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hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4

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 +241 -0
best-model.pt ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:8a24c9d1278fed56e4389df00ab1203802089c83ba54e80de532f13d8b023a24
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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 22:56:35 0.0000 0.3141 0.0931 0.7989 0.6034 0.6875 0.5356
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+ 2 22:57:48 0.0000 0.0815 0.0789 0.6585 0.7890 0.7179 0.5772
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+ 3 22:59:01 0.0000 0.0534 0.0775 0.7722 0.7722 0.7722 0.6421
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+ 4 23:00:14 0.0000 0.0349 0.0964 0.6678 0.8397 0.7439 0.6049
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+ 5 23:01:26 0.0000 0.0248 0.0978 0.7434 0.8312 0.7849 0.6611
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+ 6 23:02:37 0.0000 0.0177 0.1009 0.7837 0.8101 0.7967 0.6761
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+ 7 23:03:48 0.0000 0.0109 0.1024 0.7462 0.8186 0.7807 0.6576
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+ 8 23:04:58 0.0000 0.0079 0.1138 0.7558 0.8228 0.7879 0.6633
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+ 9 23:06:09 0.0000 0.0047 0.1116 0.7795 0.8354 0.8065 0.6899
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+ 10 23:07:21 0.0000 0.0033 0.1145 0.7823 0.8186 0.8000 0.6807
test.tsv ADDED
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training.log ADDED
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+ 2023-10-16 22:55:24,117 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 22:55:24,118 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 22:55:24,118 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 22:55:24,118 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 22:55:24,118 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 22:55:24,118 Train: 6183 sentences
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+ 2023-10-16 22:55:24,118 (train_with_dev=False, train_with_test=False)
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+ 2023-10-16 22:55:24,118 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 22:55:24,118 Training Params:
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+ 2023-10-16 22:55:24,118 - learning_rate: "3e-05"
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+ 2023-10-16 22:55:24,118 - mini_batch_size: "4"
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+ 2023-10-16 22:55:24,119 - max_epochs: "10"
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+ 2023-10-16 22:55:24,119 - shuffle: "True"
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+ 2023-10-16 22:55:24,119 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 22:55:24,119 Plugins:
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+ 2023-10-16 22:55:24,119 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-16 22:55:24,119 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 22:55:24,119 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-16 22:55:24,119 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-16 22:55:24,119 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 22:55:24,119 Computation:
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+ 2023-10-16 22:55:24,119 - compute on device: cuda:0
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+ 2023-10-16 22:55:24,119 - embedding storage: none
