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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-5/best-model.pt +3 -0
  2. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5/dev.tsv +0 -0
  3. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5/final-model.pt +3 -0
  4. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5/loss.tsv +11 -0
  5. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5/test.tsv +0 -0
  6. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5/training.log +245 -0
hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5/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-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-lr3e-05-poolingfirst-layers-1-crfFalse-5/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-5/loss.tsv ADDED
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+ EPOCH TIMESTAMP LEARNING_RATE TRAIN_LOSS DEV_LOSS DEV_PRECISION DEV_RECALL DEV_F1 DEV_ACCURACY
2
+ 1 23:32:31 0.0000 0.6444 0.2032 0.4155 0.5575 0.4761 0.3192
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+ 2 23:34:19 0.0000 0.1618 0.1588 0.7193 0.7154 0.7174 0.5795
4
+ 3 23:36:08 0.0000 0.0940 0.1649 0.7295 0.7443 0.7368 0.5987
5
+ 4 23:37:56 0.0000 0.0568 0.1610 0.7525 0.7678 0.7601 0.6295
6
+ 5 23:39:44 0.0000 0.0361 0.1914 0.7275 0.7952 0.7598 0.6333
7
+ 6 23:41:31 0.0000 0.0239 0.2092 0.7523 0.7834 0.7675 0.6431
8
+ 7 23:43:17 0.0000 0.0156 0.2316 0.7682 0.7826 0.7754 0.6487
9
+ 8 23:45:01 0.0000 0.0099 0.2163 0.7601 0.8124 0.7853 0.6643
10
+ 9 23:46:48 0.0000 0.0067 0.2261 0.7784 0.8100 0.7939 0.6749
11
+ 10 23:48:33 0.0000 0.0042 0.2306 0.7764 0.8061 0.7909 0.6717
hmbench-hipe2020/de-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/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5/training.log ADDED
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+ 2023-09-03 23:30:48,661 ----------------------------------------------------------------------------------------------------
2
+ 2023-09-03 23:30:48,662 Model: "SequenceTagger(
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+ (embeddings): TransformerWordEmbeddings(
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+ (model): BertModel(
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+ (embeddings): BertEmbeddings(
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+ (word_embeddings): Embedding(32001, 768)
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+ (position_embeddings): Embedding(512, 768)
8
+ (token_type_embeddings): Embedding(2, 768)
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+ (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
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+ (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)
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+ (key): Linear(in_features=768, out_features=768, bias=True)
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+ (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)
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+ (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
25
+ (dropout): Dropout(p=0.1, inplace=False)
26
+ )
27
+ )
28
+ (intermediate): BertIntermediate(
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+ (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 23:30:48,662 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 23:30:48,662 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
