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hmbench-topres19th-en-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1

Token Classification
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sequence-tagger-model
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+ 2023-10-25 10:07:45,243 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 10:07:45,244 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(64001, 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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+ 2023-10-25 10:07:45,244 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 10:07:45,244 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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+ 2023-10-25 10:07:45,245 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 10:07:45,245 Train: 6183 sentences
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+ 2023-10-25 10:07:45,245 (train_with_dev=False, train_with_test=False)
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+ 2023-10-25 10:07:45,245 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 10:07:45,245 Training Params:
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+ 2023-10-25 10:07:45,245 - learning_rate: "3e-05"
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+ 2023-10-25 10:07:45,245 - mini_batch_size: "4"
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+ 2023-10-25 10:07:45,245 - max_epochs: "10"
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+ 2023-10-25 10:07:45,245 - shuffle: "True"
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+ 2023-10-25 10:07:45,245 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 10:07:45,245 Plugins:
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+ 2023-10-25 10:07:45,245 - TensorboardLogger
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+ 2023-10-25 10:07:45,245 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-25 10:07:45,245 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 10:07:45,245 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-25 10:07:45,245 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-25 10:07:45,245 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 10:07:45,245 Computation:
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+ 2023-10-25 10:07:45,245 - compute on device: cuda:0
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+ 2023-10-25 10:07:45,245 - embedding storage: none
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+ 2023-10-25 10:07:45,245 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 10:07:45,245 Model training base path: "hmbench-topres19th/en-dbmdz/bert-base-historic-multilingual-64k-td-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1"
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+ 2023-10-25 10:07:45,245 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 10:07:45,245 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 10:07:45,245 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-25 10:07:54,382 epoch 1 - iter 154/1546 - loss 1.95898349 - time (sec): 9.14 - samples/sec: 1382.04 - lr: 0.000003 - momentum: 0.000000
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+ 2023-10-25 10:08:03,947 epoch 1 - iter 308/1546 - loss 1.08649823 - time (sec): 18.70 - samples/sec: 1356.53 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-25 10:08:13,323 epoch 1 - iter 462/1546 - loss 0.77749102 - time (sec): 28.08 - samples/sec: 1339.78 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-25 10:08:22,401 epoch 1 - iter 616/1546 - loss 0.61557554 - time (sec): 37.15 - samples/sec: 1350.74 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-25 10:08:31,651 epoch 1 - iter 770/1546 - loss 0.52609909 - time (sec): 46.40 - samples/sec: 1327.24 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-25 10:08:41,081 epoch 1 - iter 924/1546 - loss 0.45827477 - time (sec): 55.83 - samples/sec: 1323.47 