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training.log

@@ -0,0 +1,506 @@
+2023-10-25 01:57:10,020 ----------------------------------------------------------------------------------------------------
+2023-10-25 01:57:10,021 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 01:57:10,021 ----------------------------------------------------------------------------------------------------
+2023-10-25 01:57:10,021 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 01:57:10,021 ----------------------------------------------------------------------------------------------------
+2023-10-25 01:57:10,021 Train:  5777 sentences
+2023-10-25 01:57:10,021         (train_with_dev=False, train_with_test=False)
+2023-10-25 01:57:10,021 ----------------------------------------------------------------------------------------------------
+2023-10-25 01:57:10,021 Training Params:
+2023-10-25 01:57:10,021  - learning_rate: "3e-05" 
+2023-10-25 01:57:10,021  - mini_batch_size: "8"
+2023-10-25 01:57:10,021  - max_epochs: "10"
+2023-10-25 01:57:10,021  - shuffle: "True"
+2023-10-25 01:57:10,021 ----------------------------------------------------------------------------------------------------
+2023-10-25 01:57:10,021 Plugins:
+2023-10-25 01:57:10,021  - TensorboardLogger
+2023-10-25 01:57:10,021  - LinearScheduler | warmup_fraction: '0.1'
+2023-10-25 01:57:10,021 ----------------------------------------------------------------------------------------------------
+2023-10-25 01:57:10,021 Final evaluation on model from best epoch (best-model.pt)
+2023-10-25 01:57:10,021  - metric: "('micro avg', 'f1-score')"
+2023-10-25 01:57:10,022 ----------------------------------------------------------------------------------------------------
+2023-10-25 01:57:10,022 Computation:
+2023-10-25 01:57:10,022  - compute on device: cuda:0
+2023-10-25 01:57:10,022  - embedding storage: none
+2023-10-25 01:57:10,022 ----------------------------------------------------------------------------------------------------
+2023-10-25 01:57:10,022 Model training base path: "hmbench-icdar/nl-dbmdz/bert-base-historic-multilingual-64k-td-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4"
+2023-10-25 01:57:10,022 ----------------------------------------------------------------------------------------------------
+2023-10-25 01:57:10,022 ----------------------------------------------------------------------------------------------------
+2023-10-25 01:57:10,022 Logging anything other than scalars to TensorBoard is currently not supported.
+2023-10-25 01:57:18,564 epoch 1 - iter 72/723 - loss 1.76923904 - time (sec): 8.54 - samples/sec: 2081.38 - lr: 0.000003 - momentum: 0.000000
+2023-10-25 01:57:27,609 epoch 1 - iter 144/723 - loss 1.02194984 - time (sec): 17.59 - samples/sec: 2076.99 - lr: 0.000006 - momentum: 0.000000
+2023-10-25 01:57:36,349 epoch 1 - iter 216/723 - loss 0.76572650 - time (sec): 26.33 - samples/sec: 2059.56 - lr: 0.000009 - momentum: 0.000000
+2023-10-25 01:57:44,054 epoch 1 - iter 288/723 - loss 0.63100097 - time (sec): 34.03 - samples/sec: 2065.69 - lr: 0.000012 - momentum: 0.000000
