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training.log

@@ -0,0 +1,506 @@
+2023-10-25 03:05:38,651 ----------------------------------------------------------------------------------------------------
+2023-10-25 03:05:38,652 Model: "SequenceTagger(
+  (embeddings): TransformerWordEmbeddings(
+    (model): BertModel(
+      (embeddings): BertEmbeddings(
+        (word_embeddings): Embedding(64001, 768)
+        (position_embeddings): Embedding(512, 768)
+        (token_type_embeddings): Embedding(2, 768)
+        (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (encoder): BertEncoder(
+        (layer): ModuleList(
+          (0): BertLayer(
+            (attention): BertAttention(
+              (self): BertSelfAttention(
+                (query): Linear(in_features=768, out_features=768, bias=True)
+                (key): Linear(in_features=768, out_features=768, bias=True)
+                (value): Linear(in_features=768, out_features=768, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (output): BertSelfOutput(
+                (dense): Linear(in_features=768, out_features=768, bias=True)
+                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+            )
+            (intermediate): BertIntermediate(
+              (dense): Linear(in_features=768, out_features=3072, bias=True)
+              (intermediate_act_fn): GELUActivation()
+            )
+            (output): BertOutput(
+              (dense): Linear(in_features=3072, out_features=768, bias=True)
+              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+          )
+          (1): BertLayer(
+            (attention): BertAttention(
+              (self): BertSelfAttention(
+                (query): Linear(in_features=768, out_features=768, bias=True)
+                (key): Linear(in_features=768, out_features=768, bias=True)
+                (value): Linear(in_features=768, out_features=768, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (output): BertSelfOutput(
+                (dense): Linear(in_features=768, out_features=768, bias=True)
+                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+            )
+            (intermediate): BertIntermediate(
+              (dense): Linear(in_features=768, out_features=3072, bias=True)
+              (intermediate_act_fn): GELUActivation()
+            )
+            (output): BertOutput(
+              (dense): Linear(in_features=3072, out_features=768, bias=True)
+              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+          )
+          (2): BertLayer(
+            (attention): BertAttention(
+              (self): BertSelfAttention(
+                (query): Linear(in_features=768, out_features=768, bias=True)
+                (key): Linear(in_features=768, out_features=768, bias=True)
+                (value): Linear(in_features=768, out_features=768, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (output): BertSelfOutput(
+                (dense): Linear(in_features=768, out_features=768, bias=True)
+                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+            )
+            (intermediate): BertIntermediate(
+              (dense): Linear(in_features=768, out_features=3072, bias=True)
+              (intermediate_act_fn): GELUActivation()
+            )
+            (output): BertOutput(
+              (dense): Linear(in_features=3072, out_features=768, bias=True)
+              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+          )
+          (3): BertLayer(
+            (attention): BertAttention(
+              (self): BertSelfAttention(
+                (query): Linear(in_features=768, out_features=768, bias=True)
+                (key): Linear(in_features=768, out_features=768, bias=True)
+                (value): Linear(in_features=768, out_features=768, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (output): BertSelfOutput(
+                (dense): Linear(in_features=768, out_features=768, bias=True)
+                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+            )
+            (intermediate): BertIntermediate(
+              (dense): Linear(in_features=768, out_features=3072, bias=True)
+              (intermediate_act_fn): GELUActivation()
+            )
