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training.log

@@ -0,0 +1,504 @@
+2023-10-24 17:53:07,606 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:53:07,607 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 17:53:07,607 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:53:07,607 MultiCorpus: 7936 train + 992 dev + 992 test sentences
+ - NER_ICDAR_EUROPEANA Corpus: 7936 train + 992 dev + 992 test sentences - /home/ubuntu/.flair/datasets/ner_icdar_europeana/fr
+2023-10-24 17:53:07,607 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:53:07,607 Train:  7936 sentences
+2023-10-24 17:53:07,607         (train_with_dev=False, train_with_test=False)
+2023-10-24 17:53:07,607 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:53:07,607 Training Params:
+2023-10-24 17:53:07,607  - learning_rate: "5e-05" 
+2023-10-24 17:53:07,607  - mini_batch_size: "8"
+2023-10-24 17:53:07,607  - max_epochs: "10"
+2023-10-24 17:53:07,607  - shuffle: "True"
+2023-10-24 17:53:07,607 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:53:07,607 Plugins:
+2023-10-24 17:53:07,607  - TensorboardLogger
+2023-10-24 17:53:07,607  - LinearScheduler | warmup_fraction: '0.1'
+2023-10-24 17:53:07,607 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:53:07,607 Final evaluation on model from best epoch (best-model.pt)
+2023-10-24 17:53:07,608  - metric: "('micro avg', 'f1-score')"
+2023-10-24 17:53:07,608 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:53:07,608 Computation:
+2023-10-24 17:53:07,608  - compute on device: cuda:0
+2023-10-24 17:53:07,608  - embedding storage: none
+2023-10-24 17:53:07,608 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:53:07,608 Model training base path: "hmbench-icdar/fr-dbmdz/bert-base-historic-multilingual-64k-td-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3"
+2023-10-24 17:53:07,608 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:53:07,608 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:53:07,608 Logging anything other than scalars to TensorBoard is currently not supported.
+2023-10-24 17:53:16,110 epoch 1 - iter 99/992 - loss 1.45019353 - time (sec): 8.50 - samples/sec: 2052.11 - lr: 0.000005 - momentum: 0.000000
+2023-10-24 17:53:24,479 epoch 1 - iter 198/992 - loss 0.90651188 - time (sec): 16.87 - samples/sec: 1995.74 - lr: 0.000010 - momentum: 0.000000
+2023-10-24 17:53:32,526 epoch 1 - iter 297/992 - loss 0.68242155 - time (sec): 24.92 - samples/sec: 1970.50 - lr: 0.000015 - momentum: 0.000000
+2023-10-24 17:53:40,903 epoch 1 - iter 396/992 - loss 0.55232472 - time (sec): 33.29 - samples/sec: 1971.25 - lr: 0.000020 - momentum: 0.000000
+2023-10-24 17:53:49,010 epoch 1 - iter 495/992 - loss 0.47511537 - time (sec): 41.40 - samples/sec: 1964.36 - lr: 0.000025 - momentum: 0.000000
+2023-10-24 17:53:57,155 epoch 1 - iter 594/992 - loss 0.42026811 - time (sec): 49.55 - samples/sec: 1960.16 - lr: 0.000030 - momentum: 0.000000
+2023-10-24 17:54:05,777 epoch 1 - iter 693/992 - loss 0.37516782 - time (sec): 58.17 - samples/sec: 1957.77 - lr: 0.000035 - momentum: 0.000000
