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stefan-it
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autotrain-flair-georgian-ner-xlm_r_large-bs4-e10-lr5e-06-1

Token Classification
Flair
PyTorch
TensorBoard
English
Georgian
sequence-tagger-model
Model card Files Metrics Community
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+ 2023-11-15 22:00:25,159 ----------------------------------------------------------------------------------------------------
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+ 2023-11-15 22:00:25,161 Model: "SequenceTagger(
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+ (embeddings): TransformerWordEmbeddings(
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+ (model): XLMRobertaModel(
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+ (embeddings): XLMRobertaEmbeddings(
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+ (word_embeddings): Embedding(250003, 1024)
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+ (position_embeddings): Embedding(514, 1024, padding_idx=1)
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+ (token_type_embeddings): Embedding(1, 1024)
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+ (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ (encoder): XLMRobertaEncoder(
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+ (layer): ModuleList(
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+ (0-23): 24 x XLMRobertaLayer(
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+ (attention): XLMRobertaAttention(
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+ (self): XLMRobertaSelfAttention(
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+ (query): Linear(in_features=1024, out_features=1024, bias=True)
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+ (key): Linear(in_features=1024, out_features=1024, bias=True)
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+ (value): Linear(in_features=1024, out_features=1024, bias=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ (output): XLMRobertaSelfOutput(
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+ (dense): Linear(in_features=1024, out_features=1024, bias=True)
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+ (LayerNorm): LayerNorm((1024,), eps=1e-05, 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): XLMRobertaIntermediate(
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+ (dense): Linear(in_features=1024, out_features=4096, bias=True)
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+ (intermediate_act_fn): GELUActivation()
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+ )
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+ (output): XLMRobertaOutput(
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+ (dense): Linear(in_features=4096, out_features=1024, bias=True)
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+ (LayerNorm): LayerNorm((1024,), eps=1e-05, 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): XLMRobertaPooler(
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+ (dense): Linear(in_features=1024, out_features=1024, 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=1024, out_features=13, bias=True)
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+ (loss_function): CrossEntropyLoss()
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+ )"
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+ 2023-11-15 22:00:25,161 ----------------------------------------------------------------------------------------------------
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+ 2023-11-15 22:00:25,161 MultiCorpus: 30000 train + 10000 dev + 10000 test sentences