+            (output): BertOutput(
+              (dense): Linear(in_features=3072, out_features=768, bias=True)
+              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+          )
+          (4): BertLayer(
+            (attention): BertAttention(
+              (self): BertSelfAttention(
+                (query): Linear(in_features=768, out_features=768, bias=True)
+                (key): Linear(in_features=768, out_features=768, bias=True)
+                (value): Linear(in_features=768, out_features=768, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (output): BertSelfOutput(
+                (dense): Linear(in_features=768, out_features=768, bias=True)
+                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+            )
+            (intermediate): BertIntermediate(
+              (dense): Linear(in_features=768, out_features=3072, bias=True)
+              (intermediate_act_fn): GELUActivation()
+            )
+            (output): BertOutput(
+              (dense): Linear(in_features=3072, out_features=768, bias=True)
+              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+          )
+          (5): BertLayer(
+            (attention): BertAttention(
+              (self): BertSelfAttention(
+                (query): Linear(in_features=768, out_features=768, bias=True)
+                (key): Linear(in_features=768, out_features=768, bias=True)
+                (value): Linear(in_features=768, out_features=768, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (output): BertSelfOutput(
+                (dense): Linear(in_features=768, out_features=768, bias=True)
+                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+            )
+            (intermediate): BertIntermediate(
+              (dense): Linear(in_features=768, out_features=3072, bias=True)
+              (intermediate_act_fn): GELUActivation()
+            )
+            (output): BertOutput(
+              (dense): Linear(in_features=3072, out_features=768, bias=True)
+              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+          )
+          (6): BertLayer(
+            (attention): BertAttention(
+              (self): BertSelfAttention(
+                (query): Linear(in_features=768, out_features=768, bias=True)
+                (key): Linear(in_features=768, out_features=768, bias=True)
+                (value): Linear(in_features=768, out_features=768, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (output): BertSelfOutput(
+                (dense): Linear(in_features=768, out_features=768, bias=True)
+                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+            )
+            (intermediate): BertIntermediate(
+              (dense): Linear(in_features=768, out_features=3072, bias=True)
+              (intermediate_act_fn): GELUActivation()
+            )
+            (output): BertOutput(
+              (dense): Linear(in_features=3072, out_features=768, bias=True)
+              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+          )
+          (7): BertLayer(
+            (attention): BertAttention(
+              (self): BertSelfAttention(
+                (query): Linear(in_features=768, out_features=768, bias=True)
+                (key): Linear(in_features=768, out_features=768, bias=True)
+                (value): Linear(in_features=768, out_features=768, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (output): BertSelfOutput(
+                (dense): Linear(in_features=768, out_features=768, bias=True)
+                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+            )
+            (intermediate): BertIntermediate(
+              (dense): Linear(in_features=768, out_features=3072, bias=True)
+              (intermediate_act_fn): GELUActivation()
+            )
+            (output): BertOutput(
+              (dense): Linear(in_features=3072, out_features=768, bias=True)
+              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+          )
+          (8): BertLayer(
+            (attention): BertAttention(
+              (self): BertSelfAttention(
+                (query): Linear(in_features=768, out_features=768, bias=True)
+                (key): Linear(in_features=768, out_features=768, bias=True)
