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autotrain-flair-hipe2022-de-hmbert

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  1. __pycache__/flair-fine-tuner.cpython-39.pyc +0 -0
  2. __pycache__/utils.cpython-39.pyc +0 -0
  3. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1/best-model.pt +3 -0
  4. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1/dev.tsv +0 -0
  5. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1/final-model.pt +3 -0
  6. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1/loss.tsv +11 -0
  7. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1/test.tsv +0 -0
  8. hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1/training.log +247 -0
  9. training_params.json +8 -0
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hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1/best-model.pt ADDED
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hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1/final-model.pt ADDED
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hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1/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 18:22:55 0.0000 0.7743 0.1859 0.5822 0.6200 0.6005 0.4473
3
+ 2 18:24:26 0.0000 0.1697 0.1287 0.7198 0.7131 0.7164 0.5761
4
+ 3 18:25:58 0.0000 0.0894 0.1226 0.7012 0.7725 0.7351 0.6010
5
+ 4 18:27:26 0.0000 0.0514 0.1439 0.7045 0.7959 0.7474 0.6207
6
+ 5 18:28:56 0.0000 0.0329 0.1667 0.7652 0.7568 0.7610 0.6290
7
+ 6 18:30:29 0.0000 0.0217 0.1837 0.7663 0.7920 0.7789 0.6552
8
+ 7 18:32:03 0.0000 0.0142 0.1973 0.7626 0.8061 0.7837 0.6609
9
+ 8 18:33:36 0.0000 0.0096 0.2098 0.7632 0.8139 0.7877 0.6665
10
+ 9 18:35:09 0.0000 0.0076 0.2160 0.7704 0.8108 0.7901 0.6682
11
+ 10 18:36:42 0.0000 0.0051 0.2176 0.7818 0.8069 0.7942 0.6754
hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1/test.tsv ADDED
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hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1/training.log ADDED
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+ 2023-09-03 18:21:28,781 ----------------------------------------------------------------------------------------------------
2
+ 2023-09-03 18:21:28,782 Model: "SequenceTagger(
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+ (embeddings): TransformerWordEmbeddings(
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+ (model): BertModel(
5
+ (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)
9
+ (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
10
+ (dropout): Dropout(p=0.1, inplace=False)
11
+ )
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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)
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(
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+ (dense): Linear(in_features=768, out_features=3072, bias=True)
30
+ (intermediate_act_fn): GELUActivation()
31
+ )
32
+ (output): BertOutput(
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+ (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(
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+ (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 18:21:28,782 ----------------------------------------------------------------------------------------------------
51
+ 2023-09-03 18:21:28,783 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 18:21:28,783 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 18:21:28,783 Train: 3575 sentences