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+ - ColumnCorpus Corpus: 20000 train + 0 dev + 0 test sentences - /root/.flair/datasets/ner_multi_xtreme/en
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+ - ColumnCorpus Corpus: 10000 train + 10000 dev + 10000 test sentences - /root/.flair/datasets/ner_multi_xtreme/ka
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+ 2023-11-15 22:00:25,162 ----------------------------------------------------------------------------------------------------
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+ 2023-11-15 22:00:25,162 Train: 30000 sentences
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+ 2023-11-15 22:00:25,162 (train_with_dev=False, train_with_test=False)
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+ 2023-11-15 22:00:25,162 ----------------------------------------------------------------------------------------------------
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+ 2023-11-15 22:00:25,162 Training Params:
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+ 2023-11-15 22:00:25,162 - learning_rate: "5e-06"
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+ 2023-11-15 22:00:25,162 - mini_batch_size: "4"
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+ 2023-11-15 22:00:25,162 - max_epochs: "10"
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+ 2023-11-15 22:00:25,162 - shuffle: "True"
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+ 2023-11-15 22:00:25,162 ----------------------------------------------------------------------------------------------------
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+ 2023-11-15 22:00:25,162 Plugins:
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+ 2023-11-15 22:00:25,162 - TensorboardLogger
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+ 2023-11-15 22:00:25,162 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-11-15 22:00:25,162 ----------------------------------------------------------------------------------------------------
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+ 2023-11-15 22:00:25,162 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-11-15 22:00:25,162 - metric: "('micro avg', 'f1-score')"
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+ 2023-11-15 22:00:25,162 ----------------------------------------------------------------------------------------------------
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+ 2023-11-15 22:00:25,162 Computation:
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+ 2023-11-15 22:00:25,162 - compute on device: cuda:0
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+ 2023-11-15 22:00:25,162 - embedding storage: none
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+ 2023-11-15 22:00:25,162 ----------------------------------------------------------------------------------------------------
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+ 2023-11-15 22:00:25,162 Model training base path: "autotrain-flair-georgian-ner-xlm_r_large-bs4-e10-lr5e-06-1"
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+ 2023-11-15 22:00:25,162 ----------------------------------------------------------------------------------------------------
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+ 2023-11-15 22:00:25,162 ----------------------------------------------------------------------------------------------------
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+ 2023-11-15 22:00:25,162 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-11-15 22:02:02,381 epoch 1 - iter 750/7500 - loss 2.97720856 - time (sec): 97.22 - samples/sec: 247.35 - lr: 0.000000 - momentum: 0.000000
