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training.log

@@ -0,0 +1,507 @@
+2023-10-24 22:47:35,121 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:47:35,122 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()
+)"
+2023-10-24 22:47:35,122 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:47:35,122 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
+2023-10-24 22:47:35,122 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:47:35,122 Train:  5777 sentences
+2023-10-24 22:47:35,122         (train_with_dev=False, train_with_test=False)
+2023-10-24 22:47:35,122 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:47:35,122 Training Params:
+2023-10-24 22:47:35,122  - learning_rate: "5e-05" 
+2023-10-24 22:47:35,123  - mini_batch_size: "8"
+2023-10-24 22:47:35,123  - max_epochs: "10"
+2023-10-24 22:47:35,123  - shuffle: "True"
+2023-10-24 22:47:35,123 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:47:35,123 Plugins:
+2023-10-24 22:47:35,123  - TensorboardLogger
+2023-10-24 22:47:35,123  - LinearScheduler | warmup_fraction: '0.1'
+2023-10-24 22:47:35,123 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:47:35,123 Final evaluation on model from best epoch (best-model.pt)
+2023-10-24 22:47:35,123  - metric: "('micro avg', 'f1-score')"
+2023-10-24 22:47:35,123 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:47:35,123 Computation:
+2023-10-24 22:47:35,123  - compute on device: cuda:0
+2023-10-24 22:47:35,123  - embedding storage: none
+2023-10-24 22:47:35,123 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:47:35,123 Model training base path: "hmbench-icdar/nl-dbmdz/bert-base-historic-multilingual-64k-td-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1"
+2023-10-24 22:47:35,123 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:47:35,123 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:47:35,123 Logging anything other than scalars to TensorBoard is currently not supported.
+2023-10-24 22:47:43,574 epoch 1 - iter 72/723 - loss 1.90099622 - time (sec): 8.45 - samples/sec: 2093.32 - lr: 0.000005 - momentum: 0.000000
+2023-10-24 22:47:52,328 epoch 1 - iter 144/723 - loss 1.09087996 - time (sec): 17.20 - samples/sec: 2047.05 - lr: 0.000010 - momentum: 0.000000
+2023-10-24 22:48:01,244 epoch 1 - iter 216/723 - loss 0.77964600 - time (sec): 26.12 - samples/sec: 2072.80 - lr: 0.000015 - momentum: 0.000000
+2023-10-24 22:48:09,443 epoch 1 - iter 288/723 - loss 0.63708806 - time (sec): 34.32 - samples/sec: 2055.00 - lr: 0.000020 - momentum: 0.000000
+2023-10-24 22:48:17,547 epoch 1 - iter 360/723 - loss 0.54313247 - time (sec): 42.42 - samples/sec: 2054.17 - lr: 0.000025 - momentum: 0.000000
+2023-10-24 22:48:25,862 epoch 1 - iter 432/723 - loss 0.48284963 - time (sec): 50.74 - samples/sec: 2053.75 - lr: 0.000030 - momentum: 0.000000
+2023-10-24 22:48:34,173 epoch 1 - iter 504/723 - loss 0.43417529 - time (sec): 59.05 - samples/sec: 2046.07 - lr: 0.000035 - momentum: 0.000000