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-25 10:08:50,887 epoch 1 - iter 1078/1546 - loss 0.40834105 - time (sec): 65.64 - samples/sec: 1316.31 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-25 10:09:00,366 epoch 1 - iter 1232/1546 - loss 0.37098150 - time (sec): 75.12 - samples/sec: 1322.36 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-25 10:09:09,449 epoch 1 - iter 1386/1546 - loss 0.34265115 - time (sec): 84.20 - samples/sec: 1322.78 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-25 10:09:18,649 epoch 1 - iter 1540/1546 - loss 0.31711290 - time (sec): 93.40 - samples/sec: 1327.83 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-25 10:09:18,971 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 10:09:18,972 EPOCH 1 done: loss 0.3168 - lr: 0.000030
90
+ 2023-10-25 10:09:23,096 DEV : loss 0.06953319162130356 - f1-score (micro avg) 0.728
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+ 2023-10-25 10:09:23,121 saving best model
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+ 2023-10-25 10:09:23,684 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 10:09:32,952 epoch 2 - iter 154/1546 - loss 0.08565728 - time (sec): 9.27 - samples/sec: 1332.61 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-25 10:09:41,220 epoch 2 - iter 308/1546 - loss 0.07829584 - time (sec): 17.53 - samples/sec: 1394.51 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-25 10:09:49,136 epoch 2 - iter 462/1546 - loss 0.08041971 - time (sec): 25.45 - samples/sec: 1449.04 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-25 10:09:57,343 epoch 2 - iter 616/1546 - loss 0.07988986 - time (sec): 33.66 - samples/sec: 1467.37 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-25 10:10:05,884 epoch 2 - iter 770/1546 - loss 0.07995253 - time (sec): 42.20 - samples/sec: 1457.42 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-25 10:10:14,384 epoch 2 - iter 924/1546 - loss 0.07950843 - time (sec): 50.70 - samples/sec: 1456.83 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-25 10:10:23,311 epoch 2 - iter 1078/1546 - loss 0.07844762 - time (sec): 59.62 - samples/sec: 1450.06 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-25 10:10:31,932 epoch 2 - iter 1232/1546 - loss 0.07929517 - time (sec): 68.25 - samples/sec: 1451.56 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-25 10:10:40,728 epoch 2 - iter 1386/1546 - loss 0.07977055 - time (sec): 77.04 - samples/sec: 1450.59 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-25 10:10:49,454 epoch 2 - iter 1540/1546 - loss 0.08051213 - time (sec): 85.77 - samples/sec: 1444.81 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-25 10:10:49,779 ----------------------------------------------------------------------------------------------------
104
+ 2023-10-25 10:10:49,779 EPOCH 2 done: loss 0.0805 - lr: 0.000027
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+ 2023-10-25 10:10:52,439 DEV : loss 0.06576813757419586 - f1-score (micro avg) 0.7718
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+ 2023-10-25 10:10:52,455 saving best model
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+ 2023-10-25 10:10:53,216 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 10:11:01,862 epoch 3 - iter 154/1546 - loss 0.04411862 - time (sec): 8.64 - samples/sec: 1443.78 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-25 10:11:10,682 epoch 3 - iter 308/1546 - loss 0.04134666 - time (sec): 17.46 - samples/sec: 1398.66 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-25 10:11:19,067 epoch 3 - iter 462/1546 - loss 0.04200707 - time (sec): 25.85 - samples/sec: 1413.11 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-25 10:11:28,242 epoch 3 - iter 616/1546 - loss 0.04883651 - time (sec): 35.02 - samples/sec: 1394.56 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-25 10:11:37,518 epoch 3 - iter 770/1546 - loss 0.04944480 - time (sec): 44.30 - samples/sec: 1380.19 