+2023-10-25 01:57:52,520 epoch 1 - iter 360/723 - loss 0.53899991 - time (sec): 42.50 - samples/sec: 2046.32 - lr: 0.000015 - momentum: 0.000000
+2023-10-25 01:58:00,735 epoch 1 - iter 432/723 - loss 0.47542277 - time (sec): 50.71 - samples/sec: 2052.53 - lr: 0.000018 - momentum: 0.000000
+2023-10-25 01:58:09,949 epoch 1 - iter 504/723 - loss 0.42433087 - time (sec): 59.93 - samples/sec: 2063.60 - lr: 0.000021 - momentum: 0.000000
+2023-10-25 01:58:17,923 epoch 1 - iter 576/723 - loss 0.39260870 - time (sec): 67.90 - samples/sec: 2064.22 - lr: 0.000024 - momentum: 0.000000
+2023-10-25 01:58:26,808 epoch 1 - iter 648/723 - loss 0.36287974 - time (sec): 76.79 - samples/sec: 2059.99 - lr: 0.000027 - momentum: 0.000000
+2023-10-25 01:58:35,478 epoch 1 - iter 720/723 - loss 0.33846946 - time (sec): 85.46 - samples/sec: 2055.41 - lr: 0.000030 - momentum: 0.000000
+2023-10-25 01:58:35,775 ----------------------------------------------------------------------------------------------------
+2023-10-25 01:58:35,775 EPOCH 1 done: loss 0.3379 - lr: 0.000030
+2023-10-25 01:58:39,066 DEV : loss 0.11231282353401184 - f1-score (micro avg)  0.674
+2023-10-25 01:58:39,078 saving best model
+2023-10-25 01:58:39,545 ----------------------------------------------------------------------------------------------------
+2023-10-25 01:58:48,181 epoch 2 - iter 72/723 - loss 0.09877093 - time (sec): 8.64 - samples/sec: 2036.26 - lr: 0.000030 - momentum: 0.000000
+2023-10-25 01:58:56,556 epoch 2 - iter 144/723 - loss 0.10109280 - time (sec): 17.01 - samples/sec: 2056.82 - lr: 0.000029 - momentum: 0.000000
+2023-10-25 01:59:05,625 epoch 2 - iter 216/723 - loss 0.09847841 - time (sec): 26.08 - samples/sec: 2047.40 - lr: 0.000029 - momentum: 0.000000
+2023-10-25 01:59:14,560 epoch 2 - iter 288/723 - loss 0.09458357 - time (sec): 35.01 - samples/sec: 2059.57 - lr: 0.000029 - momentum: 0.000000
+2023-10-25 01:59:23,407 epoch 2 - iter 360/723 - loss 0.09619714 - time (sec): 43.86 - samples/sec: 2056.17 - lr: 0.000028 - momentum: 0.000000
+2023-10-25 01:59:32,184 epoch 2 - iter 432/723 - loss 0.09830646 - time (sec): 52.64 - samples/sec: 2040.11 - lr: 0.000028 - momentum: 0.000000
+2023-10-25 01:59:40,263 epoch 2 - iter 504/723 - loss 0.09849915 - time (sec): 60.72 - samples/sec: 2044.13 - lr: 0.000028 - momentum: 0.000000
+2023-10-25 01:59:48,249 epoch 2 - iter 576/723 - loss 0.09981080 - time (sec): 68.70 - samples/sec: 2042.04 - lr: 0.000027 - momentum: 0.000000
+2023-10-25 01:59:56,577 epoch 2 - iter 648/723 - loss 0.09929029 - time (sec): 77.03 - samples/sec: 2042.02 - lr: 0.000027 - momentum: 0.000000
+2023-10-25 02:00:05,960 epoch 2 - iter 720/723 - loss 0.09794570 - time (sec): 86.41 - samples/sec: 2033.04 - lr: 0.000027 - momentum: 0.000000
+2023-10-25 02:00:06,223 ----------------------------------------------------------------------------------------------------
+2023-10-25 02:00:06,223 EPOCH 2 done: loss 0.0980 - lr: 0.000027
+2023-10-25 02:00:09,924 DEV : loss 0.07828789204359055 - f1-score (micro avg)  0.806
+2023-10-25 02:00:09,935 saving best model
+2023-10-25 02:00:10,523 ----------------------------------------------------------------------------------------------------