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+ 2023-10-16 22:55:24,119 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 22:55:24,119 Model training base path: "hmbench-topres19th/en-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4"
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+ 2023-10-16 22:55:24,119 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 22:55:24,119 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 22:55:30,952 epoch 1 - iter 154/1546 - loss 2.02538090 - time (sec): 6.83 - samples/sec: 1748.19 - lr: 0.000003 - momentum: 0.000000
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+ 2023-10-16 22:55:37,944 epoch 1 - iter 308/1546 - loss 1.10084678 - time (sec): 13.82 - samples/sec: 1753.65 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-16 22:55:45,034 epoch 1 - iter 462/1546 - loss 0.78647526 - time (sec): 20.91 - samples/sec: 1743.25 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-16 22:55:51,980 epoch 1 - iter 616/1546 - loss 0.62446099 - time (sec): 27.86 - samples/sec: 1741.14 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-16 22:55:58,942 epoch 1 - iter 770/1546 - loss 0.52442561 - time (sec): 34.82 - samples/sec: 1740.59 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-16 22:56:06,056 epoch 1 - iter 924/1546 - loss 0.45595178 - time (sec): 41.94 - samples/sec: 1747.95 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-16 22:56:12,979 epoch 1 - iter 1078/1546 - loss 0.40906220 - time (sec): 48.86 - samples/sec: 1748.23 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-16 22:56:19,931 epoch 1 - iter 1232/1546 - loss 0.36522487 - time (sec): 55.81 - samples/sec: 1782.15 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-16 22:56:26,757 epoch 1 - iter 1386/1546 - loss 0.33735166 - time (sec): 62.64 - samples/sec: 1778.01 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-16 22:56:33,780 epoch 1 - iter 1540/1546 - loss 0.31510388 - time (sec): 69.66 - samples/sec: 1778.34 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-16 22:56:34,038 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 22:56:34,038 EPOCH 1 done: loss 0.3141 - lr: 0.000030
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+ 2023-10-16 22:56:35,849 DEV : loss 0.09308421611785889 - f1-score (micro avg) 0.6875
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+ 2023-10-16 22:56:35,862 saving best model
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+ 2023-10-16 22:56:36,248 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 22:56:43,180 epoch 2 - iter 154/1546 - loss 0.08398905 - time (sec): 6.93 - samples/sec: 1878.88 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-16 22:56:50,219 epoch 2 - iter 308/1546 - loss 0.09542073 - time (sec): 13.97 - samples/sec: 1876.03 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-16 22:56:57,201 epoch 2 - iter 462/1546 - loss 0.08471087 - time (sec): 20.95 - samples/sec: 1892.41 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-16 22:57:04,205 epoch 2 - iter 616/1546 - loss 0.08071299 - time (sec): 27.95 - samples/sec: 1880.42 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-16 22:57:11,180 epoch 2 - iter 770/1546 - loss 0.08054955 - time (sec): 34.93 - samples/sec: 1852.22 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-16 22:57:18,042 epoch 2 - iter 924/1546 - loss 0.08042291 - time (sec): 41.79 - samples/sec: 1822.50 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-16 22:57:24,993 epoch 2 - iter 1078/1546 - loss 0.07999482 - time (sec): 48.74 - samples/sec: 