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training.log

@@ -0,0 +1,504 @@
+2023-10-24 16:25:58,391 ----------------------------------------------------------------------------------------------------
+2023-10-24 16:25:58,392 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 16:25:58,392 ----------------------------------------------------------------------------------------------------
+2023-10-24 16:25:58,393 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 16:25:58,393 ----------------------------------------------------------------------------------------------------
+2023-10-24 16:25:58,393 Train:  7936 sentences
+2023-10-24 16:25:58,393         (train_with_dev=False, train_with_test=False)
+2023-10-24 16:25:58,393 ----------------------------------------------------------------------------------------------------
+2023-10-24 16:25:58,393 Training Params:
+2023-10-24 16:25:58,393  - learning_rate: "3e-05" 
+2023-10-24 16:25:58,393  - mini_batch_size: "8"
+2023-10-24 16:25:58,393  - max_epochs: "10"
+2023-10-24 16:25:58,393  - shuffle: "True"
+2023-10-24 16:25:58,393 ----------------------------------------------------------------------------------------------------
+2023-10-24 16:25:58,393 Plugins:
+2023-10-24 16:25:58,393  - TensorboardLogger
+2023-10-24 16:25:58,393  - LinearScheduler | warmup_fraction: '0.1'
+2023-10-24 16:25:58,393 ----------------------------------------------------------------------------------------------------
+2023-10-24 16:25:58,393 Final evaluation on model from best epoch (best-model.pt)
+2023-10-24 16:25:58,393  - metric: "('micro avg', 'f1-score')"
+2023-10-24 16:25:58,393 ----------------------------------------------------------------------------------------------------
+2023-10-24 16:25:58,393 Computation:
+2023-10-24 16:25:58,393  - compute on device: cuda:0
+2023-10-24 16:25:58,393  - embedding storage: none
+2023-10-24 16:25:58,393 ----------------------------------------------------------------------------------------------------
+2023-10-24 16:25:58,393 Model training base path: "hmbench-icdar/fr-dbmdz/bert-base-historic-multilingual-64k-td-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2"
+2023-10-24 16:25:58,393 ----------------------------------------------------------------------------------------------------
+2023-10-24 16:25:58,393 ----------------------------------------------------------------------------------------------------
+2023-10-24 16:25:58,393 Logging anything other than scalars to TensorBoard is currently not supported.
+2023-10-24 16:26:06,342 epoch 1 - iter 99/992 - loss 1.84117328 - time (sec): 7.95 - samples/sec: 1981.13 - lr: 0.000003 - momentum: 0.000000
+2023-10-24 16:26:14,855 epoch 1 - iter 198/992 - loss 1.10138130 - time (sec): 16.46 - samples/sec: 1996.76 - lr: 0.000006 - momentum: 0.000000
+2023-10-24 16:26:23,310 epoch 1 - iter 297/992 - loss 0.81486082 - time (sec): 24.92 - samples/sec: 2001.63 - lr: 0.000009 - momentum: 0.000000
+2023-10-24 16:26:31,932 epoch 1 - iter 396/992 - loss 0.64687710 - time (sec): 33.54 - samples/sec: 2015.43 - lr: 0.000012 - momentum: 0.000000
+2023-10-24 16:26:39,914 epoch 1 - iter 495/992 - loss 0.55583741 - time (sec): 41.52 - samples/sec: 1997.30 - lr: 0.000015 - momentum: 0.000000