+2023-10-24 17:54:14,242 epoch 1 - iter 792/992 - loss 0.34277188 - time (sec): 66.63 - samples/sec: 1955.19 - lr: 0.000040 - momentum: 0.000000
+2023-10-24 17:54:22,644 epoch 1 - iter 891/992 - loss 0.32033942 - time (sec): 75.04 - samples/sec: 1963.07 - lr: 0.000045 - momentum: 0.000000
+2023-10-24 17:54:31,054 epoch 1 - iter 990/992 - loss 0.30228680 - time (sec): 83.45 - samples/sec: 1960.73 - lr: 0.000050 - momentum: 0.000000
+2023-10-24 17:54:31,234 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:54:31,235 EPOCH 1 done: loss 0.3019 - lr: 0.000050
+2023-10-24 17:54:34,306 DEV : loss 0.08691307157278061 - f1-score (micro avg)  0.7201
+2023-10-24 17:54:34,321 saving best model
+2023-10-24 17:54:34,791 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:54:42,930 epoch 2 - iter 99/992 - loss 0.09527419 - time (sec): 8.14 - samples/sec: 2003.53 - lr: 0.000049 - momentum: 0.000000
+2023-10-24 17:54:51,239 epoch 2 - iter 198/992 - loss 0.09556154 - time (sec): 16.45 - samples/sec: 1975.24 - lr: 0.000049 - momentum: 0.000000
+2023-10-24 17:54:59,412 epoch 2 - iter 297/992 - loss 0.09905757 - time (sec): 24.62 - samples/sec: 1984.65 - lr: 0.000048 - momentum: 0.000000
+2023-10-24 17:55:07,962 epoch 2 - iter 396/992 - loss 0.10241445 - time (sec): 33.17 - samples/sec: 1977.76 - lr: 0.000048 - momentum: 0.000000
+2023-10-24 17:55:16,323 epoch 2 - iter 495/992 - loss 0.10157096 - time (sec): 41.53 - samples/sec: 1984.19 - lr: 0.000047 - momentum: 0.000000
+2023-10-24 17:55:24,569 epoch 2 - iter 594/992 - loss 0.10195348 - time (sec): 49.78 - samples/sec: 1983.29 - lr: 0.000047 - momentum: 0.000000
+2023-10-24 17:55:33,030 epoch 2 - iter 693/992 - loss 0.10065037 - time (sec): 58.24 - samples/sec: 1983.87 - lr: 0.000046 - momentum: 0.000000
+2023-10-24 17:55:41,369 epoch 2 - iter 792/992 - loss 0.09922714 - time (sec): 66.58 - samples/sec: 1970.91 - lr: 0.000046 - momentum: 0.000000
+2023-10-24 17:55:49,709 epoch 2 - iter 891/992 - loss 0.09994013 - time (sec): 74.92 - samples/sec: 1964.70 - lr: 0.000045 - momentum: 0.000000
+2023-10-24 17:55:58,196 epoch 2 - iter 990/992 - loss 0.10114388 - time (sec): 83.40 - samples/sec: 1963.04 - lr: 0.000044 - momentum: 0.000000
+2023-10-24 17:55:58,341 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:55:58,341 EPOCH 2 done: loss 0.1011 - lr: 0.000044
+2023-10-24 17:56:01,444 DEV : loss 0.09098362177610397 - f1-score (micro avg)  0.743
+2023-10-24 17:56:01,459 saving best model
+2023-10-24 17:56:02,049 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:56:10,259 epoch 3 - iter 99/992 - loss 0.06165896 - time (sec): 8.21 - samples/sec: 1971.22 - lr: 0.000044 - momentum: 0.000000
+2023-10-24 17:56:18,758 epoch 3 - iter 198/992 - loss 0.06593462 - time (sec): 16.71 - samples/sec: 1971.74 - lr: 0.000043 - momentum: 0.000000
+2023-10-24 17:56:27,234 epoch 3 - iter 297/992 - loss 0.07089123 - time (sec): 25.18 - samples/sec: 1940.51 - lr: 0.000043 - momentum: 0.000000
