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hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3

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 sha256:5938d6a9fff852358aa494006e31ec9b2be543ff04f74aae58d06bac13fa9e21
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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 22:14:44 0.0000 0.3292 0.0686 0.6357 0.7806 0.7008 0.5589
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+ 2 22:15:55 0.0000 0.0854 0.0617 0.7287 0.7595 0.7438 0.6102
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+ 3 22:17:07 0.0000 0.0547 0.0620 0.7647 0.7679 0.7663 0.6364
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+ 4 22:18:19 0.0000 0.0394 0.0907 0.7529 0.8101 0.7805 0.6486
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+ 5 22:19:31 0.0000 0.0267 0.1010 0.7626 0.8270 0.7935 0.6735
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+ 6 22:20:42 0.0000 0.0168 0.1026 0.7661 0.8017 0.7835 0.6620
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+ 7 22:21:53 0.0000 0.0106 0.1110 0.7480 0.8017 0.7739 0.6529
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+ 8 22:23:04 0.0000 0.0076 0.1139 0.7837 0.8101 0.7967 0.6784
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+ 9 22:24:16 0.0000 0.0062 0.1138 0.7720 0.8143 0.7926 0.6748
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+ 10 22:25:26 0.0000 0.0035 0.1129 0.7854 0.8186 0.8017 0.6855
test.tsv ADDED
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training.log ADDED
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+ 2023-10-16 22:13:33,990 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 22:13:33,991 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 22:13:33,991 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 22:13:33,991 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 22:13:33,991 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 22:13:33,991 Train: 6183 sentences
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+ 2023-10-16 22:13:33,991 (train_with_dev=False, train_with_test=False)
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+ 2023-10-16 22:13:33,991 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 22:13:33,991 Training Params:
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+ 2023-10-16 22:13:33,992 - learning_rate: "3e-05"
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+ 2023-10-16 22:13:33,992 - mini_batch_size: "4"
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+ 2023-10-16 22:13:33,992 - max_epochs: "10"
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+ 2023-10-16 22:13:33,992 - shuffle: "True"
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+ 2023-10-16 22:13:33,992 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 22:13:33,992 Plugins:
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+ 2023-10-16 22:13:33,992 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-16 22:13:33,992 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 22:13:33,992 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-16 22:13:33,992 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-16 22:13:33,992 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 22:13:33,992 Computation:
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+ 2023-10-16 22:13:33,992 - compute on device: cuda:0
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+ 2023-10-16 22:13:33,992 - embedding storage: none
