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hmbench-icdar-nl-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5

Token Classification
Flair
PyTorch
Dutch
sequence-tagger-model
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  1. best-model.pt +3 -0
  2. dev.tsv +0 -0
  3. loss.tsv +11 -0
  4. test.tsv +0 -0
  5. training.log +238 -0
best-model.pt ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:99e512486b3a7b0ca6c7421e9898d02e00cacc7de601adcc944dbd964e3a3e63
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+ size 443311111
dev.tsv ADDED
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loss.tsv ADDED
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+ EPOCH TIMESTAMP LEARNING_RATE TRAIN_LOSS DEV_LOSS DEV_PRECISION DEV_RECALL DEV_F1 DEV_ACCURACY
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+ 1 11:21:08 0.0000 0.3429 0.1257 0.6958 0.6736 0.6845 0.5314
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+ 2 11:22:25 0.0000 0.1043 0.0794 0.8366 0.7882 0.8117 0.6930
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+ 3 11:23:43 0.0000 0.0724 0.0914 0.7994 0.8275 0.8132 0.6996
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+ 4 11:25:02 0.0000 0.0516 0.1312 0.8566 0.7283 0.7873 0.6583
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+ 5 11:26:22 0.0000 0.0391 0.1291 0.8064 0.8048 0.8056 0.6943
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+ 6 11:27:42 0.0000 0.0297 0.1542 0.8273 0.7965 0.8116 0.6971
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+ 7 11:29:02 0.0000 0.0208 0.2023 0.8714 0.7417 0.8013 0.6825
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+ 8 11:30:21 0.0000 0.0168 0.1653 0.8628 0.7665 0.8118 0.6967
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+ 9 11:31:38 0.0000 0.0102 0.1807 0.8558 0.7727 0.8122 0.6958
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+ 10 11:32:57 0.0000 0.0079 0.1895 0.8570 0.7676 0.8098 0.6925
test.tsv ADDED
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training.log ADDED
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+ 2023-10-14 11:19:51,579 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 11:19:51,580 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)
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+ (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)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ (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)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ )
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+ (intermediate): BertIntermediate(
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+ (dense): Linear(in_features=768, out_features=3072, bias=True)
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+ (intermediate_act_fn): GELUActivation()
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+ )
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+ (output): BertOutput(
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+ (dense): Linear(in_features=3072, out_features=768, bias=True)
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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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+ )
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+ )
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+ )
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+ (pooler): BertPooler(
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+ (dense): Linear(in_features=768, out_features=768, bias=True)
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+ (activation): Tanh()
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+ )
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+ )
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+ )
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+ (locked_dropout): LockedDropout(p=0.5)