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+ 2023-09-03 23:30:48,662 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 23:30:48,663 Train: 3575 sentences
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+ 2023-09-03 23:30:48,663 (train_with_dev=False, train_with_test=False)
56
+ 2023-09-03 23:30:48,663 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 23:30:48,663 Training Params:
58
+ 2023-09-03 23:30:48,663 - learning_rate: "3e-05"
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+ 2023-09-03 23:30:48,663 - mini_batch_size: "4"
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+ 2023-09-03 23:30:48,663 - max_epochs: "10"
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+ 2023-09-03 23:30:48,663 - shuffle: "True"
62
+ 2023-09-03 23:30:48,663 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 23:30:48,663 Plugins:
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+ 2023-09-03 23:30:48,663 - LinearScheduler | warmup_fraction: '0.1'
65
+ 2023-09-03 23:30:48,663 ----------------------------------------------------------------------------------------------------
66
+ 2023-09-03 23:30:48,663 Final evaluation on model from best epoch (best-model.pt)
67
+ 2023-09-03 23:30:48,663 - metric: "('micro avg', 'f1-score')"
68
+ 2023-09-03 23:30:48,663 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 23:30:48,663 Computation:
70
+ 2023-09-03 23:30:48,663 - compute on device: cuda:0
71
+ 2023-09-03 23:30:48,663 - embedding storage: none
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+ 2023-09-03 23:30:48,663 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 23:30:48,663 Model training base path: "hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5"
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+ 2023-09-03 23:30:48,664 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 23:30:48,664 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 23:30:58,989 epoch 1 - iter 89/894 - loss 2.74311561 - time (sec): 10.32 - samples/sec: 955.50 - lr: 0.000003 - momentum: 0.000000
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+ 2023-09-03 23:31:08,172 epoch 1 - iter 178/894 - loss 1.81044363 - time (sec): 19.51 - samples/sec: 977.05 - lr: 0.000006 - momentum: 0.000000
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+ 2023-09-03 23:31:17,157 epoch 1 - iter 267/894 - loss 1.41836922 - time (sec): 28.49 - samples/sec: 961.79 - lr: 0.000009 - momentum: 0.000000
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+ 2023-09-03 23:31:26,621 epoch 1 - iter 356/894 - loss 1.16298770 - time (sec): 37.96 - samples/sec: 953.98 - lr: 0.000012 - momentum: 0.000000
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+ 2023-09-03 23:31:35,482 epoch 1 - iter 445/894 - loss 0.99950668 - time (sec): 46.82 - samples/sec: 958.72 - lr: 0.000015 - momentum: 0.000000
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+ 2023-09-03 23:31:44,475 epoch 1 - iter 534/894 - loss 0.88532676 - time (sec): 55.81 - samples/sec: 954.46 - lr: 0.000018 - momentum: 0.000000
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+ 2023-09-03 23:31:53,559 epoch 1 - iter 623/894 - loss 0.80230519 - time (sec): 64.89 - samples/sec: 952.79 - lr: 0.000021 - momentum: 0.000000
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+ 2023-09-03 23:32:02,315 epoch 1 - iter 712/894 - loss 0.73704514 - time (sec): 73.65 - samples/sec: 950.17 - lr: 0.000024 - momentum: 0.000000