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+ 2023-10-16 22:13:33,992 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 22:13:33,992 Model training base path: "hmbench-topres19th/en-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3"
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+ 2023-10-16 22:13:33,992 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 22:13:33,992 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 22:13:41,036 epoch 1 - iter 154/1546 - loss 1.89169363 - time (sec): 7.04 - samples/sec: 1856.25 - lr: 0.000003 - momentum: 0.000000
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+ 2023-10-16 22:13:47,976 epoch 1 - iter 308/1546 - loss 1.11226889 - time (sec): 13.98 - samples/sec: 1798.15 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-16 22:13:54,826 epoch 1 - iter 462/1546 - loss 0.79922596 - time (sec): 20.83 - samples/sec: 1834.89 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-16 22:14:01,596 epoch 1 - iter 616/1546 - loss 0.63837166 - time (sec): 27.60 - samples/sec: 1822.95 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-16 22:14:08,419 epoch 1 - iter 770/1546 - loss 0.53584832 - time (sec): 34.43 - samples/sec: 1820.11 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-16 22:14:15,218 epoch 1 - iter 924/1546 - loss 0.47208580 - time (sec): 41.22 - samples/sec: 1803.72 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-16 22:14:21,994 epoch 1 - iter 1078/1546 - loss 0.42137026 - time (sec): 48.00 - samples/sec: 1805.09 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-16 22:14:28,765 epoch 1 - iter 1232/1546 - loss 0.38283230 - time (sec): 54.77 - samples/sec: 1806.79 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-16 22:14:35,670 epoch 1 - iter 1386/1546 - loss 0.35327797 - time (sec): 61.68 - samples/sec: 1808.79 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-16 22:14:42,498 epoch 1 - iter 1540/1546 - loss 0.32990339 - time (sec): 68.51 - samples/sec: 1808.50 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-16 22:14:42,763 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 22:14:42,763 EPOCH 1 done: loss 0.3292 - lr: 0.000030
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+ 2023-10-16 22:14:44,800 DEV : loss 0.06861560046672821 - f1-score (micro avg) 0.7008
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+ 2023-10-16 22:14:44,813 saving best model
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+ 2023-10-16 22:14:45,144 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 22:14:51,840 epoch 2 - iter 154/1546 - loss 0.10594349 - time (sec): 6.70 - samples/sec: 1833.60 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-16 22:14:58,684 epoch 2 - iter 308/1546 - loss 0.09339103 - time (sec): 13.54 - samples/sec: 1878.86 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-16 22:15:05,450 epoch 2 - iter 462/1546 - loss 0.09323722 - time (sec): 20.30 - samples/sec: 1823.80 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-16 22:15:12,348 epoch 2 - iter 616/1546 - loss 0.08968150 - time (sec): 27.20 - samples/sec: 1814.58 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-16 22:15:19,198 epoch 2 - iter 770/1546 - loss 0.09342737 - time (sec): 34.05 - samples/sec: 1798.90 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-16 22:15:26,224 epoch 2 - iter 924/1546 - loss 0.08829761 - time (sec): 41.08 - samples/sec: 1820.13 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-16 22:15:33,082 epoch 2 - iter 1078/1546 - loss 0.08746364 - time (sec): 47.94 - samples/sec: 1810.86 