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+ 2023-09-03 18:21:28,783 (train_with_dev=False, train_with_test=False)
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+ 2023-09-03 18:21:28,783 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 18:21:28,783 Training Params:
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+ 2023-09-03 18:21:28,783 - learning_rate: "3e-05"
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+ 2023-09-03 18:21:28,783 - mini_batch_size: "8"
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+ 2023-09-03 18:21:28,783 - max_epochs: "10"
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+ 2023-09-03 18:21:28,783 - shuffle: "True"
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+ 2023-09-03 18:21:28,783 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 18:21:28,783 Plugins:
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+ 2023-09-03 18:21:28,783 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-09-03 18:21:28,783 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 18:21:28,783 Final evaluation on model from best epoch (best-model.pt)
67
+ 2023-09-03 18:21:28,783 - metric: "('micro avg', 'f1-score')"
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+ 2023-09-03 18:21:28,783 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 18:21:28,783 Computation:
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+ 2023-09-03 18:21:28,783 - compute on device: cuda:0
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+ 2023-09-03 18:21:28,783 - embedding storage: none
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+ 2023-09-03 18:21:28,783 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 18:21:28,784 Model training base path: "hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1"
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+ 2023-09-03 18:21:28,784 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 18:21:28,784 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 18:21:37,415 epoch 1 - iter 44/447 - loss 3.11470317 - time (sec): 8.63 - samples/sec: 1101.62 - lr: 0.000003 - momentum: 0.000000
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+ 2023-09-03 18:21:44,132 epoch 1 - iter 88/447 - loss 2.46591154 - time (sec): 15.35 - samples/sec: 1132.48 - lr: 0.000006 - momentum: 0.000000
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+ 2023-09-03 18:21:50,463 epoch 1 - iter 132/447 - loss 1.86942740 - time (sec): 21.68 - samples/sec: 1154.71 - lr: 0.000009 - momentum: 0.000000
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+ 2023-09-03 18:21:57,523 epoch 1 - iter 176/447 - loss 1.51187376 - time (sec): 28.74 - samples/sec: 1163.96 - lr: 0.000012 - momentum: 0.000000
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+ 2023-09-03 18:22:04,676 epoch 1 - iter 220/447 - loss 1.29508937 - time (sec): 35.89 - samples/sec: 1158.01 - lr: 0.000015 - momentum: 0.000000
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+ 2023-09-03 18:22:12,403 epoch 1 - iter 264/447 - loss 1.12696614 - time (sec): 43.62 - samples/sec: 1155.38 - lr: 0.000018 - momentum: 0.000000
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+ 2023-09-03 18:22:19,810 epoch 1 - iter 308/447 - loss 1.00970315 - time (sec): 51.03 - samples/sec: 1156.00 - lr: 0.000021 - momentum: 0.000000
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+ 2023-09-03 18:22:27,968 epoch 1 - iter 352/447 - loss 0.91037509 - time (sec): 59.18 - samples/sec: 1149.49 - lr: 0.000024 - momentum: 0.000000