+2023-10-24 16:26:48,202 epoch 1 - iter 594/992 - loss 0.48695047 - time (sec): 49.81 - samples/sec: 1991.39 - lr: 0.000018 - momentum: 0.000000
+2023-10-24 16:26:56,126 epoch 1 - iter 693/992 - loss 0.44276893 - time (sec): 57.73 - samples/sec: 1982.49 - lr: 0.000021 - momentum: 0.000000
+2023-10-24 16:27:04,291 epoch 1 - iter 792/992 - loss 0.40513633 - time (sec): 65.90 - samples/sec: 1978.17 - lr: 0.000024 - momentum: 0.000000
+2023-10-24 16:27:13,005 epoch 1 - iter 891/992 - loss 0.37397311 - time (sec): 74.61 - samples/sec: 1975.31 - lr: 0.000027 - momentum: 0.000000
+2023-10-24 16:27:21,230 epoch 1 - iter 990/992 - loss 0.35036716 - time (sec): 82.84 - samples/sec: 1973.58 - lr: 0.000030 - momentum: 0.000000
+2023-10-24 16:27:21,425 ----------------------------------------------------------------------------------------------------
+2023-10-24 16:27:21,425 EPOCH 1 done: loss 0.3496 - lr: 0.000030
+2023-10-24 16:27:24,457 DEV : loss 0.09255984425544739 - f1-score (micro avg)  0.7088
+2023-10-24 16:27:24,472 saving best model
+2023-10-24 16:27:24,943 ----------------------------------------------------------------------------------------------------
+2023-10-24 16:27:33,171 epoch 2 - iter 99/992 - loss 0.10446376 - time (sec): 8.23 - samples/sec: 2021.90 - lr: 0.000030 - momentum: 0.000000
+2023-10-24 16:27:41,671 epoch 2 - iter 198/992 - loss 0.10760078 - time (sec): 16.73 - samples/sec: 1971.38 - lr: 0.000029 - momentum: 0.000000
+2023-10-24 16:27:49,862 epoch 2 - iter 297/992 - loss 0.10514711 - time (sec): 24.92 - samples/sec: 1966.95 - lr: 0.000029 - momentum: 0.000000
+2023-10-24 16:27:58,454 epoch 2 - iter 396/992 - loss 0.10491517 - time (sec): 33.51 - samples/sec: 1965.67 - lr: 0.000029 - momentum: 0.000000
+2023-10-24 16:28:06,674 epoch 2 - iter 495/992 - loss 0.10303159 - time (sec): 41.73 - samples/sec: 1962.28 - lr: 0.000028 - momentum: 0.000000
+2023-10-24 16:28:15,062 epoch 2 - iter 594/992 - loss 0.10348052 - time (sec): 50.12 - samples/sec: 1961.05 - lr: 0.000028 - momentum: 0.000000
+2023-10-24 16:28:23,656 epoch 2 - iter 693/992 - loss 0.10177488 - time (sec): 58.71 - samples/sec: 1964.56 - lr: 0.000028 - momentum: 0.000000
+2023-10-24 16:28:32,361 epoch 2 - iter 792/992 - loss 0.10172499 - time (sec): 67.42 - samples/sec: 1957.58 - lr: 0.000027 - momentum: 0.000000
+2023-10-24 16:28:40,452 epoch 2 - iter 891/992 - loss 0.10087184 - time (sec): 75.51 - samples/sec: 1956.03 - lr: 0.000027 - momentum: 0.000000
+2023-10-24 16:28:48,445 epoch 2 - iter 990/992 - loss 0.09922248 - time (sec): 83.50 - samples/sec: 1961.60 - lr: 0.000027 - momentum: 0.000000
+2023-10-24 16:28:48,581 ----------------------------------------------------------------------------------------------------
+2023-10-24 16:28:48,581 EPOCH 2 done: loss 0.0993 - lr: 0.000027
+2023-10-24 16:28:51,691 DEV : loss 0.09279114753007889 - f1-score (micro avg)  0.7279
+2023-10-24 16:28:51,706 saving best model
+2023-10-24 16:28:52,375 ----------------------------------------------------------------------------------------------------
+2023-10-24 16:29:01,133 epoch 3 - iter 99/992 - loss 0.07605989 - time (sec): 8.76 - samples/sec: 1917.87 - lr: 0.000026 - momentum: 0.000000