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+ 2023-11-15 22:03:39,323 epoch 1 - iter 1500/7500 - loss 2.41069745 - time (sec): 194.16 - samples/sec: 251.96 - lr: 0.000001 - momentum: 0.000000
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+ 2023-11-15 22:05:13,923 epoch 1 - iter 2250/7500 - loss 2.11659763 - time (sec): 288.76 - samples/sec: 250.97 - lr: 0.000001 - momentum: 0.000000
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+ 2023-11-15 22:06:49,395 epoch 1 - iter 3000/7500 - loss 1.85845398 - time (sec): 384.23 - samples/sec: 250.76 - lr: 0.000002 - momentum: 0.000000
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+ 2023-11-15 22:08:25,760 epoch 1 - iter 3750/7500 - loss 1.64125510 - time (sec): 480.60 - samples/sec: 250.04 - lr: 0.000002 - momentum: 0.000000
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+ 2023-11-15 22:09:57,628 epoch 1 - iter 4500/7500 - loss 1.46644056 - time (sec): 572.46 - samples/sec: 251.97 - lr: 0.000003 - momentum: 0.000000
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+ 2023-11-15 22:11:32,160 epoch 1 - iter 5250/7500 - loss 1.33869112 - time (sec): 667.00 - samples/sec: 252.39 - lr: 0.000003 - momentum: 0.000000
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+ 2023-11-15 22:13:07,470 epoch 1 - iter 6000/7500 - loss 1.23507864 - time (sec): 762.31 - samples/sec: 252.62 - lr: 0.000004 - momentum: 0.000000
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+ 2023-11-15 22:14:40,413 epoch 1 - iter 6750/7500 - loss 1.14834855 - time (sec): 855.25 - samples/sec: 253.64 - lr: 0.000004 - momentum: 0.000000
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+ 2023-11-15 22:16:12,674 epoch 1 - iter 7500/7500 - loss 1.08078087 - time (sec): 947.51 - samples/sec: 254.14 - lr: 0.000005 - momentum: 0.000000
89
+ 2023-11-15 22:16:12,676 ----------------------------------------------------------------------------------------------------
90
+ 2023-11-15 22:16:12,676 EPOCH 1 done: loss 1.0808 - lr: 0.000005
91
+ 2023-11-15 22:16:40,276 DEV : loss 0.28522568941116333 - f1-score (micro avg) 0.7689
92
+ 2023-11-15 22:16:42,355 saving best model
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+ 2023-11-15 22:16:45,087 ----------------------------------------------------------------------------------------------------
94
+ 2023-11-15 22:18:19,302 epoch 2 - iter 750/7500 - loss 0.40628767 - time (sec): 94.21 - samples/sec: 252.98 - lr: 0.000005 - momentum: 0.000000
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+ 2023-11-15 22:19:54,957 epoch 2 - iter 1500/7500 - loss 0.39913376 - time (sec): 189.87 - samples/sec: 252.13 - lr: 0.000005 - momentum: 0.000000
96
+ 2023-11-15 22:21:30,957 epoch 2 - iter 2250/7500 - loss 0.39502501 - time (sec): 285.87 - samples/sec: 252.08 - lr: 0.000005 - momentum: 0.000000
97
+ 2023-11-15 22:23:08,084 epoch 2 - iter 3000/7500 - loss 0.39217047 - time (sec): 382.99 - samples/sec: 252.19 - lr: 0.000005 - momentum: 0.000000
98
+ 2023-11-15 22:24:44,510 epoch 2 - iter 3750/7500 - loss 0.39363076 - time (sec): 479.42 - samples/sec: 251.43 - lr: 0.000005 - momentum: 0.000000
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+ 2023-11-15 22:26:22,214 epoch 2 - iter 4500/7500 - loss 0.39486610 - time (sec): 577.12 - samples/sec: 250.52 - lr: 0.000005 - momentum: 0.000000
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+ 2023-11-15 22:28:03,826 epoch 2 - iter 5250/7500 - loss 0.39412988 - time (sec): 678.74 - samples/sec: 248.23 - lr: 0.000005 - momentum: 0.000000
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+ 2023-11-15 22:29:45,422 epoch 2 - iter 6000/7500 - loss 0.39244545 - time (sec): 780.33 - samples/sec: 246.43 - lr: 0.000005 - momentum: 0.000000