1795.97 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-16 22:57:31,929 epoch 2 - iter 1232/1546 - loss 0.08098848 - time (sec): 55.68 - samples/sec: 1795.29 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-16 22:57:38,721 epoch 2 - iter 1386/1546 - loss 0.08104893 - time (sec): 62.47 - samples/sec: 1792.03 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-16 22:57:45,660 epoch 2 - iter 1540/1546 - loss 0.08159740 - time (sec): 69.41 - samples/sec: 1785.43 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-16 22:57:45,929 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 22:57:45,930 EPOCH 2 done: loss 0.0815 - lr: 0.000027
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+ 2023-10-16 22:57:48,443 DEV : loss 0.07893380522727966 - f1-score (micro avg) 0.7179
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+ 2023-10-16 22:57:48,456 saving best model
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+ 2023-10-16 22:57:48,916 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 22:57:55,989 epoch 3 - iter 154/1546 - loss 0.05779724 - time (sec): 7.07 - samples/sec: 1866.59 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-16 22:58:02,957 epoch 3 - iter 308/1546 - loss 0.05329053 - time (sec): 14.04 - samples/sec: 1877.95 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-16 22:58:09,919 epoch 3 - iter 462/1546 - loss 0.05290610 - time (sec): 21.00 - samples/sec: 1827.68 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-16 22:58:16,734 epoch 3 - iter 616/1546 - loss 0.05251651 - time (sec): 27.82 - samples/sec: 1852.75 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-16 22:58:23,802 epoch 3 - iter 770/1546 - loss 0.05620252 - time (sec): 34.88 - samples/sec: 1830.94 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-16 22:58:30,728 epoch 3 - iter 924/1546 - loss 0.05327156 - time (sec): 41.81 - samples/sec: 1810.74 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-16 22:58:37,749 epoch 3 - iter 1078/1546 - loss 0.05179250 - time (sec): 48.83 - samples/sec: 1801.70 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-16 22:58:44,683 epoch 3 - iter 1232/1546 - loss 0.05111602 - time (sec): 55.76 - samples/sec: 1787.00 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-16 22:58:51,659 epoch 3 - iter 1386/1546 - loss 0.05503167 - time (sec): 62.74 - samples/sec: 1784.02 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-16 22:58:58,674 epoch 3 - iter 1540/1546 - loss 0.05356182 - time (sec): 69.76 - samples/sec: 1775.63 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-16 22:58:58,969 ----------------------------------------------------------------------------------------------------
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+ 2023-10-16 22:58:58,969 EPOCH 3 done: loss 0.0534 - lr: 0.000023
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+ 2023-10-16 22:59:01,104 DEV : loss 0.07748623192310333 - f1-score (micro avg) 0.7722
119
+ 2023-10-16 22:59:01,123 saving best model
120
+ 2023-10-16 22:59:01,558 ----------------------------------------------------------------------------------------------------
121
