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hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1

Token Classification
Flair
PyTorch
English
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 +242 -0
best-model.pt ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:8399de8e6e2912722ec0d1e124fd3a09ac1d9ad9048cd4849aa1f78f123c41b4
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+ size 443311175
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 20:50:14 0.0000 0.3384 0.0691 0.7016 0.7637 0.7313 0.5954
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+ 2 20:51:30 0.0000 0.0897 0.0624 0.6895 0.8059 0.7432 0.6006
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+ 3 20:52:43 0.0000 0.0600 0.0780 0.6972 0.8354 0.7601 0.6346
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+ 4 20:53:55 0.0000 0.0392 0.1011 0.7826 0.7595 0.7709 0.6475
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+ 5 20:55:07 0.0000 0.0271 0.1039 0.7598 0.8143 0.7862 0.6655
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+ 6 20:56:20 0.0000 0.0205 0.1033 0.7538 0.8270 0.7887 0.6712
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+ 7 20:57:33 0.0000 0.0141 0.1038 0.7649 0.8101 0.7869 0.6621
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+ 8 20:58:46 0.0000 0.0091 0.1193 0.7815 0.7848 0.7832 0.6643
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+ 9 20:59:59 0.0000 0.0064 0.1136 0.7559 0.8101 0.7821 0.6598
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+ 10 21:01:12 0.0000 0.0041 0.1175 0.7689 0.8143 0.7910 0.6748
test.tsv ADDED
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training.log ADDED
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+ 2023-10-16 20:49:01,964 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 20:49:01,965 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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+ 2023-10-16 20:49:01,965 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 20:49:01,965 MultiCorpus: 6183 train + 680 dev + 2113 test sentences
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+ - NER_HIPE_2022 Corpus: 6183 train + 680 dev + 2113 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/topres19th/en/with_doc_seperator
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+ 2023-10-16 20:49:01,965 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 20:49:01,965 Train: 6183 sentences
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+ 2023-10-16 20:49:01,965 (train_with_dev=False, train_with_test=False)
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+ 2023-10-16 20:49:01,965 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 20:49:01,965 Training Params:
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+ 2023-10-16 20:49:01,965 - learning_rate: "3e-05"
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+ 2023-10-16 20:49:01,965 - mini_batch_size: "4"
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+ 2023-10-16 20:49:01,965 - max_epochs: "10"
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+ 2023-10-16 20:49:01,965 - shuffle: "True"
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+ 2023-10-16 20:49:01,965 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 20:49:01,965 Plugins:
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+ 2023-10-16 20:49:01,965 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-16 20:49:01,965 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 20:49:01,965 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-16 20:49:01,965 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-16 20:49:01,965 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 20:49:01,965 Computation:
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+ 2023-10-16 20:49:01,965 - compute on device: cuda:0
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+ 2023-10-16 20:49:01,966 - embedding storage: none
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+ 2023-10-16 20:49:01,966 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 20:49:01,966 Model training base path: "hmbench-topres19th/en-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1"