+2023-10-24 17:56:35,331 epoch 3 - iter 396/992 - loss 0.06905276 - time (sec): 33.28 - samples/sec: 1946.67 - lr: 0.000042 - momentum: 0.000000
+2023-10-24 17:56:44,066 epoch 3 - iter 495/992 - loss 0.06665018 - time (sec): 42.02 - samples/sec: 1961.30 - lr: 0.000042 - momentum: 0.000000
+2023-10-24 17:56:52,473 epoch 3 - iter 594/992 - loss 0.06883315 - time (sec): 50.42 - samples/sec: 1959.12 - lr: 0.000041 - momentum: 0.000000
+2023-10-24 17:57:00,614 epoch 3 - iter 693/992 - loss 0.06972989 - time (sec): 58.56 - samples/sec: 1959.41 - lr: 0.000041 - momentum: 0.000000
+2023-10-24 17:57:09,003 epoch 3 - iter 792/992 - loss 0.06920701 - time (sec): 66.95 - samples/sec: 1961.79 - lr: 0.000040 - momentum: 0.000000
+2023-10-24 17:57:17,427 epoch 3 - iter 891/992 - loss 0.06875715 - time (sec): 75.38 - samples/sec: 1961.18 - lr: 0.000039 - momentum: 0.000000
+2023-10-24 17:57:25,554 epoch 3 - iter 990/992 - loss 0.06866266 - time (sec): 83.50 - samples/sec: 1960.90 - lr: 0.000039 - momentum: 0.000000
+2023-10-24 17:57:25,711 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:57:25,711 EPOCH 3 done: loss 0.0686 - lr: 0.000039
+2023-10-24 17:57:28,825 DEV : loss 0.10797995328903198 - f1-score (micro avg)  0.7225
+2023-10-24 17:57:28,840 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:57:36,940 epoch 4 - iter 99/992 - loss 0.04262884 - time (sec): 8.10 - samples/sec: 1951.86 - lr: 0.000038 - momentum: 0.000000
+2023-10-24 17:57:45,681 epoch 4 - iter 198/992 - loss 0.04995344 - time (sec): 16.84 - samples/sec: 1947.00 - lr: 0.000038 - momentum: 0.000000
+2023-10-24 17:57:54,126 epoch 4 - iter 297/992 - loss 0.04925014 - time (sec): 25.28 - samples/sec: 1947.25 - lr: 0.000037 - momentum: 0.000000
+2023-10-24 17:58:02,267 epoch 4 - iter 396/992 - loss 0.05068279 - time (sec): 33.43 - samples/sec: 1948.76 - lr: 0.000037 - momentum: 0.000000
+2023-10-24 17:58:10,475 epoch 4 - iter 495/992 - loss 0.05043456 - time (sec): 41.63 - samples/sec: 1960.24 - lr: 0.000036 - momentum: 0.000000
+2023-10-24 17:58:18,115 epoch 4 - iter 594/992 - loss 0.04948632 - time (sec): 49.27 - samples/sec: 1955.28 - lr: 0.000036 - momentum: 0.000000
+2023-10-24 17:58:26,645 epoch 4 - iter 693/992 - loss 0.05029241 - time (sec): 57.80 - samples/sec: 1963.09 - lr: 0.000035 - momentum: 0.000000
+2023-10-24 17:58:35,036 epoch 4 - iter 792/992 - loss 0.05054238 - time (sec): 66.19 - samples/sec: 1959.75 - lr: 0.000034 - momentum: 0.000000
+2023-10-24 17:58:43,142 epoch 4 - iter 891/992 - loss 0.04973429 - time (sec): 74.30 - samples/sec: 1968.56 - lr: 0.000034 - momentum: 0.000000
+2023-10-24 17:58:52,072 epoch 4 - iter 990/992 - loss 0.04918421 - time (sec): 83.23 - samples/sec: 1966.28 - lr: 0.000033 - momentum: 0.000000
+2023-10-24 17:58:52,222 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:58:52,222 EPOCH 4 done: loss 0.0491 - lr: 0.000033
+2023-10-24 17:58:55,339 DEV : loss 0.16018341481685638 - f1-score (micro avg)  0.7368