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+ 2023-09-03 18:22:35,226 epoch 1 - iter 396/447 - loss 0.83951962 - time (sec): 66.44 - samples/sec: 1148.53 - lr: 0.000027 - momentum: 0.000000
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+ 2023-09-03 18:22:43,504 epoch 1 - iter 440/447 - loss 0.78127074 - time (sec): 74.72 - samples/sec: 1143.13 - lr: 0.000029 - momentum: 0.000000
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+ 2023-09-03 18:22:44,641 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 18:22:44,641 EPOCH 1 done: loss 0.7743 - lr: 0.000029
88
+ 2023-09-03 18:22:55,295 DEV : loss 0.18594643473625183 - f1-score (micro avg) 0.6005
89
+ 2023-09-03 18:22:55,320 saving best model
90
+ 2023-09-03 18:22:55,824 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 18:23:02,965 epoch 2 - iter 44/447 - loss 0.18936146 - time (sec): 7.14 - samples/sec: 1197.29 - lr: 0.000030 - momentum: 0.000000
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+ 2023-09-03 18:23:10,629 epoch 2 - iter 88/447 - loss 0.20698027 - time (sec): 14.80 - samples/sec: 1151.07 - lr: 0.000029 - momentum: 0.000000
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+ 2023-09-03 18:23:18,076 epoch 2 - iter 132/447 - loss 0.20176059 - time (sec): 22.25 - samples/sec: 1154.89 - lr: 0.000029 - momentum: 0.000000
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+ 2023-09-03 18:23:26,066 epoch 2 - iter 176/447 - loss 0.19297787 - time (sec): 30.24 - samples/sec: 1127.24 - lr: 0.000029 - momentum: 0.000000
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+ 2023-09-03 18:23:33,176 epoch 2 - iter 220/447 - loss 0.18884083 - time (sec): 37.35 - samples/sec: 1123.49 - lr: 0.000028 - momentum: 0.000000
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+ 2023-09-03 18:23:41,142 epoch 2 - iter 264/447 - loss 0.17774497 - time (sec): 45.32 - samples/sec: 1115.72 - lr: 0.000028 - momentum: 0.000000
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+ 2023-09-03 18:23:49,190 epoch 2 - iter 308/447 - loss 0.17609029 - time (sec): 53.36 - samples/sec: 1121.18 - lr: 0.000028 - momentum: 0.000000
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+ 2023-09-03 18:23:56,535 epoch 2 - iter 352/447 - loss 0.17573283 - time (sec): 60.71 - samples/sec: 1117.53 - lr: 0.000027 - momentum: 0.000000
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+ 2023-09-03 18:24:03,661 epoch 2 - iter 396/447 - loss 0.17493628 - time (sec): 67.84 - samples/sec: 1118.95 - lr: 0.000027 - momentum: 0.000000
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+ 2023-09-03 18:24:12,041 epoch 2 - iter 440/447 - loss 0.17070290 - time (sec): 76.22 - samples/sec: 1119.71 - lr: 0.000027 - momentum: 0.000000
101
+ 2023-09-03 18:24:13,175 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 18:24:13,175 EPOCH 2 done: loss 0.1697 - lr: 0.000027
103
+ 2023-09-03 18:24:26,624 DEV : loss 0.1287333220243454 - f1-score (micro avg) 0.7164
104
+ 2023-09-03 18:24:26,650 saving best model
105
+ 2023-09-03 18:24:28,027 ----------------------------------------------------------------------------------------------------
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+ 2023-09-03 18:24:34,995 epoch 3 - iter 44/447 - loss 0.09495783 - time (sec): 6.97 - samples/sec: 1107.10 - lr: 0.000026 - momentum: 0.000000