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+ (linear): Linear(in_features=768, out_features=13, bias=True)
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+ (loss_function): CrossEntropyLoss()
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+ )"
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+ 2023-10-14 11:19:51,580 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 11:19:51,581 MultiCorpus: 5777 train + 722 dev + 723 test sentences
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+ - NER_ICDAR_EUROPEANA Corpus: 5777 train + 722 dev + 723 test sentences - /root/.flair/datasets/ner_icdar_europeana/nl
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+ 2023-10-14 11:19:51,581 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 11:19:51,581 Train: 5777 sentences
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+ 2023-10-14 11:19:51,581 (train_with_dev=False, train_with_test=False)
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+ 2023-10-14 11:19:51,581 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 11:19:51,581 Training Params:
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+ 2023-10-14 11:19:51,581 - learning_rate: "3e-05"
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+ 2023-10-14 11:19:51,581 - mini_batch_size: "4"
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+ 2023-10-14 11:19:51,581 - max_epochs: "10"
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+ 2023-10-14 11:19:51,581 - shuffle: "True"
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+ 2023-10-14 11:19:51,581 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 11:19:51,581 Plugins:
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+ 2023-10-14 11:19:51,581 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-14 11:19:51,581 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 11:19:51,581 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-14 11:19:51,581 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-14 11:19:51,581 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 11:19:51,581 Computation:
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+ 2023-10-14 11:19:51,581 - compute on device: cuda:0
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+ 2023-10-14 11:19:51,581 - embedding storage: none
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+ 2023-10-14 11:19:51,581 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 11:19:51,581 Model training base path: "hmbench-icdar/nl-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5"
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+ 2023-10-14 11:19:51,581 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 11:19:51,581 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 11:19:58,951 epoch 1 - iter 144/1445 - loss 1.75565661 - time (sec): 7.37 - samples/sec: 2517.03 - lr: 0.000003 - momentum: 0.000000
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+ 2023-10-14 11:20:06,337 epoch 1 - iter 288/1445 - loss 1.03252900 - time (sec): 14.75 - samples/sec: 2434.39 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-14 11:20:13,778 epoch 1 - iter 432/1445 - loss 0.76745105 - time (sec): 22.20 - samples/sec: 2402.87 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-14 11:20:21,198 epoch 1 - iter 576/1445 - loss 0.62697231 - time (sec): 29.62 - samples/sec: 2409.91 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-14 11:20:28,524 epoch 1 - iter 720/1445 - loss 0.53763328 - time (sec): 36.94 - samples/sec: 2400.64 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-14 11:20:35,915 epoch 1 - iter 864/1445 - loss 0.47592185 - time (sec): 44.33 - samples/sec: 2396.57 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-14 11:20:43,308 epoch 1 - iter 1008/1445 - loss 0.43188433 - time (sec): 51.73 - samples/sec: 2396.84 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-14 11:20:50,476 epoch 1 - iter 1152/1445 - loss 0.39731712 - time (sec): 58.89 - samples/sec: 2381.34 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-14 11:20:57,640 epoch 1 - iter 1296/1445 - loss 0.36675744 - time (sec): 66.06 - samples/sec: 2389.98 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-14 11:21:04,925 epoch 1 - iter 1440/1445 - loss 0.34335442 - time (sec): 73.34 - samples/sec: 2394.82 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-14 11:21:05,157 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 11:21:05,158 EPOCH 1 done: loss 0.3429 - lr: 0.000030