+2023-10-24 17:58:55,354 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:59:03,855 epoch 5 - iter 99/992 - loss 0.03275354 - time (sec): 8.50 - samples/sec: 1998.54 - lr: 0.000033 - momentum: 0.000000
+2023-10-24 17:59:12,128 epoch 5 - iter 198/992 - loss 0.03475824 - time (sec): 16.77 - samples/sec: 1968.33 - lr: 0.000032 - momentum: 0.000000
+2023-10-24 17:59:20,968 epoch 5 - iter 297/992 - loss 0.03421394 - time (sec): 25.61 - samples/sec: 1935.93 - lr: 0.000032 - momentum: 0.000000
+2023-10-24 17:59:29,192 epoch 5 - iter 396/992 - loss 0.03443327 - time (sec): 33.84 - samples/sec: 1928.20 - lr: 0.000031 - momentum: 0.000000
+2023-10-24 17:59:37,436 epoch 5 - iter 495/992 - loss 0.03822480 - time (sec): 42.08 - samples/sec: 1946.88 - lr: 0.000031 - momentum: 0.000000
+2023-10-24 17:59:45,453 epoch 5 - iter 594/992 - loss 0.03666575 - time (sec): 50.10 - samples/sec: 1952.70 - lr: 0.000030 - momentum: 0.000000
+2023-10-24 17:59:54,159 epoch 5 - iter 693/992 - loss 0.03753706 - time (sec): 58.80 - samples/sec: 1951.41 - lr: 0.000029 - momentum: 0.000000
+2023-10-24 18:00:02,511 epoch 5 - iter 792/992 - loss 0.03848741 - time (sec): 67.16 - samples/sec: 1952.21 - lr: 0.000029 - momentum: 0.000000
+2023-10-24 18:00:10,602 epoch 5 - iter 891/992 - loss 0.03850444 - time (sec): 75.25 - samples/sec: 1953.08 - lr: 0.000028 - momentum: 0.000000
+2023-10-24 18:00:19,094 epoch 5 - iter 990/992 - loss 0.03746891 - time (sec): 83.74 - samples/sec: 1954.15 - lr: 0.000028 - momentum: 0.000000
+2023-10-24 18:00:19,260 ----------------------------------------------------------------------------------------------------
+2023-10-24 18:00:19,260 EPOCH 5 done: loss 0.0374 - lr: 0.000028
+2023-10-24 18:00:22,383 DEV : loss 0.17979347705841064 - f1-score (micro avg)  0.7377
+2023-10-24 18:00:22,399 ----------------------------------------------------------------------------------------------------
+2023-10-24 18:00:30,989 epoch 6 - iter 99/992 - loss 0.02323895 - time (sec): 8.59 - samples/sec: 1890.29 - lr: 0.000027 - momentum: 0.000000
+2023-10-24 18:00:39,421 epoch 6 - iter 198/992 - loss 0.02299469 - time (sec): 17.02 - samples/sec: 1940.04 - lr: 0.000027 - momentum: 0.000000
+2023-10-24 18:00:47,699 epoch 6 - iter 297/992 - loss 0.02440000 - time (sec): 25.30 - samples/sec: 1959.56 - lr: 0.000026 - momentum: 0.000000
+2023-10-24 18:00:55,811 epoch 6 - iter 396/992 - loss 0.02517086 - time (sec): 33.41 - samples/sec: 1970.95 - lr: 0.000026 - momentum: 0.000000
+2023-10-24 18:01:04,347 epoch 6 - iter 495/992 - loss 0.02699649 - time (sec): 41.95 - samples/sec: 1970.74 - lr: 0.000025 - momentum: 0.000000
+2023-10-24 18:01:12,638 epoch 6 - iter 594/992 - loss 0.02691355 - time (sec): 50.24 - samples/sec: 1963.22 - lr: 0.000024 - momentum: 0.000000
+2023-10-24 18:01:20,840 epoch 6 - iter 693/992 - loss 0.02767072 - time (sec): 58.44 - samples/sec: 1957.76 - lr: 0.000024 - momentum: 0.000000
+2023-10-24 18:01:29,206 epoch 6 - iter 792/992 - loss 0.02671390 - time (sec): 66.81 - samples/sec: 1956.90 - lr: 0.000023 - momentum: 0.000000