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+ 2023-09-03 18:24:42,160 epoch 3 - iter 88/447 - loss 0.09050438 - time (sec): 14.13 - samples/sec: 1129.37 - lr: 0.000026 - momentum: 0.000000
108
+ 2023-09-03 18:24:49,492 epoch 3 - iter 132/447 - loss 0.09851004 - time (sec): 21.46 - samples/sec: 1118.88 - lr: 0.000026 - momentum: 0.000000
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+ 2023-09-03 18:24:57,792 epoch 3 - iter 176/447 - loss 0.09053540 - time (sec): 29.76 - samples/sec: 1104.37 - lr: 0.000025 - momentum: 0.000000
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+ 2023-09-03 18:25:06,412 epoch 3 - iter 220/447 - loss 0.09334204 - time (sec): 38.38 - samples/sec: 1092.60 - lr: 0.000025 - momentum: 0.000000
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+ 2023-09-03 18:25:13,477 epoch 3 - iter 264/447 - loss 0.08944533 - time (sec): 45.45 - samples/sec: 1111.97 - lr: 0.000025 - momentum: 0.000000
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+ 2023-09-03 18:25:20,942 epoch 3 - iter 308/447 - loss 0.08936581 - time (sec): 52.91 - samples/sec: 1117.84 - lr: 0.000024 - momentum: 0.000000
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+ 2023-09-03 18:25:28,640 epoch 3 - iter 352/447 - loss 0.08872997 - time (sec): 60.61 - samples/sec: 1118.60 - lr: 0.000024 - momentum: 0.000000
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+ 2023-09-03 18:25:35,687 epoch 3 - iter 396/447 - loss 0.09035623 - time (sec): 67.66 - samples/sec: 1125.10 - lr: 0.000024 - momentum: 0.000000
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+ 2023-09-03 18:25:44,366 epoch 3 - iter 440/447 - loss 0.08953185 - time (sec): 76.34 - samples/sec: 1119.46 - lr: 0.000023 - momentum: 0.000000
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+ 2023-09-03 18:25:45,340 ----------------------------------------------------------------------------------------------------
117
+ 2023-09-03 18:25:45,340 EPOCH 3 done: loss 0.0894 - lr: 0.000023
118
+ 2023-09-03 18:25:58,086 DEV : loss 0.1225898340344429 - f1-score (micro avg) 0.7351
119
+ 2023-09-03 18:25:58,112 saving best model
120
+ 2023-09-03 18:25:59,469 ----------------------------------------------------------------------------------------------------
121
+ 2023-09-03 18:26:06,724 epoch 4 - iter 44/447 - loss 0.06395735 - time (sec): 7.25 - samples/sec: 1236.05 - lr: 0.000023 - momentum: 0.000000
122
+ 2023-09-03 18:26:13,500 epoch 4 - iter 88/447 - loss 0.05982834 - time (sec): 14.03 - samples/sec: 1212.04 - lr: 0.000023 - momentum: 0.000000
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+ 2023-09-03 18:26:21,427 epoch 4 - iter 132/447 - loss 0.05652974 - time (sec): 21.96 - samples/sec: 1186.41 - lr: 0.000022 - momentum: 0.000000
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+ 2023-09-03 18:26:29,661 epoch 4 - iter 176/447 - loss 0.05525616 - time (sec): 30.19 - samples/sec: 1181.96 - lr: 0.000022 - momentum: 0.000000
125
+ 2023-09-03 18:26:37,306 epoch 4 - iter 220/447 - loss 0.05254340 - time (sec): 37.84 - samples/sec: 1172.27 - lr: 0.000022 - momentum: 0.000000
126
+ 2023-09-03 18:26:44,944 epoch 4 - iter 264/447 - loss 0.05385835 - time (sec): 45.47 - samples/sec: 1165.24 - lr: 0.000021 - momentum: 0.000000
127
+ 2023-09-03 18:26:51,760 epoch 4 - iter 308/447 - loss 0.05369138 - time (sec): 52.29 - samples/sec: 1172.55 - lr: 0.000021 - momentum: 0.000000
128
+ 2023-09-03 18:26:58,807 epoch 4 - iter 352/447 - loss 0.05338970 - time (sec): 59.34 - samples/sec: 1174.43 - lr: 0.000021 - momentum: 0.000000