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-25 10:11:46,001 epoch 3 - iter 924/1546 - loss 0.04932366 - time (sec): 52.78 - samples/sec: 1403.01 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-25 10:11:54,294 epoch 3 - iter 1078/1546 - loss 0.05053819 - time (sec): 61.08 - samples/sec: 1417.76 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-25 10:12:02,472 epoch 3 - iter 1232/1546 - loss 0.05116452 - time (sec): 69.25 - samples/sec: 1428.39 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-25 10:12:10,945 epoch 3 - iter 1386/1546 - loss 0.05124381 - time (sec): 77.73 - samples/sec: 1429.55 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-25 10:12:19,189 epoch 3 - iter 1540/1546 - loss 0.05213069 - time (sec): 85.97 - samples/sec: 1438.07 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-25 10:12:19,499 ----------------------------------------------------------------------------------------------------
119
+ 2023-10-25 10:12:19,499 EPOCH 3 done: loss 0.0521 - lr: 0.000023
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+ 2023-10-25 10:12:22,512 DEV : loss 0.08034052699804306 - f1-score (micro avg) 0.7676
121
+ 2023-10-25 10:12:22,534 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 10:12:31,143 epoch 4 - iter 154/1546 - loss 0.03047819 - time (sec): 8.61 - samples/sec: 1494.64 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-25 10:12:39,417 epoch 4 - iter 308/1546 - loss 0.03210798 - time (sec): 16.88 - samples/sec: 1449.02 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-25 10:12:47,693 epoch 4 - iter 462/1546 - loss 0.03589036 - time (sec): 25.16 - samples/sec: 1492.13 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-25 10:12:56,033 epoch 4 - iter 616/1546 - loss 0.03542909 - time (sec): 33.50 - samples/sec: 1506.75 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-25 10:13:04,396 epoch 4 - iter 770/1546 - loss 0.03538510 - time (sec): 41.86 - samples/sec: 1487.02 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-25 10:13:12,795 epoch 4 - iter 924/1546 - loss 0.03504173 - time (sec): 50.26 - samples/sec: 1489.25 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-25 10:13:21,058 epoch 4 - iter 1078/1546 - loss 0.03479597 - time (sec): 58.52 - samples/sec: 1501.68 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-25 10:13:29,439 epoch 4 - iter 1232/1546 - loss 0.03494097 - time (sec): 66.90 - samples/sec: 1495.78 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-25 10:13:37,640 epoch 4 - iter 1386/1546 - loss 0.03530495 - time (sec): 75.10 - samples/sec: 1491.99 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-25 10:13:45,873 epoch 4 - iter 1540/1546 - loss 0.03579614 - time (sec): 83.34 - samples/sec: 1485.33 - lr: 0.000020 - momentum: 0.000000
132
+ 2023-10-25 10:13:46,164 ----------------------------------------------------------------------------------------------------
133
+ 2023-10-25 10:13:46,164 EPOCH 4 done: loss 0.0358 - lr: 0.000020
134
+ 2023-10-25 10:13:49,325 DEV : loss 0.10077176988124847 - f1-score (micro avg) 0.7794
135
+ 2023-10-25 10:13:49,343 saving best model
136
+ 2023-10-25 10:13:50,397 ----------------------------------------------------------------------------------------------------
137
+ 2023-10-25 10:13:58,433 epoch 5 - iter 154/1546 - loss 0.02159984 - time (sec): 8.03 - samples/sec: 1417.90 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-25 10:14:06,479 epoch 5 - iter 308/1546 - loss 0.01984275 - time (sec): 16.08 - samples/sec: 1531.20 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-25 10:14:14,358 epoch 5 - iter 462/1546 - loss 0.01943297 - time (sec): 23.96 - samples/sec: 1561.52 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-25 10:14:22,405 epoch 5 - iter 616/1546 - loss 0.02182829 - time (sec): 32.01 - samples/sec: 1557.29 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-25 10:14:30,348 epoch 5 - iter 770/1546 - loss 