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+ 2023-09-03 23:32:11,028 epoch 1 - iter 801/894 - loss 0.68927269 - time (sec): 82.36 - samples/sec: 946.58 - lr: 0.000027 - momentum: 0.000000
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+ 2023-09-03 23:32:19,927 epoch 1 - iter 890/894 - loss 0.64616785 - time (sec): 91.26 - samples/sec: 944.78 - lr: 0.000030 - momentum: 0.000000
86
+ 2023-09-03 23:32:20,319 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 23:32:20,319 EPOCH 1 done: loss 0.6444 - lr: 0.000030
88
+ 2023-09-03 23:32:31,409 DEV : loss 0.20319941639900208 - f1-score (micro avg) 0.4761
89
+ 2023-09-03 23:32:31,436 saving best model
90
+ 2023-09-03 23:32:31,891 ----------------------------------------------------------------------------------------------------
91
+ 2023-09-03 23:32:41,596 epoch 2 - iter 89/894 - loss 0.21499267 - time (sec): 9.70 - samples/sec: 952.60 - lr: 0.000030 - momentum: 0.000000
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+ 2023-09-03 23:32:51,096 epoch 2 - iter 178/894 - loss 0.20265125 - time (sec): 19.20 - samples/sec: 923.85 - lr: 0.000029 - momentum: 0.000000
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+ 2023-09-03 23:33:00,247 epoch 2 - iter 267/894 - loss 0.18434207 - time (sec): 28.36 - samples/sec: 915.14 - lr: 0.000029 - momentum: 0.000000
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+ 2023-09-03 23:33:09,056 epoch 2 - iter 356/894 - loss 0.18344703 - time (sec): 37.16 - samples/sec: 902.95 - lr: 0.000029 - momentum: 0.000000
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+ 2023-09-03 23:33:18,307 epoch 2 - iter 445/894 - loss 0.17751486 - time (sec): 46.41 - samples/sec: 907.39 - lr: 0.000028 - momentum: 0.000000
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+ 2023-09-03 23:33:27,595 epoch 2 - iter 534/894 - loss 0.17722887 - time (sec): 55.70 - samples/sec: 909.92 - lr: 0.000028 - momentum: 0.000000
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+ 2023-09-03 23:33:36,865 epoch 2 - iter 623/894 - loss 0.17577434 - time (sec): 64.97 - samples/sec: 913.79 - lr: 0.000028 - momentum: 0.000000
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+ 2023-09-03 23:33:46,352 epoch 2 - iter 712/894 - loss 0.17072853 - time (sec): 74.46 - samples/sec: 915.30 - lr: 0.000027 - momentum: 0.000000
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+ 2023-09-03 23:33:56,140 epoch 2 - iter 801/894 - loss 0.16591060 - time (sec): 84.25 - samples/sec: 915.37 - lr: 0.000027 - momentum: 0.000000
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+ 2023-09-03 23:34:05,723 epoch 2 - iter 890/894 - loss 0.16170980 - time (sec): 93.83 - samples/sec: 916.81 - lr: 0.000027 - momentum: 0.000000
101
+ 2023-09-03 23:34:06,167 ----------------------------------------------------------------------------------------------------
102
+ 2023-09-03 23:34:06,167 EPOCH 2 done: loss 0.1618 - lr: 0.000027
103
+ 2023-09-03 23:34:19,317 DEV : loss 0.1587580442428589 - f1-score (micro avg) 0.7174
104
+ 2023-09-03 23:34:19,343 saving best model
105
+ 2023-09-03 23:34:20,654 ----------------------------------------------------------------------------------------------------
106
+ 2023-09-03 23:34:29,750 epoch 3 - iter 89/894 - loss 0.09211103 - time (sec): 9.10 - samples/sec: 1010.60 - lr: 0.000026 - momentum: 0.000000