129
+ 2023-09-03 18:27:05,146 epoch 4 - iter 396/447 - loss 0.05132492 - time (sec): 65.68 - samples/sec: 1170.43 - lr: 0.000020 - momentum: 0.000000
130
+ 2023-09-03 18:27:12,845 epoch 4 - iter 440/447 - loss 0.05151521 - time (sec): 73.37 - samples/sec: 1163.44 - lr: 0.000020 - momentum: 0.000000
131
+ 2023-09-03 18:27:13,894 ----------------------------------------------------------------------------------------------------
132
+ 2023-09-03 18:27:13,894 EPOCH 4 done: loss 0.0514 - lr: 0.000020
133
+ 2023-09-03 18:27:26,856 DEV : loss 0.1438855081796646 - f1-score (micro avg) 0.7474
134
+ 2023-09-03 18:27:26,893 saving best model
135
+ 2023-09-03 18:27:28,273 ----------------------------------------------------------------------------------------------------
136
+ 2023-09-03 18:27:37,200 epoch 5 - iter 44/447 - loss 0.04007260 - time (sec): 8.93 - samples/sec: 1080.28 - lr: 0.000020 - momentum: 0.000000
137
+ 2023-09-03 18:27:44,006 epoch 5 - iter 88/447 - loss 0.03678430 - time (sec): 15.73 - samples/sec: 1120.83 - lr: 0.000019 - momentum: 0.000000
138
+ 2023-09-03 18:27:51,611 epoch 5 - iter 132/447 - loss 0.03344633 - time (sec): 23.34 - samples/sec: 1124.95 - lr: 0.000019 - momentum: 0.000000
139
+ 2023-09-03 18:27:58,422 epoch 5 - iter 176/447 - loss 0.03311853 - time (sec): 30.15 - samples/sec: 1140.02 - lr: 0.000019 - momentum: 0.000000
140
+ 2023-09-03 18:28:06,463 epoch 5 - iter 220/447 - loss 0.03289270 - time (sec): 38.19 - samples/sec: 1136.03 - lr: 0.000018 - momentum: 0.000000
141
+ 2023-09-03 18:28:13,614 epoch 5 - iter 264/447 - loss 0.03272367 - time (sec): 45.34 - samples/sec: 1147.21 - lr: 0.000018 - momentum: 0.000000
142
+ 2023-09-03 18:28:20,716 epoch 5 - iter 308/447 - loss 0.03211904 - time (sec): 52.44 - samples/sec: 1145.62 - lr: 0.000018 - momentum: 0.000000
143
+ 2023-09-03 18:28:28,282 epoch 5 - iter 352/447 - loss 0.03099804 - time (sec): 60.01 - samples/sec: 1145.99 - lr: 0.000017 - momentum: 0.000000
144
+ 2023-09-03 18:28:35,561 epoch 5 - iter 396/447 - loss 0.03134117 - time (sec): 67.29 - samples/sec: 1139.40 - lr: 0.000017 - momentum: 0.000000
145
+ 2023-09-03 18:28:42,759 epoch 5 - iter 440/447 - loss 0.03292082 - time (sec): 74.48 - samples/sec: 1144.88 - lr: 0.000017 - momentum: 0.000000
146
+ 2023-09-03 18:28:43,886 ----------------------------------------------------------------------------------------------------
147
+ 2023-09-03 18:28:43,887 EPOCH 5 done: loss 0.0329 - lr: 0.000017
148
+ 2023-09-03 18:28:56,796 DEV : loss 0.16667184233665466 - f1-score (micro avg) 0.761
149
+ 2023-09-03 18:28:56,822 saving best model
150
+ 2023-09-03 18:28:58,193 ----------------------------------------------------------------------------------------------------
151
+ 2023-09-03 18:29:06,052 epoch 6 - iter 44/447 - loss 0.02307461 - time (sec): 7.86 - samples/sec: 1093.56 - lr: 0.000016 - momentum: 0.000000
152
+ 2023-09-03 18:29:12,868 epoch 6 - iter 88/447 - loss 0.01945012 - time (sec): 14.67 - samples/sec: 1103.26 - lr: 0.000016 - momentum: 0.000000
153
+ 2023-09-03 18:29:20,967 epoch 6 - iter 132/447 - loss 0.01718198 - time (sec): 22.77 - samples/sec: 1099.68 - lr: 0.000016 - momentum: 0.000000
154
+ 2023-09-03 18:29:28,847 epoch 6 - iter 176/447 - loss 0.01824429 - time (sec): 30.65 - samples/sec: 1108.00 - lr: 0.000015 - momentum: 0.000000
155
