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training.log

@@ -0,0 +1,509 @@
+2023-10-25 16:18:00,357 ----------------------------------------------------------------------------------------------------
+2023-10-25 16:18:00,358 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 16:18:00,359 ----------------------------------------------------------------------------------------------------
+2023-10-25 16:18:00,359 MultiCorpus: 14465 train + 1392 dev + 2432 test sentences
+ - NER_HIPE_2022 Corpus: 14465 train + 1392 dev + 2432 test sentences - /home/ubuntu/.flair/datasets/ner_hipe_2022/v2.1/letemps/fr/with_doc_seperator
+2023-10-25 16:18:00,359 ----------------------------------------------------------------------------------------------------
+2023-10-25 16:18:00,359 Train:  14465 sentences
+2023-10-25 16:18:00,359         (train_with_dev=False, train_with_test=False)
+2023-10-25 16:18:00,359 ----------------------------------------------------------------------------------------------------
+2023-10-25 16:18:00,359 Training Params:
+2023-10-25 16:18:00,359  - learning_rate: "5e-05" 
+2023-10-25 16:18:00,359  - mini_batch_size: "4"
+2023-10-25 16:18:00,359  - max_epochs: "10"
+2023-10-25 16:18:00,359  - shuffle: "True"
+2023-10-25 16:18:00,359 ----------------------------------------------------------------------------------------------------
+2023-10-25 16:18:00,359 Plugins:
+2023-10-25 16:18:00,359  - TensorboardLogger
+2023-10-25 16:18:00,359  - LinearScheduler | warmup_fraction: '0.1'
+2023-10-25 16:18:00,359 ----------------------------------------------------------------------------------------------------
+2023-10-25 16:18:00,359 Final evaluation on model from best epoch (best-model.pt)
+2023-10-25 16:18:00,359  - metric: "('micro avg', 'f1-score')"
+2023-10-25 16:18:00,359 ----------------------------------------------------------------------------------------------------
+2023-10-25 16:18:00,359 Computation:
+2023-10-25 16:18:00,359  - compute on device: cuda:0
+2023-10-25 16:18:00,359  - embedding storage: none
+2023-10-25 16:18:00,359 ----------------------------------------------------------------------------------------------------
+2023-10-25 16:18:00,359 Model training base path: "hmbench-letemps/fr-dbmdz/bert-base-historic-multilingual-64k-td-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4"
+2023-10-25 16:18:00,359 ----------------------------------------------------------------------------------------------------
+2023-10-25 16:18:00,359 ----------------------------------------------------------------------------------------------------
+2023-10-25 16:18:00,359 Logging anything other than scalars to TensorBoard is currently not supported.
+2023-10-25 16:18:22,857 epoch 1 - iter 361/3617 - loss 0.98721839 - time (sec): 22.50 - samples/sec: 1664.55 - lr: 0.000005 - momentum: 0.000000
+2023-10-25 16:18:45,720 epoch 1 - iter 722/3617 - loss 0.56997509 - time (sec): 45.36 - samples/sec: 1684.75 - lr: 0.000010 - momentum: 0.000000
+2023-10-25 16:19:08,178 epoch 1 - iter 1083/3617 - loss 0.42947665 - time (sec): 67.82 - samples/sec: 1669.91 - lr: 0.000015 - momentum: 0.000000
+2023-10-25 16:19:30,856 epoch 1 - iter 1444/3617 - loss 0.34909939 - time (sec): 90.50 - samples/sec: 1678.64 - lr: 0.000020 - momentum: 0.000000