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+ 2023-09-03 23:34:38,880 epoch 3 - iter 178/894 - loss 0.08964218 - time (sec): 18.23 - samples/sec: 983.85 - lr: 0.000026 - momentum: 0.000000
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+ 2023-09-03 23:34:49,364 epoch 3 - iter 267/894 - loss 0.09340800 - time (sec): 28.71 - samples/sec: 949.92 - lr: 0.000026 - momentum: 0.000000
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+ 2023-09-03 23:34:58,874 epoch 3 - iter 356/894 - loss 0.09572858 - time (sec): 38.22 - samples/sec: 949.71 - lr: 0.000025 - momentum: 0.000000
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+ 2023-09-03 23:35:08,189 epoch 3 - iter 445/894 - loss 0.09520323 - time (sec): 47.53 - samples/sec: 934.60 - lr: 0.000025 - momentum: 0.000000
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+ 2023-09-03 23:35:17,175 epoch 3 - iter 534/894 - loss 0.09332333 - time (sec): 56.52 - samples/sec: 934.15 - lr: 0.000025 - momentum: 0.000000
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+ 2023-09-03 23:35:26,360 epoch 3 - iter 623/894 - loss 0.09366015 - time (sec): 65.70 - samples/sec: 924.15 - lr: 0.000024 - momentum: 0.000000
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+ 2023-09-03 23:35:35,978 epoch 3 - iter 712/894 - loss 0.09376044 - time (sec): 75.32 - samples/sec: 919.53 - lr: 0.000024 - momentum: 0.000000
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+ 2023-09-03 23:35:45,082 epoch 3 - iter 801/894 - loss 0.09398189 - time (sec): 84.43 - samples/sec: 923.80 - lr: 0.000024 - momentum: 0.000000
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+ 2023-09-03 23:35:54,172 epoch 3 - iter 890/894 - loss 0.09338918 - time (sec): 93.52 - samples/sec: 921.91 - lr: 0.000023 - momentum: 0.000000
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+ 2023-09-03 23:35:54,583 ----------------------------------------------------------------------------------------------------
117
+ 2023-09-03 23:35:54,583 EPOCH 3 done: loss 0.0940 - lr: 0.000023
118
+ 2023-09-03 23:36:08,494 DEV : loss 0.16491389274597168 - f1-score (micro avg) 0.7368
119
+ 2023-09-03 23:36:08,520 saving best model
120
+ 2023-09-03 23:36:09,849 ----------------------------------------------------------------------------------------------------
121
+ 2023-09-03 23:36:19,396 epoch 4 - iter 89/894 - loss 0.06002206 - time (sec): 9.55 - samples/sec: 973.70 - lr: 0.000023 - momentum: 0.000000
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+ 2023-09-03 23:36:28,236 epoch 4 - iter 178/894 - loss 0.05670894 - time (sec): 18.39 - samples/sec: 951.51 - lr: 0.000023 - momentum: 0.000000
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+ 2023-09-03 23:36:38,929 epoch 4 - iter 267/894 - loss 0.05584137 - time (sec): 29.08 - samples/sec: 950.62 - lr: 0.000022 - momentum: 0.000000
124
+ 2023-09-03 23:36:47,859 epoch 4 - iter 356/894 - loss 0.05527686 - time (sec): 38.01 - samples/sec: 939.19 - lr: 0.000022 - momentum: 0.000000
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+ 2023-09-03 23:36:57,012 epoch 4 - iter 445/894 - loss 0.05568304 - time (sec): 47.16 - samples/sec: 936.11 - lr: 0.000022 - momentum: 0.000000
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+ 2023-09-03 23:37:06,694 epoch 4 - iter 534/894 - loss 0.05670899 - time (sec): 56.84 - samples/sec: 941.39 - lr: 0.000021 - momentum: 0.000000
127
+ 2023-09-03 23:37:15,687 epoch 4 - iter 623/894 - loss 0.05689945 - time (sec): 65.84 - samples/sec: 936.89 - lr: 0.000021 - momentum: 0.000000