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+ 2023-11-15 22:31:27,285 epoch 2 - iter 6750/7500 - loss 0.39014491 - time (sec): 882.20 - samples/sec: 245.94 - lr: 0.000005 - momentum: 0.000000
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+ 2023-11-15 22:33:08,358 epoch 2 - iter 7500/7500 - loss 0.38962978 - time (sec): 983.27 - samples/sec: 244.89 - lr: 0.000004 - momentum: 0.000000
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+ 2023-11-15 22:33:08,361 ----------------------------------------------------------------------------------------------------
105
+ 2023-11-15 22:33:08,361 EPOCH 2 done: loss 0.3896 - lr: 0.000004
106
+ 2023-11-15 22:33:34,136 DEV : loss 0.22754639387130737 - f1-score (micro avg) 0.8657
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+ 2023-11-15 22:33:36,396 saving best model
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+ 2023-11-15 22:33:39,256 ----------------------------------------------------------------------------------------------------
109
+ 2023-11-15 22:35:17,903 epoch 3 - iter 750/7500 - loss 0.34214105 - time (sec): 98.64 - samples/sec: 244.93 - lr: 0.000004 - momentum: 0.000000
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+ 2023-11-15 22:36:57,436 epoch 3 - iter 1500/7500 - loss 0.33960084 - time (sec): 198.18 - samples/sec: 243.91 - lr: 0.000004 - momentum: 0.000000
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+ 2023-11-15 22:38:36,787 epoch 3 - iter 2250/7500 - loss 0.34100409 - time (sec): 297.53 - samples/sec: 245.57 - lr: 0.000004 - momentum: 0.000000
112
+ 2023-11-15 22:40:16,845 epoch 3 - iter 3000/7500 - loss 0.34997877 - time (sec): 397.59 - samples/sec: 242.72 - lr: 0.000004 - momentum: 0.000000
113
+ 2023-11-15 22:41:55,617 epoch 3 - iter 3750/7500 - loss 0.34973124 - time (sec): 496.36 - samples/sec: 242.81 - lr: 0.000004 - momentum: 0.000000
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+ 2023-11-15 22:43:33,457 epoch 3 - iter 4500/7500 - loss 0.35049763 - time (sec): 594.20 - samples/sec: 242.95 - lr: 0.000004 - momentum: 0.000000
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+ 2023-11-15 22:45:13,590 epoch 3 - iter 5250/7500 - loss 0.34990880 - time (sec): 694.33 - samples/sec: 242.50 - lr: 0.000004 - momentum: 0.000000
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+ 2023-11-15 22:46:52,454 epoch 3 - iter 6000/7500 - loss 0.35059132 - time (sec): 793.19 - samples/sec: 242.62 - lr: 0.000004 - momentum: 0.000000
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+ 2023-11-15 22:48:30,458 epoch 3 - iter 6750/7500 - loss 0.34738568 - time (sec): 891.20 - samples/sec: 242.64 - lr: 0.000004 - momentum: 0.000000
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+ 2023-11-15 22:50:08,763 epoch 3 - iter 7500/7500 - loss 0.34558871 - time (sec): 989.50 - samples/sec: 243.35 - lr: 0.000004 - momentum: 0.000000
119
+ 2023-11-15 22:50:08,766 ----------------------------------------------------------------------------------------------------
120
+ 2023-11-15 22:50:08,766 EPOCH 3 done: loss 0.3456 - lr: 0.000004
121
+ 2023-11-15 22:50:36,792 DEV : loss 0.2620299756526947 - f1-score (micro avg) 0.8807
122
+ 2023-11-15 22:50:39,427 saving best model
123
+ 2023-11-15 22:50:42,689 ----------------------------------------------------------------------------------------------------
124
+ 2023-11-15 22:52:20,669 epoch 4 - iter 750/7500 - loss 0.28881601 - time (sec): 97.97 - samples/sec: 249.59 - lr: 0.000004 - momentum: 0.000000
125
+ 2023-11-15 22:53:59,111 epoch 4 - iter 1500/7500 - loss 0.29772971 - time (sec): 196.42 - samples/sec: 247.27 - lr: 0.000004 - momentum: 0.000000