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+ 2023-10-16 20:49:01,966 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 20:49:01,966 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 20:49:10,232 epoch 1 - iter 154/1546 - loss 2.04050695 - time (sec): 8.27 - samples/sec: 1543.19 - lr: 0.000003 - momentum: 0.000000
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+ 2023-10-16 20:49:17,106 epoch 1 - iter 308/1546 - loss 1.18613454 - time (sec): 15.14 - samples/sec: 1607.15 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-16 20:49:23,884 epoch 1 - iter 462/1546 - loss 0.84944225 - time (sec): 21.92 - samples/sec: 1650.35 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-16 20:49:30,718 epoch 1 - iter 616/1546 - loss 0.67863507 - time (sec): 28.75 - samples/sec: 1680.50 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-16 20:49:37,588 epoch 1 - iter 770/1546 - loss 0.57353723 - time (sec): 35.62 - samples/sec: 1685.60 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-16 20:49:44,597 epoch 1 - iter 924/1546 - loss 0.49451384 - time (sec): 42.63 - samples/sec: 1706.02 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-16 20:49:51,543 epoch 1 - iter 1078/1546 - loss 0.43902727 - time (sec): 49.58 - samples/sec: 1721.20 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-16 20:49:58,427 epoch 1 - iter 1232/1546 - loss 0.39809745 - time (sec): 56.46 - samples/sec: 1739.61 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-16 20:50:05,581 epoch 1 - iter 1386/1546 - loss 0.36547430 - time (sec): 63.61 - samples/sec: 1747.62 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-16 20:50:12,568 epoch 1 - iter 1540/1546 - loss 0.33904665 - time (sec): 70.60 - samples/sec: 1755.91 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-16 20:50:12,830 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 20:50:12,830 EPOCH 1 done: loss 0.3384 - lr: 0.000030
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+ 2023-10-16 20:50:14,597 DEV : loss 0.06913874298334122 - f1-score (micro avg) 0.7313
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+ 2023-10-16 20:50:14,612 saving best model
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+ 2023-10-16 20:50:15,166 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 20:50:23,126 epoch 2 - iter 154/1546 - loss 0.08438780 - time (sec): 7.96 - samples/sec: 1597.76 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-16 20:50:31,248 epoch 2 - iter 308/1546 - loss 0.09627467 - time (sec): 16.08 - samples/sec: 1582.08 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-16 20:50:38,715 epoch 2 - iter 462/1546 - loss 0.09832161 - time (sec): 23.55 - samples/sec: 1600.82 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-16 20:50:46,164 epoch 2 - iter 616/1546 - loss 0.09084457 - time (sec): 31.00 - samples/sec: 1635.83 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-16 20:50:53,052 epoch 2 - iter 770/1546 - loss 0.09363305 - time (sec): 37.88 - samples/sec: 1655.86 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-16 20:50:59,879 epoch 2 - iter 924/1546 - loss 0.09333953 - time (sec): 44.71 - samples/sec: 1682.51 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-16 20:51:06,756 epoch 2 - iter 1078/1546 - loss 0.09219892 - time (sec): 51.59 - samples/sec: 1692.81 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-16 20:51:13,668 epoch 2 - iter 1232/1546 - loss 0.09015760 - time (sec): 58.50 - samples/sec: 1693.79 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-16 20:51:20,778 epoch 2 - iter 1386/1546 - loss 0.08869022 - time (sec): 65.61 - samples/sec: 1697.34 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-16 20:51:27,731 epoch 2 - iter 1540/1546 - loss 0.08975901 - time (sec): 72.56 - samples/sec: 1704.23 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-16 20:51:28,036 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 20:51:28,036 EPOCH 2 done: loss 0.0897 - lr: 0.000027
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+ 2023-10-16 20:51:30,497 DEV : loss 0.062400542199611664 - f1-score (micro avg) 0.7432
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+ 2023-10-16 20:51:30,511 saving best model