+2023-10-24 16:29:09,138 epoch 3 - iter 198/992 - loss 0.07095587 - time (sec): 16.76 - samples/sec: 1941.89 - lr: 0.000026 - momentum: 0.000000
+2023-10-24 16:29:17,484 epoch 3 - iter 297/992 - loss 0.06933994 - time (sec): 25.11 - samples/sec: 1968.54 - lr: 0.000026 - momentum: 0.000000
+2023-10-24 16:29:25,795 epoch 3 - iter 396/992 - loss 0.06953657 - time (sec): 33.42 - samples/sec: 1984.05 - lr: 0.000025 - momentum: 0.000000
+2023-10-24 16:29:34,059 epoch 3 - iter 495/992 - loss 0.06985299 - time (sec): 41.68 - samples/sec: 1966.49 - lr: 0.000025 - momentum: 0.000000
+2023-10-24 16:29:42,455 epoch 3 - iter 594/992 - loss 0.07018513 - time (sec): 50.08 - samples/sec: 1957.35 - lr: 0.000025 - momentum: 0.000000
+2023-10-24 16:29:50,658 epoch 3 - iter 693/992 - loss 0.06885542 - time (sec): 58.28 - samples/sec: 1963.79 - lr: 0.000024 - momentum: 0.000000
+2023-10-24 16:29:58,686 epoch 3 - iter 792/992 - loss 0.06830171 - time (sec): 66.31 - samples/sec: 1969.72 - lr: 0.000024 - momentum: 0.000000
+2023-10-24 16:30:06,906 epoch 3 - iter 891/992 - loss 0.06866294 - time (sec): 74.53 - samples/sec: 1970.78 - lr: 0.000024 - momentum: 0.000000
+2023-10-24 16:30:15,479 epoch 3 - iter 990/992 - loss 0.06874566 - time (sec): 83.10 - samples/sec: 1970.06 - lr: 0.000023 - momentum: 0.000000
+2023-10-24 16:30:15,620 ----------------------------------------------------------------------------------------------------
+2023-10-24 16:30:15,621 EPOCH 3 done: loss 0.0687 - lr: 0.000023
+2023-10-24 16:30:19,034 DEV : loss 0.10878178477287292 - f1-score (micro avg)  0.7642
+2023-10-24 16:30:19,049 saving best model
+2023-10-24 16:30:19,637 ----------------------------------------------------------------------------------------------------
+2023-10-24 16:30:28,163 epoch 4 - iter 99/992 - loss 0.04392941 - time (sec): 8.52 - samples/sec: 1987.79 - lr: 0.000023 - momentum: 0.000000
+2023-10-24 16:30:36,328 epoch 4 - iter 198/992 - loss 0.04639438 - time (sec): 16.69 - samples/sec: 1952.85 - lr: 0.000023 - momentum: 0.000000
+2023-10-24 16:30:44,969 epoch 4 - iter 297/992 - loss 0.04736008 - time (sec): 25.33 - samples/sec: 1973.99 - lr: 0.000022 - momentum: 0.000000
+2023-10-24 16:30:53,150 epoch 4 - iter 396/992 - loss 0.04778313 - time (sec): 33.51 - samples/sec: 1968.61 - lr: 0.000022 - momentum: 0.000000
+2023-10-24 16:31:01,433 epoch 4 - iter 495/992 - loss 0.04940814 - time (sec): 41.79 - samples/sec: 1968.99 - lr: 0.000022 - momentum: 0.000000
+2023-10-24 16:31:09,947 epoch 4 - iter 594/992 - loss 0.04959742 - time (sec): 50.31 - samples/sec: 1965.94 - lr: 0.000021 - momentum: 0.000000
+2023-10-24 16:31:17,969 epoch 4 - iter 693/992 - loss 0.04901512 - time (sec): 58.33 - samples/sec: 1965.80 - lr: 0.000021 - momentum: 0.000000
+2023-10-24 16:31:26,565 epoch 4 - iter 792/992 - loss 0.05033168 - time (sec): 66.93 - samples/sec: 1958.37 - lr: 0.000021 - momentum: 0.000000
+2023-10-24 16:31:34,725 epoch 4 - iter 891/992 - loss 0.05069359 - time (sec): 75.09 - samples/sec: 1965.14 - lr: 0.000020 - momentum: 0.000000
+2023-10-24 16:31:42,979 epoch 4 - iter 990/992 - loss 0.04985751 - time (sec): 83.34 - samples/sec: 1964.11 - lr: 0.000020 - momentum: 0.000000