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+ 2023-11-15 22:55:37,524 epoch 4 - iter 2250/7500 - loss 0.29353995 - time (sec): 294.83 - samples/sec: 246.88 - lr: 0.000004 - momentum: 0.000000
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+ 2023-11-15 22:57:10,987 epoch 4 - iter 3000/7500 - loss 0.29232593 - time (sec): 388.29 - samples/sec: 249.82 - lr: 0.000004 - momentum: 0.000000
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+ 2023-11-15 22:58:45,878 epoch 4 - iter 3750/7500 - loss 0.29565608 - time (sec): 483.18 - samples/sec: 250.22 - lr: 0.000004 - momentum: 0.000000
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+ 2023-11-15 23:00:15,561 epoch 4 - iter 4500/7500 - loss 0.29546503 - time (sec): 572.87 - samples/sec: 252.61 - lr: 0.000004 - momentum: 0.000000
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+ 2023-11-15 23:01:46,907 epoch 4 - iter 5250/7500 - loss 0.29295260 - time (sec): 664.21 - samples/sec: 254.49 - lr: 0.000004 - momentum: 0.000000
131
+ 2023-11-15 23:03:20,758 epoch 4 - iter 6000/7500 - loss 0.29538906 - time (sec): 758.06 - samples/sec: 254.97 - lr: 0.000003 - momentum: 0.000000
132
+ 2023-11-15 23:04:54,701 epoch 4 - iter 6750/7500 - loss 0.29413686 - time (sec): 852.01 - samples/sec: 254.92 - lr: 0.000003 - momentum: 0.000000
133
+ 2023-11-15 23:06:27,232 epoch 4 - iter 7500/7500 - loss 0.29473517 - time (sec): 944.54 - samples/sec: 254.94 - lr: 0.000003 - momentum: 0.000000
134
+ 2023-11-15 23:06:27,234 ----------------------------------------------------------------------------------------------------
135
+ 2023-11-15 23:06:27,234 EPOCH 4 done: loss 0.2947 - lr: 0.000003
136
+ 2023-11-15 23:06:55,453 DEV : loss 0.2627362310886383 - f1-score (micro avg) 0.8931
137
+ 2023-11-15 23:06:57,258 saving best model
138
+ 2023-11-15 23:07:00,052 ----------------------------------------------------------------------------------------------------
139
+ 2023-11-15 23:08:33,866 epoch 5 - iter 750/7500 - loss 0.23475035 - time (sec): 93.81 - samples/sec: 257.86 - lr: 0.000003 - momentum: 0.000000
140
+ 2023-11-15 23:10:06,704 epoch 5 - iter 1500/7500 - loss 0.25020039 - time (sec): 186.65 - samples/sec: 257.30 - lr: 0.000003 - momentum: 0.000000
141
+ 2023-11-15 23:11:40,952 epoch 5 - iter 2250/7500 - loss 0.24718727 - time (sec): 280.90 - samples/sec: 257.62 - lr: 0.000003 - momentum: 0.000000
142
+ 2023-11-15 23:13:19,252 epoch 5 - iter 3000/7500 - loss 0.24230280 - time (sec): 379.20 - samples/sec: 255.38 - lr: 0.000003 - momentum: 0.000000
143
+ 2023-11-15 23:14:53,432 epoch 5 - iter 3750/7500 - loss 0.24621586 - time (sec): 473.38 - samples/sec: 255.15 - lr: 0.000003 - momentum: 0.000000
144
+ 2023-11-15 23:16:28,060 epoch 5 - iter 4500/7500 - loss 0.25446598 - time (sec): 568.01 - samples/sec: 254.99 - lr: 0.000003 - momentum: 0.000000
145
+ 2023-11-15 23:18:01,296 epoch 5 - iter 5250/7500 - loss 0.25785483 - time (sec): 661.24 - samples/sec: 255.64 - lr: 0.000003 - momentum: 0.000000
146
+ 2023-11-15 23:19:34,321 epoch 5 - iter 6000/7500 - loss 0.25542150 - time (sec): 754.27 - samples/sec: 255.29 - lr: 0.000003 - momentum: 0.000000
147
+ 2023-11-15 23:21:06,401 epoch 5 - iter 6750/7500 - loss 0.25788299 - time (sec): 846.35 - samples/sec: 256.25 - lr: 0.000003 - momentum: 0.000000
148
+ 2023-11-15 23:22:42,749 epoch 5 - iter 7500/7500 - loss 0.25897971 - time (sec): 942.69 - samples/sec: 255.43 - lr: 0.000003 - momentum: 0.000000
149
+ 2023-11-15 23:22:42,752 ----------------------------------------------------------------------------------------------------
150
+ 2023-11-15 23:22:42,752 EPOCH 5 done: loss 0.2590 - lr: 0.000003