+2023-10-25 02:00:18,673 epoch 3 - iter 72/723 - loss 0.06198109 - time (sec): 8.15 - samples/sec: 2012.08 - lr: 0.000026 - momentum: 0.000000
+2023-10-25 02:00:27,884 epoch 3 - iter 144/723 - loss 0.06527874 - time (sec): 17.36 - samples/sec: 2022.88 - lr: 0.000026 - momentum: 0.000000
+2023-10-25 02:00:37,322 epoch 3 - iter 216/723 - loss 0.06427805 - time (sec): 26.80 - samples/sec: 2020.15 - lr: 0.000026 - momentum: 0.000000
+2023-10-25 02:00:45,394 epoch 3 - iter 288/723 - loss 0.06175598 - time (sec): 34.87 - samples/sec: 2037.50 - lr: 0.000025 - momentum: 0.000000
+2023-10-25 02:00:54,010 epoch 3 - iter 360/723 - loss 0.06098889 - time (sec): 43.49 - samples/sec: 2039.72 - lr: 0.000025 - momentum: 0.000000
+2023-10-25 02:01:02,672 epoch 3 - iter 432/723 - loss 0.06065212 - time (sec): 52.15 - samples/sec: 2028.25 - lr: 0.000025 - momentum: 0.000000
+2023-10-25 02:01:11,492 epoch 3 - iter 504/723 - loss 0.06224962 - time (sec): 60.97 - samples/sec: 2025.42 - lr: 0.000024 - momentum: 0.000000
+2023-10-25 02:01:19,924 epoch 3 - iter 576/723 - loss 0.06131250 - time (sec): 69.40 - samples/sec: 2032.42 - lr: 0.000024 - momentum: 0.000000
+2023-10-25 02:01:28,012 epoch 3 - iter 648/723 - loss 0.06236989 - time (sec): 77.49 - samples/sec: 2030.94 - lr: 0.000024 - momentum: 0.000000
+2023-10-25 02:01:36,993 epoch 3 - iter 720/723 - loss 0.06197838 - time (sec): 86.47 - samples/sec: 2030.66 - lr: 0.000023 - momentum: 0.000000
+2023-10-25 02:01:37,283 ----------------------------------------------------------------------------------------------------
+2023-10-25 02:01:37,283 EPOCH 3 done: loss 0.0619 - lr: 0.000023
+2023-10-25 02:01:40,715 DEV : loss 0.07889249920845032 - f1-score (micro avg)  0.8187
+2023-10-25 02:01:40,727 saving best model
+2023-10-25 02:01:41,604 ----------------------------------------------------------------------------------------------------
+2023-10-25 02:01:49,477 epoch 4 - iter 72/723 - loss 0.03547065 - time (sec): 7.87 - samples/sec: 2115.66 - lr: 0.000023 - momentum: 0.000000
+2023-10-25 02:01:57,245 epoch 4 - iter 144/723 - loss 0.03615245 - time (sec): 15.64 - samples/sec: 2088.46 - lr: 0.000023 - momentum: 0.000000
+2023-10-25 02:02:06,293 epoch 4 - iter 216/723 - loss 0.03524641 - time (sec): 24.69 - samples/sec: 2067.24 - lr: 0.000022 - momentum: 0.000000
+2023-10-25 02:02:15,868 epoch 4 - iter 288/723 - loss 0.03601960 - time (sec): 34.26 - samples/sec: 2036.14 - lr: 0.000022 - momentum: 0.000000
+2023-10-25 02:02:24,460 epoch 4 - iter 360/723 - loss 0.03839060 - time (sec): 42.86 - samples/sec: 2035.86 - lr: 0.000022 - momentum: 0.000000
+2023-10-25 02:02:32,074 epoch 4 - iter 432/723 - loss 0.03960256 - time (sec): 50.47 - samples/sec: 2039.66 - lr: 0.000021 - momentum: 0.000000
+2023-10-25 02:02:41,518 epoch 4 - iter 504/723 - loss 0.03882792 - time (sec): 59.91 - samples/sec: 2043.37 - lr: 0.000021 - momentum: 0.000000
+2023-10-25 02:02:50,069 epoch 4 - iter 576/723 - loss 0.04084126 - time (sec): 68.46 - samples/sec: 2051.86 - lr: 0.000021 - momentum: 0.000000