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-16 22:15:39,985 epoch 2 - iter 1232/1546 - loss 0.08799371 - time (sec): 54.84 - samples/sec: 1803.28 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-16 22:15:46,769 epoch 2 - iter 1386/1546 - loss 0.08684286 - time (sec): 61.62 - samples/sec: 1799.34 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-16 22:15:53,640 epoch 2 - iter 1540/1546 - loss 0.08551341 - time (sec): 68.49 - samples/sec: 1808.43 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-16 22:15:53,897 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 22:15:53,897 EPOCH 2 done: loss 0.0854 - lr: 0.000027
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+ 2023-10-16 22:15:55,946 DEV : loss 0.061695486307144165 - f1-score (micro avg) 0.7438
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+ 2023-10-16 22:15:55,958 saving best model
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+ 2023-10-16 22:15:56,336 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 22:16:03,116 epoch 3 - iter 154/1546 - loss 0.05352142 - time (sec): 6.78 - samples/sec: 1856.48 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-16 22:16:10,018 epoch 3 - iter 308/1546 - loss 0.05381165 - time (sec): 13.68 - samples/sec: 1831.30 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-16 22:16:16,836 epoch 3 - iter 462/1546 - loss 0.05138306 - time (sec): 20.50 - samples/sec: 1818.79 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-16 22:16:23,700 epoch 3 - iter 616/1546 - loss 0.05202487 - time (sec): 27.36 - samples/sec: 1799.35 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-16 22:16:30,519 epoch 3 - iter 770/1546 - loss 0.05270760 - time (sec): 34.18 - samples/sec: 1781.49 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-16 22:16:37,305 epoch 3 - iter 924/1546 - loss 0.05313926 - time (sec): 40.97 - samples/sec: 1769.51 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-16 22:16:44,171 epoch 3 - iter 1078/1546 - loss 0.05425075 - time (sec): 47.83 - samples/sec: 1774.25 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-16 22:16:51,082 epoch 3 - iter 1232/1546 - loss 0.05410382 - time (sec): 54.75 - samples/sec: 1782.41 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-16 22:16:57,962 epoch 3 - iter 1386/1546 - loss 0.05476332 - time (sec): 61.62 - samples/sec: 1789.99 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-16 22:17:04,918 epoch 3 - iter 1540/1546 - loss 0.05489666 - time (sec): 68.58 - samples/sec: 1805.90 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-16 22:17:05,181 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 22:17:05,181 EPOCH 3 done: loss 0.0547 - lr: 0.000023
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+ 2023-10-16 22:17:07,529 DEV : loss 0.061977606266736984 - f1-score (micro avg) 0.7663
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+ 2023-10-16 22:17:07,541 saving best model
120
+ 2023-10-16 22:17:08,003 ----------------------------------------------------------------------------------------------------
121