+                (value): Linear(in_features=768, out_features=768, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (output): BertSelfOutput(
+                (dense): Linear(in_features=768, out_features=768, bias=True)
+                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+            )
+            (intermediate): BertIntermediate(
+              (dense): Linear(in_features=768, out_features=3072, bias=True)
+              (intermediate_act_fn): GELUActivation()
+            )
+            (output): BertOutput(
+              (dense): Linear(in_features=3072, out_features=768, bias=True)
+              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+          )
+          (9): BertLayer(
+            (attention): BertAttention(
+              (self): BertSelfAttention(
+                (query): Linear(in_features=768, out_features=768, bias=True)
+                (key): Linear(in_features=768, out_features=768, bias=True)
+                (value): Linear(in_features=768, out_features=768, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (output): BertSelfOutput(
+                (dense): Linear(in_features=768, out_features=768, bias=True)
+                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+            )
+            (intermediate): BertIntermediate(
+              (dense): Linear(in_features=768, out_features=3072, bias=True)
+              (intermediate_act_fn): GELUActivation()
+            )
+            (output): BertOutput(
+              (dense): Linear(in_features=3072, out_features=768, bias=True)
+              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+          )
+          (10): BertLayer(
+            (attention): BertAttention(
+              (self): BertSelfAttention(
+                (query): Linear(in_features=768, out_features=768, bias=True)
+                (key): Linear(in_features=768, out_features=768, bias=True)
+                (value): Linear(in_features=768, out_features=768, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (output): BertSelfOutput(
+                (dense): Linear(in_features=768, out_features=768, bias=True)
+                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+            )
+            (intermediate): BertIntermediate(
+              (dense): Linear(in_features=768, out_features=3072, bias=True)
+              (intermediate_act_fn): GELUActivation()
+            )
+            (output): BertOutput(
+              (dense): Linear(in_features=3072, out_features=768, bias=True)
+              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+          )
+          (11): BertLayer(
+            (attention): BertAttention(
+              (self): BertSelfAttention(
+                (query): Linear(in_features=768, out_features=768, bias=True)
+                (key): Linear(in_features=768, out_features=768, bias=True)
+                (value): Linear(in_features=768, out_features=768, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (output): BertSelfOutput(
+                (dense): Linear(in_features=768, out_features=768, bias=True)
+                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+            )
+            (intermediate): BertIntermediate(
+              (dense): Linear(in_features=768, out_features=3072, bias=True)
+              (intermediate_act_fn): GELUActivation()
+            )
+            (output): BertOutput(
+              (dense): Linear(in_features=3072, out_features=768, bias=True)
+              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+          )
+        )
+      )
+      (pooler): BertPooler(
+        (dense): Linear(in_features=768, out_features=768, bias=True)
+        (activation): Tanh()
+      )
+    )
+  )
+  (locked_dropout): LockedDropout(p=0.5)
+  (linear): Linear(in_features=768, out_features=13, bias=True)
+  (loss_function): CrossEntropyLoss()
+)"
+2023-10-25 03:05:38,652 ----------------------------------------------------------------------------------------------------
+2023-10-25 03:05:38,652 MultiCorpus: 5777 train + 722 dev + 723 test sentences
+ - NER_ICDAR_EUROPEANA Corpus: 5777 train + 722 dev + 723 test sentences - /home/ubuntu/.flair/datasets/ner_icdar_europeana/nl
+2023-10-25 03:05:38,652 ----------------------------------------------------------------------------------------------------