151
+ 2023-11-15 23:23:10,474 DEV : loss 0.28726592659950256 - f1-score (micro avg) 0.8965
152
+ 2023-11-15 23:23:12,645 saving best model
153
+ 2023-11-15 23:23:15,948 ----------------------------------------------------------------------------------------------------
154
+ 2023-11-15 23:24:53,113 epoch 6 - iter 750/7500 - loss 0.21973774 - time (sec): 97.16 - samples/sec: 249.95 - lr: 0.000003 - momentum: 0.000000
155
+ 2023-11-15 23:26:26,815 epoch 6 - iter 1500/7500 - loss 0.21332096 - time (sec): 190.86 - samples/sec: 253.62 - lr: 0.000003 - momentum: 0.000000
156
+ 2023-11-15 23:27:59,646 epoch 6 - iter 2250/7500 - loss 0.21491622 - time (sec): 283.69 - samples/sec: 254.10 - lr: 0.000003 - momentum: 0.000000
157
+ 2023-11-15 23:29:32,191 epoch 6 - iter 3000/7500 - loss 0.21457413 - time (sec): 376.24 - samples/sec: 255.67 - lr: 0.000003 - momentum: 0.000000
158
+ 2023-11-15 23:31:04,817 epoch 6 - iter 3750/7500 - loss 0.21967125 - time (sec): 468.87 - samples/sec: 257.41 - lr: 0.000003 - momentum: 0.000000
159
+ 2023-11-15 23:32:36,784 epoch 6 - iter 4500/7500 - loss 0.22261148 - time (sec): 560.83 - samples/sec: 257.57 - lr: 0.000002 - momentum: 0.000000
160
+ 2023-11-15 23:34:09,162 epoch 6 - iter 5250/7500 - loss 0.22064338 - time (sec): 653.21 - samples/sec: 257.83 - lr: 0.000002 - momentum: 0.000000
161
+ 2023-11-15 23:35:42,101 epoch 6 - iter 6000/7500 - loss 0.21731885 - time (sec): 746.15 - samples/sec: 258.55 - lr: 0.000002 - momentum: 0.000000
162
+ 2023-11-15 23:37:15,361 epoch 6 - iter 6750/7500 - loss 0.21808265 - time (sec): 839.41 - samples/sec: 257.99 - lr: 0.000002 - momentum: 0.000000
163
+ 2023-11-15 23:38:49,020 epoch 6 - iter 7500/7500 - loss 0.21803191 - time (sec): 933.07 - samples/sec: 258.07 - lr: 0.000002 - momentum: 0.000000
164
+ 2023-11-15 23:38:49,030 ----------------------------------------------------------------------------------------------------
165
+ 2023-11-15 23:38:49,030 EPOCH 6 done: loss 0.2180 - lr: 0.000002
166
+ 2023-11-15 23:39:16,657 DEV : loss 0.2947460412979126 - f1-score (micro avg) 0.8946
167
+ 2023-11-15 23:39:18,346 ----------------------------------------------------------------------------------------------------
168
+ 2023-11-15 23:40:52,475 epoch 7 - iter 750/7500 - loss 0.18291047 - time (sec): 94.13 - samples/sec: 255.74 - lr: 0.000002 - momentum: 0.000000
169
+ 2023-11-15 23:42:24,451 epoch 7 - iter 1500/7500 - loss 0.18433171 - time (sec): 186.10 - samples/sec: 255.14 - lr: 0.000002 - momentum: 0.000000
170
+ 2023-11-15 23:43:58,604 epoch 7 - iter 2250/7500 - loss 0.18998389 - time (sec): 280.25 - samples/sec: 253.23 - lr: 0.000002 - momentum: 0.000000
171
+ 2023-11-15 23:45:30,028 epoch 7 - iter 3000/7500 - loss 0.18175644 - time (sec): 371.68 - samples/sec: 256.35 - lr: 0.000002 - momentum: 0.000000
172
+ 2023-11-15 23:47:03,392 epoch 7 - iter 3750/7500 - loss 0.18696273 - time (sec): 465.04 - samples/sec: 257.12 - lr: 0.000002 - momentum: 0.000000
173
+ 2023-11-15 23:48:37,150 epoch 7 - iter 4500/7500 - loss 0.18321438 - time (sec): 558.80 - samples/sec: 257.32 - lr: 0.000002 - momentum: 0.000000
174
+ 2023-11-15 23:50:10,852 epoch 7 - iter 5250/7500 - loss 0.18492056 - time (sec): 652.50 - samples/sec: 257.36 - lr: 0.000002 - momentum: 0.000000
175
+ 2023-11-15 23:51:45,033 epoch 7 - iter 6000/7500 - loss 0.18451583 - time (sec): 746.68 - samples/sec: 256.74 - lr: 0.000002 - momentum: 0.000000
176
+ 2023-11-15 23:53:17,929 epoch 7 - iter 6750/7500 - loss 0.18613635 - time (sec): 839.58 - samples/sec: 257.56 - lr: 0.000002 - momentum: 0.000000
177