128
+ 2023-09-03 23:37:24,648 epoch 4 - iter 712/894 - loss 0.05695848 - time (sec): 74.80 - samples/sec: 934.31 - lr: 0.000021 - momentum: 0.000000
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+ 2023-09-03 23:37:33,640 epoch 4 - iter 801/894 - loss 0.05762958 - time (sec): 83.79 - samples/sec: 930.98 - lr: 0.000020 - momentum: 0.000000
130
+ 2023-09-03 23:37:42,783 epoch 4 - iter 890/894 - loss 0.05704236 - time (sec): 92.93 - samples/sec: 927.84 - lr: 0.000020 - momentum: 0.000000
131
+ 2023-09-03 23:37:43,141 ----------------------------------------------------------------------------------------------------
132
+ 2023-09-03 23:37:43,141 EPOCH 4 done: loss 0.0568 - lr: 0.000020
133
+ 2023-09-03 23:37:56,337 DEV : loss 0.16102342307567596 - f1-score (micro avg) 0.7601
134
+ 2023-09-03 23:37:56,363 saving best model
135
+ 2023-09-03 23:37:57,891 ----------------------------------------------------------------------------------------------------
136
+ 2023-09-03 23:38:07,278 epoch 5 - iter 89/894 - loss 0.04237017 - time (sec): 9.39 - samples/sec: 929.34 - lr: 0.000020 - momentum: 0.000000
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+ 2023-09-03 23:38:16,495 epoch 5 - iter 178/894 - loss 0.04053170 - time (sec): 18.60 - samples/sec: 915.63 - lr: 0.000019 - momentum: 0.000000
138
+ 2023-09-03 23:38:26,293 epoch 5 - iter 267/894 - loss 0.04022232 - time (sec): 28.40 - samples/sec: 923.94 - lr: 0.000019 - momentum: 0.000000
139
+ 2023-09-03 23:38:35,225 epoch 5 - iter 356/894 - loss 0.03940618 - time (sec): 37.33 - samples/sec: 931.91 - lr: 0.000019 - momentum: 0.000000
140
+ 2023-09-03 23:38:44,805 epoch 5 - iter 445/894 - loss 0.03797463 - time (sec): 46.91 - samples/sec: 939.52 - lr: 0.000018 - momentum: 0.000000
141
+ 2023-09-03 23:38:54,351 epoch 5 - iter 534/894 - loss 0.03759381 - time (sec): 56.46 - samples/sec: 934.25 - lr: 0.000018 - momentum: 0.000000
142
+ 2023-09-03 23:39:03,289 epoch 5 - iter 623/894 - loss 0.03731125 - time (sec): 65.40 - samples/sec: 933.80 - lr: 0.000018 - momentum: 0.000000
143
+ 2023-09-03 23:39:12,853 epoch 5 - iter 712/894 - loss 0.03703168 - time (sec): 74.96 - samples/sec: 932.07 - lr: 0.000017 - momentum: 0.000000
144
+ 2023-09-03 23:39:21,676 epoch 5 - iter 801/894 - loss 0.03792050 - time (sec): 83.78 - samples/sec: 928.22 - lr: 0.000017 - momentum: 0.000000
145
+ 2023-09-03 23:39:30,935 epoch 5 - iter 890/894 - loss 0.03624499 - time (sec): 93.04 - samples/sec: 925.72 - lr: 0.000017 - momentum: 0.000000
146
+ 2023-09-03 23:39:31,374 ----------------------------------------------------------------------------------------------------
147
+ 2023-09-03 23:39:31,374 EPOCH 5 done: loss 0.0361 - lr: 0.000017
148
+ 2023-09-03 23:39:44,550 DEV : loss 0.19139504432678223 - f1-score (micro avg) 0.7598
149
+ 2023-09-03 23:39:44,576 ----------------------------------------------------------------------------------------------------
150
+ 2023-09-03 23:39:53,391 epoch 6 - iter 89/894 - loss 0.02273816 - time (sec): 8.81 - samples/sec: 949.03 - lr: 0.000016 - momentum: 0.000000
151
+ 2023-09-03 23:40:03,138 epoch 6 - iter 178/894 - loss 0.03094662 - time (sec): 18.56 - samples/sec: 903.56 - lr: 0.000016 - momentum: 0.000000
152
+ 2023-09-03 23:40:13,803 epoch 6 - iter 267/894 - loss 0.02784261 - time (sec): 29.23 - samples/sec: 927.87 - lr: 0.000016 - momentum: 0.000000