+2023-10-25 16:19:53,548 epoch 1 - iter 1805/3617 - loss 0.30295916 - time (sec): 113.19 - samples/sec: 1677.11 - lr: 0.000025 - momentum: 0.000000
+2023-10-25 16:20:16,240 epoch 1 - iter 2166/3617 - loss 0.27214151 - time (sec): 135.88 - samples/sec: 1684.86 - lr: 0.000030 - momentum: 0.000000
+2023-10-25 16:20:38,797 epoch 1 - iter 2527/3617 - loss 0.25109284 - time (sec): 158.44 - samples/sec: 1682.20 - lr: 0.000035 - momentum: 0.000000
+2023-10-25 16:21:01,498 epoch 1 - iter 2888/3617 - loss 0.23564479 - time (sec): 181.14 - samples/sec: 1683.93 - lr: 0.000040 - momentum: 0.000000
+2023-10-25 16:21:24,087 epoch 1 - iter 3249/3617 - loss 0.22390402 - time (sec): 203.73 - samples/sec: 1680.45 - lr: 0.000045 - momentum: 0.000000
+2023-10-25 16:21:46,423 epoch 1 - iter 3610/3617 - loss 0.21417050 - time (sec): 226.06 - samples/sec: 1677.12 - lr: 0.000050 - momentum: 0.000000
+2023-10-25 16:21:46,872 ----------------------------------------------------------------------------------------------------
+2023-10-25 16:21:46,873 EPOCH 1 done: loss 0.2139 - lr: 0.000050
+2023-10-25 16:21:51,373 DEV : loss 0.1173805445432663 - f1-score (micro avg)  0.5928
+2023-10-25 16:21:51,394 saving best model
+2023-10-25 16:21:51,945 ----------------------------------------------------------------------------------------------------
+2023-10-25 16:22:14,928 epoch 2 - iter 361/3617 - loss 0.11371088 - time (sec): 22.98 - samples/sec: 1698.15 - lr: 0.000049 - momentum: 0.000000
+2023-10-25 16:22:37,559 epoch 2 - iter 722/3617 - loss 0.11014365 - time (sec): 45.61 - samples/sec: 1676.19 - lr: 0.000049 - momentum: 0.000000
+2023-10-25 16:23:00,367 epoch 2 - iter 1083/3617 - loss 0.10881076 - time (sec): 68.42 - samples/sec: 1668.81 - lr: 0.000048 - momentum: 0.000000
+2023-10-25 16:23:23,009 epoch 2 - iter 1444/3617 - loss 0.10813684 - time (sec): 91.06 - samples/sec: 1673.56 - lr: 0.000048 - momentum: 0.000000
+2023-10-25 16:23:45,565 epoch 2 - iter 1805/3617 - loss 0.10693539 - time (sec): 113.62 - samples/sec: 1661.67 - lr: 0.000047 - momentum: 0.000000
+2023-10-25 16:24:08,644 epoch 2 - iter 2166/3617 - loss 0.10638248 - time (sec): 136.70 - samples/sec: 1677.19 - lr: 0.000047 - momentum: 0.000000
+2023-10-25 16:24:31,270 epoch 2 - iter 2527/3617 - loss 0.10544641 - time (sec): 159.32 - samples/sec: 1672.56 - lr: 0.000046 - momentum: 0.000000
+2023-10-25 16:24:54,391 epoch 2 - iter 2888/3617 - loss 0.10605920 - time (sec): 182.45 - samples/sec: 1666.72 - lr: 0.000046 - momentum: 0.000000
+2023-10-25 16:25:17,015 epoch 2 - iter 3249/3617 - loss 0.10581619 - time (sec): 205.07 - samples/sec: 1670.42 - lr: 0.000045 - momentum: 0.000000
+2023-10-25 16:25:39,603 epoch 2 - iter 3610/3617 - loss 0.10642989 - time (sec): 227.66 - samples/sec: 1665.97 - lr: 0.000044 - momentum: 0.000000
+2023-10-25 16:25:40,033 ----------------------------------------------------------------------------------------------------
+2023-10-25 16:25:40,033 EPOCH 2 done: loss 0.1064 - lr: 0.000044
+2023-10-25 16:25:44,767 DEV : loss 0.12259281426668167 - f1-score (micro avg)  0.5151
+2023-10-25 16:25:44,790 ----------------------------------------------------------------------------------------------------