+ 2023-11-15 23:54:48,507 epoch 7 - iter 7500/7500 - loss 0.18639933 - time (sec): 930.16 - samples/sec: 258.88 - lr: 0.000002 - momentum: 0.000000
178
+ 2023-11-15 23:54:48,510 ----------------------------------------------------------------------------------------------------
179
+ 2023-11-15 23:54:48,510 EPOCH 7 done: loss 0.1864 - lr: 0.000002
180
+ 2023-11-15 23:55:15,442 DEV : loss 0.3085970878601074 - f1-score (micro avg) 0.8966
181
+ 2023-11-15 23:55:18,420 saving best model
182
+ 2023-11-15 23:55:21,094 ----------------------------------------------------------------------------------------------------
183
+ 2023-11-15 23:56:54,732 epoch 8 - iter 750/7500 - loss 0.17466309 - time (sec): 93.63 - samples/sec: 253.48 - lr: 0.000002 - momentum: 0.000000
184
+ 2023-11-15 23:58:27,121 epoch 8 - iter 1500/7500 - loss 0.16813816 - time (sec): 186.02 - samples/sec: 257.36 - lr: 0.000002 - momentum: 0.000000
185
+ 2023-11-15 23:59:58,131 epoch 8 - iter 2250/7500 - loss 0.16489442 - time (sec): 277.03 - samples/sec: 259.32 - lr: 0.000002 - momentum: 0.000000
186
+ 2023-11-16 00:01:31,801 epoch 8 - iter 3000/7500 - loss 0.16611691 - time (sec): 370.70 - samples/sec: 260.01 - lr: 0.000001 - momentum: 0.000000
187
+ 2023-11-16 00:03:04,777 epoch 8 - iter 3750/7500 - loss 0.15963682 - time (sec): 463.68 - samples/sec: 260.64 - lr: 0.000001 - momentum: 0.000000
188
+ 2023-11-16 00:04:37,159 epoch 8 - iter 4500/7500 - loss 0.15855342 - time (sec): 556.06 - samples/sec: 260.73 - lr: 0.000001 - momentum: 0.000000
189
+ 2023-11-16 00:06:08,858 epoch 8 - iter 5250/7500 - loss 0.15795009 - time (sec): 647.76 - samples/sec: 260.56 - lr: 0.000001 - momentum: 0.000000
190
+ 2023-11-16 00:07:40,645 epoch 8 - iter 6000/7500 - loss 0.15834278 - time (sec): 739.55 - samples/sec: 260.14 - lr: 0.000001 - momentum: 0.000000
191
+ 2023-11-16 00:09:13,294 epoch 8 - iter 6750/7500 - loss 0.15728929 - time (sec): 832.19 - samples/sec: 259.82 - lr: 0.000001 - momentum: 0.000000
192
+ 2023-11-16 00:10:45,101 epoch 8 - iter 7500/7500 - loss 0.15715876 - time (sec): 924.00 - samples/sec: 260.60 - lr: 0.000001 - momentum: 0.000000
193
+ 2023-11-16 00:10:45,104 ----------------------------------------------------------------------------------------------------
194
+ 2023-11-16 00:10:45,104 EPOCH 8 done: loss 0.1572 - lr: 0.000001
195
+ 2023-11-16 00:11:12,824 DEV : loss 0.3132772743701935 - f1-score (micro avg) 0.8987
196
+ 2023-11-16 00:11:14,864 saving best model
197
+ 2023-11-16 00:11:17,496 ----------------------------------------------------------------------------------------------------
198
+ 2023-11-16 00:12:50,277 epoch 9 - iter 750/7500 - loss 0.13402991 - time (sec): 92.78 - samples/sec: 262.49 - lr: 0.000001 - momentum: 0.000000
199
+ 2023-11-16 00:14:25,042 epoch 9 - iter 1500/7500 - loss 0.13544134 - time (sec): 187.54 - samples/sec: 260.51 - lr: 0.000001 - momentum: 0.000000
200
+ 2023-11-16 00:16:00,925 epoch 9 - iter 2250/7500 - loss 0.13605938 - time (sec): 283.43 - samples/sec: 256.17 - lr: 0.000001 - momentum: 0.000000
201
+ 2023-11-16 00:17:34,205 epoch 9 - iter 3000/7500 - loss 0.13264017 - time (sec): 376.71 - samples/sec: 257.28 - lr: 0.000001 - momentum: 0.000000
202
+ 2023-11-16 00:19:09,897 epoch 9 - iter 3750/7500 - loss 0.13248311 - time (sec): 472.40 - samples/sec: 257.65 - lr: 0.000001 - momentum: 0.000000
203
+ 2023-11-16 00:20:46,186 epoch 9 - iter 4500/7500 - loss 0.13242849 - time (sec): 568.69 - samples/sec: 255.77 - lr: 0.000001 - momentum: 0.000000
204
+ 2023-11-16 00:22:19,408 epoch 9 - iter 5250/7500 - loss 0.13193630 - time (sec): 661.91 - samples/sec: 256.28 - lr: 0.000001 - momentum: 0.000000
205