153
+ 2023-09-03 23:40:23,166 epoch 6 - iter 356/894 - loss 0.02655943 - time (sec): 38.59 - samples/sec: 928.62 - lr: 0.000015 - momentum: 0.000000
154
+ 2023-09-03 23:40:33,069 epoch 6 - iter 445/894 - loss 0.02461255 - time (sec): 48.49 - samples/sec: 940.90 - lr: 0.000015 - momentum: 0.000000
155
+ 2023-09-03 23:40:41,923 epoch 6 - iter 534/894 - loss 0.02450099 - time (sec): 57.35 - samples/sec: 933.99 - lr: 0.000015 - momentum: 0.000000
156
+ 2023-09-03 23:40:51,009 epoch 6 - iter 623/894 - loss 0.02418424 - time (sec): 66.43 - samples/sec: 927.04 - lr: 0.000014 - momentum: 0.000000
157
+ 2023-09-03 23:40:59,851 epoch 6 - iter 712/894 - loss 0.02423121 - time (sec): 75.27 - samples/sec: 929.15 - lr: 0.000014 - momentum: 0.000000
158
+ 2023-09-03 23:41:09,094 epoch 6 - iter 801/894 - loss 0.02349068 - time (sec): 84.52 - samples/sec: 928.36 - lr: 0.000014 - momentum: 0.000000
159
+ 2023-09-03 23:41:17,949 epoch 6 - iter 890/894 - loss 0.02383666 - time (sec): 93.37 - samples/sec: 922.88 - lr: 0.000013 - momentum: 0.000000
160
+ 2023-09-03 23:41:18,323 ----------------------------------------------------------------------------------------------------
161
+ 2023-09-03 23:41:18,323 EPOCH 6 done: loss 0.0239 - lr: 0.000013
162
+ 2023-09-03 23:41:31,323 DEV : loss 0.20917759835720062 - f1-score (micro avg) 0.7675
163
+ 2023-09-03 23:41:31,357 saving best model
164
+ 2023-09-03 23:41:32,698 ----------------------------------------------------------------------------------------------------
165
+ 2023-09-03 23:41:41,274 epoch 7 - iter 89/894 - loss 0.02131126 - time (sec): 8.58 - samples/sec: 910.88 - lr: 0.000013 - momentum: 0.000000
166
+ 2023-09-03 23:41:50,208 epoch 7 - iter 178/894 - loss 0.01558822 - time (sec): 17.51 - samples/sec: 894.18 - lr: 0.000013 - momentum: 0.000000
167
+ 2023-09-03 23:42:00,140 epoch 7 - iter 267/894 - loss 0.01541116 - time (sec): 27.44 - samples/sec: 924.48 - lr: 0.000012 - momentum: 0.000000
168
+ 2023-09-03 23:42:09,256 epoch 7 - iter 356/894 - loss 0.01482310 - time (sec): 36.56 - samples/sec: 933.49 - lr: 0.000012 - momentum: 0.000000
169
+ 2023-09-03 23:42:18,691 epoch 7 - iter 445/894 - loss 0.01455277 - time (sec): 45.99 - samples/sec: 937.26 - lr: 0.000012 - momentum: 0.000000
170
+ 2023-09-03 23:42:27,885 epoch 7 - iter 534/894 - loss 0.01453793 - time (sec): 55.19 - samples/sec: 930.23 - lr: 0.000011 - momentum: 0.000000
171
+ 2023-09-03 23:42:36,796 epoch 7 - iter 623/894 - loss 0.01625399 - time (sec): 64.10 - samples/sec: 929.60 - lr: 0.000011 - momentum: 0.000000
172
+ 2023-09-03 23:42:46,677 epoch 7 - iter 712/894 - loss 0.01580634 - time (sec): 73.98 - samples/sec: 930.54 - lr: 0.000011 - momentum: 0.000000
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+ 2023-09-03 23:42:55,514 epoch 7 - iter 801/894 - loss 0.01613378 - time (sec): 82.81 - samples/sec: 934.94 - lr: 0.000010 - momentum: 0.000000
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+ 2023-09-03 23:43:04,603 epoch 7 - iter 890/894 - loss 0.01538471 - time (sec): 91.90 - samples/sec: 937.58 - lr: 0.000010 - momentum: 0.000000
175
+ 2023-09-03 23:43:04,991 ----------------------------------------------------------------------------------------------------
176
+ 2023-09-03 23:43:04,991 EPOCH 7 done: loss 0.0156 - lr: 0.000010
177
+ 2023-09-03 23:43:17,386 DEV : loss 0.23162303864955902 - f1-score (micro avg) 0.7754
178