+2023-10-25 02:02:59,175 epoch 4 - iter 648/723 - loss 0.04148523 - time (sec): 77.57 - samples/sec: 2039.34 - lr: 0.000020 - momentum: 0.000000
+2023-10-25 02:03:07,642 epoch 4 - iter 720/723 - loss 0.04215552 - time (sec): 86.04 - samples/sec: 2040.91 - lr: 0.000020 - momentum: 0.000000
+2023-10-25 02:03:08,002 ----------------------------------------------------------------------------------------------------
+2023-10-25 02:03:08,002 EPOCH 4 done: loss 0.0422 - lr: 0.000020
+2023-10-25 02:03:11,428 DEV : loss 0.08857569843530655 - f1-score (micro avg)  0.8152
+2023-10-25 02:03:11,440 ----------------------------------------------------------------------------------------------------
+2023-10-25 02:03:20,580 epoch 5 - iter 72/723 - loss 0.03032337 - time (sec): 9.14 - samples/sec: 2016.26 - lr: 0.000020 - momentum: 0.000000
+2023-10-25 02:03:28,862 epoch 5 - iter 144/723 - loss 0.03022295 - time (sec): 17.42 - samples/sec: 2045.28 - lr: 0.000019 - momentum: 0.000000
+2023-10-25 02:03:37,784 epoch 5 - iter 216/723 - loss 0.03156000 - time (sec): 26.34 - samples/sec: 2026.79 - lr: 0.000019 - momentum: 0.000000
+2023-10-25 02:03:46,416 epoch 5 - iter 288/723 - loss 0.03054376 - time (sec): 34.98 - samples/sec: 2024.47 - lr: 0.000019 - momentum: 0.000000
+2023-10-25 02:03:55,592 epoch 5 - iter 360/723 - loss 0.03181466 - time (sec): 44.15 - samples/sec: 2018.45 - lr: 0.000018 - momentum: 0.000000
+2023-10-25 02:04:04,054 epoch 5 - iter 432/723 - loss 0.03195174 - time (sec): 52.61 - samples/sec: 2031.68 - lr: 0.000018 - momentum: 0.000000
+2023-10-25 02:04:13,084 epoch 5 - iter 504/723 - loss 0.03082310 - time (sec): 61.64 - samples/sec: 2019.75 - lr: 0.000018 - momentum: 0.000000
+2023-10-25 02:04:21,429 epoch 5 - iter 576/723 - loss 0.03053546 - time (sec): 69.99 - samples/sec: 2021.67 - lr: 0.000017 - momentum: 0.000000
+2023-10-25 02:04:29,957 epoch 5 - iter 648/723 - loss 0.03077615 - time (sec): 78.52 - samples/sec: 2018.21 - lr: 0.000017 - momentum: 0.000000
+2023-10-25 02:04:38,508 epoch 5 - iter 720/723 - loss 0.03132395 - time (sec): 87.07 - samples/sec: 2017.06 - lr: 0.000017 - momentum: 0.000000
+2023-10-25 02:04:38,820 ----------------------------------------------------------------------------------------------------
+2023-10-25 02:04:38,820 EPOCH 5 done: loss 0.0313 - lr: 0.000017
+2023-10-25 02:04:42,571 DEV : loss 0.13429175317287445 - f1-score (micro avg)  0.8056
+2023-10-25 02:04:42,583 ----------------------------------------------------------------------------------------------------
+2023-10-25 02:04:51,054 epoch 6 - iter 72/723 - loss 0.01367169 - time (sec): 8.47 - samples/sec: 2080.33 - lr: 0.000016 - momentum: 0.000000
+2023-10-25 02:04:59,541 epoch 6 - iter 144/723 - loss 0.01585753 - time (sec): 16.96 - samples/sec: 2057.02 - lr: 0.000016 - momentum: 0.000000
+2023-10-25 02:05:08,192 epoch 6 - iter 216/723 - loss 0.02218546 - time (sec): 25.61 - samples/sec: 2067.32 - lr: 0.000016 - momentum: 0.000000
+2023-10-25 02:05:16,920 epoch 6 - iter 288/723 - loss 0.02190850 - time (sec): 34.34 - samples/sec: 2058.93 - lr: 0.000015 - momentum: 0.000000
+2023-10-25 02:05:26,395 epoch 6 - iter 360/723 - loss 0.02267499 - time (sec): 43.81 - samples/sec: 2046.85 - lr: 0.000015 - momentum: 0.000000