+2023-10-25 03:05:38,652 Train:  5777 sentences
+2023-10-25 03:05:38,652         (train_with_dev=False, train_with_test=False)
+2023-10-25 03:05:38,652 ----------------------------------------------------------------------------------------------------
+2023-10-25 03:05:38,652 Training Params:
+2023-10-25 03:05:38,652  - learning_rate: "3e-05" 
+2023-10-25 03:05:38,652  - mini_batch_size: "8"
+2023-10-25 03:05:38,652  - max_epochs: "10"
+2023-10-25 03:05:38,652  - shuffle: "True"
+2023-10-25 03:05:38,652 ----------------------------------------------------------------------------------------------------
+2023-10-25 03:05:38,652 Plugins:
+2023-10-25 03:05:38,652  - TensorboardLogger
+2023-10-25 03:05:38,652  - LinearScheduler | warmup_fraction: '0.1'
+2023-10-25 03:05:38,652 ----------------------------------------------------------------------------------------------------
+2023-10-25 03:05:38,652 Final evaluation on model from best epoch (best-model.pt)
+2023-10-25 03:05:38,652  - metric: "('micro avg', 'f1-score')"
+2023-10-25 03:05:38,652 ----------------------------------------------------------------------------------------------------
+2023-10-25 03:05:38,652 Computation:
+2023-10-25 03:05:38,653  - compute on device: cuda:0
+2023-10-25 03:05:38,653  - embedding storage: none
+2023-10-25 03:05:38,653 ----------------------------------------------------------------------------------------------------
+2023-10-25 03:05:38,653 Model training base path: "hmbench-icdar/nl-dbmdz/bert-base-historic-multilingual-64k-td-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5"
+2023-10-25 03:05:38,653 ----------------------------------------------------------------------------------------------------
+2023-10-25 03:05:38,653 ----------------------------------------------------------------------------------------------------
+2023-10-25 03:05:38,653 Logging anything other than scalars to TensorBoard is currently not supported.
+2023-10-25 03:05:47,361 epoch 1 - iter 72/723 - loss 1.99617329 - time (sec): 8.71 - samples/sec: 1924.42 - lr: 0.000003 - momentum: 0.000000
+2023-10-25 03:05:55,310 epoch 1 - iter 144/723 - loss 1.16280577 - time (sec): 16.66 - samples/sec: 1978.56 - lr: 0.000006 - momentum: 0.000000
+2023-10-25 03:06:03,657 epoch 1 - iter 216/723 - loss 0.84060964 - time (sec): 25.00 - samples/sec: 2007.83 - lr: 0.000009 - momentum: 0.000000
+2023-10-25 03:06:12,528 epoch 1 - iter 288/723 - loss 0.66558228 - time (sec): 33.87 - samples/sec: 2021.42 - lr: 0.000012 - momentum: 0.000000
+2023-10-25 03:06:20,701 epoch 1 - iter 360/723 - loss 0.56766766 - time (sec): 42.05 - samples/sec: 2024.88 - lr: 0.000015 - momentum: 0.000000
+2023-10-25 03:06:29,450 epoch 1 - iter 432/723 - loss 0.49389313 - time (sec): 50.80 - samples/sec: 2042.64 - lr: 0.000018 - momentum: 0.000000
+2023-10-25 03:06:38,106 epoch 1 - iter 504/723 - loss 0.44520564 - time (sec): 59.45 - samples/sec: 2046.47 - lr: 0.000021 - momentum: 0.000000
+2023-10-25 03:06:46,545 epoch 1 - iter 576/723 - loss 0.40819214 - time (sec): 67.89 - samples/sec: 2045.50 - lr: 0.000024 - momentum: 0.000000
+2023-10-25 03:06:55,306 epoch 1 - iter 648/723 - loss 0.37477357 - time (sec): 76.65 - samples/sec: 2054.01 - lr: 0.000027 - momentum: 0.000000
+2023-10-25 03:07:04,267 epoch 1 - iter 720/723 - loss 0.35059174 - time (sec): 85.61 - samples/sec: 2050.77 - lr: 0.000030 - momentum: 0.000000
+2023-10-25 03:07:04,591 ----------------------------------------------------------------------------------------------------
+2023-10-25 03:07:04,591 EPOCH 1 done: loss 0.3498 - lr: 0.000030
+2023-10-25 03:07:07,856 DEV : loss 0.12723077833652496 - f1-score (micro avg)  0.605
+2023-10-25 03:07:07,867 saving best model
+2023-10-25 03:07:08,331 ----------------------------------------------------------------------------------------------------
+2023-10-25 03:07:16,780 epoch 2 - iter 72/723 - loss 0.12378288 - time (sec): 8.45 - samples/sec: 2025.34 - lr: 0.000030 - momentum: 0.000000
+2023-10-25 03:07:25,019 epoch 2 - iter 144/723 - loss 0.10929827 - time (sec): 16.69 - samples/sec: 2057.29 - lr: 0.000029 - momentum: 0.000000
+2023-10-25 03:07:33,376 epoch 2 - iter 216/723 - loss 0.10475478 - time (sec): 25.04 - samples/sec: 2064.54 - lr: 0.000029 - momentum: 0.000000