+2023-10-24 22:48:43,285 epoch 1 - iter 576/723 - loss 0.39662721 - time (sec): 68.16 - samples/sec: 2037.13 - lr: 0.000040 - momentum: 0.000000
+2023-10-24 22:48:51,944 epoch 1 - iter 648/723 - loss 0.36490823 - time (sec): 76.82 - samples/sec: 2044.94 - lr: 0.000045 - momentum: 0.000000
+2023-10-24 22:49:01,070 epoch 1 - iter 720/723 - loss 0.33855338 - time (sec): 85.95 - samples/sec: 2044.95 - lr: 0.000050 - momentum: 0.000000
+2023-10-24 22:49:01,321 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:49:01,321 EPOCH 1 done: loss 0.3381 - lr: 0.000050
+2023-10-24 22:49:04,603 DEV : loss 0.13358977437019348 - f1-score (micro avg)  0.5559
+2023-10-24 22:49:04,615 saving best model
+2023-10-24 22:49:05,173 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:49:13,532 epoch 2 - iter 72/723 - loss 0.11563641 - time (sec): 8.36 - samples/sec: 2039.66 - lr: 0.000049 - momentum: 0.000000
+2023-10-24 22:49:21,466 epoch 2 - iter 144/723 - loss 0.11274613 - time (sec): 16.29 - samples/sec: 2051.20 - lr: 0.000049 - momentum: 0.000000
+2023-10-24 22:49:29,822 epoch 2 - iter 216/723 - loss 0.10902633 - time (sec): 24.65 - samples/sec: 2053.51 - lr: 0.000048 - momentum: 0.000000
+2023-10-24 22:49:38,964 epoch 2 - iter 288/723 - loss 0.10367207 - time (sec): 33.79 - samples/sec: 2049.90 - lr: 0.000048 - momentum: 0.000000
+2023-10-24 22:49:48,274 epoch 2 - iter 360/723 - loss 0.09939488 - time (sec): 43.10 - samples/sec: 2053.73 - lr: 0.000047 - momentum: 0.000000
+2023-10-24 22:49:57,605 epoch 2 - iter 432/723 - loss 0.09748051 - time (sec): 52.43 - samples/sec: 2046.48 - lr: 0.000047 - momentum: 0.000000
+2023-10-24 22:50:06,005 epoch 2 - iter 504/723 - loss 0.09514922 - time (sec): 60.83 - samples/sec: 2045.92 - lr: 0.000046 - momentum: 0.000000
+2023-10-24 22:50:13,666 epoch 2 - iter 576/723 - loss 0.09875432 - time (sec): 68.49 - samples/sec: 2047.89 - lr: 0.000046 - momentum: 0.000000
+2023-10-24 22:50:22,101 epoch 2 - iter 648/723 - loss 0.09818627 - time (sec): 76.93 - samples/sec: 2048.27 - lr: 0.000045 - momentum: 0.000000
+2023-10-24 22:50:30,670 epoch 2 - iter 720/723 - loss 0.09775475 - time (sec): 85.50 - samples/sec: 2053.69 - lr: 0.000044 - momentum: 0.000000
+2023-10-24 22:50:30,916 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:50:30,916 EPOCH 2 done: loss 0.0977 - lr: 0.000044
+2023-10-24 22:50:34,642 DEV : loss 0.09923986345529556 - f1-score (micro avg)  0.7434
+2023-10-24 22:50:34,654 saving best model
+2023-10-24 22:50:35,378 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:50:44,044 epoch 3 - iter 72/723 - loss 0.07128608 - time (sec): 8.66 - samples/sec: 2017.83 - lr: 0.000044 - momentum: 0.000000
+2023-10-24 22:50:52,525 epoch 3 - iter 144/723 - loss 0.05959143 - time (sec): 17.15 - samples/sec: 2039.09 - lr: 0.000043 - momentum: 0.000000
+2023-10-24 22:51:00,812 epoch 3 - iter 216/723 - loss 0.06657607 - time (sec): 25.43 - samples/sec: 2055.01 - lr: 0.000043 - momentum: 0.000000