+ 2023-11-16 00:23:53,091 epoch 9 - iter 6000/7500 - loss 0.13145249 - time (sec): 755.59 - samples/sec: 256.01 - lr: 0.000001 - momentum: 0.000000
206
+ 2023-11-16 00:25:24,254 epoch 9 - iter 6750/7500 - loss 0.13171967 - time (sec): 846.75 - samples/sec: 256.47 - lr: 0.000001 - momentum: 0.000000
207
+ 2023-11-16 00:26:55,146 epoch 9 - iter 7500/7500 - loss 0.13410642 - time (sec): 937.65 - samples/sec: 256.81 - lr: 0.000001 - momentum: 0.000000
208
+ 2023-11-16 00:26:55,148 ----------------------------------------------------------------------------------------------------
209
+ 2023-11-16 00:26:55,148 EPOCH 9 done: loss 0.1341 - lr: 0.000001
210
+ 2023-11-16 00:27:22,622 DEV : loss 0.33312830328941345 - f1-score (micro avg) 0.8995
211
+ 2023-11-16 00:27:24,615 saving best model
212
+ 2023-11-16 00:27:27,266 ----------------------------------------------------------------------------------------------------
213
+ 2023-11-16 00:29:01,701 epoch 10 - iter 750/7500 - loss 0.12076654 - time (sec): 94.43 - samples/sec: 257.39 - lr: 0.000001 - momentum: 0.000000
214
+ 2023-11-16 00:30:35,121 epoch 10 - iter 1500/7500 - loss 0.11879889 - time (sec): 187.85 - samples/sec: 259.58 - lr: 0.000000 - momentum: 0.000000
215
+ 2023-11-16 00:32:08,364 epoch 10 - iter 2250/7500 - loss 0.11450840 - time (sec): 281.09 - samples/sec: 258.55 - lr: 0.000000 - momentum: 0.000000
216
+ 2023-11-16 00:33:41,838 epoch 10 - iter 3000/7500 - loss 0.10939028 - time (sec): 374.57 - samples/sec: 258.62 - lr: 0.000000 - momentum: 0.000000
217
+ 2023-11-16 00:35:14,502 epoch 10 - iter 3750/7500 - loss 0.10864189 - time (sec): 467.23 - samples/sec: 259.77 - lr: 0.000000 - momentum: 0.000000
218
+ 2023-11-16 00:36:47,096 epoch 10 - iter 4500/7500 - loss 0.11020150 - time (sec): 559.83 - samples/sec: 259.27 - lr: 0.000000 - momentum: 0.000000
219
+ 2023-11-16 00:38:19,472 epoch 10 - iter 5250/7500 - loss 0.11284750 - time (sec): 652.20 - samples/sec: 259.40 - lr: 0.000000 - momentum: 0.000000
220
+ 2023-11-16 00:39:51,965 epoch 10 - iter 6000/7500 - loss 0.11442017 - time (sec): 744.70 - samples/sec: 259.20 - lr: 0.000000 - momentum: 0.000000
221
+ 2023-11-16 00:41:24,113 epoch 10 - iter 6750/7500 - loss 0.11440977 - time (sec): 836.84 - samples/sec: 259.01 - lr: 0.000000 - momentum: 0.000000
222
+ 2023-11-16 00:42:56,471 epoch 10 - iter 7500/7500 - loss 0.11765761 - time (sec): 929.20 - samples/sec: 259.14 - lr: 0.000000 - momentum: 0.000000
223
+ 2023-11-16 00:42:56,474 ----------------------------------------------------------------------------------------------------
224
+ 2023-11-16 00:42:56,474 EPOCH 10 done: loss 0.1177 - lr: 0.000000
225
+ 2023-11-16 00:43:23,420 DEV : loss 0.3264077305793762 - f1-score (micro avg) 0.9005
226
+ 2023-11-16 00:43:25,353 saving best model
227
+ 2023-11-16 00:43:30,017 ----------------------------------------------------------------------------------------------------
228
+ 2023-11-16 00:43:30,019 Loading model from best epoch ...
229
+ 2023-11-16 00:43:39,075 SequenceTagger predicts: Dictionary with 13 tags: O, S-LOC, B-LOC, E-LOC, I-LOC, S-ORG, B-ORG, E-ORG, I-ORG, S-PER, B-PER, E-PER, I-PER
230
+ 2023-11-16 00:44:07,251
231
+ Results:
232
+ - F-score (micro) 0.9036
233
+ - F-score (macro) 0.9025
234
+ - Accuracy 0.8526
235
+
236
+ By class:
237
+ precision recall f1-score support
238
+
239
+ LOC 0.9015 0.9153 0.9083 5288
240
+ PER 0.9170 0.9430 0.9298 3962
241
+ ORG 0.8680 0.8708 0.8694 3807
242
+
243
+ micro avg 0.8966 0.9107 0.9036 13057
244
+ macro avg 0.8955 0.9097 0.9025 13057
245
+ weighted avg 0.8964 0.9107 0.9035 13057
246
+
247
+ 2023-11-16 00:44:07,251 ----------------------------------------------------------------------------------------------------