+ 2023-10-16 22:59:08,656 epoch 4 - iter 154/1546 - loss 0.02829162 - time (sec): 7.10 - samples/sec: 1872.64 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-16 22:59:15,863 epoch 4 - iter 308/1546 - loss 0.03022453 - time (sec): 14.30 - samples/sec: 1765.31 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-16 22:59:23,032 epoch 4 - iter 462/1546 - loss 0.03061131 - time (sec): 21.47 - samples/sec: 1784.48 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-16 22:59:29,960 epoch 4 - iter 616/1546 - loss 0.03200286 - time (sec): 28.40 - samples/sec: 1768.39 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-16 22:59:36,962 epoch 4 - iter 770/1546 - loss 0.03054981 - time (sec): 35.40 - samples/sec: 1754.04 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-16 22:59:44,121 epoch 4 - iter 924/1546 - loss 0.03188868 - time (sec): 42.56 - samples/sec: 1770.99 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-16 22:59:51,180 epoch 4 - iter 1078/1546 - loss 0.03290334 - time (sec): 49.62 - samples/sec: 1762.82 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-16 22:59:58,373 epoch 4 - iter 1232/1546 - loss 0.03474654 - time (sec): 56.81 - samples/sec: 1764.27 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-16 23:00:05,304 epoch 4 - iter 1386/1546 - loss 0.03409150 - time (sec): 63.74 - samples/sec: 1761.62 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-16 23:00:12,109 epoch 4 - iter 1540/1546 - loss 0.03479952 - time (sec): 70.55 - samples/sec: 1757.31 - lr: 0.000020 - momentum: 0.000000
131
+ 2023-10-16 23:00:12,360 ----------------------------------------------------------------------------------------------------
132
+ 2023-10-16 23:00:12,360 EPOCH 4 done: loss 0.0349 - lr: 0.000020
133
+ 2023-10-16 23:00:14,474 DEV : loss 0.096384696662426 - f1-score (micro avg) 0.7439
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+ 2023-10-16 23:00:14,487 ----------------------------------------------------------------------------------------------------
135
+ 2023-10-16 23:00:21,420 epoch 5 - iter 154/1546 - loss 0.02586135 - time (sec): 6.93 - samples/sec: 1708.87 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-16 23:00:28,521 epoch 5 - iter 308/1546 - loss 0.02697778 - time (sec): 14.03 - samples/sec: 1731.12 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-16 23:00:35,515 epoch 5 - iter 462/1546 - loss 0.02633540 - time (sec): 21.03 - samples/sec: 1744.38 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-16 23:00:42,412 epoch 5 - iter 616/1546 - loss 0.02506817 - time (sec): 27.92 - samples/sec: 1769.86 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-16 23:00:49,241 epoch 5 - iter 770/1546 - loss 0.02333240 - time (sec): 34.75 - samples/sec: 1771.95 - lr: 0.000018 - momentum: 0.000000
140
+ 2023-10-16 23:00:56,162 epoch 5 - iter 924/1546 - loss 0.02299940 - time (sec): 41.67 - samples/sec: 1782.02 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-16 23:01:03,068 epoch 5 - iter 1078/1546 - loss 0.02391515 - time (sec): 48.58 - samples/sec: 1787.02 - lr: 0.000018 - momentum: 0.000000
142
+ 2023-10-16 23:01:09,932 epoch 5 - iter 1232/1546 - loss 0.02530811 - time (sec): 55.44 - samples/sec: 1782.95 - lr: 0.000017 - momentum: 0.000000
143
+ 2023-10-16 23:01:16,872 epoch 5 - iter 1386/1546 - loss 0.02533599 - time (sec): 62.38 - samples/sec: 1780.89 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-16 23:01:23,709 epoch 5 - iter 1540/1546 - loss 0.02481519 - time (sec): 69.22 - samples/sec: 1791.49 - lr: 0.000017 - momentum: 0.000000
145
+ 2023-10-16 23:01:23,966 ----------------------------------------------------------------------------------------------------
146
+ 2023-10-16 23:01:23,966 EPOCH 5 done: loss 0.0248 - lr: 0.000017
147
+ 2023-10-16 23:01:26,008 DEV : loss 0.09776511788368225 - f1-score (micro avg) 0.7849
148
+ 2023-10-16 23:01:26,022 saving best model
149