+2023-10-25 02:05:35,128 epoch 6 - iter 432/723 - loss 0.02297097 - time (sec): 52.54 - samples/sec: 2041.22 - lr: 0.000015 - momentum: 0.000000
+2023-10-25 02:05:43,638 epoch 6 - iter 504/723 - loss 0.02330794 - time (sec): 61.05 - samples/sec: 2029.07 - lr: 0.000014 - momentum: 0.000000
+2023-10-25 02:05:52,188 epoch 6 - iter 576/723 - loss 0.02379736 - time (sec): 69.60 - samples/sec: 2023.47 - lr: 0.000014 - momentum: 0.000000
+2023-10-25 02:06:01,280 epoch 6 - iter 648/723 - loss 0.02466991 - time (sec): 78.70 - samples/sec: 2020.22 - lr: 0.000014 - momentum: 0.000000
+2023-10-25 02:06:09,367 epoch 6 - iter 720/723 - loss 0.02437194 - time (sec): 86.78 - samples/sec: 2024.28 - lr: 0.000013 - momentum: 0.000000
+2023-10-25 02:06:09,695 ----------------------------------------------------------------------------------------------------
+2023-10-25 02:06:09,695 EPOCH 6 done: loss 0.0245 - lr: 0.000013
+2023-10-25 02:06:13,131 DEV : loss 0.14080575108528137 - f1-score (micro avg)  0.8217
+2023-10-25 02:06:13,143 saving best model
+2023-10-25 02:06:13,735 ----------------------------------------------------------------------------------------------------
+2023-10-25 02:06:23,735 epoch 7 - iter 72/723 - loss 0.01098581 - time (sec): 10.00 - samples/sec: 1887.19 - lr: 0.000013 - momentum: 0.000000
+2023-10-25 02:06:33,054 epoch 7 - iter 144/723 - loss 0.01537901 - time (sec): 19.32 - samples/sec: 1911.08 - lr: 0.000013 - momentum: 0.000000
+2023-10-25 02:06:41,678 epoch 7 - iter 216/723 - loss 0.01497113 - time (sec): 27.94 - samples/sec: 1932.38 - lr: 0.000012 - momentum: 0.000000
+2023-10-25 02:06:50,197 epoch 7 - iter 288/723 - loss 0.01604563 - time (sec): 36.46 - samples/sec: 1964.82 - lr: 0.000012 - momentum: 0.000000
+2023-10-25 02:06:58,743 epoch 7 - iter 360/723 - loss 0.01505398 - time (sec): 45.01 - samples/sec: 1993.38 - lr: 0.000012 - momentum: 0.000000
+2023-10-25 02:07:06,856 epoch 7 - iter 432/723 - loss 0.01538597 - time (sec): 53.12 - samples/sec: 2011.13 - lr: 0.000011 - momentum: 0.000000
+2023-10-25 02:07:15,150 epoch 7 - iter 504/723 - loss 0.01617676 - time (sec): 61.41 - samples/sec: 2008.32 - lr: 0.000011 - momentum: 0.000000
+2023-10-25 02:07:23,595 epoch 7 - iter 576/723 - loss 0.01694236 - time (sec): 69.86 - samples/sec: 2013.89 - lr: 0.000011 - momentum: 0.000000
+2023-10-25 02:07:32,512 epoch 7 - iter 648/723 - loss 0.01645753 - time (sec): 78.78 - samples/sec: 2023.17 - lr: 0.000010 - momentum: 0.000000
+2023-10-25 02:07:40,519 epoch 7 - iter 720/723 - loss 0.01637803 - time (sec): 86.78 - samples/sec: 2025.51 - lr: 0.000010 - momentum: 0.000000
+2023-10-25 02:07:40,756 ----------------------------------------------------------------------------------------------------
+2023-10-25 02:07:40,757 EPOCH 7 done: loss 0.0164 - lr: 0.000010
+2023-10-25 02:07:44,192 DEV : loss 0.15570510923862457 - f1-score (micro avg)  0.8346
+2023-10-25 02:07:44,204 saving best model
+2023-10-25 02:07:44,786 ----------------------------------------------------------------------------------------------------