0.02422414 - time (sec): 39.95 - samples/sec: 1552.65 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-25 10:14:38,271 epoch 5 - iter 924/1546 - loss 0.02304780 - time (sec): 47.87 - samples/sec: 1558.65 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-25 10:14:46,226 epoch 5 - iter 1078/1546 - loss 0.02401278 - time (sec): 55.83 - samples/sec: 1554.19 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-25 10:14:54,088 epoch 5 - iter 1232/1546 - loss 0.02393514 - time (sec): 63.69 - samples/sec: 1556.48 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-25 10:15:02,055 epoch 5 - iter 1386/1546 - loss 0.02427300 - time (sec): 71.66 - samples/sec: 1557.22 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-25 10:15:09,948 epoch 5 - iter 1540/1546 - loss 0.02413449 - time (sec): 79.55 - samples/sec: 1557.93 - lr: 0.000017 - momentum: 0.000000
147
+ 2023-10-25 10:15:10,234 ----------------------------------------------------------------------------------------------------
148
+ 2023-10-25 10:15:10,234 EPOCH 5 done: loss 0.0241 - lr: 0.000017
149
+ 2023-10-25 10:15:13,016 DEV : loss 0.10452549159526825 - f1-score (micro avg) 0.7705
150
+ 2023-10-25 10:15:13,036 ----------------------------------------------------------------------------------------------------
151
+ 2023-10-25 10:15:21,266 epoch 6 - iter 154/1546 - loss 0.01168533 - time (sec): 8.23 - samples/sec: 1528.49 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-25 10:15:29,267 epoch 6 - iter 308/1546 - loss 0.01065419 - time (sec): 16.23 - samples/sec: 1537.60 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-25 10:15:37,298 epoch 6 - iter 462/1546 - loss 0.01526262 - time (sec): 24.26 - samples/sec: 1541.69 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-25 10:15:45,424 epoch 6 - iter 616/1546 - loss 0.01612627 - time (sec): 32.39 - samples/sec: 1542.05 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-25 10:15:53,337 epoch 6 - iter 770/1546 - loss 0.01582392 - time (sec): 40.30 - samples/sec: 1513.88 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-25 10:16:01,223 epoch 6 - iter 924/1546 - loss 0.01644036 - time (sec): 48.19 - samples/sec: 1517.02 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-25 10:16:09,384 epoch 6 - iter 1078/1546 - loss 0.01548608 - time (sec): 56.35 - samples/sec: 1520.60 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-25 10:16:17,233 epoch 6 - iter 1232/1546 - loss 0.01600009 - time (sec): 64.20 - samples/sec: 1541.94 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-25 10:16:25,302 epoch 6 - iter 1386/1546 - loss 0.01604283 - time (sec): 72.26 - samples/sec: 1542.64 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-25 10:16:33,560 epoch 6 - iter 1540/1546 - loss 0.01611172 - time (sec): 80.52 - samples/sec: 1538.36 - lr: 0.000013 - momentum: 0.000000
161
+ 2023-10-25 10:16:33,865 ----------------------------------------------------------------------------------------------------
162
+ 2023-10-25 10:16:33,865 EPOCH 6 done: loss 0.0161 - lr: 0.000013
163
+ 2023-10-25 10:16:37,133 DEV : loss 0.10936635732650757 - f1-score (micro avg) 0.7838
164
+ 2023-10-25 10:16:37,151 saving best model
165
+ 2023-10-25 10:16:37,850 ----------------------------------------------------------------------------------------------------
166
+ 2023-10-25 10:16:46,325 epoch 7 - iter 154/1546 - loss 0.01008689 - time (sec): 8.47 - samples/sec: 1438.72 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-25 10:16:54,564 epoch 7 - iter 308/1546 - loss 0.01216195 - time (sec): 16.71 - samples/sec: 1492.79 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-25 10:17:02,857 epoch 7 - iter 462/1546 - loss 0.00994628 - time (sec): 25.00 - samples/sec: 1525.72 - lr: 0.000012 - momentum: 0.000000
169
+ 2023-10-25 10:17:11,081 epoch 7 - iter 616/1546 - loss 0.01019350 - time (sec): 33.23 - samples/sec: 1513.71 - lr: 0.000012 - momentum: 0.000000
170