+2023-10-24 22:51:09,580 epoch 3 - iter 288/723 - loss 0.06577637 - time (sec): 34.20 - samples/sec: 2060.35 - lr: 0.000042 - momentum: 0.000000
+2023-10-24 22:51:18,393 epoch 3 - iter 360/723 - loss 0.06437789 - time (sec): 43.01 - samples/sec: 2050.83 - lr: 0.000042 - momentum: 0.000000
+2023-10-24 22:51:27,526 epoch 3 - iter 432/723 - loss 0.06488597 - time (sec): 52.15 - samples/sec: 2052.47 - lr: 0.000041 - momentum: 0.000000
+2023-10-24 22:51:35,847 epoch 3 - iter 504/723 - loss 0.06625285 - time (sec): 60.47 - samples/sec: 2041.55 - lr: 0.000041 - momentum: 0.000000
+2023-10-24 22:51:44,188 epoch 3 - iter 576/723 - loss 0.06508266 - time (sec): 68.81 - samples/sec: 2036.26 - lr: 0.000040 - momentum: 0.000000
+2023-10-24 22:51:52,876 epoch 3 - iter 648/723 - loss 0.06525563 - time (sec): 77.50 - samples/sec: 2036.55 - lr: 0.000039 - momentum: 0.000000
+2023-10-24 22:52:01,622 epoch 3 - iter 720/723 - loss 0.06417277 - time (sec): 86.24 - samples/sec: 2039.48 - lr: 0.000039 - momentum: 0.000000
+2023-10-24 22:52:01,826 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:52:01,827 EPOCH 3 done: loss 0.0642 - lr: 0.000039
+2023-10-24 22:52:05,562 DEV : loss 0.08431313186883926 - f1-score (micro avg)  0.8162
+2023-10-24 22:52:05,574 saving best model
+2023-10-24 22:52:06,287 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:52:14,602 epoch 4 - iter 72/723 - loss 0.04275318 - time (sec): 8.31 - samples/sec: 2104.71 - lr: 0.000038 - momentum: 0.000000
+2023-10-24 22:52:23,182 epoch 4 - iter 144/723 - loss 0.04122534 - time (sec): 16.89 - samples/sec: 2056.08 - lr: 0.000038 - momentum: 0.000000
+2023-10-24 22:52:30,986 epoch 4 - iter 216/723 - loss 0.04387147 - time (sec): 24.70 - samples/sec: 2053.76 - lr: 0.000037 - momentum: 0.000000
+2023-10-24 22:52:39,468 epoch 4 - iter 288/723 - loss 0.04477684 - time (sec): 33.18 - samples/sec: 2026.76 - lr: 0.000037 - momentum: 0.000000
+2023-10-24 22:52:48,425 epoch 4 - iter 360/723 - loss 0.04492124 - time (sec): 42.14 - samples/sec: 2037.30 - lr: 0.000036 - momentum: 0.000000
+2023-10-24 22:52:57,346 epoch 4 - iter 432/723 - loss 0.04583451 - time (sec): 51.06 - samples/sec: 2038.10 - lr: 0.000036 - momentum: 0.000000
+2023-10-24 22:53:06,429 epoch 4 - iter 504/723 - loss 0.04611188 - time (sec): 60.14 - samples/sec: 2037.05 - lr: 0.000035 - momentum: 0.000000
+2023-10-24 22:53:15,120 epoch 4 - iter 576/723 - loss 0.04469752 - time (sec): 68.83 - samples/sec: 2040.59 - lr: 0.000034 - momentum: 0.000000
+2023-10-24 22:53:23,881 epoch 4 - iter 648/723 - loss 0.04421455 - time (sec): 77.59 - samples/sec: 2036.88 - lr: 0.000034 - momentum: 0.000000
+2023-10-24 22:53:32,384 epoch 4 - iter 720/723 - loss 0.04381095 - time (sec): 86.10 - samples/sec: 2041.83 - lr: 0.000033 - momentum: 0.000000
+2023-10-24 22:53:32,612 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:53:32,612 EPOCH 4 done: loss 0.0440 - lr: 0.000033