+2023-10-25 02:07:53,036 epoch 8 - iter 72/723 - loss 0.01309712 - time (sec): 8.25 - samples/sec: 2153.09 - lr: 0.000010 - momentum: 0.000000
+2023-10-25 02:08:01,661 epoch 8 - iter 144/723 - loss 0.01223716 - time (sec): 16.87 - samples/sec: 2119.06 - lr: 0.000009 - momentum: 0.000000
+2023-10-25 02:08:10,351 epoch 8 - iter 216/723 - loss 0.01227785 - time (sec): 25.56 - samples/sec: 2072.17 - lr: 0.000009 - momentum: 0.000000
+2023-10-25 02:08:19,092 epoch 8 - iter 288/723 - loss 0.01256366 - time (sec): 34.31 - samples/sec: 2062.39 - lr: 0.000009 - momentum: 0.000000
+2023-10-25 02:08:27,865 epoch 8 - iter 360/723 - loss 0.01225996 - time (sec): 43.08 - samples/sec: 2059.21 - lr: 0.000008 - momentum: 0.000000
+2023-10-25 02:08:36,583 epoch 8 - iter 432/723 - loss 0.01189296 - time (sec): 51.80 - samples/sec: 2067.58 - lr: 0.000008 - momentum: 0.000000
+2023-10-25 02:08:44,833 epoch 8 - iter 504/723 - loss 0.01204948 - time (sec): 60.05 - samples/sec: 2071.09 - lr: 0.000008 - momentum: 0.000000
+2023-10-25 02:08:52,800 epoch 8 - iter 576/723 - loss 0.01168071 - time (sec): 68.01 - samples/sec: 2062.87 - lr: 0.000007 - momentum: 0.000000
+2023-10-25 02:09:01,566 epoch 8 - iter 648/723 - loss 0.01278714 - time (sec): 76.78 - samples/sec: 2056.45 - lr: 0.000007 - momentum: 0.000000
+2023-10-25 02:09:10,329 epoch 8 - iter 720/723 - loss 0.01274436 - time (sec): 85.54 - samples/sec: 2052.17 - lr: 0.000007 - momentum: 0.000000
+2023-10-25 02:09:10,650 ----------------------------------------------------------------------------------------------------
+2023-10-25 02:09:10,650 EPOCH 8 done: loss 0.0127 - lr: 0.000007
+2023-10-25 02:09:14,365 DEV : loss 0.1717204749584198 - f1-score (micro avg)  0.8326
+2023-10-25 02:09:14,377 ----------------------------------------------------------------------------------------------------
+2023-10-25 02:09:23,098 epoch 9 - iter 72/723 - loss 0.00789912 - time (sec): 8.72 - samples/sec: 2081.85 - lr: 0.000006 - momentum: 0.000000
+2023-10-25 02:09:30,935 epoch 9 - iter 144/723 - loss 0.00864702 - time (sec): 16.56 - samples/sec: 2058.46 - lr: 0.000006 - momentum: 0.000000
+2023-10-25 02:09:39,734 epoch 9 - iter 216/723 - loss 0.00868959 - time (sec): 25.36 - samples/sec: 2036.57 - lr: 0.000006 - momentum: 0.000000
+2023-10-25 02:09:48,226 epoch 9 - iter 288/723 - loss 0.00778029 - time (sec): 33.85 - samples/sec: 2043.96 - lr: 0.000005 - momentum: 0.000000
+2023-10-25 02:09:56,296 epoch 9 - iter 360/723 - loss 0.00691008 - time (sec): 41.92 - samples/sec: 2048.82 - lr: 0.000005 - momentum: 0.000000
+2023-10-25 02:10:05,211 epoch 9 - iter 432/723 - loss 0.00740542 - time (sec): 50.83 - samples/sec: 2058.24 - lr: 0.000005 - momentum: 0.000000
+2023-10-25 02:10:13,970 epoch 9 - iter 504/723 - loss 0.00767085 - time (sec): 59.59 - samples/sec: 2060.52 - lr: 0.000004 - momentum: 0.000000
+2023-10-25 02:10:22,837 epoch 9 - iter 576/723 - loss 0.00813665 - time (sec): 68.46 - samples/sec: 2047.75 - lr: 0.000004 - momentum: 0.000000
+2023-10-25 02:10:31,665 epoch 9 - iter 648/723 - loss 0.00835648 - time (sec): 77.29 - samples/sec: 2046.98 - lr: 0.000004 - momentum: 0.000000