+ 2023-09-03 18:29:35,589 epoch 6 - iter 220/447 - loss 0.01838533 - time (sec): 37.39 - samples/sec: 1111.07 - lr: 0.000015 - momentum: 0.000000
156
+ 2023-09-03 18:29:43,043 epoch 6 - iter 264/447 - loss 0.01877602 - time (sec): 44.85 - samples/sec: 1105.93 - lr: 0.000015 - momentum: 0.000000
157
+ 2023-09-03 18:29:50,277 epoch 6 - iter 308/447 - loss 0.02057658 - time (sec): 52.08 - samples/sec: 1101.92 - lr: 0.000014 - momentum: 0.000000
158
+ 2023-09-03 18:29:57,389 epoch 6 - iter 352/447 - loss 0.02153178 - time (sec): 59.19 - samples/sec: 1114.71 - lr: 0.000014 - momentum: 0.000000
159
+ 2023-09-03 18:30:06,767 epoch 6 - iter 396/447 - loss 0.02220110 - time (sec): 68.57 - samples/sec: 1110.88 - lr: 0.000014 - momentum: 0.000000
160
+ 2023-09-03 18:30:15,051 epoch 6 - iter 440/447 - loss 0.02172317 - time (sec): 76.86 - samples/sec: 1108.89 - lr: 0.000013 - momentum: 0.000000
161
+ 2023-09-03 18:30:16,130 ----------------------------------------------------------------------------------------------------
162
+ 2023-09-03 18:30:16,130 EPOCH 6 done: loss 0.0217 - lr: 0.000013
163
+ 2023-09-03 18:30:29,605 DEV : loss 0.1836623251438141 - f1-score (micro avg) 0.7789
164
+ 2023-09-03 18:30:29,632 saving best model
165
+ 2023-09-03 18:30:31,501 ----------------------------------------------------------------------------------------------------
166
+ 2023-09-03 18:30:38,866 epoch 7 - iter 44/447 - loss 0.01419595 - time (sec): 7.36 - samples/sec: 1187.09 - lr: 0.000013 - momentum: 0.000000
167
+ 2023-09-03 18:30:46,088 epoch 7 - iter 88/447 - loss 0.01603520 - time (sec): 14.59 - samples/sec: 1157.28 - lr: 0.000013 - momentum: 0.000000
168
+ 2023-09-03 18:30:55,966 epoch 7 - iter 132/447 - loss 0.01574985 - time (sec): 24.46 - samples/sec: 1102.88 - lr: 0.000012 - momentum: 0.000000
169
+ 2023-09-03 18:31:03,653 epoch 7 - iter 176/447 - loss 0.01383093 - time (sec): 32.15 - samples/sec: 1098.70 - lr: 0.000012 - momentum: 0.000000
170
+ 2023-09-03 18:31:11,583 epoch 7 - iter 220/447 - loss 0.01544671 - time (sec): 40.08 - samples/sec: 1099.46 - lr: 0.000012 - momentum: 0.000000
171
+ 2023-09-03 18:31:18,562 epoch 7 - iter 264/447 - loss 0.01556334 - time (sec): 47.06 - samples/sec: 1105.80 - lr: 0.000011 - momentum: 0.000000
172
+ 2023-09-03 18:31:25,934 epoch 7 - iter 308/447 - loss 0.01431665 - time (sec): 54.43 - samples/sec: 1105.32 - lr: 0.000011 - momentum: 0.000000
173
+ 2023-09-03 18:31:33,925 epoch 7 - iter 352/447 - loss 0.01409783 - time (sec): 62.42 - samples/sec: 1097.26 - lr: 0.000011 - momentum: 0.000000
174
+ 2023-09-03 18:31:41,095 epoch 7 - iter 396/447 - loss 0.01484694 - time (sec): 69.59 - samples/sec: 1094.28 - lr: 0.000010 - momentum: 0.000000
175
+ 2023-09-03 18:31:48,109 epoch 7 - iter 440/447 - loss 0.01446960 - time (sec): 76.61 - samples/sec: 1100.22 - lr: 0.000010 - momentum: 0.000000
176
+ 2023-09-03 18:31:50,291 ----------------------------------------------------------------------------------------------------
177
+ 2023-09-03 18:31:50,291 EPOCH 7 done: loss 0.0142 - lr: 0.000010
178
+ 2023-09-03 18:32:03,818 DEV : loss 0.1973269134759903 - f1-score (micro avg) 0.7837
179
+ 2023-09-03 18:32:03,845 saving best model
180
+ 2023-09-03 18:32:05,222 ----------------------------------------------------------------------------------------------------
181
+ 2023-09-03 18:32:13,220 epoch 8 - iter 44/447 - loss 0.01029987 - time (sec): 8.00 - samples/sec: 1071.49 - lr: 0.000010 - momentum: 0.000000
182