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+ 2023-10-16 20:51:31,004 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 20:51:37,901 epoch 3 - iter 154/1546 - loss 0.06053105 - time (sec): 6.89 - samples/sec: 1630.47 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-16 20:51:44,765 epoch 3 - iter 308/1546 - loss 0.05492019 - time (sec): 13.76 - samples/sec: 1745.54 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-16 20:51:51,617 epoch 3 - iter 462/1546 - loss 0.06003718 - time (sec): 20.61 - samples/sec: 1764.49 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-16 20:51:58,419 epoch 3 - iter 616/1546 - loss 0.05518600 - time (sec): 27.41 - samples/sec: 1771.94 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-16 20:52:05,480 epoch 3 - iter 770/1546 - loss 0.05822749 - time (sec): 34.47 - samples/sec: 1779.59 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-16 20:52:12,376 epoch 3 - iter 924/1546 - loss 0.05981956 - time (sec): 41.37 - samples/sec: 1781.70 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-16 20:52:19,309 epoch 3 - iter 1078/1546 - loss 0.05732479 - time (sec): 48.30 - samples/sec: 1784.82 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-16 20:52:26,317 epoch 3 - iter 1232/1546 - loss 0.06037730 - time (sec): 55.31 - samples/sec: 1784.84 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-16 20:52:33,347 epoch 3 - iter 1386/1546 - loss 0.05953915 - time (sec): 62.34 - samples/sec: 1779.99 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-16 20:52:40,504 epoch 3 - iter 1540/1546 - loss 0.06029278 - time (sec): 69.50 - samples/sec: 1778.49 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-16 20:52:40,824 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 20:52:40,824 EPOCH 3 done: loss 0.0600 - lr: 0.000023
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+ 2023-10-16 20:52:42,988 DEV : loss 0.07797159254550934 - f1-score (micro avg) 0.7601
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+ 2023-10-16 20:52:43,006 saving best model
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+ 2023-10-16 20:52:43,593 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 20:52:50,785 epoch 4 - iter 154/1546 - loss 0.04625746 - time (sec): 7.19 - samples/sec: 1675.40 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-16 20:52:57,832 epoch 4 - iter 308/1546 - loss 0.04071889 - time (sec): 14.24 - samples/sec: 1805.67 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-16 20:53:04,736 epoch 4 - iter 462/1546 - loss 0.04042915 - time (sec): 21.14 - samples/sec: 1771.77 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-16 20:53:11,614 epoch 4 - iter 616/1546 - loss 0.03895070 - time (sec): 28.02 - samples/sec: 1788.97 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-16 20:53:18,562 epoch 4 - iter 770/1546 - loss 0.03791720 - time (sec): 34.97 - samples/sec: 1806.84 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-16 20:53:25,352 epoch 4 - iter 924/1546 - loss 0.03865879 - time (sec): 41.76 - samples/sec: 1805.06 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-16 20:53:32,304 epoch 4 - iter 1078/1546 - loss 0.03898360 - time (sec): 48.71 - samples/sec: 1803.59 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-16 20:53:39,033 epoch 4 - iter 1232/1546 - loss 0.03898183 - time (sec): 55.44 - samples/sec: 1798.60 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-16 20:53:46,010 epoch 4 - iter 1386/1546 - loss 0.03954707 - time (sec): 62.41 - samples/sec: 1803.81 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-16 20:53:52,758 epoch 4 - iter 1540/1546 - loss 0.03922934 - time (sec): 69.16 - samples/sec: 1791.34 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-16 20:53:53,022 ----------------------------------------------------------------------------------------------------
132
+ 2023-10-16 20:53:53,022 EPOCH 4 done: loss 0.0392 - lr: 0.000020
133
+ 2023-10-16 20:53:55,308 DEV : loss 0.1010911613702774 - f1-score (micro avg) 0.7709
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+ 2023-10-16 20:53:55,321 saving best model
135
+ 2023-10-16 20:53:55,833 ----------------------------------------------------------------------------------------------------
136
