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training.log

@@ -0,0 +1,505 @@
+2023-10-24 22:31:59,069 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:31:59,070 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 22:31:59,071 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:31:59,071 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 22:31:59,071 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:31:59,071 Train:  5777 sentences
+2023-10-24 22:31:59,071         (train_with_dev=False, train_with_test=False)
+2023-10-24 22:31:59,071 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:31:59,071 Training Params:
+2023-10-24 22:31:59,071  - learning_rate: "3e-05" 
+2023-10-24 22:31:59,071  - mini_batch_size: "8"
+2023-10-24 22:31:59,071  - max_epochs: "10"
+2023-10-24 22:31:59,071  - shuffle: "True"
+2023-10-24 22:31:59,071 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:31:59,071 Plugins:
+2023-10-24 22:31:59,071  - TensorboardLogger
+2023-10-24 22:31:59,072  - LinearScheduler | warmup_fraction: '0.1'
+2023-10-24 22:31:59,072 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:31:59,072 Final evaluation on model from best epoch (best-model.pt)
+2023-10-24 22:31:59,072  - metric: "('micro avg', 'f1-score')"
+2023-10-24 22:31:59,072 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:31:59,072 Computation:
+2023-10-24 22:31:59,072  - compute on device: cuda:0
+2023-10-24 22:31:59,072  - embedding storage: none
+2023-10-24 22:31:59,072 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:31:59,072 Model training base path: "hmbench-icdar/nl-dbmdz/bert-base-historic-multilingual-64k-td-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1"
+2023-10-24 22:31:59,072 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:31:59,072 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:31:59,072 Logging anything other than scalars to TensorBoard is currently not supported.
+2023-10-24 22:32:07,563 epoch 1 - iter 72/723 - loss 2.31947311 - time (sec): 8.49 - samples/sec: 2083.66 - lr: 0.000003 - momentum: 0.000000
+2023-10-24 22:32:16,346 epoch 1 - iter 144/723 - loss 1.32909159 - time (sec): 17.27 - samples/sec: 2038.93 - lr: 0.000006 - momentum: 0.000000
+2023-10-24 22:32:25,292 epoch 1 - iter 216/723 - loss 0.94456255 - time (sec): 26.22 - samples/sec: 2064.96 - lr: 0.000009 - momentum: 0.000000
+2023-10-24 22:32:33,521 epoch 1 - iter 288/723 - loss 0.76663770 - time (sec): 34.45 - samples/sec: 2047.33 - lr: 0.000012 - momentum: 0.000000
+2023-10-24 22:32:41,645 epoch 1 - iter 360/723 - loss 0.64951811 - time (sec): 42.57 - samples/sec: 2047.01 - lr: 0.000015 - momentum: 0.000000
+2023-10-24 22:32:49,977 epoch 1 - iter 432/723 - loss 0.57340174 - time (sec): 50.90 - samples/sec: 2047.02 - lr: 0.000018 - momentum: 0.000000
+2023-10-24 22:32:58,323 epoch 1 - iter 504/723 - loss 0.51388230 - time (sec): 59.25 - samples/sec: 2039.16 - lr: 0.000021 - momentum: 0.000000
+2023-10-24 22:33:07,442 epoch 1 - iter 576/723 - loss 0.46626848 - time (sec): 68.37 - samples/sec: 2030.93 - lr: 0.000024 - momentum: 0.000000
+2023-10-24 22:33:16,119 epoch 1 - iter 648/723 - loss 0.42649097 - time (sec): 77.05 - samples/sec: 2038.95 - lr: 0.000027 - momentum: 0.000000
+2023-10-24 22:33:25,266 epoch 1 - iter 720/723 - loss 0.39365540 - time (sec): 86.19 - samples/sec: 2039.10 - lr: 0.000030 - momentum: 0.000000