+ 2023-09-03 18:32:21,316 epoch 8 - iter 88/447 - loss 0.00934919 - time (sec): 16.09 - samples/sec: 1093.03 - lr: 0.000009 - momentum: 0.000000
183
+ 2023-09-03 18:32:29,012 epoch 8 - iter 132/447 - loss 0.00805343 - time (sec): 23.79 - samples/sec: 1120.87 - lr: 0.000009 - momentum: 0.000000
184
+ 2023-09-03 18:32:37,827 epoch 8 - iter 176/447 - loss 0.00742876 - time (sec): 32.60 - samples/sec: 1107.86 - lr: 0.000009 - momentum: 0.000000
185
+ 2023-09-03 18:32:45,039 epoch 8 - iter 220/447 - loss 0.00944281 - time (sec): 39.82 - samples/sec: 1102.57 - lr: 0.000008 - momentum: 0.000000
186
+ 2023-09-03 18:32:52,908 epoch 8 - iter 264/447 - loss 0.01021476 - time (sec): 47.68 - samples/sec: 1090.24 - lr: 0.000008 - momentum: 0.000000
187
+ 2023-09-03 18:33:00,532 epoch 8 - iter 308/447 - loss 0.00971741 - time (sec): 55.31 - samples/sec: 1101.87 - lr: 0.000008 - momentum: 0.000000
188
+ 2023-09-03 18:33:07,723 epoch 8 - iter 352/447 - loss 0.00925926 - time (sec): 62.50 - samples/sec: 1108.00 - lr: 0.000007 - momentum: 0.000000
189
+ 2023-09-03 18:33:14,970 epoch 8 - iter 396/447 - loss 0.01013440 - time (sec): 69.75 - samples/sec: 1109.62 - lr: 0.000007 - momentum: 0.000000
190
+ 2023-09-03 18:33:22,431 epoch 8 - iter 440/447 - loss 0.00973599 - time (sec): 77.21 - samples/sec: 1104.79 - lr: 0.000007 - momentum: 0.000000
191
+ 2023-09-03 18:33:23,503 ----------------------------------------------------------------------------------------------------
192
+ 2023-09-03 18:33:23,503 EPOCH 8 done: loss 0.0096 - lr: 0.000007
193
+ 2023-09-03 18:33:36,556 DEV : loss 0.20980410277843475 - f1-score (micro avg) 0.7877
194
+ 2023-09-03 18:33:36,582 saving best model
195
+ 2023-09-03 18:33:38,290 ----------------------------------------------------------------------------------------------------
196
+ 2023-09-03 18:33:45,638 epoch 9 - iter 44/447 - loss 0.00992836 - time (sec): 7.35 - samples/sec: 1108.84 - lr: 0.000006 - momentum: 0.000000
197
+ 2023-09-03 18:33:53,704 epoch 9 - iter 88/447 - loss 0.00727258 - time (sec): 15.41 - samples/sec: 1127.51 - lr: 0.000006 - momentum: 0.000000
198
+ 2023-09-03 18:34:01,989 epoch 9 - iter 132/447 - loss 0.00686773 - time (sec): 23.70 - samples/sec: 1085.59 - lr: 0.000006 - momentum: 0.000000
199
+ 2023-09-03 18:34:10,168 epoch 9 - iter 176/447 - loss 0.00593792 - time (sec): 31.88 - samples/sec: 1093.36 - lr: 0.000005 - momentum: 0.000000
200
+ 2023-09-03 18:34:18,970 epoch 9 - iter 220/447 - loss 0.00648321 - time (sec): 40.68 - samples/sec: 1073.40 - lr: 0.000005 - momentum: 0.000000
201
+ 2023-09-03 18:34:26,084 epoch 9 - iter 264/447 - loss 0.00789765 - time (sec): 47.79 - samples/sec: 1085.54 - lr: 0.000005 - momentum: 0.000000
202
+ 2023-09-03 18:34:34,436 epoch 9 - iter 308/447 - loss 0.00712320 - time (sec): 56.14 - samples/sec: 1094.59 - lr: 0.000004 - momentum: 0.000000
203
+ 2023-09-03 18:34:41,491 epoch 9 - iter 352/447 - loss 0.00662608 - time (sec): 63.20 - samples/sec: 1099.33 - lr: 0.000004 - momentum: 0.000000
204
+ 2023-09-03 18:34:48,326 epoch 9 - iter 396/447 - loss 0.00674970 - time (sec): 70.03 - samples/sec: 1105.13 - lr: 0.000004 - momentum: 0.000000
205
+ 2023-09-03 18:34:55,784 epoch 9 - iter 440/447 - loss 0.00751222 - time (sec): 77.49 - samples/sec: 1101.10 - lr: 0.000003 - momentum: 0.000000
206
+ 2023-09-03 18:34:56,819 ----------------------------------------------------------------------------------------------------
207