+ 2023-09-03 23:43:17,412 saving best model
179
+ 2023-09-03 23:43:18,737 ----------------------------------------------------------------------------------------------------
180
+ 2023-09-03 23:43:27,954 epoch 8 - iter 89/894 - loss 0.01009460 - time (sec): 9.22 - samples/sec: 917.53 - lr: 0.000010 - momentum: 0.000000
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+ 2023-09-03 23:43:36,766 epoch 8 - iter 178/894 - loss 0.00709218 - time (sec): 18.03 - samples/sec: 923.51 - lr: 0.000009 - momentum: 0.000000
182
+ 2023-09-03 23:43:46,236 epoch 8 - iter 267/894 - loss 0.00777061 - time (sec): 27.50 - samples/sec: 948.29 - lr: 0.000009 - momentum: 0.000000
183
+ 2023-09-03 23:43:55,678 epoch 8 - iter 356/894 - loss 0.00668856 - time (sec): 36.94 - samples/sec: 959.88 - lr: 0.000009 - momentum: 0.000000
184
+ 2023-09-03 23:44:04,921 epoch 8 - iter 445/894 - loss 0.00864465 - time (sec): 46.18 - samples/sec: 950.74 - lr: 0.000008 - momentum: 0.000000
185
+ 2023-09-03 23:44:13,549 epoch 8 - iter 534/894 - loss 0.00899618 - time (sec): 54.81 - samples/sec: 955.17 - lr: 0.000008 - momentum: 0.000000
186
+ 2023-09-03 23:44:22,382 epoch 8 - iter 623/894 - loss 0.00881135 - time (sec): 63.64 - samples/sec: 956.37 - lr: 0.000008 - momentum: 0.000000
187
+ 2023-09-03 23:44:31,077 epoch 8 - iter 712/894 - loss 0.01010302 - time (sec): 72.34 - samples/sec: 957.98 - lr: 0.000007 - momentum: 0.000000
188
+ 2023-09-03 23:44:39,891 epoch 8 - iter 801/894 - loss 0.00991379 - time (sec): 81.15 - samples/sec: 955.84 - lr: 0.000007 - momentum: 0.000000
189
+ 2023-09-03 23:44:49,009 epoch 8 - iter 890/894 - loss 0.00985983 - time (sec): 90.27 - samples/sec: 954.91 - lr: 0.000007 - momentum: 0.000000
190
+ 2023-09-03 23:44:49,366 ----------------------------------------------------------------------------------------------------
191
+ 2023-09-03 23:44:49,367 EPOCH 8 done: loss 0.0099 - lr: 0.000007
192
+ 2023-09-03 23:45:01,950 DEV : loss 0.2163342833518982 - f1-score (micro avg) 0.7853
193
+ 2023-09-03 23:45:01,977 saving best model
194
+ 2023-09-03 23:45:03,335 ----------------------------------------------------------------------------------------------------
195
+ 2023-09-03 23:45:12,384 epoch 9 - iter 89/894 - loss 0.01046440 - time (sec): 9.05 - samples/sec: 911.56 - lr: 0.000006 - momentum: 0.000000
196
+ 2023-09-03 23:45:22,249 epoch 9 - iter 178/894 - loss 0.00684178 - time (sec): 18.91 - samples/sec: 939.00 - lr: 0.000006 - momentum: 0.000000
197
+ 2023-09-03 23:45:31,727 epoch 9 - iter 267/894 - loss 0.00585405 - time (sec): 28.39 - samples/sec: 945.14 - lr: 0.000006 - momentum: 0.000000
198
+ 2023-09-03 23:45:40,741 epoch 9 - iter 356/894 - loss 0.00581434 - time (sec): 37.40 - samples/sec: 944.49 - lr: 0.000005 - momentum: 0.000000
199
+ 2023-09-03 23:45:49,876 epoch 9 - iter 445/894 - loss 0.00601538 - time (sec): 46.54 - samples/sec: 934.86 - lr: 0.000005 - momentum: 0.000000
200
+ 2023-09-03 23:45:58,848 epoch 9 - iter 534/894 - loss 0.00637338 - time (sec): 55.51 - samples/sec: 938.35 - lr: 0.000005 - momentum: 0.000000
201
+ 2023-09-03 23:46:07,969 epoch 9 - iter 623/894 - loss 0.00712429 - time (sec): 64.63 - samples/sec: 940.30 - lr: 0.000004 - momentum: 0.000000
202
+ 2023-09-03 23:46:16,776 epoch 9 - iter 712/894 - loss 0.00713056 - time (sec): 73.44 - samples/sec: 944.52 - lr: 0.000004 - momentum: 0.000000
203