+2023-10-25 03:07:41,993 epoch 2 - iter 288/723 - loss 0.10331239 - time (sec): 33.66 - samples/sec: 2056.30 - lr: 0.000029 - momentum: 0.000000
+2023-10-25 03:07:50,632 epoch 2 - iter 360/723 - loss 0.09975351 - time (sec): 42.30 - samples/sec: 2045.34 - lr: 0.000028 - momentum: 0.000000
+2023-10-25 03:07:59,130 epoch 2 - iter 432/723 - loss 0.09772803 - time (sec): 50.80 - samples/sec: 2043.69 - lr: 0.000028 - momentum: 0.000000
+2023-10-25 03:08:07,642 epoch 2 - iter 504/723 - loss 0.09812027 - time (sec): 59.31 - samples/sec: 2041.26 - lr: 0.000028 - momentum: 0.000000
+2023-10-25 03:08:16,048 epoch 2 - iter 576/723 - loss 0.09695368 - time (sec): 67.72 - samples/sec: 2044.92 - lr: 0.000027 - momentum: 0.000000
+2023-10-25 03:08:25,214 epoch 2 - iter 648/723 - loss 0.09757941 - time (sec): 76.88 - samples/sec: 2041.28 - lr: 0.000027 - momentum: 0.000000
+2023-10-25 03:08:34,573 epoch 2 - iter 720/723 - loss 0.09551261 - time (sec): 86.24 - samples/sec: 2036.63 - lr: 0.000027 - momentum: 0.000000
+2023-10-25 03:08:34,936 ----------------------------------------------------------------------------------------------------
+2023-10-25 03:08:34,936 EPOCH 2 done: loss 0.0956 - lr: 0.000027
+2023-10-25 03:08:38,643 DEV : loss 0.08566790819168091 - f1-score (micro avg)  0.7745
+2023-10-25 03:08:38,655 saving best model
+2023-10-25 03:08:39,247 ----------------------------------------------------------------------------------------------------
+2023-10-25 03:08:47,889 epoch 3 - iter 72/723 - loss 0.05916132 - time (sec): 8.64 - samples/sec: 1981.91 - lr: 0.000026 - momentum: 0.000000
+2023-10-25 03:08:56,374 epoch 3 - iter 144/723 - loss 0.06201111 - time (sec): 17.13 - samples/sec: 2019.26 - lr: 0.000026 - momentum: 0.000000
+2023-10-25 03:09:05,644 epoch 3 - iter 216/723 - loss 0.05888965 - time (sec): 26.40 - samples/sec: 2030.27 - lr: 0.000026 - momentum: 0.000000
+2023-10-25 03:09:14,319 epoch 3 - iter 288/723 - loss 0.05851997 - time (sec): 35.07 - samples/sec: 2041.19 - lr: 0.000025 - momentum: 0.000000
+2023-10-25 03:09:22,895 epoch 3 - iter 360/723 - loss 0.05764096 - time (sec): 43.65 - samples/sec: 2054.60 - lr: 0.000025 - momentum: 0.000000
+2023-10-25 03:09:31,205 epoch 3 - iter 432/723 - loss 0.06016911 - time (sec): 51.96 - samples/sec: 2050.28 - lr: 0.000025 - momentum: 0.000000
+2023-10-25 03:09:39,400 epoch 3 - iter 504/723 - loss 0.05918448 - time (sec): 60.15 - samples/sec: 2049.80 - lr: 0.000024 - momentum: 0.000000
+2023-10-25 03:09:47,713 epoch 3 - iter 576/723 - loss 0.05969279 - time (sec): 68.46 - samples/sec: 2053.93 - lr: 0.000024 - momentum: 0.000000
+2023-10-25 03:09:55,945 epoch 3 - iter 648/723 - loss 0.06014119 - time (sec): 76.70 - samples/sec: 2057.74 - lr: 0.000024 - momentum: 0.000000
+2023-10-25 03:10:04,767 epoch 3 - iter 720/723 - loss 0.05977622 - time (sec): 85.52 - samples/sec: 2051.90 - lr: 0.000023 - momentum: 0.000000
+2023-10-25 03:10:05,178 ----------------------------------------------------------------------------------------------------
+2023-10-25 03:10:05,178 EPOCH 3 done: loss 0.0596 - lr: 0.000023
+2023-10-25 03:10:08,906 DEV : loss 0.09248801320791245 - f1-score (micro avg)  0.8169
+2023-10-25 03:10:08,918 saving best model
+2023-10-25 03:10:09,504 ----------------------------------------------------------------------------------------------------
+2023-10-25 03:10:17,921 epoch 4 - iter 72/723 - loss 0.03098176 - time (sec): 8.42 - samples/sec: 1998.29 - lr: 0.000023 - momentum: 0.000000
+2023-10-25 03:10:26,602 epoch 4 - iter 144/723 - loss 0.03944408 - time (sec): 17.10 - samples/sec: 2035.54 - lr: 0.000023 - momentum: 0.000000
+2023-10-25 03:10:35,744 epoch 4 - iter 216/723 - loss 0.04237264 - time (sec): 26.24 - samples/sec: 2032.29 - lr: 0.000022 - momentum: 0.000000
+2023-10-25 03:10:45,044 epoch 4 - iter 288/723 - loss 0.04386620 - time (sec): 35.54 - samples/sec: 1999.13 - lr: 0.000022 - momentum: 0.000000
+2023-10-25 03:10:53,659 epoch 4 - iter 360/723 - loss 0.04538680 - time (sec): 44.15 - samples/sec: 2010.14 - lr: 0.000022 - momentum: 0.000000
+2023-10-25 03:11:01,969 epoch 4 - iter 432/723 - loss 0.04433392 - time (sec): 52.46 - samples/sec: 2009.08 - lr: 0.000021 - momentum: 0.000000
+2023-10-25 03:11:10,884 epoch 4 - iter 504/723 - loss 0.04255925 - time (sec): 61.38 - samples/sec: 2023.36 - lr: 0.000021 - momentum: 0.000000