+ 2023-10-25 10:17:19,490 epoch 7 - iter 770/1546 - loss 0.01005783 - time (sec): 41.64 - samples/sec: 1499.81 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-25 10:17:27,757 epoch 7 - iter 924/1546 - loss 0.01009497 - time (sec): 49.90 - samples/sec: 1482.68 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-25 10:17:36,242 epoch 7 - iter 1078/1546 - loss 0.01001395 - time (sec): 58.39 - samples/sec: 1488.54 - lr: 0.000011 - momentum: 0.000000
173
+ 2023-10-25 10:17:44,675 epoch 7 - iter 1232/1546 - loss 0.00998899 - time (sec): 66.82 - samples/sec: 1488.34 - lr: 0.000011 - momentum: 0.000000
174
+ 2023-10-25 10:17:53,200 epoch 7 - iter 1386/1546 - loss 0.01038485 - time (sec): 75.35 - samples/sec: 1479.83 - lr: 0.000010 - momentum: 0.000000
175
+ 2023-10-25 10:18:01,461 epoch 7 - iter 1540/1546 - loss 0.01017453 - time (sec): 83.61 - samples/sec: 1478.58 - lr: 0.000010 - momentum: 0.000000
176
+ 2023-10-25 10:18:01,791 ----------------------------------------------------------------------------------------------------
177
+ 2023-10-25 10:18:01,791 EPOCH 7 done: loss 0.0102 - lr: 0.000010
178
+ 2023-10-25 10:18:05,007 DEV : loss 0.1250709891319275 - f1-score (micro avg) 0.7574
179
+ 2023-10-25 10:18:05,026 ----------------------------------------------------------------------------------------------------
180
+ 2023-10-25 10:18:13,181 epoch 8 - iter 154/1546 - loss 0.00665759 - time (sec): 8.15 - samples/sec: 1538.14 - lr: 0.000010 - momentum: 0.000000
181
+ 2023-10-25 10:18:21,404 epoch 8 - iter 308/1546 - loss 0.00574229 - time (sec): 16.38 - samples/sec: 1574.33 - lr: 0.000009 - momentum: 0.000000
182
+ 2023-10-25 10:18:29,281 epoch 8 - iter 462/1546 - loss 0.00845782 - time (sec): 24.25 - samples/sec: 1557.05 - lr: 0.000009 - momentum: 0.000000
183
+ 2023-10-25 10:18:37,411 epoch 8 - iter 616/1546 - loss 0.00893510 - time (sec): 32.38 - samples/sec: 1529.51 - lr: 0.000009 - momentum: 0.000000
184
+ 2023-10-25 10:18:45,240 epoch 8 - iter 770/1546 - loss 0.00897181 - time (sec): 40.21 - samples/sec: 1523.81 - lr: 0.000008 - momentum: 0.000000
185
+ 2023-10-25 10:18:53,140 epoch 8 - iter 924/1546 - loss 0.00912196 - time (sec): 48.11 - samples/sec: 1520.34 - lr: 0.000008 - momentum: 0.000000
186
+ 2023-10-25 10:19:01,009 epoch 8 - iter 1078/1546 - loss 0.00899284 - time (sec): 55.98 - samples/sec: 1517.08 - lr: 0.000008 - momentum: 0.000000
187
+ 2023-10-25 10:19:09,092 epoch 8 - iter 1232/1546 - loss 0.00792079 - time (sec): 64.06 - samples/sec: 1537.54 - lr: 0.000007 - momentum: 0.000000
188
+ 2023-10-25 10:19:17,137 epoch 8 - iter 1386/1546 - loss 0.00751409 - time (sec): 72.11 - samples/sec: 1545.03 - lr: 0.000007 - momentum: 0.000000
189
+ 2023-10-25 10:19:25,122 epoch 8 - iter 1540/1546 - loss 0.00765103 - time (sec): 80.09 - samples/sec: 1545.22 - lr: 0.000007 - momentum: 0.000000
190
+ 2023-10-25 10:19:25,437 ----------------------------------------------------------------------------------------------------
191
+ 2023-10-25 10:19:25,438 EPOCH 8 done: loss 0.0076 - lr: 0.000007
192
+ 2023-10-25 10:19:28,416 DEV : loss 0.138199582695961 - f1-score (micro avg) 0.7826
193
+ 2023-10-25 10:19:28,435 ----------------------------------------------------------------------------------------------------
194
+ 2023-10-25 10:19:36,386 epoch 9 - iter 154/1546 - loss 0.00388127 - time (sec): 7.95 - samples/sec: 1454.52 - lr: 0.000006 - momentum: 0.000000
195
+ 2023-10-25 10:19:44,184 epoch 9 - iter 308/1546 - loss 0.00445294 - time (sec): 15.75 - samples/sec: 1513.85 - lr: 0.000006 - momentum: 0.000000
196
+ 2023-10-25 10:19:52,139 epoch 9 - iter 462/1546 - loss 0.00384866 - time (sec): 23.70 - samples/sec: 1546.23 - lr: 0.000006 - momentum: 0.000000
197
+ 2023-10-25 10:19:59,987 epoch 9 - iter 616/1546 - loss 0.00454109 - time (sec): 31.55 - samples/sec: 1565.36 - lr: 0.000005 - momentum: 0.000000
198