+2023-10-24 22:33:25,516 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:33:25,516 EPOCH 1 done: loss 0.3931 - lr: 0.000030
+2023-10-24 22:33:28,789 DEV : loss 0.13080401718616486 - f1-score (micro avg)  0.5705
+2023-10-24 22:33:28,801 saving best model
+2023-10-24 22:33:29,271 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:33:37,630 epoch 2 - iter 72/723 - loss 0.11662981 - time (sec): 8.36 - samples/sec: 2039.80 - lr: 0.000030 - momentum: 0.000000
+2023-10-24 22:33:45,571 epoch 2 - iter 144/723 - loss 0.11114776 - time (sec): 16.30 - samples/sec: 2050.45 - lr: 0.000029 - momentum: 0.000000
+2023-10-24 22:33:53,911 epoch 2 - iter 216/723 - loss 0.10664717 - time (sec): 24.64 - samples/sec: 2054.36 - lr: 0.000029 - momentum: 0.000000
+2023-10-24 22:34:03,044 epoch 2 - iter 288/723 - loss 0.10255450 - time (sec): 33.77 - samples/sec: 2051.08 - lr: 0.000029 - momentum: 0.000000
+2023-10-24 22:34:12,351 epoch 2 - iter 360/723 - loss 0.09854358 - time (sec): 43.08 - samples/sec: 2054.75 - lr: 0.000028 - momentum: 0.000000
+2023-10-24 22:34:21,686 epoch 2 - iter 432/723 - loss 0.09691315 - time (sec): 52.41 - samples/sec: 2047.19 - lr: 0.000028 - momentum: 0.000000
+2023-10-24 22:34:30,078 epoch 2 - iter 504/723 - loss 0.09403782 - time (sec): 60.81 - samples/sec: 2046.78 - lr: 0.000028 - momentum: 0.000000
+2023-10-24 22:34:37,733 epoch 2 - iter 576/723 - loss 0.09689121 - time (sec): 68.46 - samples/sec: 2048.84 - lr: 0.000027 - momentum: 0.000000
+2023-10-24 22:34:46,175 epoch 2 - iter 648/723 - loss 0.09667821 - time (sec): 76.90 - samples/sec: 2048.92 - lr: 0.000027 - momentum: 0.000000
+2023-10-24 22:34:54,725 epoch 2 - iter 720/723 - loss 0.09635443 - time (sec): 85.45 - samples/sec: 2054.74 - lr: 0.000027 - momentum: 0.000000
+2023-10-24 22:34:54,971 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:34:54,971 EPOCH 2 done: loss 0.0964 - lr: 0.000027
+2023-10-24 22:34:58,678 DEV : loss 0.07759504020214081 - f1-score (micro avg)  0.8195
+2023-10-24 22:34:58,690 saving best model
+2023-10-24 22:34:59,285 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:35:07,943 epoch 3 - iter 72/723 - loss 0.06713425 - time (sec): 8.66 - samples/sec: 2019.48 - lr: 0.000026 - momentum: 0.000000
+2023-10-24 22:35:16,408 epoch 3 - iter 144/723 - loss 0.05848043 - time (sec): 17.12 - samples/sec: 2041.94 - lr: 0.000026 - momentum: 0.000000
+2023-10-24 22:35:24,682 epoch 3 - iter 216/723 - loss 0.06611272 - time (sec): 25.40 - samples/sec: 2057.98 - lr: 0.000026 - momentum: 0.000000
+2023-10-24 22:35:33,448 epoch 3 - iter 288/723 - loss 0.06373711 - time (sec): 34.16 - samples/sec: 2062.71 - lr: 0.000025 - momentum: 0.000000
+2023-10-24 22:35:42,258 epoch 3 - iter 360/723 - loss 0.06368511 - time (sec): 42.97 - samples/sec: 2052.83 - lr: 0.000025 - momentum: 0.000000
+2023-10-24 22:35:51,380 epoch 3 - iter 432/723 - loss 0.06405055 - time (sec): 52.09 - samples/sec: 2054.55 - lr: 0.000025 - momentum: 0.000000
+2023-10-24 22:35:59,698 epoch 3 - iter 504/723 - loss 0.06454130 - time (sec): 60.41 - samples/sec: 2043.44 - lr: 0.000024 - momentum: 0.000000
+2023-10-24 22:36:08,056 epoch 3 - iter 576/723 - loss 0.06344541 - time (sec): 68.77 - samples/sec: 2037.40 - lr: 0.000024 - momentum: 0.000000
+2023-10-24 22:36:16,742 epoch 3 - iter 648/723 - loss 0.06359618 - time (sec): 77.46 - samples/sec: 2037.63 - lr: 0.000024 - momentum: 0.000000