+2023-10-25 03:11:19,328 epoch 4 - iter 576/723 - loss 0.04165806 - time (sec): 69.82 - samples/sec: 2025.62 - lr: 0.000021 - momentum: 0.000000
+2023-10-25 03:11:27,196 epoch 4 - iter 648/723 - loss 0.04211832 - time (sec): 77.69 - samples/sec: 2031.28 - lr: 0.000020 - momentum: 0.000000
+2023-10-25 03:11:35,625 epoch 4 - iter 720/723 - loss 0.04183929 - time (sec): 86.12 - samples/sec: 2042.24 - lr: 0.000020 - momentum: 0.000000
+2023-10-25 03:11:35,879 ----------------------------------------------------------------------------------------------------
+2023-10-25 03:11:35,880 EPOCH 4 done: loss 0.0418 - lr: 0.000020
+2023-10-25 03:11:39,311 DEV : loss 0.10374626517295837 - f1-score (micro avg)  0.7998
+2023-10-25 03:11:39,323 ----------------------------------------------------------------------------------------------------
+2023-10-25 03:11:47,653 epoch 5 - iter 72/723 - loss 0.03199178 - time (sec): 8.33 - samples/sec: 2125.79 - lr: 0.000020 - momentum: 0.000000
+2023-10-25 03:11:56,611 epoch 5 - iter 144/723 - loss 0.03213636 - time (sec): 17.29 - samples/sec: 2049.33 - lr: 0.000019 - momentum: 0.000000
+2023-10-25 03:12:04,995 epoch 5 - iter 216/723 - loss 0.03115061 - time (sec): 25.67 - samples/sec: 2057.92 - lr: 0.000019 - momentum: 0.000000
+2023-10-25 03:12:13,192 epoch 5 - iter 288/723 - loss 0.03097977 - time (sec): 33.87 - samples/sec: 2042.62 - lr: 0.000019 - momentum: 0.000000
+2023-10-25 03:12:21,459 epoch 5 - iter 360/723 - loss 0.02861777 - time (sec): 42.14 - samples/sec: 2046.34 - lr: 0.000018 - momentum: 0.000000
+2023-10-25 03:12:30,963 epoch 5 - iter 432/723 - loss 0.02807181 - time (sec): 51.64 - samples/sec: 2026.17 - lr: 0.000018 - momentum: 0.000000
+2023-10-25 03:12:39,302 epoch 5 - iter 504/723 - loss 0.02949394 - time (sec): 59.98 - samples/sec: 2025.30 - lr: 0.000018 - momentum: 0.000000
+2023-10-25 03:12:48,150 epoch 5 - iter 576/723 - loss 0.03091767 - time (sec): 68.83 - samples/sec: 2028.32 - lr: 0.000017 - momentum: 0.000000
+2023-10-25 03:12:57,221 epoch 5 - iter 648/723 - loss 0.03041035 - time (sec): 77.90 - samples/sec: 2032.00 - lr: 0.000017 - momentum: 0.000000
+2023-10-25 03:13:05,882 epoch 5 - iter 720/723 - loss 0.03003918 - time (sec): 86.56 - samples/sec: 2030.99 - lr: 0.000017 - momentum: 0.000000
+2023-10-25 03:13:06,115 ----------------------------------------------------------------------------------------------------
+2023-10-25 03:13:06,116 EPOCH 5 done: loss 0.0300 - lr: 0.000017
+2023-10-25 03:13:09,854 DEV : loss 0.10400616377592087 - f1-score (micro avg)  0.8329
+2023-10-25 03:13:09,866 saving best model
+2023-10-25 03:13:10,453 ----------------------------------------------------------------------------------------------------
+2023-10-25 03:13:19,082 epoch 6 - iter 72/723 - loss 0.02189838 - time (sec): 8.63 - samples/sec: 2088.50 - lr: 0.000016 - momentum: 0.000000
+2023-10-25 03:13:26,772 epoch 6 - iter 144/723 - loss 0.02550434 - time (sec): 16.32 - samples/sec: 2089.07 - lr: 0.000016 - momentum: 0.000000
+2023-10-25 03:13:35,285 epoch 6 - iter 216/723 - loss 0.02613427 - time (sec): 24.83 - samples/sec: 2096.33 - lr: 0.000016 - momentum: 0.000000
+2023-10-25 03:13:44,917 epoch 6 - iter 288/723 - loss 0.02482502 - time (sec): 34.46 - samples/sec: 2066.30 - lr: 0.000015 - momentum: 0.000000
+2023-10-25 03:13:53,581 epoch 6 - iter 360/723 - loss 0.02424077 - time (sec): 43.13 - samples/sec: 2064.26 - lr: 0.000015 - momentum: 0.000000
+2023-10-25 03:14:02,495 epoch 6 - iter 432/723 - loss 0.02422193 - time (sec): 52.04 - samples/sec: 2054.09 - lr: 0.000015 - momentum: 0.000000
+2023-10-25 03:14:11,533 epoch 6 - iter 504/723 - loss 0.02433358 - time (sec): 61.08 - samples/sec: 2039.82 - lr: 0.000014 - momentum: 0.000000
+2023-10-25 03:14:19,993 epoch 6 - iter 576/723 - loss 0.02425909 - time (sec): 69.54 - samples/sec: 2039.67 - lr: 0.000014 - momentum: 0.000000
+2023-10-25 03:14:27,983 epoch 6 - iter 648/723 - loss 0.02400181 - time (sec): 77.53 - samples/sec: 2042.61 - lr: 0.000014 - momentum: 0.000000
+2023-10-25 03:14:37,020 epoch 6 - iter 720/723 - loss 0.02315851 - time (sec): 86.57 - samples/sec: 2030.27 - lr: 0.000013 - momentum: 0.000000
+2023-10-25 03:14:37,321 ----------------------------------------------------------------------------------------------------