+ 2023-10-16 20:54:02,895 epoch 5 - iter 154/1546 - loss 0.03491660 - time (sec): 7.06 - samples/sec: 1891.66 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-16 20:54:09,782 epoch 5 - iter 308/1546 - loss 0.03112251 - time (sec): 13.94 - samples/sec: 1894.77 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-16 20:54:16,689 epoch 5 - iter 462/1546 - loss 0.02985586 - time (sec): 20.85 - samples/sec: 1863.01 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-16 20:54:23,397 epoch 5 - iter 616/1546 - loss 0.02761481 - time (sec): 27.56 - samples/sec: 1839.34 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-16 20:54:30,218 epoch 5 - iter 770/1546 - loss 0.02541894 - time (sec): 34.38 - samples/sec: 1800.90 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-16 20:54:37,025 epoch 5 - iter 924/1546 - loss 0.02635009 - time (sec): 41.19 - samples/sec: 1789.32 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-16 20:54:43,921 epoch 5 - iter 1078/1546 - loss 0.02918319 - time (sec): 48.08 - samples/sec: 1769.74 - lr: 0.000018 - momentum: 0.000000
143
+ 2023-10-16 20:54:51,045 epoch 5 - iter 1232/1546 - loss 0.02832300 - time (sec): 55.21 - samples/sec: 1778.57 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-16 20:54:58,031 epoch 5 - iter 1386/1546 - loss 0.02761248 - time (sec): 62.19 - samples/sec: 1782.93 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-16 20:55:05,177 epoch 5 - iter 1540/1546 - loss 0.02672369 - time (sec): 69.34 - samples/sec: 1785.53 - lr: 0.000017 - momentum: 0.000000
146
+ 2023-10-16 20:55:05,463 ----------------------------------------------------------------------------------------------------
147
+ 2023-10-16 20:55:05,463 EPOCH 5 done: loss 0.0271 - lr: 0.000017
148
+ 2023-10-16 20:55:07,686 DEV : loss 0.10391230136156082 - f1-score (micro avg) 0.7862
149
+ 2023-10-16 20:55:07,703 saving best model
150
+ 2023-10-16 20:55:08,185 ----------------------------------------------------------------------------------------------------
151
+ 2023-10-16 20:55:15,057 epoch 6 - iter 154/1546 - loss 0.02493865 - time (sec): 6.87 - samples/sec: 1834.39 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-16 20:55:21,919 epoch 6 - iter 308/1546 - loss 0.02188742 - time (sec): 13.73 - samples/sec: 1789.72 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-16 20:55:28,980 epoch 6 - iter 462/1546 - loss 0.02105559 - time (sec): 20.79 - samples/sec: 1779.32 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-16 20:55:35,949 epoch 6 - iter 616/1546 - loss 0.01961893 - time (sec): 27.76 - samples/sec: 1787.26 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-16 20:55:42,862 epoch 6 - iter 770/1546 - loss 0.02113852 - time (sec): 34.68 - samples/sec: 1793.89 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-16 20:55:49,800 epoch 6 - iter 924/1546 - loss 0.02137338 - time (sec): 41.61 - samples/sec: 1790.78 - lr: 0.000015 - momentum: 0.000000
157
+ 2023-10-16 20:55:56,804 epoch 6 - iter 1078/1546 - loss 0.02060005 - time (sec): 48.62 - samples/sec: 1792.52 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-16 20:56:03,662 epoch 6 - iter 1232/1546 - loss 0.02034437 - time (sec): 55.48 - samples/sec: 1798.00 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-16 20:56:10,662 epoch 6 - iter 1386/1546 - loss 0.02021798 - time (sec): 62.48 - samples/sec: 1792.01 - lr: 0.000014 - momentum: 0.000000
160
+ 2023-10-16 20:56:17,575 epoch 6 - iter 1540/1546 - loss 0.02057104 - time (sec): 69.39 - samples/sec: 1783.53 - lr: 0.000013 - momentum: 0.000000
161
+ 2023-10-16 20:56:17,837 ----------------------------------------------------------------------------------------------------
162
+ 2023-10-16 20:56:17,837 EPOCH 6 done: loss 0.0205 - lr: 0.000013
163
+ 2023-10-16 20:56:20,024 DEV : loss 0.10331042110919952 - f1-score (micro avg) 0.7887
164
+ 2023-10-16 20:56:20,038 saving best model
165
+ 2023-10-16 20:56:20,460 ----------------------------------------------------------------------------------------------------
166
+ 2023-10-16 20:56:27,345 epoch 7 - iter 154/1546 - loss 0.01205076 - time (sec): 6.88 - samples/sec: 1702.65 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-16 20:56:34,241 epoch 7 - iter 308/1546 - loss 0.01298242 - time (sec): 13.78 - samples/sec: 1755.29 - lr: 0.000013 - momentum: 0.000000