+2023-10-24 22:36:25,498 epoch 3 - iter 720/723 - loss 0.06294517 - time (sec): 86.21 - samples/sec: 2040.20 - lr: 0.000023 - momentum: 0.000000
+2023-10-24 22:36:25,702 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:36:25,702 EPOCH 3 done: loss 0.0631 - lr: 0.000023
+2023-10-24 22:36:29,121 DEV : loss 0.06691966950893402 - f1-score (micro avg)  0.8335
+2023-10-24 22:36:29,133 saving best model
+2023-10-24 22:36:29,728 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:36:38,347 epoch 4 - iter 72/723 - loss 0.04289293 - time (sec): 8.62 - samples/sec: 2030.37 - lr: 0.000023 - momentum: 0.000000
+2023-10-24 22:36:46,920 epoch 4 - iter 144/723 - loss 0.04393330 - time (sec): 17.19 - samples/sec: 2020.50 - lr: 0.000023 - momentum: 0.000000
+2023-10-24 22:36:54,721 epoch 4 - iter 216/723 - loss 0.04626933 - time (sec): 24.99 - samples/sec: 2029.60 - lr: 0.000022 - momentum: 0.000000
+2023-10-24 22:37:03,203 epoch 4 - iter 288/723 - loss 0.04679271 - time (sec): 33.47 - samples/sec: 2008.90 - lr: 0.000022 - momentum: 0.000000
+2023-10-24 22:37:12,171 epoch 4 - iter 360/723 - loss 0.04474950 - time (sec): 42.44 - samples/sec: 2022.65 - lr: 0.000022 - momentum: 0.000000
+2023-10-24 22:37:21,110 epoch 4 - iter 432/723 - loss 0.04661379 - time (sec): 51.38 - samples/sec: 2025.29 - lr: 0.000021 - momentum: 0.000000
+2023-10-24 22:37:30,179 epoch 4 - iter 504/723 - loss 0.04617695 - time (sec): 60.45 - samples/sec: 2026.61 - lr: 0.000021 - momentum: 0.000000
+2023-10-24 22:37:38,883 epoch 4 - iter 576/723 - loss 0.04515587 - time (sec): 69.15 - samples/sec: 2031.07 - lr: 0.000021 - momentum: 0.000000
+2023-10-24 22:37:47,642 epoch 4 - iter 648/723 - loss 0.04439814 - time (sec): 77.91 - samples/sec: 2028.48 - lr: 0.000020 - momentum: 0.000000
+2023-10-24 22:37:56,158 epoch 4 - iter 720/723 - loss 0.04362410 - time (sec): 86.43 - samples/sec: 2033.94 - lr: 0.000020 - momentum: 0.000000
+2023-10-24 22:37:56,386 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:37:56,387 EPOCH 4 done: loss 0.0437 - lr: 0.000020
+2023-10-24 22:37:59,816 DEV : loss 0.09226194024085999 - f1-score (micro avg)  0.8141
+2023-10-24 22:37:59,828 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:38:08,904 epoch 5 - iter 72/723 - loss 0.03491869 - time (sec): 9.08 - samples/sec: 2016.25 - lr: 0.000020 - momentum: 0.000000
+2023-10-24 22:38:18,027 epoch 5 - iter 144/723 - loss 0.03697398 - time (sec): 18.20 - samples/sec: 1966.32 - lr: 0.000019 - momentum: 0.000000
+2023-10-24 22:38:26,766 epoch 5 - iter 216/723 - loss 0.03299498 - time (sec): 26.94 - samples/sec: 1980.81 - lr: 0.000019 - momentum: 0.000000
+2023-10-24 22:38:36,258 epoch 5 - iter 288/723 - loss 0.03395920 - time (sec): 36.43 - samples/sec: 1983.77 - lr: 0.000019 - momentum: 0.000000
+2023-10-24 22:38:44,704 epoch 5 - iter 360/723 - loss 0.03351756 - time (sec): 44.88 - samples/sec: 1993.52 - lr: 0.000018 - momentum: 0.000000
+2023-10-24 22:38:53,420 epoch 5 - iter 432/723 - loss 0.03259007 - time (sec): 53.59 - samples/sec: 2008.90 - lr: 0.000018 - momentum: 0.000000
+2023-10-24 22:39:01,181 epoch 5 - iter 504/723 - loss 0.03251132 - time (sec): 61.35 - samples/sec: 2013.69 - lr: 0.000018 - momentum: 0.000000
+2023-10-24 22:39:09,786 epoch 5 - iter 576/723 - loss 0.03147036 - time (sec): 69.96 - samples/sec: 2022.20 - lr: 0.000017 - momentum: 0.000000