+ 2023-10-16 22:17:14,831 epoch 4 - iter 154/1546 - loss 0.03348088 - time (sec): 6.82 - samples/sec: 1727.26 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-16 22:17:21,744 epoch 4 - iter 308/1546 - loss 0.03828523 - time (sec): 13.73 - samples/sec: 1842.44 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-16 22:17:28,540 epoch 4 - iter 462/1546 - loss 0.03893862 - time (sec): 20.53 - samples/sec: 1804.31 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-16 22:17:35,383 epoch 4 - iter 616/1546 - loss 0.03748261 - time (sec): 27.37 - samples/sec: 1791.42 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-16 22:17:42,259 epoch 4 - iter 770/1546 - loss 0.03477433 - time (sec): 34.25 - samples/sec: 1810.90 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-16 22:17:49,167 epoch 4 - iter 924/1546 - loss 0.03501152 - time (sec): 41.15 - samples/sec: 1825.66 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-16 22:17:55,980 epoch 4 - iter 1078/1546 - loss 0.03703932 - time (sec): 47.97 - samples/sec: 1801.41 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-16 22:18:02,902 epoch 4 - iter 1232/1546 - loss 0.03858821 - time (sec): 54.89 - samples/sec: 1807.98 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-16 22:18:09,684 epoch 4 - iter 1386/1546 - loss 0.03914559 - time (sec): 61.67 - samples/sec: 1802.93 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-16 22:18:17,009 epoch 4 - iter 1540/1546 - loss 0.03933452 - time (sec): 69.00 - samples/sec: 1796.19 - lr: 0.000020 - momentum: 0.000000
131
+ 2023-10-16 22:18:17,268 ----------------------------------------------------------------------------------------------------
132
+ 2023-10-16 22:18:17,269 EPOCH 4 done: loss 0.0394 - lr: 0.000020
133
+ 2023-10-16 22:18:19,309 DEV : loss 0.0907452255487442 - f1-score (micro avg) 0.7805
134
+ 2023-10-16 22:18:19,323 saving best model
135
+ 2023-10-16 22:18:19,768 ----------------------------------------------------------------------------------------------------
136
+ 2023-10-16 22:18:26,862 epoch 5 - iter 154/1546 - loss 0.02856650 - time (sec): 7.09 - samples/sec: 1732.93 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-16 22:18:33,828 epoch 5 - iter 308/1546 - loss 0.02438427 - time (sec): 14.06 - samples/sec: 1786.41 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-16 22:18:40,685 epoch 5 - iter 462/1546 - loss 0.02487299 - time (sec): 20.91 - samples/sec: 1793.05 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-16 22:18:47,656 epoch 5 - iter 616/1546 - loss 0.02566201 - time (sec): 27.88 - samples/sec: 1825.66 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-16 22:18:54,562 epoch 5 - iter 770/1546 - loss 0.02715511 - time (sec): 34.79 - samples/sec: 1825.46 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-16 22:19:01,378 epoch 5 - iter 924/1546 - loss 0.02767064 - time (sec): 41.61 - samples/sec: 1828.00 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-16 22:19:08,338 epoch 5 - iter 1078/1546 - loss 0.02706633 - time (sec): 48.57 - samples/sec: 1834.40 - lr: 0.000018 - momentum: 0.000000
143
+ 2023-10-16 22:19:15,159 epoch 5 - iter 1232/1546 - loss 0.02726636 - time (sec): 55.39 - samples/sec: 1823.57 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-16 22:19:21,988 epoch 5 - iter 1386/1546 - loss 0.02736502 - time (sec): 62.22 - samples/sec: 1808.57 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-16 22:19:28,800 epoch 5 - iter 1540/1546 - loss 0.02672501 - time (sec): 69.03 - samples/sec: 1795.96 - lr: 0.000017 - momentum: 0.000000
146
+ 2023-10-16 22:19:29,066 ----------------------------------------------------------------------------------------------------
147
+ 2023-10-16 22:19:29,067 EPOCH 5 done: loss 0.0267 - lr: 0.000017
148
+ 2023-10-16 22:19:31,125 DEV : loss 0.10103413462638855 - f1-score (micro avg) 0.7935
149
+ 2023-10-16 22:19:31,138 saving best model
150
+ 2023-10-16 22:19:31,609 ----------------------------------------------------------------------------------------------------