+2023-10-25 16:26:07,266 epoch 3 - iter 361/3617 - loss 0.07575995 - time (sec): 22.48 - samples/sec: 1670.01 - lr: 0.000044 - momentum: 0.000000
+2023-10-25 16:26:30,145 epoch 3 - iter 722/3617 - loss 0.07769008 - time (sec): 45.35 - samples/sec: 1678.96 - lr: 0.000043 - momentum: 0.000000
+2023-10-25 16:26:52,861 epoch 3 - iter 1083/3617 - loss 0.07934303 - time (sec): 68.07 - samples/sec: 1684.63 - lr: 0.000043 - momentum: 0.000000
+2023-10-25 16:27:15,407 epoch 3 - iter 1444/3617 - loss 0.08201725 - time (sec): 90.62 - samples/sec: 1671.59 - lr: 0.000042 - momentum: 0.000000
+2023-10-25 16:27:38,068 epoch 3 - iter 1805/3617 - loss 0.08413864 - time (sec): 113.28 - samples/sec: 1671.83 - lr: 0.000042 - momentum: 0.000000
+2023-10-25 16:28:00,588 epoch 3 - iter 2166/3617 - loss 0.08234846 - time (sec): 135.80 - samples/sec: 1678.32 - lr: 0.000041 - momentum: 0.000000
+2023-10-25 16:28:23,268 epoch 3 - iter 2527/3617 - loss 0.08197610 - time (sec): 158.48 - samples/sec: 1680.92 - lr: 0.000041 - momentum: 0.000000
+2023-10-25 16:28:45,562 epoch 3 - iter 2888/3617 - loss 0.08211964 - time (sec): 180.77 - samples/sec: 1675.98 - lr: 0.000040 - momentum: 0.000000
+2023-10-25 16:29:08,559 epoch 3 - iter 3249/3617 - loss 0.08210844 - time (sec): 203.77 - samples/sec: 1677.64 - lr: 0.000039 - momentum: 0.000000
+2023-10-25 16:29:31,049 epoch 3 - iter 3610/3617 - loss 0.08220886 - time (sec): 226.26 - samples/sec: 1675.52 - lr: 0.000039 - momentum: 0.000000
+2023-10-25 16:29:31,512 ----------------------------------------------------------------------------------------------------
+2023-10-25 16:29:31,513 EPOCH 3 done: loss 0.0822 - lr: 0.000039
+2023-10-25 16:29:36,772 DEV : loss 0.22915546596050262 - f1-score (micro avg)  0.6111
+2023-10-25 16:29:36,794 saving best model
+2023-10-25 16:29:37,445 ----------------------------------------------------------------------------------------------------
+2023-10-25 16:30:00,261 epoch 4 - iter 361/3617 - loss 0.05161137 - time (sec): 22.81 - samples/sec: 1693.39 - lr: 0.000038 - momentum: 0.000000
+2023-10-25 16:30:22,792 epoch 4 - iter 722/3617 - loss 0.05675682 - time (sec): 45.35 - samples/sec: 1702.28 - lr: 0.000038 - momentum: 0.000000
+2023-10-25 16:30:45,543 epoch 4 - iter 1083/3617 - loss 0.05633822 - time (sec): 68.10 - samples/sec: 1701.43 - lr: 0.000037 - momentum: 0.000000
+2023-10-25 16:31:08,137 epoch 4 - iter 1444/3617 - loss 0.05836208 - time (sec): 90.69 - samples/sec: 1674.64 - lr: 0.000037 - momentum: 0.000000
+2023-10-25 16:31:30,671 epoch 4 - iter 1805/3617 - loss 0.05808428 - time (sec): 113.22 - samples/sec: 1669.96 - lr: 0.000036 - momentum: 0.000000
+2023-10-25 16:31:53,629 epoch 4 - iter 2166/3617 - loss 0.05945872 - time (sec): 136.18 - samples/sec: 1682.21 - lr: 0.000036 - momentum: 0.000000
+2023-10-25 16:32:16,372 epoch 4 - iter 2527/3617 - loss 0.05987102 - time (sec): 158.93 - samples/sec: 1682.29 - lr: 0.000035 - momentum: 0.000000
+2023-10-25 16:32:38,940 epoch 4 - iter 2888/3617 - loss 0.06229445 - time (sec): 181.49 - samples/sec: 1679.33 - lr: 0.000034 - momentum: 0.000000
+2023-10-25 16:33:01,490 epoch 4 - iter 3249/3617 - loss 0.06184988 - time (sec): 204.04 - samples/sec: 1677.22 - lr: 0.000034 - momentum: 0.000000