+2023-10-24 22:39:18,155 epoch 5 - iter 648/723 - loss 0.03152705 - time (sec): 78.33 - samples/sec: 2016.73 - lr: 0.000017 - momentum: 0.000000
+2023-10-24 22:39:26,576 epoch 5 - iter 720/723 - loss 0.03165355 - time (sec): 86.75 - samples/sec: 2022.49 - lr: 0.000017 - momentum: 0.000000
+2023-10-24 22:39:26,978 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:39:26,979 EPOCH 5 done: loss 0.0317 - lr: 0.000017
+2023-10-24 22:39:30,691 DEV : loss 0.1143888533115387 - f1-score (micro avg)  0.8319
+2023-10-24 22:39:30,703 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:39:39,467 epoch 6 - iter 72/723 - loss 0.01995720 - time (sec): 8.76 - samples/sec: 1955.67 - lr: 0.000016 - momentum: 0.000000
+2023-10-24 22:39:47,873 epoch 6 - iter 144/723 - loss 0.02213871 - time (sec): 17.17 - samples/sec: 2001.18 - lr: 0.000016 - momentum: 0.000000
+2023-10-24 22:39:57,180 epoch 6 - iter 216/723 - loss 0.02122386 - time (sec): 26.48 - samples/sec: 2013.81 - lr: 0.000016 - momentum: 0.000000
+2023-10-24 22:40:05,862 epoch 6 - iter 288/723 - loss 0.02103884 - time (sec): 35.16 - samples/sec: 1996.29 - lr: 0.000015 - momentum: 0.000000
+2023-10-24 22:40:14,288 epoch 6 - iter 360/723 - loss 0.02213591 - time (sec): 43.58 - samples/sec: 2004.08 - lr: 0.000015 - momentum: 0.000000
+2023-10-24 22:40:22,944 epoch 6 - iter 432/723 - loss 0.02306774 - time (sec): 52.24 - samples/sec: 2015.55 - lr: 0.000015 - momentum: 0.000000
+2023-10-24 22:40:31,393 epoch 6 - iter 504/723 - loss 0.02312191 - time (sec): 60.69 - samples/sec: 2032.38 - lr: 0.000014 - momentum: 0.000000
+2023-10-24 22:40:40,001 epoch 6 - iter 576/723 - loss 0.02377623 - time (sec): 69.30 - samples/sec: 2032.40 - lr: 0.000014 - momentum: 0.000000
+2023-10-24 22:40:48,317 epoch 6 - iter 648/723 - loss 0.02402287 - time (sec): 77.61 - samples/sec: 2042.80 - lr: 0.000014 - momentum: 0.000000
+2023-10-24 22:40:56,641 epoch 6 - iter 720/723 - loss 0.02443272 - time (sec): 85.94 - samples/sec: 2044.18 - lr: 0.000013 - momentum: 0.000000
+2023-10-24 22:40:56,909 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:40:56,910 EPOCH 6 done: loss 0.0244 - lr: 0.000013
+2023-10-24 22:41:00,364 DEV : loss 0.12616097927093506 - f1-score (micro avg)  0.8405
+2023-10-24 22:41:00,376 saving best model
+2023-10-24 22:41:01,246 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:41:09,652 epoch 7 - iter 72/723 - loss 0.01313625 - time (sec): 8.40 - samples/sec: 2128.95 - lr: 0.000013 - momentum: 0.000000
+2023-10-24 22:41:18,753 epoch 7 - iter 144/723 - loss 0.01797221 - time (sec): 17.51 - samples/sec: 2020.29 - lr: 0.000013 - momentum: 0.000000
+2023-10-24 22:41:27,131 epoch 7 - iter 216/723 - loss 0.01779934 - time (sec): 25.88 - samples/sec: 2033.59 - lr: 0.000012 - momentum: 0.000000
+2023-10-24 22:41:35,855 epoch 7 - iter 288/723 - loss 0.01742948 - time (sec): 34.61 - samples/sec: 2048.28 - lr: 0.000012 - momentum: 0.000000
+2023-10-24 22:41:44,958 epoch 7 - iter 360/723 - loss 0.01829595 - time (sec): 43.71 - samples/sec: 2038.76 - lr: 0.000012 - momentum: 0.000000
+2023-10-24 22:41:53,223 epoch 7 - iter 432/723 - loss 0.01860388 - time (sec): 51.98 - samples/sec: 2027.05 - lr: 0.000011 - momentum: 0.000000
+2023-10-24 22:42:01,586 epoch 7 - iter 504/723 - loss 0.01859120 - time (sec): 60.34 - samples/sec: 2027.45 - lr: 0.000011 - momentum: 0.000000