+2023-10-24 22:53:36,048 DEV : loss 0.0921085774898529 - f1-score (micro avg)  0.8061
+2023-10-24 22:53:36,059 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:53:45,135 epoch 5 - iter 72/723 - loss 0.02981632 - time (sec): 9.07 - samples/sec: 2016.43 - lr: 0.000033 - momentum: 0.000000
+2023-10-24 22:53:54,262 epoch 5 - iter 144/723 - loss 0.03293464 - time (sec): 18.20 - samples/sec: 1965.96 - lr: 0.000032 - momentum: 0.000000
+2023-10-24 22:54:02,987 epoch 5 - iter 216/723 - loss 0.02903229 - time (sec): 26.93 - samples/sec: 1981.65 - lr: 0.000032 - momentum: 0.000000
+2023-10-24 22:54:12,480 epoch 5 - iter 288/723 - loss 0.02921469 - time (sec): 36.42 - samples/sec: 1984.35 - lr: 0.000031 - momentum: 0.000000
+2023-10-24 22:54:20,920 epoch 5 - iter 360/723 - loss 0.03035987 - time (sec): 44.86 - samples/sec: 1994.23 - lr: 0.000031 - momentum: 0.000000
+2023-10-24 22:54:29,626 epoch 5 - iter 432/723 - loss 0.03109785 - time (sec): 53.57 - samples/sec: 2009.90 - lr: 0.000030 - momentum: 0.000000
+2023-10-24 22:54:37,390 epoch 5 - iter 504/723 - loss 0.03348098 - time (sec): 61.33 - samples/sec: 2014.44 - lr: 0.000029 - momentum: 0.000000
+2023-10-24 22:54:46,312 epoch 5 - iter 576/723 - loss 0.03272635 - time (sec): 70.25 - samples/sec: 2013.74 - lr: 0.000029 - momentum: 0.000000
+2023-10-24 22:54:54,679 epoch 5 - iter 648/723 - loss 0.03250020 - time (sec): 78.62 - samples/sec: 2009.22 - lr: 0.000028 - momentum: 0.000000
+2023-10-24 22:55:03,105 epoch 5 - iter 720/723 - loss 0.03217828 - time (sec): 87.04 - samples/sec: 2015.60 - lr: 0.000028 - momentum: 0.000000
+2023-10-24 22:55:03,508 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:55:03,508 EPOCH 5 done: loss 0.0323 - lr: 0.000028
+2023-10-24 22:55:06,952 DEV : loss 0.12793748080730438 - f1-score (micro avg)  0.8201
+2023-10-24 22:55:06,964 saving best model
+2023-10-24 22:55:07,671 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:55:16,444 epoch 6 - iter 72/723 - loss 0.01771827 - time (sec): 8.77 - samples/sec: 1953.71 - lr: 0.000027 - momentum: 0.000000
+2023-10-24 22:55:24,853 epoch 6 - iter 144/723 - loss 0.02179612 - time (sec): 17.18 - samples/sec: 1999.81 - lr: 0.000027 - momentum: 0.000000
+2023-10-24 22:55:34,164 epoch 6 - iter 216/723 - loss 0.02091712 - time (sec): 26.49 - samples/sec: 2012.54 - lr: 0.000026 - momentum: 0.000000
+2023-10-24 22:55:42,847 epoch 6 - iter 288/723 - loss 0.02115975 - time (sec): 35.18 - samples/sec: 1995.33 - lr: 0.000026 - momentum: 0.000000
+2023-10-24 22:55:51,268 epoch 6 - iter 360/723 - loss 0.02279607 - time (sec): 43.60 - samples/sec: 2003.54 - lr: 0.000025 - momentum: 0.000000
+2023-10-24 22:55:59,917 epoch 6 - iter 432/723 - loss 0.02299399 - time (sec): 52.25 - samples/sec: 2015.37 - lr: 0.000024 - momentum: 0.000000
+2023-10-24 22:56:08,369 epoch 6 - iter 504/723 - loss 0.02253209 - time (sec): 60.70 - samples/sec: 2032.10 - lr: 0.000024 - momentum: 0.000000