151
+ 2023-10-16 22:19:38,520 epoch 6 - iter 154/1546 - loss 0.01115327 - time (sec): 6.91 - samples/sec: 1773.33 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-16 22:19:45,472 epoch 6 - iter 308/1546 - loss 0.01261767 - time (sec): 13.86 - samples/sec: 1775.01 - lr: 0.000016 - momentum: 0.000000
153
+ 2023-10-16 22:19:52,353 epoch 6 - iter 462/1546 - loss 0.01720971 - time (sec): 20.74 - samples/sec: 1760.86 - lr: 0.000016 - momentum: 0.000000
154
+ 2023-10-16 22:19:59,182 epoch 6 - iter 616/1546 - loss 0.01700125 - time (sec): 27.57 - samples/sec: 1789.53 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-16 22:20:05,960 epoch 6 - iter 770/1546 - loss 0.01632348 - time (sec): 34.35 - samples/sec: 1797.98 - lr: 0.000015 - momentum: 0.000000
156
+ 2023-10-16 22:20:12,846 epoch 6 - iter 924/1546 - loss 0.01648542 - time (sec): 41.23 - samples/sec: 1822.12 - lr: 0.000015 - momentum: 0.000000
157
+ 2023-10-16 22:20:19,764 epoch 6 - iter 1078/1546 - loss 0.01624090 - time (sec): 48.15 - samples/sec: 1807.88 - lr: 0.000014 - momentum: 0.000000
158
+ 2023-10-16 22:20:26,576 epoch 6 - iter 1232/1546 - loss 0.01732653 - time (sec): 54.96 - samples/sec: 1798.95 - lr: 0.000014 - momentum: 0.000000
159
+ 2023-10-16 22:20:33,435 epoch 6 - iter 1386/1546 - loss 0.01698745 - time (sec): 61.82 - samples/sec: 1796.22 - lr: 0.000014 - momentum: 0.000000
160
+ 2023-10-16 22:20:40,310 epoch 6 - iter 1540/1546 - loss 0.01680358 - time (sec): 68.70 - samples/sec: 1804.24 - lr: 0.000013 - momentum: 0.000000
161
+ 2023-10-16 22:20:40,579 ----------------------------------------------------------------------------------------------------
162
+ 2023-10-16 22:20:40,579 EPOCH 6 done: loss 0.0168 - lr: 0.000013
163
+ 2023-10-16 22:20:42,593 DEV : loss 0.10263525694608688 - f1-score (micro avg) 0.7835
164
+ 2023-10-16 22:20:42,605 ----------------------------------------------------------------------------------------------------
165
+ 2023-10-16 22:20:49,419 epoch 7 - iter 154/1546 - loss 0.01073330 - time (sec): 6.81 - samples/sec: 1767.10 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-16 22:20:56,281 epoch 7 - iter 308/1546 - loss 0.01268942 - time (sec): 13.67 - samples/sec: 1748.02 - lr: 0.000013 - momentum: 0.000000
167
+ 2023-10-16 22:21:03,138 epoch 7 - iter 462/1546 - loss 0.01130454 - time (sec): 20.53 - samples/sec: 1771.55 - lr: 0.000012 - momentum: 0.000000
168
+ 2023-10-16 22:21:09,977 epoch 7 - iter 616/1546 - loss 0.01193437 - time (sec): 27.37 - samples/sec: 1775.93 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-16 22:21:16,911 epoch 7 - iter 770/1546 - loss 0.01224745 - time (sec): 34.30 - samples/sec: 1789.42 - lr: 0.000012 - momentum: 0.000000
170
+ 2023-10-16 22:21:23,792 epoch 7 - iter 924/1546 - loss 0.01174877 - time (sec): 41.19 - samples/sec: 1805.47 - lr: 0.000011 - momentum: 0.000000
171
+ 2023-10-16 22:21:30,715 epoch 7 - iter 1078/1546 - loss 0.01116054 - time (sec): 48.11 - samples/sec: 1807.28 - lr: 0.000011 - momentum: 0.000000
172
+ 2023-10-16 22:21:37,649 epoch 7 - iter 1232/1546 - loss 0.01077128 - time (sec): 55.04 - samples/sec: 1799.13 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-16 22:21:44,464 epoch 7 - iter 1386/1546 - loss 0.01068182 - time (sec): 61.86 - samples/sec: 1799.59 - lr: 0.000010 - momentum: 0.000000
174
+ 2023-10-16 22:21:51,345 epoch 7 - iter 1540/1546 - loss 0.01058741 - time (sec): 68.74 - samples/sec: 1803.61 - lr: 0.000010 - momentum: 0.000000
175