+2023-10-24 22:42:10,140 epoch 7 - iter 576/723 - loss 0.01846148 - time (sec): 68.89 - samples/sec: 2029.92 - lr: 0.000011 - momentum: 0.000000
+2023-10-24 22:42:18,997 epoch 7 - iter 648/723 - loss 0.01760306 - time (sec): 77.75 - samples/sec: 2032.59 - lr: 0.000010 - momentum: 0.000000
+2023-10-24 22:42:27,610 epoch 7 - iter 720/723 - loss 0.01743141 - time (sec): 86.36 - samples/sec: 2032.66 - lr: 0.000010 - momentum: 0.000000
+2023-10-24 22:42:27,974 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:42:27,975 EPOCH 7 done: loss 0.0174 - lr: 0.000010
+2023-10-24 22:42:31,416 DEV : loss 0.1625511348247528 - f1-score (micro avg)  0.8273
+2023-10-24 22:42:31,428 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:42:40,071 epoch 8 - iter 72/723 - loss 0.01217969 - time (sec): 8.64 - samples/sec: 2041.96 - lr: 0.000010 - momentum: 0.000000
+2023-10-24 22:42:49,190 epoch 8 - iter 144/723 - loss 0.01421228 - time (sec): 17.76 - samples/sec: 1996.70 - lr: 0.000009 - momentum: 0.000000
+2023-10-24 22:42:57,430 epoch 8 - iter 216/723 - loss 0.01295467 - time (sec): 26.00 - samples/sec: 2040.84 - lr: 0.000009 - momentum: 0.000000
+2023-10-24 22:43:06,802 epoch 8 - iter 288/723 - loss 0.01304675 - time (sec): 35.37 - samples/sec: 2069.04 - lr: 0.000009 - momentum: 0.000000
+2023-10-24 22:43:15,158 epoch 8 - iter 360/723 - loss 0.01147798 - time (sec): 43.73 - samples/sec: 2062.23 - lr: 0.000008 - momentum: 0.000000
+2023-10-24 22:43:23,633 epoch 8 - iter 432/723 - loss 0.01210736 - time (sec): 52.20 - samples/sec: 2063.22 - lr: 0.000008 - momentum: 0.000000
+2023-10-24 22:43:32,367 epoch 8 - iter 504/723 - loss 0.01300207 - time (sec): 60.94 - samples/sec: 2051.84 - lr: 0.000008 - momentum: 0.000000
+2023-10-24 22:43:40,103 epoch 8 - iter 576/723 - loss 0.01331943 - time (sec): 68.67 - samples/sec: 2042.03 - lr: 0.000007 - momentum: 0.000000
+2023-10-24 22:43:48,376 epoch 8 - iter 648/723 - loss 0.01306185 - time (sec): 76.95 - samples/sec: 2042.52 - lr: 0.000007 - momentum: 0.000000
+2023-10-24 22:43:57,171 epoch 8 - iter 720/723 - loss 0.01323653 - time (sec): 85.74 - samples/sec: 2046.87 - lr: 0.000007 - momentum: 0.000000
+2023-10-24 22:43:57,642 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:43:57,642 EPOCH 8 done: loss 0.0132 - lr: 0.000007
+2023-10-24 22:44:01,377 DEV : loss 0.14701317250728607 - f1-score (micro avg)  0.8396
+2023-10-24 22:44:01,389 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:44:10,338 epoch 9 - iter 72/723 - loss 0.00421793 - time (sec): 8.95 - samples/sec: 2094.09 - lr: 0.000006 - momentum: 0.000000
+2023-10-24 22:44:18,282 epoch 9 - iter 144/723 - loss 0.00746426 - time (sec): 16.89 - samples/sec: 2075.48 - lr: 0.000006 - momentum: 0.000000
+2023-10-24 22:44:27,384 epoch 9 - iter 216/723 - loss 0.00859736 - time (sec): 25.99 - samples/sec: 2060.21 - lr: 0.000006 - momentum: 0.000000
+2023-10-24 22:44:36,026 epoch 9 - iter 288/723 - loss 0.00966802 - time (sec): 34.64 - samples/sec: 2050.75 - lr: 0.000005 - momentum: 0.000000
+2023-10-24 22:44:44,734 epoch 9 - iter 360/723 - loss 0.00936446 - time (sec): 43.34 - samples/sec: 2037.71 - lr: 0.000005 - momentum: 0.000000
+2023-10-24 22:44:53,267 epoch 9 - iter 432/723 - loss 0.00876449 - time (sec): 51.88 - samples/sec: 2046.44 - lr: 0.000005 - momentum: 0.000000