+2023-10-24 18:01:37,458 epoch 6 - iter 891/992 - loss 0.02828160 - time (sec): 75.06 - samples/sec: 1948.57 - lr: 0.000023 - momentum: 0.000000
+2023-10-24 18:01:45,697 epoch 6 - iter 990/992 - loss 0.02810958 - time (sec): 83.30 - samples/sec: 1965.05 - lr: 0.000022 - momentum: 0.000000
+2023-10-24 18:01:45,857 ----------------------------------------------------------------------------------------------------
+2023-10-24 18:01:45,857 EPOCH 6 done: loss 0.0281 - lr: 0.000022
+2023-10-24 18:01:48,981 DEV : loss 0.18152180314064026 - f1-score (micro avg)  0.7691
+2023-10-24 18:01:48,996 saving best model
+2023-10-24 18:01:49,629 ----------------------------------------------------------------------------------------------------
+2023-10-24 18:01:58,369 epoch 7 - iter 99/992 - loss 0.02452345 - time (sec): 8.74 - samples/sec: 1920.30 - lr: 0.000022 - momentum: 0.000000
+2023-10-24 18:02:06,462 epoch 7 - iter 198/992 - loss 0.02583170 - time (sec): 16.83 - samples/sec: 1928.62 - lr: 0.000021 - momentum: 0.000000
+2023-10-24 18:02:15,105 epoch 7 - iter 297/992 - loss 0.02293195 - time (sec): 25.48 - samples/sec: 1913.00 - lr: 0.000021 - momentum: 0.000000
+2023-10-24 18:02:23,523 epoch 7 - iter 396/992 - loss 0.01981722 - time (sec): 33.89 - samples/sec: 1901.12 - lr: 0.000020 - momentum: 0.000000
+2023-10-24 18:02:31,697 epoch 7 - iter 495/992 - loss 0.01985616 - time (sec): 42.07 - samples/sec: 1910.50 - lr: 0.000019 - momentum: 0.000000
+2023-10-24 18:02:40,392 epoch 7 - iter 594/992 - loss 0.01977100 - time (sec): 50.76 - samples/sec: 1926.84 - lr: 0.000019 - momentum: 0.000000
+2023-10-24 18:02:48,921 epoch 7 - iter 693/992 - loss 0.02041123 - time (sec): 59.29 - samples/sec: 1935.23 - lr: 0.000018 - momentum: 0.000000
+2023-10-24 18:02:57,131 epoch 7 - iter 792/992 - loss 0.02070652 - time (sec): 67.50 - samples/sec: 1941.00 - lr: 0.000018 - momentum: 0.000000
+2023-10-24 18:03:05,223 epoch 7 - iter 891/992 - loss 0.02081829 - time (sec): 75.59 - samples/sec: 1949.40 - lr: 0.000017 - momentum: 0.000000
+2023-10-24 18:03:13,362 epoch 7 - iter 990/992 - loss 0.02144692 - time (sec): 83.73 - samples/sec: 1952.78 - lr: 0.000017 - momentum: 0.000000
+2023-10-24 18:03:13,536 ----------------------------------------------------------------------------------------------------
+2023-10-24 18:03:13,536 EPOCH 7 done: loss 0.0214 - lr: 0.000017
+2023-10-24 18:03:16,649 DEV : loss 0.18771061301231384 - f1-score (micro avg)  0.7667
+2023-10-24 18:03:16,664 ----------------------------------------------------------------------------------------------------
+2023-10-24 18:03:25,249 epoch 8 - iter 99/992 - loss 0.01897961 - time (sec): 8.58 - samples/sec: 2021.42 - lr: 0.000016 - momentum: 0.000000
+2023-10-24 18:03:33,938 epoch 8 - iter 198/992 - loss 0.01571900 - time (sec): 17.27 - samples/sec: 1977.75 - lr: 0.000016 - momentum: 0.000000
+2023-10-24 18:03:42,080 epoch 8 - iter 297/992 - loss 0.01464608 - time (sec): 25.41 - samples/sec: 1955.74 - lr: 0.000015 - momentum: 0.000000