+ 2023-10-16 22:21:51,605 ----------------------------------------------------------------------------------------------------
176
+ 2023-10-16 22:21:51,605 EPOCH 7 done: loss 0.0106 - lr: 0.000010
177
+ 2023-10-16 22:21:53,621 DEV : loss 0.11100788414478302 - f1-score (micro avg) 0.7739
178
+ 2023-10-16 22:21:53,634 ----------------------------------------------------------------------------------------------------
179
+ 2023-10-16 22:22:00,462 epoch 8 - iter 154/1546 - loss 0.00712344 - time (sec): 6.83 - samples/sec: 1667.10 - lr: 0.000010 - momentum: 0.000000
180
+ 2023-10-16 22:22:07,489 epoch 8 - iter 308/1546 - loss 0.00737014 - time (sec): 13.85 - samples/sec: 1786.24 - lr: 0.000009 - momentum: 0.000000
181
+ 2023-10-16 22:22:14,432 epoch 8 - iter 462/1546 - loss 0.00844824 - time (sec): 20.80 - samples/sec: 1819.72 - lr: 0.000009 - momentum: 0.000000
182
+ 2023-10-16 22:22:21,368 epoch 8 - iter 616/1546 - loss 0.00756277 - time (sec): 27.73 - samples/sec: 1839.47 - lr: 0.000009 - momentum: 0.000000
183
+ 2023-10-16 22:22:28,177 epoch 8 - iter 770/1546 - loss 0.00738767 - time (sec): 34.54 - samples/sec: 1825.85 - lr: 0.000008 - momentum: 0.000000
184
+ 2023-10-16 22:22:34,927 epoch 8 - iter 924/1546 - loss 0.00797400 - time (sec): 41.29 - samples/sec: 1809.26 - lr: 0.000008 - momentum: 0.000000
185
+ 2023-10-16 22:22:41,703 epoch 8 - iter 1078/1546 - loss 0.00749557 - time (sec): 48.07 - samples/sec: 1812.26 - lr: 0.000008 - momentum: 0.000000
186
+ 2023-10-16 22:22:48,592 epoch 8 - iter 1232/1546 - loss 0.00775779 - time (sec): 54.96 - samples/sec: 1814.63 - lr: 0.000007 - momentum: 0.000000
187
+ 2023-10-16 22:22:55,442 epoch 8 - iter 1386/1546 - loss 0.00804980 - time (sec): 61.81 - samples/sec: 1814.91 - lr: 0.000007 - momentum: 0.000000
188
+ 2023-10-16 22:23:02,230 epoch 8 - iter 1540/1546 - loss 0.00758625 - time (sec): 68.60 - samples/sec: 1804.27 - lr: 0.000007 - momentum: 0.000000
189
+ 2023-10-16 22:23:02,492 ----------------------------------------------------------------------------------------------------
190
+ 2023-10-16 22:23:02,492 EPOCH 8 done: loss 0.0076 - lr: 0.000007
191
+ 2023-10-16 22:23:04,849 DEV : loss 0.11394007503986359 - f1-score (micro avg) 0.7967
192
+ 2023-10-16 22:23:04,862 saving best model
193
+ 2023-10-16 22:23:05,318 ----------------------------------------------------------------------------------------------------
194
+ 2023-10-16 22:23:12,200 epoch 9 - iter 154/1546 - loss 0.00573957 - time (sec): 6.88 - samples/sec: 1709.00 - lr: 0.000006 - momentum: 0.000000
195
+ 2023-10-16 22:23:19,147 epoch 9 - iter 308/1546 - loss 0.00372078 - time (sec): 13.83 - samples/sec: 1719.76 - lr: 0.000006 - momentum: 0.000000
196
+ 2023-10-16 22:23:26,016 epoch 9 - iter 462/1546 - loss 0.00469241 - time (sec): 20.70 - samples/sec: 1796.59 - lr: 0.000006 - momentum: 0.000000
197
+ 2023-10-16 22:23:32,827 epoch 9 - iter 616/1546 - loss 0.00728260 - time (sec): 27.51 - samples/sec: 1786.73 - lr: 0.000005 - momentum: 0.000000
198
+ 2023-10-16 22:23:39,731 epoch 9 - iter 770/1546 - loss 0.00701758 - time (sec): 34.41 - samples/sec: 1807.80 - lr: 0.000005 - momentum: 0.000000
199
+ 2023-10-16 22:23:46,646 epoch 9 - iter 924/1546 - loss 0.00660586 - time (sec): 41.33 - samples/sec: 1797.68 - lr: 0.000005 - momentum: 0.000000
200
+ 2023-10-16 22:23:53,527 epoch 9 - iter 1078/1546 - loss 0.00602231 - time (sec): 48.21 - samples/sec: 1805.05 - lr: 0.000004 - momentum: 0.000000
201