+2023-10-24 22:45:01,957 epoch 9 - iter 504/723 - loss 0.00947271 - time (sec): 60.57 - samples/sec: 2046.52 - lr: 0.000004 - momentum: 0.000000
+2023-10-24 22:45:10,195 epoch 9 - iter 576/723 - loss 0.00911775 - time (sec): 68.81 - samples/sec: 2050.71 - lr: 0.000004 - momentum: 0.000000
+2023-10-24 22:45:18,773 epoch 9 - iter 648/723 - loss 0.00878954 - time (sec): 77.38 - samples/sec: 2047.93 - lr: 0.000004 - momentum: 0.000000
+2023-10-24 22:45:27,488 epoch 9 - iter 720/723 - loss 0.00902168 - time (sec): 86.10 - samples/sec: 2042.23 - lr: 0.000003 - momentum: 0.000000
+2023-10-24 22:45:27,703 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:45:27,703 EPOCH 9 done: loss 0.0090 - lr: 0.000003
+2023-10-24 22:45:31,141 DEV : loss 0.16477558016777039 - f1-score (micro avg)  0.8348
+2023-10-24 22:45:31,153 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:45:39,876 epoch 10 - iter 72/723 - loss 0.00532785 - time (sec): 8.72 - samples/sec: 2001.01 - lr: 0.000003 - momentum: 0.000000
+2023-10-24 22:45:48,365 epoch 10 - iter 144/723 - loss 0.00549018 - time (sec): 17.21 - samples/sec: 2063.17 - lr: 0.000003 - momentum: 0.000000
+2023-10-24 22:45:57,326 epoch 10 - iter 216/723 - loss 0.00530542 - time (sec): 26.17 - samples/sec: 2080.27 - lr: 0.000002 - momentum: 0.000000
+2023-10-24 22:46:06,688 epoch 10 - iter 288/723 - loss 0.00594591 - time (sec): 35.53 - samples/sec: 2048.79 - lr: 0.000002 - momentum: 0.000000
+2023-10-24 22:46:15,127 epoch 10 - iter 360/723 - loss 0.00637310 - time (sec): 43.97 - samples/sec: 2036.30 - lr: 0.000002 - momentum: 0.000000
+2023-10-24 22:46:24,052 epoch 10 - iter 432/723 - loss 0.00626112 - time (sec): 52.90 - samples/sec: 2018.61 - lr: 0.000001 - momentum: 0.000000
+2023-10-24 22:46:32,684 epoch 10 - iter 504/723 - loss 0.00700602 - time (sec): 61.53 - samples/sec: 2017.36 - lr: 0.000001 - momentum: 0.000000
+2023-10-24 22:46:41,008 epoch 10 - iter 576/723 - loss 0.00726040 - time (sec): 69.85 - samples/sec: 2026.17 - lr: 0.000001 - momentum: 0.000000
+2023-10-24 22:46:49,862 epoch 10 - iter 648/723 - loss 0.00745651 - time (sec): 78.71 - samples/sec: 2015.95 - lr: 0.000000 - momentum: 0.000000
+2023-10-24 22:46:58,135 epoch 10 - iter 720/723 - loss 0.00724114 - time (sec): 86.98 - samples/sec: 2021.35 - lr: 0.000000 - momentum: 0.000000
+2023-10-24 22:46:58,346 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:46:58,347 EPOCH 10 done: loss 0.0072 - lr: 0.000000
+2023-10-24 22:47:01,783 DEV : loss 0.16929292678833008 - f1-score (micro avg)  0.8392
+2023-10-24 22:47:02,271 ----------------------------------------------------------------------------------------------------
+2023-10-24 22:47:02,272 Loading model from best epoch ...
+2023-10-24 22:47:04,037 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 22:47:07,593 
+Results:
+- F-score (micro) 0.8156
+- F-score (macro) 0.6995
+- Accuracy 0.6985
+
+By class:
+              precision    recall  f1-score   support
+
+         PER     0.8537    0.8112    0.8319       482
+         LOC     0.8956    0.8057    0.8483       458
+         ORG     0.5610    0.3333    0.4182        69
+
+   micro avg     0.8595    0.7760    0.8156      1009
+   macro avg     0.7701    0.6501    0.6995      1009
+weighted avg     0.8527    0.7760    0.8110      1009
+
+2023-10-24 22:47:07,593 ----------------------------------------------------------------------------------------------------