+2023-10-24 18:03:50,487 epoch 8 - iter 396/992 - loss 0.01451842 - time (sec): 33.82 - samples/sec: 1945.87 - lr: 0.000014 - momentum: 0.000000
+2023-10-24 18:03:58,546 epoch 8 - iter 495/992 - loss 0.01464158 - time (sec): 41.88 - samples/sec: 1950.62 - lr: 0.000014 - momentum: 0.000000
+2023-10-24 18:04:07,019 epoch 8 - iter 594/992 - loss 0.01494271 - time (sec): 50.35 - samples/sec: 1962.71 - lr: 0.000013 - momentum: 0.000000
+2023-10-24 18:04:15,324 epoch 8 - iter 693/992 - loss 0.01427937 - time (sec): 58.66 - samples/sec: 1965.49 - lr: 0.000013 - momentum: 0.000000
+2023-10-24 18:04:23,141 epoch 8 - iter 792/992 - loss 0.01444238 - time (sec): 66.48 - samples/sec: 1961.96 - lr: 0.000012 - momentum: 0.000000
+2023-10-24 18:04:31,590 epoch 8 - iter 891/992 - loss 0.01470428 - time (sec): 74.93 - samples/sec: 1960.87 - lr: 0.000012 - momentum: 0.000000
+2023-10-24 18:04:39,955 epoch 8 - iter 990/992 - loss 0.01468421 - time (sec): 83.29 - samples/sec: 1964.56 - lr: 0.000011 - momentum: 0.000000
+2023-10-24 18:04:40,104 ----------------------------------------------------------------------------------------------------
+2023-10-24 18:04:40,104 EPOCH 8 done: loss 0.0147 - lr: 0.000011
+2023-10-24 18:04:43,222 DEV : loss 0.2224731296300888 - f1-score (micro avg)  0.7444
+2023-10-24 18:04:43,237 ----------------------------------------------------------------------------------------------------
+2023-10-24 18:04:51,724 epoch 9 - iter 99/992 - loss 0.01508641 - time (sec): 8.49 - samples/sec: 1869.36 - lr: 0.000011 - momentum: 0.000000
+2023-10-24 18:04:59,936 epoch 9 - iter 198/992 - loss 0.01126348 - time (sec): 16.70 - samples/sec: 1893.49 - lr: 0.000010 - momentum: 0.000000
+2023-10-24 18:05:08,057 epoch 9 - iter 297/992 - loss 0.01011817 - time (sec): 24.82 - samples/sec: 1903.84 - lr: 0.000009 - momentum: 0.000000
+2023-10-24 18:05:17,194 epoch 9 - iter 396/992 - loss 0.01014998 - time (sec): 33.96 - samples/sec: 1904.20 - lr: 0.000009 - momentum: 0.000000
+2023-10-24 18:05:25,870 epoch 9 - iter 495/992 - loss 0.00901112 - time (sec): 42.63 - samples/sec: 1918.22 - lr: 0.000008 - momentum: 0.000000
+2023-10-24 18:05:34,447 epoch 9 - iter 594/992 - loss 0.00957614 - time (sec): 51.21 - samples/sec: 1921.71 - lr: 0.000008 - momentum: 0.000000
+2023-10-24 18:05:42,470 epoch 9 - iter 693/992 - loss 0.00985237 - time (sec): 59.23 - samples/sec: 1932.18 - lr: 0.000007 - momentum: 0.000000
+2023-10-24 18:05:50,719 epoch 9 - iter 792/992 - loss 0.00966421 - time (sec): 67.48 - samples/sec: 1935.77 - lr: 0.000007 - momentum: 0.000000
+2023-10-24 18:05:58,741 epoch 9 - iter 891/992 - loss 0.00952795 - time (sec): 75.50 - samples/sec: 1944.93 - lr: 0.000006 - momentum: 0.000000
+2023-10-24 18:06:06,945 epoch 9 - iter 990/992 - loss 0.00951350 - time (sec): 83.71 - samples/sec: 1955.66 - lr: 0.000006 - momentum: 0.000000
+2023-10-24 18:06:07,091 ----------------------------------------------------------------------------------------------------