+2023-10-25 03:14:37,322 EPOCH 6 done: loss 0.0233 - lr: 0.000013
+2023-10-25 03:14:40,761 DEV : loss 0.1466233879327774 - f1-score (micro avg)  0.8271
+2023-10-25 03:14:40,773 ----------------------------------------------------------------------------------------------------
+2023-10-25 03:14:49,901 epoch 7 - iter 72/723 - loss 0.01604370 - time (sec): 9.13 - samples/sec: 1997.10 - lr: 0.000013 - momentum: 0.000000
+2023-10-25 03:14:58,957 epoch 7 - iter 144/723 - loss 0.01674537 - time (sec): 18.18 - samples/sec: 2010.15 - lr: 0.000013 - momentum: 0.000000
+2023-10-25 03:15:07,312 epoch 7 - iter 216/723 - loss 0.01646182 - time (sec): 26.54 - samples/sec: 2011.93 - lr: 0.000012 - momentum: 0.000000
+2023-10-25 03:15:16,127 epoch 7 - iter 288/723 - loss 0.01562860 - time (sec): 35.35 - samples/sec: 2010.60 - lr: 0.000012 - momentum: 0.000000
+2023-10-25 03:15:24,723 epoch 7 - iter 360/723 - loss 0.01653965 - time (sec): 43.95 - samples/sec: 2014.53 - lr: 0.000012 - momentum: 0.000000
+2023-10-25 03:15:33,248 epoch 7 - iter 432/723 - loss 0.01654614 - time (sec): 52.47 - samples/sec: 2019.73 - lr: 0.000011 - momentum: 0.000000
+2023-10-25 03:15:41,936 epoch 7 - iter 504/723 - loss 0.01772708 - time (sec): 61.16 - samples/sec: 2012.83 - lr: 0.000011 - momentum: 0.000000
+2023-10-25 03:15:51,042 epoch 7 - iter 576/723 - loss 0.01751254 - time (sec): 70.27 - samples/sec: 2014.86 - lr: 0.000011 - momentum: 0.000000
+2023-10-25 03:15:59,505 epoch 7 - iter 648/723 - loss 0.01722334 - time (sec): 78.73 - samples/sec: 2017.94 - lr: 0.000010 - momentum: 0.000000
+2023-10-25 03:16:07,618 epoch 7 - iter 720/723 - loss 0.01748532 - time (sec): 86.84 - samples/sec: 2020.86 - lr: 0.000010 - momentum: 0.000000
+2023-10-25 03:16:08,082 ----------------------------------------------------------------------------------------------------
+2023-10-25 03:16:08,082 EPOCH 7 done: loss 0.0175 - lr: 0.000010
+2023-10-25 03:16:11,526 DEV : loss 0.14099310338497162 - f1-score (micro avg)  0.8444
+2023-10-25 03:16:11,538 saving best model
+2023-10-25 03:16:12,132 ----------------------------------------------------------------------------------------------------
+2023-10-25 03:16:20,471 epoch 8 - iter 72/723 - loss 0.02389593 - time (sec): 8.34 - samples/sec: 2029.40 - lr: 0.000010 - momentum: 0.000000
+2023-10-25 03:16:28,527 epoch 8 - iter 144/723 - loss 0.01519053 - time (sec): 16.39 - samples/sec: 2070.04 - lr: 0.000009 - momentum: 0.000000
+2023-10-25 03:16:36,685 epoch 8 - iter 216/723 - loss 0.01528692 - time (sec): 24.55 - samples/sec: 2055.74 - lr: 0.000009 - momentum: 0.000000
+2023-10-25 03:16:45,368 epoch 8 - iter 288/723 - loss 0.01454248 - time (sec): 33.23 - samples/sec: 2030.79 - lr: 0.000009 - momentum: 0.000000
+2023-10-25 03:16:53,990 epoch 8 - iter 360/723 - loss 0.01392144 - time (sec): 41.86 - samples/sec: 2028.37 - lr: 0.000008 - momentum: 0.000000
+2023-10-25 03:17:02,707 epoch 8 - iter 432/723 - loss 0.01333604 - time (sec): 50.57 - samples/sec: 2028.92 - lr: 0.000008 - momentum: 0.000000
+2023-10-25 03:17:11,757 epoch 8 - iter 504/723 - loss 0.01255700 - time (sec): 59.62 - samples/sec: 2012.47 - lr: 0.000008 - momentum: 0.000000
+2023-10-25 03:17:20,313 epoch 8 - iter 576/723 - loss 0.01296982 - time (sec): 68.18 - samples/sec: 2017.55 - lr: 0.000007 - momentum: 0.000000
+2023-10-25 03:17:28,983 epoch 8 - iter 648/723 - loss 0.01386353 - time (sec): 76.85 - samples/sec: 2033.29 - lr: 0.000007 - momentum: 0.000000
+2023-10-25 03:17:38,555 epoch 8 - iter 720/723 - loss 0.01336908 - time (sec): 86.42 - samples/sec: 2032.31 - lr: 0.000007 - momentum: 0.000000
+2023-10-25 03:17:38,804 ----------------------------------------------------------------------------------------------------
+2023-10-25 03:17:38,805 EPOCH 8 done: loss 0.0134 - lr: 0.000007
+2023-10-25 03:17:42,543 DEV : loss 0.16321606934070587 - f1-score (micro avg)  0.8331
+2023-10-25 03:17:42,555 ----------------------------------------------------------------------------------------------------
+2023-10-25 03:17:52,154 epoch 9 - iter 72/723 - loss 0.00928599 - time (sec): 9.60 - samples/sec: 1891.08 - lr: 0.000006 - momentum: 0.000000
+2023-10-25 03:18:00,763 epoch 9 - iter 144/723 - loss 0.01114101 - time (sec): 18.21 - samples/sec: 1981.42 - lr: 0.000006 - momentum: 0.000000