+ 2023-10-16 22:24:00,289 epoch 9 - iter 1232/1546 - loss 0.00642192 - time (sec): 54.97 - samples/sec: 1801.13 - lr: 0.000004 - momentum: 0.000000
202
+ 2023-10-16 22:24:07,070 epoch 9 - iter 1386/1546 - loss 0.00626127 - time (sec): 61.75 - samples/sec: 1800.99 - lr: 0.000004 - momentum: 0.000000
203
+ 2023-10-16 22:24:13,888 epoch 9 - iter 1540/1546 - loss 0.00615296 - time (sec): 68.57 - samples/sec: 1807.95 - lr: 0.000003 - momentum: 0.000000
204
+ 2023-10-16 22:24:14,136 ----------------------------------------------------------------------------------------------------
205
+ 2023-10-16 22:24:14,136 EPOCH 9 done: loss 0.0062 - lr: 0.000003
206
+ 2023-10-16 22:24:16,156 DEV : loss 0.11375954002141953 - f1-score (micro avg) 0.7926
207
+ 2023-10-16 22:24:16,169 ----------------------------------------------------------------------------------------------------
208
+ 2023-10-16 22:24:22,994 epoch 10 - iter 154/1546 - loss 0.00252186 - time (sec): 6.82 - samples/sec: 1789.34 - lr: 0.000003 - momentum: 0.000000
209
+ 2023-10-16 22:24:29,820 epoch 10 - iter 308/1546 - loss 0.00144410 - time (sec): 13.65 - samples/sec: 1820.64 - lr: 0.000003 - momentum: 0.000000
210
+ 2023-10-16 22:24:36,641 epoch 10 - iter 462/1546 - loss 0.00151338 - time (sec): 20.47 - samples/sec: 1816.93 - lr: 0.000002 - momentum: 0.000000
211
+ 2023-10-16 22:24:43,491 epoch 10 - iter 616/1546 - loss 0.00162804 - time (sec): 27.32 - samples/sec: 1819.88 - lr: 0.000002 - momentum: 0.000000
212
+ 2023-10-16 22:24:50,236 epoch 10 - iter 770/1546 - loss 0.00277701 - time (sec): 34.07 - samples/sec: 1826.95 - lr: 0.000002 - momentum: 0.000000
213
+ 2023-10-16 22:24:57,155 epoch 10 - iter 924/1546 - loss 0.00280504 - time (sec): 40.98 - samples/sec: 1826.24 - lr: 0.000001 - momentum: 0.000000
214
+ 2023-10-16 22:25:04,089 epoch 10 - iter 1078/1546 - loss 0.00303272 - time (sec): 47.92 - samples/sec: 1814.17 - lr: 0.000001 - momentum: 0.000000
215
+ 2023-10-16 22:25:10,898 epoch 10 - iter 1232/1546 - loss 0.00317340 - time (sec): 54.73 - samples/sec: 1815.67 - lr: 0.000001 - momentum: 0.000000
216
+ 2023-10-16 22:25:17,708 epoch 10 - iter 1386/1546 - loss 0.00315536 - time (sec): 61.54 - samples/sec: 1813.27 - lr: 0.000000 - momentum: 0.000000
217
+ 2023-10-16 22:25:24,582 epoch 10 - iter 1540/1546 - loss 0.00355373 - time (sec): 68.41 - samples/sec: 1811.86 - lr: 0.000000 - momentum: 0.000000
218
+ 2023-10-16 22:25:24,842 ----------------------------------------------------------------------------------------------------
219
+ 2023-10-16 22:25:24,842 EPOCH 10 done: loss 0.0035 - lr: 0.000000
220
+ 2023-10-16 22:25:26,849 DEV : loss 0.11292611062526703 - f1-score (micro avg) 0.8017
221
+ 2023-10-16 22:25:26,861 saving best model
222
+ 2023-10-16 22:25:27,661 ----------------------------------------------------------------------------------------------------
223
+ 2023-10-16 22:25:27,663 Loading model from best epoch ...
224
+ 2023-10-16 22:25:29,149 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 22:25:35,105
226
+ Results:
227
+ - F-score (micro) 0.8044
228
+ - F-score (macro) 0.7149
229
+ - Accuracy 0.6943
230
+
231
+ By class:
232
+ precision recall f1-score support
233
+
234
+ LOC 0.8392 0.8605 0.8497 946
235
+ BUILDING 0.6329 0.5405 0.5831 185
236
+ STREET 0.6774 0.7500 0.7119 56
237
+
238
+ micro avg 0.8034 0.8054 0.8044 1187
239
+ macro avg 0.7165 0.7170 0.7149 1187
240
+ weighted avg 0.7994 0.8054 0.8016 1187
241
+
242
+ 2023-10-16 22:25:35,105 ----------------------------------------------------------------------------------------------------