+2023-10-25 02:10:40,427 epoch 9 - iter 720/723 - loss 0.00850227 - time (sec): 86.05 - samples/sec: 2042.09 - lr: 0.000003 - momentum: 0.000000
+2023-10-25 02:10:40,686 ----------------------------------------------------------------------------------------------------
+2023-10-25 02:10:40,687 EPOCH 9 done: loss 0.0085 - lr: 0.000003
+2023-10-25 02:10:44,419 DEV : loss 0.1841525286436081 - f1-score (micro avg)  0.825
+2023-10-25 02:10:44,431 ----------------------------------------------------------------------------------------------------
+2023-10-25 02:10:52,780 epoch 10 - iter 72/723 - loss 0.00481871 - time (sec): 8.35 - samples/sec: 2007.01 - lr: 0.000003 - momentum: 0.000000
+2023-10-25 02:11:01,397 epoch 10 - iter 144/723 - loss 0.00626100 - time (sec): 16.97 - samples/sec: 2046.98 - lr: 0.000003 - momentum: 0.000000
+2023-10-25 02:11:09,955 epoch 10 - iter 216/723 - loss 0.00618329 - time (sec): 25.52 - samples/sec: 2053.89 - lr: 0.000002 - momentum: 0.000000
+2023-10-25 02:11:19,139 epoch 10 - iter 288/723 - loss 0.00580240 - time (sec): 34.71 - samples/sec: 2024.11 - lr: 0.000002 - momentum: 0.000000
+2023-10-25 02:11:27,711 epoch 10 - iter 360/723 - loss 0.00586626 - time (sec): 43.28 - samples/sec: 2019.33 - lr: 0.000002 - momentum: 0.000000
+2023-10-25 02:11:36,279 epoch 10 - iter 432/723 - loss 0.00601919 - time (sec): 51.85 - samples/sec: 2027.37 - lr: 0.000001 - momentum: 0.000000
+2023-10-25 02:11:44,889 epoch 10 - iter 504/723 - loss 0.00603383 - time (sec): 60.46 - samples/sec: 2033.59 - lr: 0.000001 - momentum: 0.000000
+2023-10-25 02:11:53,227 epoch 10 - iter 576/723 - loss 0.00737357 - time (sec): 68.80 - samples/sec: 2028.89 - lr: 0.000001 - momentum: 0.000000
+2023-10-25 02:12:01,809 epoch 10 - iter 648/723 - loss 0.00682436 - time (sec): 77.38 - samples/sec: 2028.68 - lr: 0.000000 - momentum: 0.000000
+2023-10-25 02:12:10,623 epoch 10 - iter 720/723 - loss 0.00665171 - time (sec): 86.19 - samples/sec: 2035.60 - lr: 0.000000 - momentum: 0.000000
+2023-10-25 02:12:10,917 ----------------------------------------------------------------------------------------------------
+2023-10-25 02:12:10,918 EPOCH 10 done: loss 0.0066 - lr: 0.000000
+2023-10-25 02:12:14,347 DEV : loss 0.19385258853435516 - f1-score (micro avg)  0.833
+2023-10-25 02:12:14,823 ----------------------------------------------------------------------------------------------------
+2023-10-25 02:12:14,823 Loading model from best epoch ...
+2023-10-25 02:12:16,332 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 02:12:19,857 
+Results:
+- F-score (micro) 0.8133
+- F-score (macro) 0.7041
+- Accuracy 0.6967
+
+By class:
+              precision    recall  f1-score   support
+
+         PER     0.8452    0.8154    0.8300       482
+         LOC     0.8847    0.8210    0.8516       458
+         ORG     0.4590    0.4058    0.4308        69
+
+   micro avg     0.8381    0.7899    0.8133      1009
+   macro avg     0.7296    0.6807    0.7041      1009
+weighted avg     0.8367    0.7899    0.8125      1009
+
+2023-10-25 02:12:19,857 ----------------------------------------------------------------------------------------------------