+ 2023-09-03 18:34:56,819 EPOCH 9 done: loss 0.0076 - lr: 0.000003
208
+ 2023-09-03 18:35:09,955 DEV : loss 0.2160281091928482 - f1-score (micro avg) 0.7901
209
+ 2023-09-03 18:35:09,982 saving best model
210
+ 2023-09-03 18:35:11,346 ----------------------------------------------------------------------------------------------------
211
+ 2023-09-03 18:35:19,098 epoch 10 - iter 44/447 - loss 0.00476283 - time (sec): 7.75 - samples/sec: 1121.94 - lr: 0.000003 - momentum: 0.000000
212
+ 2023-09-03 18:35:25,973 epoch 10 - iter 88/447 - loss 0.00479459 - time (sec): 14.63 - samples/sec: 1131.01 - lr: 0.000003 - momentum: 0.000000
213
+ 2023-09-03 18:35:33,160 epoch 10 - iter 132/447 - loss 0.00414159 - time (sec): 21.81 - samples/sec: 1147.93 - lr: 0.000002 - momentum: 0.000000
214
+ 2023-09-03 18:35:40,912 epoch 10 - iter 176/447 - loss 0.00424893 - time (sec): 29.56 - samples/sec: 1138.91 - lr: 0.000002 - momentum: 0.000000
215
+ 2023-09-03 18:35:49,645 epoch 10 - iter 220/447 - loss 0.00517905 - time (sec): 38.30 - samples/sec: 1117.89 - lr: 0.000002 - momentum: 0.000000
216
+ 2023-09-03 18:35:58,183 epoch 10 - iter 264/447 - loss 0.00508760 - time (sec): 46.84 - samples/sec: 1100.36 - lr: 0.000001 - momentum: 0.000000
217
+ 2023-09-03 18:36:06,411 epoch 10 - iter 308/447 - loss 0.00478898 - time (sec): 55.06 - samples/sec: 1096.34 - lr: 0.000001 - momentum: 0.000000
218
+ 2023-09-03 18:36:13,313 epoch 10 - iter 352/447 - loss 0.00492933 - time (sec): 61.96 - samples/sec: 1103.01 - lr: 0.000001 - momentum: 0.000000
219
+ 2023-09-03 18:36:20,461 epoch 10 - iter 396/447 - loss 0.00523232 - time (sec): 69.11 - samples/sec: 1105.83 - lr: 0.000000 - momentum: 0.000000
220
+ 2023-09-03 18:36:28,541 epoch 10 - iter 440/447 - loss 0.00518352 - time (sec): 77.19 - samples/sec: 1100.36 - lr: 0.000000 - momentum: 0.000000
221
+ 2023-09-03 18:36:29,888 ----------------------------------------------------------------------------------------------------
222
+ 2023-09-03 18:36:29,889 EPOCH 10 done: loss 0.0051 - lr: 0.000000
223
+ 2023-09-03 18:36:42,963 DEV : loss 0.21760693192481995 - f1-score (micro avg) 0.7942
224
+ 2023-09-03 18:36:42,989 saving best model
225
+ 2023-09-03 18:36:44,882 ----------------------------------------------------------------------------------------------------
226
+ 2023-09-03 18:36:44,883 Loading model from best epoch ...
227
+ 2023-09-03 18:36:47,178 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
228
+ 2023-09-03 18:36:58,110
229
+ Results:
230
+ - F-score (micro) 0.7599
231
+ - F-score (macro) 0.7005
232
+ - Accuracy 0.632
233
+
234
+ By class:
235
+ precision recall f1-score support
236
+
237
+ loc 0.8386 0.8540 0.8462 596
238
+ pers 0.6746 0.7658 0.7173 333
239
+ org 0.5328 0.4924 0.5118 132
240
+ prod 0.7872 0.5606 0.6549 66
241
+ time 0.7500 0.7959 0.7723 49
242
+
243
+ micro avg 0.7504 0.7696 0.7599 1176
244
+ macro avg 0.7166 0.6937 0.7005 1176
245
+ weighted avg 0.7512 0.7696 0.7584 1176
246
+
247
+ 2023-09-03 18:36:58,110 ----------------------------------------------------------------------------------------------------
training_params.json ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "username": "stefan-it",
3
+ "project_name": "/tmp/model",
4
+ "data_path": "stefan-it/autotrain-flair-hipe2022-de-hmbert",
5
+ "token": "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",
6
+ "script_path": "/home/stefan/Repositories/hmTEAMS/bench",
7
+ "env": {}
8
+ }