+ 2023-10-16 23:01:26,478 ----------------------------------------------------------------------------------------------------
150
+ 2023-10-16 23:01:33,699 epoch 6 - iter 154/1546 - loss 0.01891335 - time (sec): 7.22 - samples/sec: 1656.83 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-16 23:01:40,520 epoch 6 - iter 308/1546 - loss 0.01505548 - time (sec): 14.04 - samples/sec: 1735.36 - lr: 0.000016 - momentum: 0.000000
152
+ 2023-10-16 23:01:47,391 epoch 6 - iter 462/1546 - loss 0.01687048 - time (sec): 20.91 - samples/sec: 1770.14 - lr: 0.000016 - momentum: 0.000000
153
+ 2023-10-16 23:01:54,201 epoch 6 - iter 616/1546 - loss 0.01676100 - time (sec): 27.72 - samples/sec: 1795.31 - lr: 0.000015 - momentum: 0.000000
154
+ 2023-10-16 23:02:01,043 epoch 6 - iter 770/1546 - loss 0.01743799 - time (sec): 34.56 - samples/sec: 1783.52 - lr: 0.000015 - momentum: 0.000000
155
+ 2023-10-16 23:02:07,867 epoch 6 - iter 924/1546 - loss 0.01719557 - time (sec): 41.39 - samples/sec: 1786.16 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-16 23:02:14,832 epoch 6 - iter 1078/1546 - loss 0.01686034 - time (sec): 48.35 - samples/sec: 1810.74 - lr: 0.000014 - momentum: 0.000000
157
+ 2023-10-16 23:02:21,723 epoch 6 - iter 1232/1546 - loss 0.01742908 - time (sec): 55.24 - samples/sec: 1811.68 - lr: 0.000014 - momentum: 0.000000
158
+ 2023-10-16 23:02:28,514 epoch 6 - iter 1386/1546 - loss 0.01704174 - time (sec): 62.03 - samples/sec: 1803.99 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-16 23:02:35,311 epoch 6 - iter 1540/1546 - loss 0.01782016 - time (sec): 68.83 - samples/sec: 1797.96 - lr: 0.000013 - momentum: 0.000000
160
+ 2023-10-16 23:02:35,582 ----------------------------------------------------------------------------------------------------
161
+ 2023-10-16 23:02:35,582 EPOCH 6 done: loss 0.0177 - lr: 0.000013
162
+ 2023-10-16 23:02:37,681 DEV : loss 0.10087885707616806 - f1-score (micro avg) 0.7967
163
+ 2023-10-16 23:02:37,694 saving best model
164
+ 2023-10-16 23:02:38,137 ----------------------------------------------------------------------------------------------------
165
+ 2023-10-16 23:02:45,059 epoch 7 - iter 154/1546 - loss 0.00882572 - time (sec): 6.92 - samples/sec: 1801.10 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-16 23:02:51,970 epoch 7 - iter 308/1546 - loss 0.00959269 - time (sec): 13.83 - samples/sec: 1837.87 - lr: 0.000013 - momentum: 0.000000
167
+ 2023-10-16 23:02:58,728 epoch 7 - iter 462/1546 - loss 0.01074153 - time (sec): 20.59 - samples/sec: 1826.45 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-16 23:03:05,614 epoch 7 - iter 616/1546 - loss 0.01103381 - time (sec): 27.48 - samples/sec: 1815.78 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-16 23:03:12,330 epoch 7 - iter 770/1546 - loss 0.01089398 - time (sec): 34.19 - samples/sec: 1795.14 - lr: 0.000012 - momentum: 0.000000
170
+ 2023-10-16 23:03:19,062 epoch 7 - iter 924/1546 - loss 0.01051405 - time (sec): 40.92 - samples/sec: 1791.40 - lr: 0.000011 - momentum: 0.000000
171
+ 2023-10-16 23:03:25,860 epoch 7 - iter 1078/1546 - loss 0.01078117 - time (sec): 47.72 - samples/sec: 1789.95 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-16 23:03:32,683 epoch 7 - iter 1232/1546 - loss 0.01046656 - time (sec): 54.55 - samples/sec: 1783.28 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-16 23:03:39,483 epoch 7 - iter 1386/1546 - loss 0.01062877 - time (sec): 61.34 - samples/sec: 1781.78 - lr: 0.000010 - momentum: 0.000000
174
+ 2023-10-16 23:03:46,278 epoch 7 - iter 1540/1546 - loss 0.01097604 - time (sec): 68.14 - samples/sec: 1816.28 - lr: 0.000010 - momentum: 0.000000