+2023-10-25 16:33:23,989 epoch 4 - iter 3610/3617 - loss 0.06154159 - time (sec): 226.54 - samples/sec: 1673.55 - lr: 0.000033 - momentum: 0.000000
+2023-10-25 16:33:24,442 ----------------------------------------------------------------------------------------------------
+2023-10-25 16:33:24,442 EPOCH 4 done: loss 0.0615 - lr: 0.000033
+2023-10-25 16:33:29,699 DEV : loss 0.2458053082227707 - f1-score (micro avg)  0.6126
+2023-10-25 16:33:29,720 saving best model
+2023-10-25 16:33:30,419 ----------------------------------------------------------------------------------------------------
+2023-10-25 16:33:53,141 epoch 5 - iter 361/3617 - loss 0.03679615 - time (sec): 22.72 - samples/sec: 1636.48 - lr: 0.000033 - momentum: 0.000000
+2023-10-25 16:34:15,545 epoch 5 - iter 722/3617 - loss 0.03349966 - time (sec): 45.13 - samples/sec: 1643.87 - lr: 0.000032 - momentum: 0.000000
+2023-10-25 16:34:38,200 epoch 5 - iter 1083/3617 - loss 0.03405818 - time (sec): 67.78 - samples/sec: 1655.79 - lr: 0.000032 - momentum: 0.000000
+2023-10-25 16:35:00,655 epoch 5 - iter 1444/3617 - loss 0.03703588 - time (sec): 90.23 - samples/sec: 1659.92 - lr: 0.000031 - momentum: 0.000000
+2023-10-25 16:35:23,213 epoch 5 - iter 1805/3617 - loss 0.04191627 - time (sec): 112.79 - samples/sec: 1665.63 - lr: 0.000031 - momentum: 0.000000
+2023-10-25 16:35:45,708 epoch 5 - iter 2166/3617 - loss 0.04211938 - time (sec): 135.29 - samples/sec: 1659.86 - lr: 0.000030 - momentum: 0.000000
+2023-10-25 16:36:08,304 epoch 5 - iter 2527/3617 - loss 0.04356578 - time (sec): 157.88 - samples/sec: 1658.25 - lr: 0.000029 - momentum: 0.000000
+2023-10-25 16:36:31,295 epoch 5 - iter 2888/3617 - loss 0.04317565 - time (sec): 180.87 - samples/sec: 1673.08 - lr: 0.000029 - momentum: 0.000000
+2023-10-25 16:36:53,829 epoch 5 - iter 3249/3617 - loss 0.04414571 - time (sec): 203.41 - samples/sec: 1668.42 - lr: 0.000028 - momentum: 0.000000
+2023-10-25 16:37:16,685 epoch 5 - iter 3610/3617 - loss 0.04383334 - time (sec): 226.27 - samples/sec: 1676.42 - lr: 0.000028 - momentum: 0.000000
+2023-10-25 16:37:17,090 ----------------------------------------------------------------------------------------------------
+2023-10-25 16:37:17,090 EPOCH 5 done: loss 0.0439 - lr: 0.000028
+2023-10-25 16:37:22,367 DEV : loss 0.29450729489326477 - f1-score (micro avg)  0.6228
+2023-10-25 16:37:22,389 saving best model
+2023-10-25 16:37:23,094 ----------------------------------------------------------------------------------------------------
+2023-10-25 16:37:45,762 epoch 6 - iter 361/3617 - loss 0.02401422 - time (sec): 22.67 - samples/sec: 1686.95 - lr: 0.000027 - momentum: 0.000000
+2023-10-25 16:38:08,579 epoch 6 - iter 722/3617 - loss 0.02513291 - time (sec): 45.48 - samples/sec: 1662.53 - lr: 0.000027 - momentum: 0.000000
+2023-10-25 16:38:31,531 epoch 6 - iter 1083/3617 - loss 0.02688665 - time (sec): 68.44 - samples/sec: 1693.79 - lr: 0.000026 - momentum: 0.000000
+2023-10-25 16:38:53,910 epoch 6 - iter 1444/3617 - loss 0.02741538 - time (sec): 90.81 - samples/sec: 1683.32 - lr: 0.000026 - momentum: 0.000000
+2023-10-25 16:39:16,700 epoch 6 - iter 1805/3617 - loss 0.02832321 - time (sec): 113.61 - samples/sec: 1689.63 - lr: 0.000025 - momentum: 0.000000