+2023-10-24 22:56:16,964 epoch 6 - iter 576/723 - loss 0.02318425 - time (sec): 69.29 - samples/sec: 2032.54 - lr: 0.000023 - momentum: 0.000000
+2023-10-24 22:56:25,279 epoch 6 - iter 648/723 - loss 0.02365793 - time (sec): 77.61 - samples/sec: 2042.94 - lr: 0.000023 - momentum: 0.000000
+2023-10-24 22:56:33,600 epoch 6 - iter 720/723 - loss 0.02442479 - time (sec): 85.93 - samples/sec: 2044.40 - lr: 0.000022 - momentum: 0.000000
+2023-10-24 22:56:33,868 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:56:33,869 EPOCH 6 done: loss 0.0244 - lr: 0.000022
+2023-10-24 22:56:37,590 DEV : loss 0.13028167188167572 - f1-score (micro avg)  0.8206
+2023-10-24 22:56:37,602 saving best model
+2023-10-24 22:56:38,305 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:56:46,715 epoch 7 - iter 72/723 - loss 0.01204270 - time (sec): 8.41 - samples/sec: 2127.69 - lr: 0.000022 - momentum: 0.000000
+2023-10-24 22:56:55,800 epoch 7 - iter 144/723 - loss 0.01568493 - time (sec): 17.49 - samples/sec: 2021.54 - lr: 0.000021 - momentum: 0.000000
+2023-10-24 22:57:04,172 epoch 7 - iter 216/723 - loss 0.01685436 - time (sec): 25.87 - samples/sec: 2034.94 - lr: 0.000021 - momentum: 0.000000
+2023-10-24 22:57:12,899 epoch 7 - iter 288/723 - loss 0.01586004 - time (sec): 34.59 - samples/sec: 2049.11 - lr: 0.000020 - momentum: 0.000000
+2023-10-24 22:57:22,002 epoch 7 - iter 360/723 - loss 0.01669915 - time (sec): 43.70 - samples/sec: 2039.42 - lr: 0.000019 - momentum: 0.000000
+2023-10-24 22:57:30,281 epoch 7 - iter 432/723 - loss 0.01664053 - time (sec): 51.98 - samples/sec: 2027.06 - lr: 0.000019 - momentum: 0.000000
+2023-10-24 22:57:38,651 epoch 7 - iter 504/723 - loss 0.01665777 - time (sec): 60.35 - samples/sec: 2027.21 - lr: 0.000018 - momentum: 0.000000
+2023-10-24 22:57:47,215 epoch 7 - iter 576/723 - loss 0.01672193 - time (sec): 68.91 - samples/sec: 2029.42 - lr: 0.000018 - momentum: 0.000000
+2023-10-24 22:57:56,065 epoch 7 - iter 648/723 - loss 0.01634593 - time (sec): 77.76 - samples/sec: 2032.35 - lr: 0.000017 - momentum: 0.000000
+2023-10-24 22:58:04,677 epoch 7 - iter 720/723 - loss 0.01622942 - time (sec): 86.37 - samples/sec: 2032.46 - lr: 0.000017 - momentum: 0.000000
+2023-10-24 22:58:05,043 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:58:05,043 EPOCH 7 done: loss 0.0162 - lr: 0.000017
+2023-10-24 22:58:08,477 DEV : loss 0.16047385334968567 - f1-score (micro avg)  0.8284
+2023-10-24 22:58:08,489 saving best model
+2023-10-24 22:58:09,187 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:58:18,118 epoch 8 - iter 72/723 - loss 0.00701010 - time (sec): 8.93 - samples/sec: 1976.12 - lr: 0.000016 - momentum: 0.000000
+2023-10-24 22:58:27,226 epoch 8 - iter 144/723 - loss 0.00950195 - time (sec): 18.04 - samples/sec: 1966.17 - lr: 0.000016 - momentum: 0.000000