+ 2023-10-25 10:20:08,351 epoch 9 - iter 770/1546 - loss 0.00384940 - time (sec): 39.91 - samples/sec: 1567.95 - lr: 0.000005 - momentum: 0.000000
199
+ 2023-10-25 10:20:16,436 epoch 9 - iter 924/1546 - loss 0.00331364 - time (sec): 48.00 - samples/sec: 1565.11 - lr: 0.000005 - momentum: 0.000000
200
+ 2023-10-25 10:20:24,623 epoch 9 - iter 1078/1546 - loss 0.00371152 - time (sec): 56.19 - samples/sec: 1570.81 - lr: 0.000004 - momentum: 0.000000
201
+ 2023-10-25 10:20:32,735 epoch 9 - iter 1232/1546 - loss 0.00348174 - time (sec): 64.30 - samples/sec: 1561.45 - lr: 0.000004 - momentum: 0.000000
202
+ 2023-10-25 10:20:40,421 epoch 9 - iter 1386/1546 - loss 0.00368389 - time (sec): 71.98 - samples/sec: 1552.80 - lr: 0.000004 - momentum: 0.000000
203
+ 2023-10-25 10:20:48,214 epoch 9 - iter 1540/1546 - loss 0.00399989 - time (sec): 79.78 - samples/sec: 1551.76 - lr: 0.000003 - momentum: 0.000000
204
+ 2023-10-25 10:20:48,507 ----------------------------------------------------------------------------------------------------
205
+ 2023-10-25 10:20:48,507 EPOCH 9 done: loss 0.0040 - lr: 0.000003
206
+ 2023-10-25 10:20:51,401 DEV : loss 0.14553460478782654 - f1-score (micro avg) 0.7775
207
+ 2023-10-25 10:20:51,418 ----------------------------------------------------------------------------------------------------
208
+ 2023-10-25 10:20:59,196 epoch 10 - iter 154/1546 - loss 0.00354279 - time (sec): 7.78 - samples/sec: 1585.87 - lr: 0.000003 - momentum: 0.000000
209
+ 2023-10-25 10:21:07,094 epoch 10 - iter 308/1546 - loss 0.00332450 - time (sec): 15.67 - samples/sec: 1497.57 - lr: 0.000003 - momentum: 0.000000
210
+ 2023-10-25 10:21:15,194 epoch 10 - iter 462/1546 - loss 0.00258446 - time (sec): 23.77 - samples/sec: 1505.04 - lr: 0.000002 - momentum: 0.000000
211
+ 2023-10-25 10:21:23,161 epoch 10 - iter 616/1546 - loss 0.00309299 - time (sec): 31.74 - samples/sec: 1515.18 - lr: 0.000002 - momentum: 0.000000
212
+ 2023-10-25 10:21:31,077 epoch 10 - iter 770/1546 - loss 0.00275583 - time (sec): 39.66 - samples/sec: 1538.77 - lr: 0.000002 - momentum: 0.000000
213
+ 2023-10-25 10:21:39,083 epoch 10 - iter 924/1546 - loss 0.00295097 - time (sec): 47.66 - samples/sec: 1538.93 - lr: 0.000001 - momentum: 0.000000
214
+ 2023-10-25 10:21:46,922 epoch 10 - iter 1078/1546 - loss 0.00274496 - time (sec): 55.50 - samples/sec: 1541.17 - lr: 0.000001 - momentum: 0.000000
215
+ 2023-10-25 10:21:54,953 epoch 10 - iter 1232/1546 - loss 0.00293898 - time (sec): 63.53 - samples/sec: 1546.02 - lr: 0.000001 - momentum: 0.000000
216
+ 2023-10-25 10:22:02,893 epoch 10 - iter 1386/1546 - loss 0.00260959 - time (sec): 71.47 - samples/sec: 1549.52 - lr: 0.000000 - momentum: 0.000000
217
+ 2023-10-25 10:22:10,803 epoch 10 - iter 1540/1546 - loss 0.00281452 - time (sec): 79.38 - samples/sec: 1559.37 - lr: 0.000000 - momentum: 0.000000
218
+ 2023-10-25 10:22:11,140 ----------------------------------------------------------------------------------------------------
219
+ 2023-10-25 10:22:11,141 EPOCH 10 done: loss 0.0028 - lr: 0.000000
220
+ 2023-10-25 10:22:14,123 DEV : loss 0.14905457198619843 - f1-score (micro avg) 0.7683
221
+ 2023-10-25 10:22:14,660 ----------------------------------------------------------------------------------------------------
222
+ 2023-10-25 10:22:14,662 Loading model from best epoch ...
223
+ 2023-10-25 10:22:16,789 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
+ 2023-10-25 10:22:26,927
225
+ Results:
226
+ - F-score (micro) 0.7887
227
+ - F-score (macro) 0.6962
228
+ - Accuracy 0.6725
229
+
230
+ By class:
231
+ precision recall f1-score support
232
+
233
+ LOC 0.8172 0.8552 0.8357 946
234
+ BUILDING 0.5736 0.6108 0.5916 185
235
+ STREET 0.6029 0.7321 0.6613 56
236
+
237
+ micro avg 0.7673 0.8113 0.7887 1187
238
+ macro avg 0.6646 0.7327 0.6962 1187
239
+ weighted avg 0.7691 0.8113 0.7895 1187
240
+
241
+ 2023-10-25 10:22:26,928 ----------------------------------------------------------------------------------------------------