168
+ 2023-10-16 20:56:41,193 epoch 7 - iter 462/1546 - loss 0.01346244 - time (sec): 20.73 - samples/sec: 1757.85 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-16 20:56:48,314 epoch 7 - iter 616/1546 - loss 0.01319763 - time (sec): 27.85 - samples/sec: 1746.00 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-16 20:56:55,311 epoch 7 - iter 770/1546 - loss 0.01263468 - time (sec): 34.85 - samples/sec: 1744.52 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-16 20:57:02,573 epoch 7 - iter 924/1546 - loss 0.01299134 - time (sec): 42.11 - samples/sec: 1749.31 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-16 20:57:09,583 epoch 7 - iter 1078/1546 - loss 0.01334880 - time (sec): 49.12 - samples/sec: 1760.41 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-16 20:57:16,841 epoch 7 - iter 1232/1546 - loss 0.01406829 - time (sec): 56.38 - samples/sec: 1749.55 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-16 20:57:23,787 epoch 7 - iter 1386/1546 - loss 0.01364924 - time (sec): 63.33 - samples/sec: 1740.60 - lr: 0.000010 - momentum: 0.000000
175
+ 2023-10-16 20:57:31,145 epoch 7 - iter 1540/1546 - loss 0.01416500 - time (sec): 70.68 - samples/sec: 1752.96 - lr: 0.000010 - momentum: 0.000000
176
+ 2023-10-16 20:57:31,419 ----------------------------------------------------------------------------------------------------
177
+ 2023-10-16 20:57:31,419 EPOCH 7 done: loss 0.0141 - lr: 0.000010
178
+ 2023-10-16 20:57:33,633 DEV : loss 0.10377668589353561 - f1-score (micro avg) 0.7869
179
+ 2023-10-16 20:57:33,646 ----------------------------------------------------------------------------------------------------
180
+ 2023-10-16 20:57:40,710 epoch 8 - iter 154/1546 - loss 0.00994108 - time (sec): 7.06 - samples/sec: 1755.04 - lr: 0.000010 - momentum: 0.000000
181
+ 2023-10-16 20:57:47,679 epoch 8 - iter 308/1546 - loss 0.01034994 - time (sec): 14.03 - samples/sec: 1737.29 - lr: 0.000009 - momentum: 0.000000
182
+ 2023-10-16 20:57:54,447 epoch 8 - iter 462/1546 - loss 0.01317594 - time (sec): 20.80 - samples/sec: 1732.51 - lr: 0.000009 - momentum: 0.000000
183
+ 2023-10-16 20:58:01,521 epoch 8 - iter 616/1546 - loss 0.01148351 - time (sec): 27.87 - samples/sec: 1744.13 - lr: 0.000009 - momentum: 0.000000
184
+ 2023-10-16 20:58:08,660 epoch 8 - iter 770/1546 - loss 0.01104651 - time (sec): 35.01 - samples/sec: 1756.59 - lr: 0.000008 - momentum: 0.000000
185
+ 2023-10-16 20:58:15,694 epoch 8 - iter 924/1546 - loss 0.00978163 - time (sec): 42.05 - samples/sec: 1733.72 - lr: 0.000008 - momentum: 0.000000
186
+ 2023-10-16 20:58:22,674 epoch 8 - iter 1078/1546 - loss 0.00959353 - time (sec): 49.03 - samples/sec: 1742.16 - lr: 0.000008 - momentum: 0.000000
187
+ 2023-10-16 20:58:29,695 epoch 8 - iter 1232/1546 - loss 0.00941851 - time (sec): 56.05 - samples/sec: 1745.65 - lr: 0.000007 - momentum: 0.000000
188
+ 2023-10-16 20:58:36,930 epoch 8 - iter 1386/1546 - loss 0.00865968 - time (sec): 63.28 - samples/sec: 1755.25 - lr: 0.000007 - momentum: 0.000000
189
+ 2023-10-16 20:58:44,146 epoch 8 - iter 1540/1546 - loss 0.00911298 - time (sec): 70.50 - samples/sec: 1756.63 - lr: 0.000007 - momentum: 0.000000
190
+ 2023-10-16 20:58:44,415 ----------------------------------------------------------------------------------------------------
191
+ 2023-10-16 20:58:44,415 EPOCH 8 done: loss 0.0091 - lr: 0.000007
192
+ 2023-10-16 20:58:46,570 DEV : loss 0.11925285309553146 - f1-score (micro avg) 0.7832
193
+ 2023-10-16 20:58:46,582 ----------------------------------------------------------------------------------------------------
194
+ 2023-10-16 20:58:53,588 epoch 9 - iter 154/1546 - loss 0.01184549 - time (sec): 7.00 - samples/sec: 1841.40 - lr: 0.000006 - momentum: 0.000000
195
+ 2023-10-16 20:59:00,766 epoch 9 - iter 308/1546 - loss 0.00842207 - time (sec): 14.18 - samples/sec: 1827.96 - lr: 0.000006 - momentum: 0.000000
196
+ 2023-10-16 20:59:07,864 epoch 9 - iter 462/1546 - loss 0.00902081 - time (sec): 21.28 - samples/sec: 1811.55 - lr: 0.000006 - momentum: 0.000000
197
+ 2023-10-16 20:59:15,008 epoch 9 - iter 616/1546 - loss 0.00794030 - time (sec): 28.42 - samples/sec: 1782.48 - lr: 0.000005 - momentum: 0.000000
