momentum: 0.000000
212
+ 2023-09-03 20:17:25,436 epoch 10 - iter 356/894 - loss 0.00405978 - time (sec): 38.16 - samples/sec: 931.25 - lr: 0.000002 - momentum: 0.000000
213
+ 2023-09-03 20:17:34,526 epoch 10 - iter 445/894 - loss 0.00424425 - time (sec): 47.25 - samples/sec: 929.10 - lr: 0.000002 - momentum: 0.000000
214
+ 2023-09-03 20:17:43,508 epoch 10 - iter 534/894 - loss 0.00505153 - time (sec): 56.23 - samples/sec: 931.94 - lr: 0.000001 - momentum: 0.000000
215
+ 2023-09-03 20:17:52,376 epoch 10 - iter 623/894 - loss 0.00504540 - time (sec): 65.10 - samples/sec: 923.65 - lr: 0.000001 - momentum: 0.000000
216
+ 2023-09-03 20:18:01,937 epoch 10 - iter 712/894 - loss 0.00501811 - time (sec): 74.66 - samples/sec: 921.08 - lr: 0.000001 - momentum: 0.000000
217
+ 2023-09-03 20:18:10,996 epoch 10 - iter 801/894 - loss 0.00563425 - time (sec): 83.72 - samples/sec: 919.02 - lr: 0.000000 - momentum: 0.000000
218
+ 2023-09-03 20:18:20,743 epoch 10 - iter 890/894 - loss 0.00575244 - time (sec): 93.47 - samples/sec: 922.76 - lr: 0.000000 - momentum: 0.000000
219
+ 2023-09-03 20:18:21,147 ----------------------------------------------------------------------------------------------------
220
+ 2023-09-03 20:18:21,147 EPOCH 10 done: loss 0.0059 - lr: 0.000000
221
+ 2023-09-03 20:18:34,886 DEV : loss 0.23916852474212646 - f1-score (micro avg) 0.7865
222
+ 2023-09-03 20:18:34,913 saving best model
223
+ 2023-09-03 20:18:36,753 ----------------------------------------------------------------------------------------------------
224
+ 2023-09-03 20:18:36,754 Loading model from best epoch ...
225
+ 2023-09-03 20:18:38,576 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
226
+ 2023-09-03 20:18:49,344
227
+ Results:
228
+ - F-score (micro) 0.7454
229
+ - F-score (macro) 0.6684
230
+ - Accuracy 0.6184
231
+
232
+ By class:
233
+ precision recall f1-score support
234
+
235
+ loc 0.8366 0.8507 0.8436 596
236
+ pers 0.6684 0.7568 0.7099 333
237
+ org 0.4752 0.5076 0.4908 132
238
+ prod 0.5962 0.4697 0.5254 66
239
+ time 0.7500 0.7959 0.7723 49
240
+
241
+ micro avg 0.7296 0.7619 0.7454 1176
242
+ macro avg 0.6653 0.6761 0.6684 1176
243
+ weighted avg 0.7313 0.7619 0.7453 1176
244
+
245
+ 2023-09-03 20:18:49,344 ----------------------------------------------------------------------------------------------------