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+ 2023-10-14 11:21:08,099 DEV : loss 0.12569260597229004 - f1-score (micro avg) 0.6845
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+ 2023-10-14 11:21:08,115 saving best model
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+ 2023-10-14 11:21:08,480 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 11:21:15,744 epoch 2 - iter 144/1445 - loss 0.11445694 - time (sec): 7.26 - samples/sec: 2355.69 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-14 11:21:22,917 epoch 2 - iter 288/1445 - loss 0.11933495 - time (sec): 14.44 - samples/sec: 2365.17 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-14 11:21:30,379 epoch 2 - iter 432/1445 - loss 0.11449909 - time (sec): 21.90 - samples/sec: 2393.79 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-14 11:21:37,825 epoch 2 - iter 576/1445 - loss 0.11286041 - time (sec): 29.34 - samples/sec: 2416.56 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-14 11:21:45,105 epoch 2 - iter 720/1445 - loss 0.11064148 - time (sec): 36.62 - samples/sec: 2402.08 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-14 11:21:52,191 epoch 2 - iter 864/1445 - loss 0.10759736 - time (sec): 43.71 - samples/sec: 2395.87 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-14 11:21:59,736 epoch 2 - iter 1008/1445 - loss 0.10670874 - time (sec): 51.25 - samples/sec: 2386.85 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-14 11:22:07,129 epoch 2 - iter 1152/1445 - loss 0.10492693 - time (sec): 58.65 - samples/sec: 2390.49 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-14 11:22:14,431 epoch 2 - iter 1296/1445 - loss 0.10543454 - time (sec): 65.95 - samples/sec: 2398.76 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-14 11:22:21,625 epoch 2 - iter 1440/1445 - loss 0.10438782 - time (sec): 73.14 - samples/sec: 2403.24 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-14 11:22:21,848 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 11:22:21,848 EPOCH 2 done: loss 0.1043 - lr: 0.000027
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+ 2023-10-14 11:22:25,893 DEV : loss 0.07940532267093658 - f1-score (micro avg) 0.8117
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+ 2023-10-14 11:22:25,913 saving best model
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+ 2023-10-14 11:22:26,438 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 11:22:33,672 epoch 3 - iter 144/1445 - loss 0.07386524 - time (sec): 7.23 - samples/sec: 2347.39 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-14 11:22:40,885 epoch 3 - iter 288/1445 - loss 0.07483343 - time (sec): 14.44 - samples/sec: 2359.93 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-14 11:22:48,224 epoch 3 - iter 432/1445 - loss 0.07412461 - time (sec): 21.78 - samples/sec: 2349.80 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-14 11:22:55,420 epoch 3 - iter 576/1445 - loss 0.07525880 - time (sec): 28.98 - samples/sec: 2344.61 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-14 11:23:02,802 epoch 3 - iter 720/1445 - loss 0.07266541 - time (sec): 36.36 - samples/sec: 2318.85 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-14 11:23:10,303 epoch 3 - iter 864/1445 - loss 0.07006827 - time (sec): 43.86 - samples/sec: 2354.04 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-14 11:23:17,543 epoch 3 - iter 1008/1445 - loss 0.07173992 - time (sec): 51.10 - samples/sec: 2356.57 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-14 11:23:25,013 epoch 3 - iter 1152/1445 - loss 0.07507578 - time (sec): 58.57 - samples/sec: 2375.48 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-14 11:23:32,575 epoch 3 - iter 1296/1445 - loss 0.07342122 - time (sec): 66.13 - samples/sec: 2372.37 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-14 11:23:40,030 epoch 3 - iter 1440/1445 - loss 0.07228349 - time (sec): 73.59 - samples/sec: 2388.87 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-14 11:23:40,254 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 11:23:40,255 EPOCH 3 done: loss 0.0724 - lr: 0.000023
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+ 2023-10-14 11:23:43,807 DEV : loss 0.0913621187210083 - f1-score (micro avg) 0.8132
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+ 2023-10-14 11:23:43,824 saving best model
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+ 2023-10-14 11:23:44,309 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 11:23:51,639 epoch 4 - iter 144/1445 - loss 0.04124556 - time (sec): 7.33 - samples/sec: 2313.25 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-14 11:23:59,115 epoch 4 - iter 288/1445 - loss 0.04481502 - time (sec): 14.80 - samples/sec: 2421.60 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-14 11:24:06,305 epoch 4 - iter 432/1445 - loss 0.04399488 - time (sec): 21.99 - samples/sec: 2410.15 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-14 11:24:14,200 epoch 4 - iter 576/1445 - loss 0.05066883 - time (sec): 29.89 - samples/sec: 2364.56 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-14 11:24:21,411 epoch 4 - iter 720/1445 - loss 0.05193458 - time (sec): 37.10 - samples/sec: 2356.32 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-14 11:24:28,873 epoch 4 - iter 864/1445 - loss 0.05279794 - time (sec): 44.56 - samples/sec: 2357.40 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-14 11:24:36,264 epoch 4 - iter 1008/1445 - loss 0.05274805 - time (sec): 51.95 - samples/sec: 2368.95 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-14 11:24:43,432 epoch 4 - iter 1152/1445 - loss 0.05186328 - time (sec): 59.12 - samples/sec: 2363.42 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-14 11:24:50,727 epoch 4 - iter 1296/1445 - loss 0.05122179 - time (sec): 66.42 - samples/sec: 2375.97 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-14 11:24:58,244 epoch 4 - iter 1440/1445 - loss 0.05165454 - time (sec): 73.93 - samples/sec: 2377.78 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-14 11:24:58,467 ----------------------------------------------------------------------------------------------------
132
+ 2023-10-14 11:24:58,467 EPOCH 4 done: loss 0.0516 - lr: 0.000020
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+ 2023-10-14 11:25:02,030 DEV : loss 0.1312413215637207 - f1-score (micro avg) 0.7873
134
+ 2023-10-14 11:25:02,052 ----------------------------------------------------------------------------------------------------
135
+ 2023-10-14 11:25:10,200 epoch 5 - iter 144/1445 - loss 0.03172829 - time (sec): 8.15 - samples/sec: 2041.29 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-14 11:25:17,599 epoch 5 - iter 288/1445 - loss 0.03587651 - time (sec): 15.54 - samples/sec: 2149.38 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-14 11:25:24,807 epoch 5 - iter 432/1445 - loss 0.03852227 - time (sec): 22.75 - samples/sec: 2222.67 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-14 11:25:32,180 epoch 5 - iter 576/1445 - loss 0.03797228 - time (sec): 30.13 - samples/sec: 2295.90 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-14 11:25:39,666 epoch 5 - iter 720/1445 - loss 0.04007370 - time (sec): 37.61 - samples/sec: 2317.85 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-14 11:25:47,202 epoch 5 - iter 864/1445 - loss 0.04030841 - time (sec): 45.15 - samples/sec: 2333.39 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-14 11:25:54,950 epoch 5 - iter 1008/1445 - loss 0.03895283 - time (sec): 52.90 - samples/sec: 2346.70 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-14 11:26:02,363 epoch 5 - iter 1152/1445 - loss 0.03892191 - time (sec): 60.31 - samples/sec: 2335.88 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-14 11:26:10,423 epoch 5 - iter 1296/1445 - loss 0.03776827 - time (sec): 68.37 - samples/sec: 2311.82 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-14 11:26:18,250 epoch 5 - iter 1440/1445 - loss 0.03917454 - time (sec): 76.20 - samples/sec: 2301.84 - lr: 0.000017 - momentum: 0.000000
145
+ 2023-10-14 11:26:18,546 ----------------------------------------------------------------------------------------------------
146
+ 2023-10-14 11:26:18,547 EPOCH 5 done: loss 0.0391 - lr: 0.000017
147
+ 2023-10-14 11:26:22,555 DEV : loss 0.12906955182552338 - f1-score (micro avg) 0.8056
148
+ 2023-10-14 11:26:22,571 ----------------------------------------------------------------------------------------------------
149
+ 2023-10-14 11:26:29,760 epoch 6 - iter 144/1445 - loss 0.02668724 - time (sec): 7.19 - samples/sec: 2352.12 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-14 11:26:37,402 epoch 6 - iter 288/1445 - loss 0.03043050 - time (sec): 14.83 - samples/sec: 2308.65 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-14 11:26:45,621 epoch 6 - iter 432/1445 - loss 0.03237606 - time (sec): 23.05 - samples/sec: 2260.11 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-14 11:26:54,021 epoch 6 - iter 576/1445 - loss 0.03461076 - time (sec): 31.45 - samples/sec: 2236.70 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-14 11:27:02,112 epoch 6 - iter 720/1445 - loss 0.03444742 - time (sec): 39.54 - samples/sec: 2263.64 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-14 11:27:09,978 epoch 6 - iter 864/1445 - loss 0.03339662 - time (sec): 47.41 - samples/sec: 2253.87 - lr: 0.000015 - momentum: 0.000000
155
+ 2023-10-14 11:27:17,058 epoch 6 - iter 1008/1445 - loss 0.03185918 - time (sec): 54.49 - samples/sec: 2269.09 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-14 11:27:24,364 epoch 6 - iter 1152/1445 - loss 0.03003098 - time (sec): 61.79 - samples/sec: 2278.81 - lr: 0.000014 - momentum: 0.000000
157
+ 2023-10-14 11:27:31,815 epoch 6 - iter 1296/1445 - loss 0.03093691 - time (sec): 69.24 - samples/sec: 2285.58 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-14 11:27:39,005 epoch 6 - iter 1440/1445 - loss 0.02981435 - time (sec): 76.43 - samples/sec: 2298.80 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-14 11:27:39,263 ----------------------------------------------------------------------------------------------------
160
+ 2023-10-14 11:27:39,263 EPOCH 6 done: loss 0.0297 - lr: 0.000013
161
+ 2023-10-14 11:27:42,895 DEV : loss 0.15421992540359497 - f1-score (micro avg) 0.8116
162
+ 2023-10-14 11:27:42,912 ----------------------------------------------------------------------------------------------------
163
+ 2023-10-14 11:27:51,172 epoch 7 - iter 144/1445 - loss 0.01588193 - time (sec): 8.26 - samples/sec: 2061.32 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-14 11:27:59,204 epoch 7 - iter 288/1445 - loss 0.01690062 - time (sec): 16.29 - samples/sec: 2101.42 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-14 11:28:07,120 epoch 7 - iter 432/1445 - loss 0.01781960 - time (sec): 24.21 - samples/sec: 2174.74 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-14 11:28:15,004 epoch 7 - iter 576/1445 - loss 0.02064476 - time (sec): 32.09 - samples/sec: 2182.17 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-14 11:28:22,356 epoch 7 - iter 720/1445 - loss 0.01923653 - time (sec): 39.44 - samples/sec: 2233.50 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-14 