+2023-10-25 16:39:39,108 epoch 6 - iter 2166/3617 - loss 0.02884619 - time (sec): 136.01 - samples/sec: 1688.38 - lr: 0.000024 - momentum: 0.000000
+2023-10-25 16:40:01,861 epoch 6 - iter 2527/3617 - loss 0.02937217 - time (sec): 158.77 - samples/sec: 1686.15 - lr: 0.000024 - momentum: 0.000000
+2023-10-25 16:40:24,473 epoch 6 - iter 2888/3617 - loss 0.03055198 - time (sec): 181.38 - samples/sec: 1681.50 - lr: 0.000023 - momentum: 0.000000
+2023-10-25 16:40:46,890 epoch 6 - iter 3249/3617 - loss 0.03075395 - time (sec): 203.80 - samples/sec: 1673.96 - lr: 0.000023 - momentum: 0.000000
+2023-10-25 16:41:09,565 epoch 6 - iter 3610/3617 - loss 0.03165445 - time (sec): 226.47 - samples/sec: 1674.14 - lr: 0.000022 - momentum: 0.000000
+2023-10-25 16:41:10,008 ----------------------------------------------------------------------------------------------------
+2023-10-25 16:41:10,008 EPOCH 6 done: loss 0.0316 - lr: 0.000022
+2023-10-25 16:41:15,282 DEV : loss 0.31113916635513306 - f1-score (micro avg)  0.6275
+2023-10-25 16:41:15,304 saving best model
+2023-10-25 16:41:16,055 ----------------------------------------------------------------------------------------------------
+2023-10-25 16:41:38,656 epoch 7 - iter 361/3617 - loss 0.01966350 - time (sec): 22.60 - samples/sec: 1685.33 - lr: 0.000022 - momentum: 0.000000
+2023-10-25 16:42:01,294 epoch 7 - iter 722/3617 - loss 0.02181364 - time (sec): 45.24 - samples/sec: 1689.13 - lr: 0.000021 - momentum: 0.000000
+2023-10-25 16:42:24,009 epoch 7 - iter 1083/3617 - loss 0.02052075 - time (sec): 67.95 - samples/sec: 1682.75 - lr: 0.000021 - momentum: 0.000000
+2023-10-25 16:42:46,833 epoch 7 - iter 1444/3617 - loss 0.02198526 - time (sec): 90.78 - samples/sec: 1691.60 - lr: 0.000020 - momentum: 0.000000
+2023-10-25 16:43:09,244 epoch 7 - iter 1805/3617 - loss 0.02198388 - time (sec): 113.19 - samples/sec: 1682.17 - lr: 0.000019 - momentum: 0.000000
+2023-10-25 16:43:31,998 epoch 7 - iter 2166/3617 - loss 0.02099104 - time (sec): 135.94 - samples/sec: 1687.85 - lr: 0.000019 - momentum: 0.000000
+2023-10-25 16:43:54,612 epoch 7 - iter 2527/3617 - loss 0.02101164 - time (sec): 158.56 - samples/sec: 1687.44 - lr: 0.000018 - momentum: 0.000000
+2023-10-25 16:44:17,265 epoch 7 - iter 2888/3617 - loss 0.02107248 - time (sec): 181.21 - samples/sec: 1680.63 - lr: 0.000018 - momentum: 0.000000
+2023-10-25 16:44:40,039 epoch 7 - iter 3249/3617 - loss 0.02067048 - time (sec): 203.98 - samples/sec: 1675.79 - lr: 0.000017 - momentum: 0.000000
+2023-10-25 16:45:02,575 epoch 7 - iter 3610/3617 - loss 0.02054673 - time (sec): 226.52 - samples/sec: 1674.01 - lr: 0.000017 - momentum: 0.000000
+2023-10-25 16:45:03,027 ----------------------------------------------------------------------------------------------------
+2023-10-25 16:45:03,027 EPOCH 7 done: loss 0.0206 - lr: 0.000017
+2023-10-25 16:45:07,782 DEV : loss 0.3365882337093353 - f1-score (micro avg)  0.6271
+2023-10-25 16:45:07,804 ----------------------------------------------------------------------------------------------------
+2023-10-25 16:45:30,470 epoch 8 - iter 361/3617 - loss 0.01421589 - time (sec): 22.67 - samples/sec: 1710.89 - lr: 0.000016 - momentum: 0.000000