+2023-10-24 22:58:35,453 epoch 8 - iter 216/723 - loss 0.01036607 - time (sec): 26.26 - samples/sec: 2020.43 - lr: 0.000015 - momentum: 0.000000
+2023-10-24 22:58:44,819 epoch 8 - iter 288/723 - loss 0.01038046 - time (sec): 35.63 - samples/sec: 2054.12 - lr: 0.000014 - momentum: 0.000000
+2023-10-24 22:58:53,155 epoch 8 - iter 360/723 - loss 0.01050105 - time (sec): 43.97 - samples/sec: 2051.11 - lr: 0.000014 - momentum: 0.000000
+2023-10-24 22:59:01,634 epoch 8 - iter 432/723 - loss 0.01077764 - time (sec): 52.45 - samples/sec: 2053.71 - lr: 0.000013 - momentum: 0.000000
+2023-10-24 22:59:10,359 epoch 8 - iter 504/723 - loss 0.01155176 - time (sec): 61.17 - samples/sec: 2044.08 - lr: 0.000013 - momentum: 0.000000
+2023-10-24 22:59:18,085 epoch 8 - iter 576/723 - loss 0.01170645 - time (sec): 68.90 - samples/sec: 2035.43 - lr: 0.000012 - momentum: 0.000000
+2023-10-24 22:59:26,361 epoch 8 - iter 648/723 - loss 0.01132420 - time (sec): 77.17 - samples/sec: 2036.57 - lr: 0.000012 - momentum: 0.000000
+2023-10-24 22:59:35,154 epoch 8 - iter 720/723 - loss 0.01110679 - time (sec): 85.97 - samples/sec: 2041.54 - lr: 0.000011 - momentum: 0.000000
+2023-10-24 22:59:35,626 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:59:35,626 EPOCH 8 done: loss 0.0111 - lr: 0.000011
+2023-10-24 22:59:39,060 DEV : loss 0.17271144688129425 - f1-score (micro avg)  0.8152
+2023-10-24 22:59:39,072 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:59:48,021 epoch 9 - iter 72/723 - loss 0.00436475 - time (sec): 8.95 - samples/sec: 2093.86 - lr: 0.000011 - momentum: 0.000000
+2023-10-24 22:59:55,966 epoch 9 - iter 144/723 - loss 0.00673411 - time (sec): 16.89 - samples/sec: 2075.39 - lr: 0.000010 - momentum: 0.000000
+2023-10-24 23:00:05,069 epoch 9 - iter 216/723 - loss 0.00655734 - time (sec): 26.00 - samples/sec: 2059.97 - lr: 0.000009 - momentum: 0.000000
+2023-10-24 23:00:13,711 epoch 9 - iter 288/723 - loss 0.00707595 - time (sec): 34.64 - samples/sec: 2050.59 - lr: 0.000009 - momentum: 0.000000
+2023-10-24 23:00:22,414 epoch 9 - iter 360/723 - loss 0.00702570 - time (sec): 43.34 - samples/sec: 2037.79 - lr: 0.000008 - momentum: 0.000000
+2023-10-24 23:00:30,933 epoch 9 - iter 432/723 - loss 0.00653348 - time (sec): 51.86 - samples/sec: 2047.10 - lr: 0.000008 - momentum: 0.000000
+2023-10-24 23:00:39,615 epoch 9 - iter 504/723 - loss 0.00730415 - time (sec): 60.54 - samples/sec: 2047.35 - lr: 0.000007 - momentum: 0.000000
+2023-10-24 23:00:47,843 epoch 9 - iter 576/723 - loss 0.00716059 - time (sec): 68.77 - samples/sec: 2051.75 - lr: 0.000007 - momentum: 0.000000
+2023-10-24 23:00:56,415 epoch 9 - iter 648/723 - loss 0.00716027 - time (sec): 77.34 - samples/sec: 2049.01 - lr: 0.000006 - momentum: 0.000000
+2023-10-24 23:01:05,125 epoch 9 - iter 720/723 - loss 0.00769558 - time (sec): 86.05 - samples/sec: 2043.30 - lr: 0.000006 - momentum: 0.000000
+2023-10-24 23:01:05,342 ----------------------------------------------------------------------------------------------------