175
+ 2023-10-16 23:03:46,535 ----------------------------------------------------------------------------------------------------
176
+ 2023-10-16 23:03:46,535 EPOCH 7 done: loss 0.0109 - lr: 0.000010
177
+ 2023-10-16 23:03:48,934 DEV : loss 0.1023801639676094 - f1-score (micro avg) 0.7807
178
+ 2023-10-16 23:03:48,947 ----------------------------------------------------------------------------------------------------
179
+ 2023-10-16 23:03:55,722 epoch 8 - iter 154/1546 - loss 0.00276108 - time (sec): 6.77 - samples/sec: 1772.81 - lr: 0.000010 - momentum: 0.000000
180
+ 2023-10-16 23:04:02,248 epoch 8 - iter 308/1546 - loss 0.00679671 - time (sec): 13.30 - samples/sec: 1827.75 - lr: 0.000009 - momentum: 0.000000
181
+ 2023-10-16 23:04:08,788 epoch 8 - iter 462/1546 - loss 0.00528715 - time (sec): 19.84 - samples/sec: 1852.41 - lr: 0.000009 - momentum: 0.000000
182
+ 2023-10-16 23:04:15,408 epoch 8 - iter 616/1546 - loss 0.00664770 - time (sec): 26.46 - samples/sec: 1867.03 - lr: 0.000009 - momentum: 0.000000
183
+ 2023-10-16 23:04:22,270 epoch 8 - iter 770/1546 - loss 0.00694105 - time (sec): 33.32 - samples/sec: 1846.72 - lr: 0.000008 - momentum: 0.000000
184
+ 2023-10-16 23:04:29,131 epoch 8 - iter 924/1546 - loss 0.00772621 - time (sec): 40.18 - samples/sec: 1837.91 - lr: 0.000008 - momentum: 0.000000
185
+ 2023-10-16 23:04:35,982 epoch 8 - iter 1078/1546 - loss 0.00795917 - time (sec): 47.03 - samples/sec: 1842.25 - lr: 0.000008 - momentum: 0.000000
186
+ 2023-10-16 23:04:42,806 epoch 8 - iter 1232/1546 - loss 0.00817950 - time (sec): 53.86 - samples/sec: 1845.29 - lr: 0.000007 - momentum: 0.000000
187
+ 2023-10-16 23:04:49,669 epoch 8 - iter 1386/1546 - loss 0.00800634 - time (sec): 60.72 - samples/sec: 1838.42 - lr: 0.000007 - momentum: 0.000000
188
+ 2023-10-16 23:04:56,447 epoch 8 - iter 1540/1546 - loss 0.00792214 - time (sec): 67.50 - samples/sec: 1834.35 - lr: 0.000007 - momentum: 0.000000
189
+ 2023-10-16 23:04:56,713 ----------------------------------------------------------------------------------------------------
190
+ 2023-10-16 23:04:56,713 EPOCH 8 done: loss 0.0079 - lr: 0.000007
191
+ 2023-10-16 23:04:58,765 DEV : loss 0.1137828379869461 - f1-score (micro avg) 0.7879
192
+ 2023-10-16 23:04:58,779 ----------------------------------------------------------------------------------------------------
193
+ 2023-10-16 23:05:05,640 epoch 9 - iter 154/1546 - loss 0.00625578 - time (sec): 6.86 - samples/sec: 1822.09 - lr: 0.000006 - momentum: 0.000000
194
+ 2023-10-16 23:05:12,362 epoch 9 - iter 308/1546 - loss 0.00569991 - time (sec): 13.58 - samples/sec: 1786.38 - lr: 0.000006 - momentum: 0.000000
195
+ 2023-10-16 23:05:19,230 epoch 9 - iter 462/1546 - loss 0.00580179 - time (sec): 20.45 - samples/sec: 1833.99 - lr: 0.000006 - momentum: 0.000000
196
+ 2023-10-16 23:05:25,993 epoch 9 - iter 616/1546 - loss 0.00507301 - time (sec): 27.21 - samples/sec: 1813.16 - lr: 0.000005 - momentum: 0.000000
197
+ 2023-10-16 23:05:32,807 epoch 9 - iter 770/1546 - loss 0.00469977 - time (sec): 34.03 - samples/sec: 1799.76 - lr: 0.000005 - momentum: 0.000000
198
+ 2023-10-16 23:05:39,691 epoch 9 - iter 924/1546 - loss 0.00605868 - time (sec): 40.91 - samples/sec: 1821.49 - lr: 0.000005 - momentum: 0.000000
199
+ 2023-10-16 23:05:46,528 epoch 9 - iter 1078/1546 - loss 0.00553326 - time (sec): 47.75 - samples/sec: 1821.57 - lr: 0.000004 - momentum: 0.000000
200