198
+ 2023-10-16 20:59:22,257 epoch 9 - iter 770/1546 - loss 0.00768491 - time (sec): 35.67 - samples/sec: 1766.33 - lr: 0.000005 - momentum: 0.000000
199
+ 2023-10-16 20:59:29,350 epoch 9 - iter 924/1546 - loss 0.00720504 - time (sec): 42.77 - samples/sec: 1766.77 - lr: 0.000005 - momentum: 0.000000
200
+ 2023-10-16 20:59:36,291 epoch 9 - iter 1078/1546 - loss 0.00677038 - time (sec): 49.71 - samples/sec: 1760.48 - lr: 0.000004 - momentum: 0.000000
201
+ 2023-10-16 20:59:43,351 epoch 9 - iter 1232/1546 - loss 0.00643558 - time (sec): 56.77 - samples/sec: 1761.58 - lr: 0.000004 - momentum: 0.000000
202
+ 2023-10-16 20:59:50,357 epoch 9 - iter 1386/1546 - loss 0.00636983 - time (sec): 63.77 - samples/sec: 1756.55 - lr: 0.000004 - momentum: 0.000000
203
+ 2023-10-16 20:59:57,350 epoch 9 - iter 1540/1546 - loss 0.00634724 - time (sec): 70.77 - samples/sec: 1751.55 - lr: 0.000003 - momentum: 0.000000
204
+ 2023-10-16 20:59:57,618 ----------------------------------------------------------------------------------------------------
205
+ 2023-10-16 20:59:57,618 EPOCH 9 done: loss 0.0064 - lr: 0.000003
206
+ 2023-10-16 20:59:59,773 DEV : loss 0.1136489063501358 - f1-score (micro avg) 0.7821
207
+ 2023-10-16 20:59:59,786 ----------------------------------------------------------------------------------------------------
208
+ 2023-10-16 21:00:06,666 epoch 10 - iter 154/1546 - loss 0.00181108 - time (sec): 6.88 - samples/sec: 1703.72 - lr: 0.000003 - momentum: 0.000000
209
+ 2023-10-16 21:00:13,607 epoch 10 - iter 308/1546 - loss 0.00269566 - time (sec): 13.82 - samples/sec: 1733.95 - lr: 0.000003 - momentum: 0.000000
210
+ 2023-10-16 21:00:20,897 epoch 10 - iter 462/1546 - loss 0.00441727 - time (sec): 21.11 - samples/sec: 1737.06 - lr: 0.000002 - momentum: 0.000000
211
+ 2023-10-16 21:00:28,009 epoch 10 - iter 616/1546 - loss 0.00384133 - time (sec): 28.22 - samples/sec: 1775.35 - lr: 0.000002 - momentum: 0.000000
212
+ 2023-10-16 21:00:35,093 epoch 10 - iter 770/1546 - loss 0.00365736 - time (sec): 35.31 - samples/sec: 1772.83 - lr: 0.000002 - momentum: 0.000000
213
+ 2023-10-16 21:00:42,021 epoch 10 - iter 924/1546 - loss 0.00403936 - time (sec): 42.23 - samples/sec: 1765.43 - lr: 0.000001 - momentum: 0.000000
214
+ 2023-10-16 21:00:49,101 epoch 10 - iter 1078/1546 - loss 0.00464171 - time (sec): 49.31 - samples/sec: 1781.32 - lr: 0.000001 - momentum: 0.000000
215
+ 2023-10-16 21:00:56,091 epoch 10 - iter 1232/1546 - loss 0.00435505 - time (sec): 56.30 - samples/sec: 1762.10 - lr: 0.000001 - momentum: 0.000000
216
+ 2023-10-16 21:01:03,204 epoch 10 - iter 1386/1546 - loss 0.00412570 - time (sec): 63.42 - samples/sec: 1764.89 - lr: 0.000000 - momentum: 0.000000
217
+ 2023-10-16 21:01:10,180 epoch 10 - iter 1540/1546 - loss 0.00413572 - time (sec): 70.39 - samples/sec: 1761.54 - lr: 0.000000 - momentum: 0.000000
218
+ 2023-10-16 21:01:10,429 ----------------------------------------------------------------------------------------------------
219
+ 2023-10-16 21:01:10,429 EPOCH 10 done: loss 0.0041 - lr: 0.000000
220
+ 2023-10-16 21:01:12,588 DEV : loss 0.11747898161411285 - f1-score (micro avg) 0.791
221
+ 2023-10-16 21:01:12,604 saving best model
222
+ 2023-10-16 21:01:13,588 ----------------------------------------------------------------------------------------------------
223
+ 2023-10-16 21:01:13,590 Loading model from best epoch ...
224
+ 2023-10-16 21:01:15,618 SequenceTagger predicts: Dictionary with 13 tags: O, S-LOC, B-LOC, E-LOC, I-LOC, S-BUILDING, B-BUILDING, E-BUILDING, I-BUILDING, S-STREET, B-STREET, E-STREET, I-STREET
225
+ 2023-10-16 21:01:22,263
226
+ Results:
227
+ - F-score (micro) 0.8175
228
+ - F-score (macro) 0.7428
229
+ - Accuracy 0.7133
230
+
231
+ By class:
232
+ precision recall f1-score support
233
+
234
+ LOC 0.8397 0.8636 0.8515 946
235
+ BUILDING 0.6584 0.7189 0.6873 185
236
+ STREET 0.6667 0.7143 0.6897 56
237
+
238
+ micro avg 0.8016 0.8340 0.8175 1187
239
+ macro avg 0.7216 0.7656 0.7428 1187
240
+ weighted avg 0.8033 0.8340 0.8183 1187
241
+
242
+ 2023-10-16 21:01:22,264 ----------------------------------------------------------------------------------------------------