11:28:29,480 epoch 7 - iter 864/1445 - loss 0.02022570 - time (sec): 46.57 - samples/sec: 2263.41 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-14 11:28:36,967 epoch 7 - iter 1008/1445 - loss 0.02153322 - time (sec): 54.05 - samples/sec: 2275.18 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-14 11:28:44,349 epoch 7 - iter 1152/1445 - loss 0.02052892 - time (sec): 61.44 - samples/sec: 2288.90 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-14 11:28:51,513 epoch 7 - iter 1296/1445 - loss 0.02095327 - time (sec): 68.60 - samples/sec: 2290.67 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-14 11:28:59,028 epoch 7 - iter 1440/1445 - loss 0.02075394 - time (sec): 76.11 - samples/sec: 2308.21 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-14 11:28:59,294 ----------------------------------------------------------------------------------------------------
174
+ 2023-10-14 11:28:59,294 EPOCH 7 done: loss 0.0208 - lr: 0.000010
175
+ 2023-10-14 11:29:02,923 DEV : loss 0.20232853293418884 - f1-score (micro avg) 0.8013
176
+ 2023-10-14 11:29:02,948 ----------------------------------------------------------------------------------------------------
177
+ 2023-10-14 11:29:10,235 epoch 8 - iter 144/1445 - loss 0.01686935 - time (sec): 7.29 - samples/sec: 2444.86 - lr: 0.000010 - momentum: 0.000000
178
+ 2023-10-14 11:29:17,403 epoch 8 - iter 288/1445 - loss 0.01651959 - time (sec): 14.45 - samples/sec: 2432.31 - lr: 0.000009 - momentum: 0.000000
179
+ 2023-10-14 11:29:24,873 epoch 8 - iter 432/1445 - loss 0.01702677 - time (sec): 21.92 - samples/sec: 2429.48 - lr: 0.000009 - momentum: 0.000000
180
+ 2023-10-14 11:29:31,981 epoch 8 - iter 576/1445 - loss 0.01781295 - time (sec): 29.03 - samples/sec: 2431.84 - lr: 0.000009 - momentum: 0.000000
181
+ 2023-10-14 11:29:39,239 epoch 8 - iter 720/1445 - loss 0.01906418 - time (sec): 36.29 - samples/sec: 2440.50 - lr: 0.000008 - momentum: 0.000000
182
+ 2023-10-14 11:29:46,360 epoch 8 - iter 864/1445 - loss 0.01854455 - time (sec): 43.41 - samples/sec: 2441.24 - lr: 0.000008 - momentum: 0.000000
183
+ 2023-10-14 11:29:53,542 epoch 8 - iter 1008/1445 - loss 0.01734082 - time (sec): 50.59 - samples/sec: 2418.61 - lr: 0.000008 - momentum: 0.000000
184
+ 2023-10-14 11:30:01,355 epoch 8 - iter 1152/1445 - loss 0.01630921 - time (sec): 58.41 - samples/sec: 2403.18 - lr: 0.000007 - momentum: 0.000000
185
+ 2023-10-14 11:30:09,931 epoch 8 - iter 1296/1445 - loss 0.01692418 - time (sec): 66.98 - samples/sec: 2366.73 - lr: 0.000007 - momentum: 0.000000
186
+ 2023-10-14 11:30:17,349 epoch 8 - iter 1440/1445 - loss 0.01674331 - time (sec): 74.40 - samples/sec: 2358.30 - lr: 0.000007 - momentum: 0.000000
187
+ 2023-10-14 11:30:17,642 ----------------------------------------------------------------------------------------------------
188
+ 2023-10-14 11:30:17,642 EPOCH 8 done: loss 0.0168 - lr: 0.000007
189
+ 2023-10-14 11:30:21,838 DEV : loss 0.165278822183609 - f1-score (micro avg) 0.8118
190
+ 2023-10-14 11:30:21,862 ----------------------------------------------------------------------------------------------------
191
+ 2023-10-14 11:30:29,160 epoch 9 - iter 144/1445 - loss 0.00480124 - time (sec): 7.30 - samples/sec: 2388.87 - lr: 0.000006 - momentum: 0.000000
192
+ 2023-10-14 11:30:36,224 epoch 9 - iter 288/1445 - loss 0.00564082 - time (sec): 14.36 - samples/sec: 2330.43 - lr: 0.000006 - momentum: 0.000000
193
+ 2023-10-14 11:30:44,078 epoch 9 - iter 432/1445 - loss 0.00787483 - time (sec): 22.21 - samples/sec: 2414.52 - lr: 0.000006 - momentum: 0.000000
194
+ 2023-10-14 11:30:51,098 epoch 9 - iter 576/1445 - loss 0.00799249 - time (sec): 29.24 - samples/sec: 2405.67 - lr: 0.000005 - momentum: 0.000000
195
+ 2023-10-14 11:30:58,424 epoch 9 - iter 720/1445 - loss 0.00802746 - time (sec): 36.56 - samples/sec: 2417.76 - lr: 0.000005 - momentum: 0.000000
196