+2023-10-25 16:45:53,207 epoch 8 - iter 722/3617 - loss 0.01334565 - time (sec): 45.40 - samples/sec: 1682.77 - lr: 0.000016 - momentum: 0.000000
+2023-10-25 16:46:15,832 epoch 8 - iter 1083/3617 - loss 0.01298190 - time (sec): 68.03 - samples/sec: 1685.59 - lr: 0.000015 - momentum: 0.000000
+2023-10-25 16:46:38,501 epoch 8 - iter 1444/3617 - loss 0.01355953 - time (sec): 90.70 - samples/sec: 1678.39 - lr: 0.000014 - momentum: 0.000000
+2023-10-25 16:47:01,119 epoch 8 - iter 1805/3617 - loss 0.01318574 - time (sec): 113.31 - samples/sec: 1673.05 - lr: 0.000014 - momentum: 0.000000
+2023-10-25 16:47:23,681 epoch 8 - iter 2166/3617 - loss 0.01290110 - time (sec): 135.88 - samples/sec: 1674.03 - lr: 0.000013 - momentum: 0.000000
+2023-10-25 16:47:46,238 epoch 8 - iter 2527/3617 - loss 0.01331098 - time (sec): 158.43 - samples/sec: 1672.50 - lr: 0.000013 - momentum: 0.000000
+2023-10-25 16:48:09,195 epoch 8 - iter 2888/3617 - loss 0.01356070 - time (sec): 181.39 - samples/sec: 1662.42 - lr: 0.000012 - momentum: 0.000000
+2023-10-25 16:48:32,130 epoch 8 - iter 3249/3617 - loss 0.01314274 - time (sec): 204.33 - samples/sec: 1670.42 - lr: 0.000012 - momentum: 0.000000
+2023-10-25 16:48:54,868 epoch 8 - iter 3610/3617 - loss 0.01341847 - time (sec): 227.06 - samples/sec: 1670.28 - lr: 0.000011 - momentum: 0.000000
+2023-10-25 16:48:55,285 ----------------------------------------------------------------------------------------------------
+2023-10-25 16:48:55,285 EPOCH 8 done: loss 0.0134 - lr: 0.000011
+2023-10-25 16:49:00,055 DEV : loss 0.40507254004478455 - f1-score (micro avg)  0.6314
+2023-10-25 16:49:00,077 saving best model
+2023-10-25 16:49:00,828 ----------------------------------------------------------------------------------------------------
+2023-10-25 16:49:23,521 epoch 9 - iter 361/3617 - loss 0.00754713 - time (sec): 22.69 - samples/sec: 1713.09 - lr: 0.000011 - momentum: 0.000000
+2023-10-25 16:49:45,939 epoch 9 - iter 722/3617 - loss 0.01019330 - time (sec): 45.11 - samples/sec: 1669.29 - lr: 0.000010 - momentum: 0.000000
+2023-10-25 16:50:08,674 epoch 9 - iter 1083/3617 - loss 0.00909325 - time (sec): 67.84 - samples/sec: 1678.55 - lr: 0.000009 - momentum: 0.000000
+2023-10-25 16:50:31,541 epoch 9 - iter 1444/3617 - loss 0.00920364 - time (sec): 90.71 - samples/sec: 1677.07 - lr: 0.000009 - momentum: 0.000000
+2023-10-25 16:50:54,228 epoch 9 - iter 1805/3617 - loss 0.00936195 - time (sec): 113.40 - samples/sec: 1685.90 - lr: 0.000008 - momentum: 0.000000
+2023-10-25 16:51:16,646 epoch 9 - iter 2166/3617 - loss 0.00947121 - time (sec): 135.82 - samples/sec: 1674.45 - lr: 0.000008 - momentum: 0.000000
+2023-10-25 16:51:39,304 epoch 9 - iter 2527/3617 - loss 0.00953719 - time (sec): 158.48 - samples/sec: 1668.32 - lr: 0.000007 - momentum: 0.000000
+2023-10-25 16:52:02,093 epoch 9 - iter 2888/3617 - loss 0.00923108 - time (sec): 181.26 - samples/sec: 1673.08 - lr: 0.000007 - momentum: 0.000000
+2023-10-25 16:52:24,810 epoch 9 - iter 3249/3617 - loss 0.00883401 - time (sec): 203.98 - samples/sec: 1673.03 - lr: 0.000006 - momentum: 0.000000
+2023-10-25 16:52:47,507 epoch 9 - iter 3610/3617 - loss 0.00869020 - time (sec): 226.68 - samples/sec: 1673.24 - lr: 0.000006 - momentum: 0.000000