+ 2023-09-03 23:46:26,040 epoch 9 - iter 801/894 - loss 0.00684114 - time (sec): 82.70 - samples/sec: 938.50 - lr: 0.000004 - momentum: 0.000000
204
+ 2023-09-03 23:46:35,144 epoch 9 - iter 890/894 - loss 0.00671261 - time (sec): 91.81 - samples/sec: 939.00 - lr: 0.000003 - momentum: 0.000000
205
+ 2023-09-03 23:46:35,515 ----------------------------------------------------------------------------------------------------
206
+ 2023-09-03 23:46:35,515 EPOCH 9 done: loss 0.0067 - lr: 0.000003
207
+ 2023-09-03 23:46:48,817 DEV : loss 0.22610056400299072 - f1-score (micro avg) 0.7939
208
+ 2023-09-03 23:46:48,845 saving best model
209
+ 2023-09-03 23:46:50,165 ----------------------------------------------------------------------------------------------------
210
+ 2023-09-03 23:46:59,180 epoch 10 - iter 89/894 - loss 0.00107317 - time (sec): 9.01 - samples/sec: 950.83 - lr: 0.000003 - momentum: 0.000000
211
+ 2023-09-03 23:47:08,078 epoch 10 - iter 178/894 - loss 0.00240391 - time (sec): 17.91 - samples/sec: 919.06 - lr: 0.000003 - momentum: 0.000000
212
+ 2023-09-03 23:47:17,198 epoch 10 - iter 267/894 - loss 0.00259919 - time (sec): 27.03 - samples/sec: 924.99 - lr: 0.000002 - momentum: 0.000000
213
+ 2023-09-03 23:47:25,814 epoch 10 - iter 356/894 - loss 0.00369997 - time (sec): 35.65 - samples/sec: 929.59 - lr: 0.000002 - momentum: 0.000000
214
+ 2023-09-03 23:47:35,665 epoch 10 - iter 445/894 - loss 0.00404340 - time (sec): 45.50 - samples/sec: 941.77 - lr: 0.000002 - momentum: 0.000000
215
+ 2023-09-03 23:47:45,431 epoch 10 - iter 534/894 - loss 0.00441361 - time (sec): 55.26 - samples/sec: 943.70 - lr: 0.000001 - momentum: 0.000000
216
+ 2023-09-03 23:47:54,188 epoch 10 - iter 623/894 - loss 0.00431692 - time (sec): 64.02 - samples/sec: 945.20 - lr: 0.000001 - momentum: 0.000000
217
+ 2023-09-03 23:48:02,892 epoch 10 - iter 712/894 - loss 0.00420207 - time (sec): 72.73 - samples/sec: 942.53 - lr: 0.000001 - momentum: 0.000000
218
+ 2023-09-03 23:48:11,986 epoch 10 - iter 801/894 - loss 0.00417998 - time (sec): 81.82 - samples/sec: 952.06 - lr: 0.000000 - momentum: 0.000000
219
+ 2023-09-03 23:48:20,669 epoch 10 - iter 890/894 - loss 0.00418087 - time (sec): 90.50 - samples/sec: 952.35 - lr: 0.000000 - momentum: 0.000000
220
+ 2023-09-03 23:48:21,030 ----------------------------------------------------------------------------------------------------
221
+ 2023-09-03 23:48:21,031 EPOCH 10 done: loss 0.0042 - lr: 0.000000
222
+ 2023-09-03 23:48:33,669 DEV : loss 0.23058366775512695 - f1-score (micro avg) 0.7909
223
+ 2023-09-03 23:48:34,151 ----------------------------------------------------------------------------------------------------
224
+ 2023-09-03 23:48:34,152 Loading model from best epoch ...
225
+ 2023-09-03 23:48:35,890 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 23:48:45,725
227
+ Results:
228
+ - F-score (micro) 0.7522
229
+ - F-score (macro) 0.6802
230
+ - Accuracy 0.6235
231
+
232
+ By class:
233
+ precision recall f1-score support
234
+
235
+ loc 0.8127 0.8591 0.8352 596
236
+ pers 0.6764 0.7658 0.7183 333
237
+ org 0.5492 0.5076 0.5276 132
238
+ prod 0.7021 0.5000 0.5841 66
239
+ time 0.6842 0.7959 0.7358 49
240
+
241
+ micro avg 0.7348 0.7704 0.7522 1176
242
+ macro avg 0.6849 0.6857 0.6802 1176
243
+ weighted avg 0.7330 0.7704 0.7494 1176
244
+
245
+ 2023-09-03 23:48:45,725 ----------------------------------------------------------------------------------------------------