+2023-10-24 18:06:07,092 EPOCH 9 done: loss 0.0095 - lr: 0.000006
+2023-10-24 18:06:10,221 DEV : loss 0.2356439083814621 - f1-score (micro avg)  0.7551
+2023-10-24 18:06:10,236 ----------------------------------------------------------------------------------------------------
+2023-10-24 18:06:18,255 epoch 10 - iter 99/992 - loss 0.00471428 - time (sec): 8.02 - samples/sec: 2021.65 - lr: 0.000005 - momentum: 0.000000
+2023-10-24 18:06:26,499 epoch 10 - iter 198/992 - loss 0.00492676 - time (sec): 16.26 - samples/sec: 1988.79 - lr: 0.000004 - momentum: 0.000000
+2023-10-24 18:06:34,960 epoch 10 - iter 297/992 - loss 0.00537611 - time (sec): 24.72 - samples/sec: 1985.92 - lr: 0.000004 - momentum: 0.000000
+2023-10-24 18:06:43,429 epoch 10 - iter 396/992 - loss 0.00591290 - time (sec): 33.19 - samples/sec: 1993.67 - lr: 0.000003 - momentum: 0.000000
+2023-10-24 18:06:51,659 epoch 10 - iter 495/992 - loss 0.00619826 - time (sec): 41.42 - samples/sec: 1987.82 - lr: 0.000003 - momentum: 0.000000
+2023-10-24 18:07:00,038 epoch 10 - iter 594/992 - loss 0.00579102 - time (sec): 49.80 - samples/sec: 1972.80 - lr: 0.000002 - momentum: 0.000000
+2023-10-24 18:07:08,440 epoch 10 - iter 693/992 - loss 0.00584032 - time (sec): 58.20 - samples/sec: 1968.97 - lr: 0.000002 - momentum: 0.000000
+2023-10-24 18:07:16,506 epoch 10 - iter 792/992 - loss 0.00552839 - time (sec): 66.27 - samples/sec: 1964.77 - lr: 0.000001 - momentum: 0.000000
+2023-10-24 18:07:25,019 epoch 10 - iter 891/992 - loss 0.00572974 - time (sec): 74.78 - samples/sec: 1962.97 - lr: 0.000001 - momentum: 0.000000
+2023-10-24 18:07:33,501 epoch 10 - iter 990/992 - loss 0.00560021 - time (sec): 83.26 - samples/sec: 1965.24 - lr: 0.000000 - momentum: 0.000000
+2023-10-24 18:07:33,670 ----------------------------------------------------------------------------------------------------
+2023-10-24 18:07:33,671 EPOCH 10 done: loss 0.0056 - lr: 0.000000
+2023-10-24 18:07:36,792 DEV : loss 0.24207349121570587 - f1-score (micro avg)  0.7541
+2023-10-24 18:07:37,277 ----------------------------------------------------------------------------------------------------
+2023-10-24 18:07:37,277 Loading model from best epoch ...
+2023-10-24 18:07:39,090 SequenceTagger predicts: Dictionary with 13 tags: O, S-PER, B-PER, E-PER, I-PER, S-LOC, B-LOC, E-LOC, I-LOC, S-ORG, B-ORG, E-ORG, I-ORG
+2023-10-24 18:07:41,834 
+Results:
+- F-score (micro) 0.7721
+- F-score (macro) 0.6822
+- Accuracy 0.6487
+
+By class:
+              precision    recall  f1-score   support
+
+         LOC     0.8067    0.8473    0.8265       655
+         PER     0.6980    0.7982    0.7448       223
+         ORG     0.6400    0.3780    0.4752       127
+
+   micro avg     0.7672    0.7771    0.7721      1005
+   macro avg     0.7149    0.6745    0.6822      1005
+weighted avg     0.7615    0.7771    0.7640      1005
+
+2023-10-24 18:07:41,834 ----------------------------------------------------------------------------------------------------