+2023-10-25 16:52:47,929 ----------------------------------------------------------------------------------------------------
+2023-10-25 16:52:47,929 EPOCH 9 done: loss 0.0087 - lr: 0.000006
+2023-10-25 16:52:53,216 DEV : loss 0.3974364399909973 - f1-score (micro avg)  0.6335
+2023-10-25 16:52:53,238 saving best model
+2023-10-25 16:52:53,901 ----------------------------------------------------------------------------------------------------
+2023-10-25 16:53:16,928 epoch 10 - iter 361/3617 - loss 0.00451968 - time (sec): 23.03 - samples/sec: 1742.22 - lr: 0.000005 - momentum: 0.000000
+2023-10-25 16:53:39,365 epoch 10 - iter 722/3617 - loss 0.00518291 - time (sec): 45.46 - samples/sec: 1692.54 - lr: 0.000004 - momentum: 0.000000
+2023-10-25 16:54:01,876 epoch 10 - iter 1083/3617 - loss 0.00458772 - time (sec): 67.97 - samples/sec: 1681.79 - lr: 0.000004 - momentum: 0.000000
+2023-10-25 16:54:24,404 epoch 10 - iter 1444/3617 - loss 0.00486760 - time (sec): 90.50 - samples/sec: 1676.06 - lr: 0.000003 - momentum: 0.000000
+2023-10-25 16:54:47,079 epoch 10 - iter 1805/3617 - loss 0.00489244 - time (sec): 113.18 - samples/sec: 1670.27 - lr: 0.000003 - momentum: 0.000000
+2023-10-25 16:55:09,930 epoch 10 - iter 2166/3617 - loss 0.00530223 - time (sec): 136.03 - samples/sec: 1676.93 - lr: 0.000002 - momentum: 0.000000
+2023-10-25 16:55:32,746 epoch 10 - iter 2527/3617 - loss 0.00531784 - time (sec): 158.84 - samples/sec: 1675.88 - lr: 0.000002 - momentum: 0.000000
+2023-10-25 16:55:55,505 epoch 10 - iter 2888/3617 - loss 0.00516961 - time (sec): 181.60 - samples/sec: 1679.56 - lr: 0.000001 - momentum: 0.000000
+2023-10-25 16:56:17,939 epoch 10 - iter 3249/3617 - loss 0.00499057 - time (sec): 204.04 - samples/sec: 1673.16 - lr: 0.000001 - momentum: 0.000000
+2023-10-25 16:56:40,508 epoch 10 - iter 3610/3617 - loss 0.00490336 - time (sec): 226.61 - samples/sec: 1673.80 - lr: 0.000000 - momentum: 0.000000
+2023-10-25 16:56:40,926 ----------------------------------------------------------------------------------------------------
+2023-10-25 16:56:40,927 EPOCH 10 done: loss 0.0049 - lr: 0.000000
+2023-10-25 16:56:46,237 DEV : loss 0.41693753004074097 - f1-score (micro avg)  0.6372
+2023-10-25 16:56:46,259 saving best model
+2023-10-25 16:56:47,509 ----------------------------------------------------------------------------------------------------
+2023-10-25 16:56:47,510 Loading model from best epoch ...
+2023-10-25 16:56:49,299 SequenceTagger predicts: Dictionary with 13 tags: O, S-loc, B-loc, E-loc, I-loc, S-pers, B-pers, E-pers, I-pers, S-org, B-org, E-org, I-org
+2023-10-25 16:56:55,016 
+Results:
+- F-score (micro) 0.6271
+- F-score (macro) 0.4748
+- Accuracy 0.4707
+
+By class:
+              precision    recall  f1-score   support
+
+         loc     0.6173    0.7259    0.6672       591
+        pers     0.5689    0.7171    0.6344       357
+         org     0.2000    0.0886    0.1228        79
+
+   micro avg     0.5864    0.6738    0.6271      1027
+   macro avg     0.4621    0.5105    0.4748      1027
+weighted avg     0.5684    0.6738    0.6139      1027
+
+2023-10-25 16:56:55,016 ----------------------------------------------------------------------------------------------------