+ 2023-10-14 11:31:05,652 epoch 9 - iter 864/1445 - loss 0.00927689 - time (sec): 43.79 - samples/sec: 2417.57 - lr: 0.000005 - momentum: 0.000000
197
+ 2023-10-14 11:31:12,868 epoch 9 - iter 1008/1445 - loss 0.00857392 - time (sec): 51.00 - samples/sec: 2418.14 - lr: 0.000004 - momentum: 0.000000
198
+ 2023-10-14 11:31:20,330 epoch 9 - iter 1152/1445 - loss 0.00976295 - time (sec): 58.47 - samples/sec: 2401.39 - lr: 0.000004 - momentum: 0.000000
199
+ 2023-10-14 11:31:27,560 epoch 9 - iter 1296/1445 - loss 0.01000819 - time (sec): 65.70 - samples/sec: 2400.94 - lr: 0.000004 - momentum: 0.000000
200
+ 2023-10-14 11:31:34,843 epoch 9 - iter 1440/1445 - loss 0.01022696 - time (sec): 72.98 - samples/sec: 2404.55 - lr: 0.000003 - momentum: 0.000000
201
+ 2023-10-14 11:31:35,106 ----------------------------------------------------------------------------------------------------
202
+ 2023-10-14 11:31:35,106 EPOCH 9 done: loss 0.0102 - lr: 0.000003
203
+ 2023-10-14 11:31:38,643 DEV : loss 0.18068867921829224 - f1-score (micro avg) 0.8122
204
+ 2023-10-14 11:31:38,665 ----------------------------------------------------------------------------------------------------
205
+ 2023-10-14 11:31:47,034 epoch 10 - iter 144/1445 - loss 0.00660655 - time (sec): 8.37 - samples/sec: 2163.54 - lr: 0.000003 - momentum: 0.000000
206
+ 2023-10-14 11:31:55,098 epoch 10 - iter 288/1445 - loss 0.00706000 - time (sec): 16.43 - samples/sec: 2169.34 - lr: 0.000003 - momentum: 0.000000
207
+ 2023-10-14 11:32:03,016 epoch 10 - iter 432/1445 - loss 0.00915700 - time (sec): 24.35 - samples/sec: 2188.35 - lr: 0.000002 - momentum: 0.000000
208
+ 2023-10-14 11:32:10,893 epoch 10 - iter 576/1445 - loss 0.00816302 - time (sec): 32.23 - samples/sec: 2228.93 - lr: 0.000002 - momentum: 0.000000
209
+ 2023-10-14 11:32:18,020 epoch 10 - iter 720/1445 - loss 0.00731694 - time (sec): 39.35 - samples/sec: 2268.04 - lr: 0.000002 - momentum: 0.000000
210
+ 2023-10-14 11:32:25,157 epoch 10 - iter 864/1445 - loss 0.00729216 - time (sec): 46.49 - samples/sec: 2297.09 - lr: 0.000001 - momentum: 0.000000
211
+ 2023-10-14 11:32:32,546 epoch 10 - iter 1008/1445 - loss 0.00743674 - time (sec): 53.88 - samples/sec: 2307.13 - lr: 0.000001 - momentum: 0.000000
212
+ 2023-10-14 11:32:39,647 epoch 10 - iter 1152/1445 - loss 0.00774300 - time (sec): 60.98 - samples/sec: 2320.04 - lr: 0.000001 - momentum: 0.000000
213
+ 2023-10-14 11:32:46,774 epoch 10 - iter 1296/1445 - loss 0.00794261 - time (sec): 68.11 - samples/sec: 2320.41 - lr: 0.000000 - momentum: 0.000000
214
+ 2023-10-14 11:32:53,945 epoch 10 - iter 1440/1445 - loss 0.00789770 - time (sec): 75.28 - samples/sec: 2332.16 - lr: 0.000000 - momentum: 0.000000
215
+ 2023-10-14 11:32:54,229 ----------------------------------------------------------------------------------------------------
216
+ 2023-10-14 11:32:54,229 EPOCH 10 done: loss 0.0079 - lr: 0.000000
217
+ 2023-10-14 11:32:57,810 DEV : loss 0.18952515721321106 - f1-score (micro avg) 0.8098
218
+ 2023-10-14 11:32:58,296 ----------------------------------------------------------------------------------------------------
219
+ 2023-10-14 11:32:58,297 Loading model from best epoch ...
220
+ 2023-10-14 11:33:00,088 SequenceTagger predicts: Dictionary with 13 tags: O, S-LOC, B-LOC, E-LOC, I-LOC, S-PER, B-PER, E-PER, I-PER, S-ORG, B-ORG, E-ORG, I-ORG
221
+ 2023-10-14 11:33:03,295
222
+ Results:
223
+ - F-score (micro) 0.8059
224
+ - F-score (macro) 0.7025
225
+ - Accuracy 0.6862
226
+
227
+ By class:
228
+ precision recall f1-score support
229
+
230
+ PER 0.8119 0.8237 0.8177 482
231
+ LOC 0.8277 0.8603 0.8437 458
232
+ ORG 0.4754 0.4203 0.4462 69
233
+
234
+ micro avg 0.7992 0.8127 0.8059 1009
235
+ macro avg 0.7050 0.7014 0.7025 1009
236
+ weighted avg 0.7961 0.8127 0.8041 1009
237
+
238
+ 2023-10-14 11:33:03,296 ----------------------------------------------------------------------------------------------------