+ 2023-10-16 23:05:53,539 epoch 9 - iter 1232/1546 - loss 0.00506175 - time (sec): 54.76 - samples/sec: 1815.38 - lr: 0.000004 - momentum: 0.000000
201
+ 2023-10-16 23:06:00,472 epoch 9 - iter 1386/1546 - loss 0.00474763 - time (sec): 61.69 - samples/sec: 1817.48 - lr: 0.000004 - momentum: 0.000000
202
+ 2023-10-16 23:06:07,322 epoch 9 - iter 1540/1546 - loss 0.00470313 - time (sec): 68.54 - samples/sec: 1802.45 - lr: 0.000003 - momentum: 0.000000
203
+ 2023-10-16 23:06:07,604 ----------------------------------------------------------------------------------------------------
204
+ 2023-10-16 23:06:07,604 EPOCH 9 done: loss 0.0047 - lr: 0.000003
205
+ 2023-10-16 23:06:09,632 DEV : loss 0.11162678897380829 - f1-score (micro avg) 0.8065
206
+ 2023-10-16 23:06:09,645 saving best model
207
+ 2023-10-16 23:06:10,098 ----------------------------------------------------------------------------------------------------
208
+ 2023-10-16 23:06:16,992 epoch 10 - iter 154/1546 - loss 0.00029590 - time (sec): 6.89 - samples/sec: 1868.58 - lr: 0.000003 - momentum: 0.000000
209
+ 2023-10-16 23:06:23,924 epoch 10 - iter 308/1546 - loss 0.00287854 - time (sec): 13.82 - samples/sec: 1847.28 - lr: 0.000003 - momentum: 0.000000
210
+ 2023-10-16 23:06:30,870 epoch 10 - iter 462/1546 - loss 0.00262317 - time (sec): 20.77 - samples/sec: 1847.50 - lr: 0.000002 - momentum: 0.000000
211
+ 2023-10-16 23:06:37,720 epoch 10 - iter 616/1546 - loss 0.00288842 - time (sec): 27.62 - samples/sec: 1830.00 - lr: 0.000002 - momentum: 0.000000
212
+ 2023-10-16 23:06:44,747 epoch 10 - iter 770/1546 - loss 0.00280402 - time (sec): 34.65 - samples/sec: 1827.44 - lr: 0.000002 - momentum: 0.000000
213
+ 2023-10-16 23:06:51,575 epoch 10 - iter 924/1546 - loss 0.00318869 - time (sec): 41.48 - samples/sec: 1806.38 - lr: 0.000001 - momentum: 0.000000
214
+ 2023-10-16 23:06:58,391 epoch 10 - iter 1078/1546 - loss 0.00298211 - time (sec): 48.29 - samples/sec: 1804.58 - lr: 0.000001 - momentum: 0.000000
215
+ 2023-10-16 23:07:05,298 epoch 10 - iter 1232/1546 - loss 0.00271568 - time (sec): 55.20 - samples/sec: 1808.08 - lr: 0.000001 - momentum: 0.000000
216
+ 2023-10-16 23:07:12,158 epoch 10 - iter 1386/1546 - loss 0.00309708 - time (sec): 62.06 - samples/sec: 1794.54 - lr: 0.000000 - momentum: 0.000000
217
+ 2023-10-16 23:07:19,086 epoch 10 - iter 1540/1546 - loss 0.00330551 - time (sec): 68.99 - samples/sec: 1793.54 - lr: 0.000000 - momentum: 0.000000
218
+ 2023-10-16 23:07:19,363 ----------------------------------------------------------------------------------------------------
219
+ 2023-10-16 23:07:19,363 EPOCH 10 done: loss 0.0033 - lr: 0.000000
220
+ 2023-10-16 23:07:21,467 DEV : loss 0.11447464674711227 - f1-score (micro avg) 0.8
221
+ 2023-10-16 23:07:21,847 ----------------------------------------------------------------------------------------------------
222
+ 2023-10-16 23:07:21,848 Loading model from best epoch ...
223
+ 2023-10-16 23:07:23,353 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
224
+ 2023-10-16 23:07:29,624
225
+ Results:
226
+ - F-score (micro) 0.8094
227
+ - F-score (macro) 0.7264
228
+ - Accuracy 0.7028
229
+
230
+ By class:
231
+ precision recall f1-score support
232
+
233
+ LOC 0.8396 0.8742 0.8566 946
234
+ BUILDING 0.5812 0.6000 0.5904 185
235
+ STREET 0.7321 0.7321 0.7321 56
236
+
237
+ micro avg 0.7946 0.8248 0.8094 1187
238
+ macro avg 0.7176 0.7355 0.7264 1187
239
+ weighted avg 0.7942 0.8248 0.8092 1187
240
+
241
+ 2023-10-16 23:07:29,625 ----------------------------------------------------------------------------------------------------