+2023-10-25 03:18:09,687 epoch 9 - iter 216/723 - loss 0.00982378 - time (sec): 27.13 - samples/sec: 2013.82 - lr: 0.000006 - momentum: 0.000000
+2023-10-25 03:18:18,372 epoch 9 - iter 288/723 - loss 0.00914376 - time (sec): 35.82 - samples/sec: 2021.99 - lr: 0.000005 - momentum: 0.000000
+2023-10-25 03:18:26,374 epoch 9 - iter 360/723 - loss 0.00876306 - time (sec): 43.82 - samples/sec: 2027.74 - lr: 0.000005 - momentum: 0.000000
+2023-10-25 03:18:34,609 epoch 9 - iter 432/723 - loss 0.00823411 - time (sec): 52.05 - samples/sec: 2024.16 - lr: 0.000005 - momentum: 0.000000
+2023-10-25 03:18:43,316 epoch 9 - iter 504/723 - loss 0.00818760 - time (sec): 60.76 - samples/sec: 2033.51 - lr: 0.000004 - momentum: 0.000000
+2023-10-25 03:18:51,923 epoch 9 - iter 576/723 - loss 0.00821037 - time (sec): 69.37 - samples/sec: 2034.49 - lr: 0.000004 - momentum: 0.000000
+2023-10-25 03:19:00,772 epoch 9 - iter 648/723 - loss 0.00872746 - time (sec): 78.22 - samples/sec: 2030.67 - lr: 0.000004 - momentum: 0.000000
+2023-10-25 03:19:09,106 epoch 9 - iter 720/723 - loss 0.00885136 - time (sec): 86.55 - samples/sec: 2030.87 - lr: 0.000003 - momentum: 0.000000
+2023-10-25 03:19:09,383 ----------------------------------------------------------------------------------------------------
+2023-10-25 03:19:09,383 EPOCH 9 done: loss 0.0088 - lr: 0.000003
+2023-10-25 03:19:13,170 DEV : loss 0.18107134103775024 - f1-score (micro avg)  0.829
+2023-10-25 03:19:13,182 ----------------------------------------------------------------------------------------------------
+2023-10-25 03:19:22,203 epoch 10 - iter 72/723 - loss 0.00646861 - time (sec): 9.02 - samples/sec: 1972.88 - lr: 0.000003 - momentum: 0.000000
+2023-10-25 03:19:31,133 epoch 10 - iter 144/723 - loss 0.00638719 - time (sec): 17.95 - samples/sec: 1994.98 - lr: 0.000003 - momentum: 0.000000
+2023-10-25 03:19:39,625 epoch 10 - iter 216/723 - loss 0.00729037 - time (sec): 26.44 - samples/sec: 1986.12 - lr: 0.000002 - momentum: 0.000000
+2023-10-25 03:19:48,001 epoch 10 - iter 288/723 - loss 0.00646852 - time (sec): 34.82 - samples/sec: 1995.23 - lr: 0.000002 - momentum: 0.000000
+2023-10-25 03:19:56,662 epoch 10 - iter 360/723 - loss 0.00687157 - time (sec): 43.48 - samples/sec: 1991.42 - lr: 0.000002 - momentum: 0.000000
+2023-10-25 03:20:05,230 epoch 10 - iter 432/723 - loss 0.00636256 - time (sec): 52.05 - samples/sec: 2003.13 - lr: 0.000001 - momentum: 0.000000
+2023-10-25 03:20:13,979 epoch 10 - iter 504/723 - loss 0.00637499 - time (sec): 60.80 - samples/sec: 1997.17 - lr: 0.000001 - momentum: 0.000000
+2023-10-25 03:20:22,735 epoch 10 - iter 576/723 - loss 0.00672918 - time (sec): 69.55 - samples/sec: 1987.42 - lr: 0.000001 - momentum: 0.000000
+2023-10-25 03:20:31,736 epoch 10 - iter 648/723 - loss 0.00714825 - time (sec): 78.55 - samples/sec: 1993.22 - lr: 0.000000 - momentum: 0.000000
+2023-10-25 03:20:40,664 epoch 10 - iter 720/723 - loss 0.00697419 - time (sec): 87.48 - samples/sec: 2006.56 - lr: 0.000000 - momentum: 0.000000
+2023-10-25 03:20:40,944 ----------------------------------------------------------------------------------------------------
+2023-10-25 03:20:40,944 EPOCH 10 done: loss 0.0070 - lr: 0.000000
+2023-10-25 03:20:44,389 DEV : loss 0.17600025236606598 - f1-score (micro avg)  0.8312
+2023-10-25 03:20:44,876 ----------------------------------------------------------------------------------------------------
+2023-10-25 03:20:44,876 Loading model from best epoch ...
+2023-10-25 03:20:46,545 SequenceTagger predicts: Dictionary with 13 tags: O, S-LOC, B-LOC, E-LOC, I-LOC, S-PER, B-PER, E-PER, I-PER, S-ORG, B-ORG, E-ORG, I-ORG
+2023-10-25 03:20:50,083 
+Results:
+- F-score (micro) 0.8055
+- F-score (macro) 0.6854
+- Accuracy 0.6868
+
+By class:
+              precision    recall  f1-score   support
+
+         PER     0.8254    0.8237    0.8245       482
+         LOC     0.9010    0.7948    0.8445       458
+         ORG     0.4364    0.3478    0.3871        69
+
+   micro avg     0.8351    0.7780    0.8055      1009
+   macro avg     0.7209    0.6554    0.6854      1009
+weighted avg     0.8331    0.7780    0.8037      1009
+
+2023-10-25 03:20:50,084 ----------------------------------------------------------------------------------------------------