+2023-10-24 23:01:05,342 EPOCH 9 done: loss 0.0077 - lr: 0.000006
+2023-10-24 23:01:09,068 DEV : loss 0.18762636184692383 - f1-score (micro avg)  0.8138
+2023-10-24 23:01:09,080 ----------------------------------------------------------------------------------------------------
+2023-10-24 23:01:17,501 epoch 10 - iter 72/723 - loss 0.00700037 - time (sec): 8.42 - samples/sec: 2072.79 - lr: 0.000005 - momentum: 0.000000
+2023-10-24 23:01:25,989 epoch 10 - iter 144/723 - loss 0.00538041 - time (sec): 16.91 - samples/sec: 2100.23 - lr: 0.000004 - momentum: 0.000000
+2023-10-24 23:01:34,941 epoch 10 - iter 216/723 - loss 0.00524047 - time (sec): 25.86 - samples/sec: 2105.37 - lr: 0.000004 - momentum: 0.000000
+2023-10-24 23:01:44,291 epoch 10 - iter 288/723 - loss 0.00587406 - time (sec): 35.21 - samples/sec: 2067.64 - lr: 0.000003 - momentum: 0.000000
+2023-10-24 23:01:52,710 epoch 10 - iter 360/723 - loss 0.00546117 - time (sec): 43.63 - samples/sec: 2052.35 - lr: 0.000003 - momentum: 0.000000
+2023-10-24 23:02:01,634 epoch 10 - iter 432/723 - loss 0.00525314 - time (sec): 52.55 - samples/sec: 2031.85 - lr: 0.000002 - momentum: 0.000000
+2023-10-24 23:02:10,260 epoch 10 - iter 504/723 - loss 0.00559956 - time (sec): 61.18 - samples/sec: 2028.96 - lr: 0.000002 - momentum: 0.000000
+2023-10-24 23:02:18,569 epoch 10 - iter 576/723 - loss 0.00567395 - time (sec): 69.49 - samples/sec: 2036.85 - lr: 0.000001 - momentum: 0.000000
+2023-10-24 23:02:27,420 epoch 10 - iter 648/723 - loss 0.00543119 - time (sec): 78.34 - samples/sec: 2025.44 - lr: 0.000001 - momentum: 0.000000
+2023-10-24 23:02:35,701 epoch 10 - iter 720/723 - loss 0.00550425 - time (sec): 86.62 - samples/sec: 2029.78 - lr: 0.000000 - momentum: 0.000000
+2023-10-24 23:02:35,912 ----------------------------------------------------------------------------------------------------
+2023-10-24 23:02:35,913 EPOCH 10 done: loss 0.0055 - lr: 0.000000
+2023-10-24 23:02:39,645 DEV : loss 0.19829346239566803 - f1-score (micro avg)  0.8156
+2023-10-24 23:02:40,213 ----------------------------------------------------------------------------------------------------
+2023-10-24 23:02:40,214 Loading model from best epoch ...
+2023-10-24 23:02:42,032 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
+2023-10-24 23:02:45,283 
+Results:
+- F-score (micro) 0.8006
+- F-score (macro) 0.6747
+- Accuracy 0.6799
+
+By class:
+              precision    recall  f1-score   support
+
+         PER     0.8527    0.7925    0.8215       482
+         LOC     0.8801    0.8013    0.8389       458
+         ORG     0.4231    0.3188    0.3636        69
+
+   micro avg     0.8408    0.7641    0.8006      1009
+   macro avg     0.7186    0.6376    0.6747      1009
+weighted avg     0.8357    0.7641    0.7981      1009
+
+2023-10-24 23:02:45,283 ----------------------------------------------------------------------------------------------------