+2023-10-24 16:31:43,127 ----------------------------------------------------------------------------------------------------
+2023-10-24 16:31:43,127 EPOCH 4 done: loss 0.0498 - lr: 0.000020
+2023-10-24 16:31:46,247 DEV : loss 0.12828028202056885 - f1-score (micro avg)  0.7563
+2023-10-24 16:31:46,262 ----------------------------------------------------------------------------------------------------
+2023-10-24 16:31:54,899 epoch 5 - iter 99/992 - loss 0.03290449 - time (sec): 8.64 - samples/sec: 1954.44 - lr: 0.000020 - momentum: 0.000000
+2023-10-24 16:32:03,134 epoch 5 - iter 198/992 - loss 0.03381169 - time (sec): 16.87 - samples/sec: 1924.32 - lr: 0.000019 - momentum: 0.000000
+2023-10-24 16:32:11,746 epoch 5 - iter 297/992 - loss 0.03697508 - time (sec): 25.48 - samples/sec: 1942.23 - lr: 0.000019 - momentum: 0.000000
+2023-10-24 16:32:19,881 epoch 5 - iter 396/992 - loss 0.03788595 - time (sec): 33.62 - samples/sec: 1937.06 - lr: 0.000019 - momentum: 0.000000
+2023-10-24 16:32:28,085 epoch 5 - iter 495/992 - loss 0.03765117 - time (sec): 41.82 - samples/sec: 1939.00 - lr: 0.000018 - momentum: 0.000000
+2023-10-24 16:32:36,427 epoch 5 - iter 594/992 - loss 0.03686130 - time (sec): 50.16 - samples/sec: 1949.84 - lr: 0.000018 - momentum: 0.000000
+2023-10-24 16:32:44,439 epoch 5 - iter 693/992 - loss 0.03765527 - time (sec): 58.18 - samples/sec: 1951.39 - lr: 0.000018 - momentum: 0.000000
+2023-10-24 16:32:52,622 epoch 5 - iter 792/992 - loss 0.03734430 - time (sec): 66.36 - samples/sec: 1952.28 - lr: 0.000017 - momentum: 0.000000
+2023-10-24 16:33:01,356 epoch 5 - iter 891/992 - loss 0.03750261 - time (sec): 75.09 - samples/sec: 1957.40 - lr: 0.000017 - momentum: 0.000000
+2023-10-24 16:33:09,599 epoch 5 - iter 990/992 - loss 0.03737905 - time (sec): 83.34 - samples/sec: 1964.25 - lr: 0.000017 - momentum: 0.000000
+2023-10-24 16:33:09,763 ----------------------------------------------------------------------------------------------------
+2023-10-24 16:33:09,763 EPOCH 5 done: loss 0.0373 - lr: 0.000017
+2023-10-24 16:33:13,201 DEV : loss 0.16802850365638733 - f1-score (micro avg)  0.7613
+2023-10-24 16:33:13,216 ----------------------------------------------------------------------------------------------------
+2023-10-24 16:33:21,537 epoch 6 - iter 99/992 - loss 0.02894776 - time (sec): 8.32 - samples/sec: 1949.39 - lr: 0.000016 - momentum: 0.000000
+2023-10-24 16:33:29,913 epoch 6 - iter 198/992 - loss 0.02934176 - time (sec): 16.70 - samples/sec: 1933.41 - lr: 0.000016 - momentum: 0.000000
+2023-10-24 16:33:38,373 epoch 6 - iter 297/992 - loss 0.02785056 - time (sec): 25.16 - samples/sec: 1915.53 - lr: 0.000016 - momentum: 0.000000
+2023-10-24 16:33:46,336 epoch 6 - iter 396/992 - loss 0.02582194 - time (sec): 33.12 - samples/sec: 1931.95 - lr: 0.000015 - momentum: 0.000000
+2023-10-24 16:33:54,785 epoch 6 - iter 495/992 - loss 0.02658002 - time (sec): 41.57 - samples/sec: 1938.95 - lr: 0.000015 - momentum: 0.000000
+2023-10-24 16:34:03,288 epoch 6 - iter 594/992 - loss 0.02696841 - time (sec): 50.07 - samples/sec: 1958.83 - lr: 0.000015 - momentum: 0.000000
+2023-10-24 16:34:11,609 epoch 6 - iter 693/992 - loss 0.02669713 - time (sec): 58.39 - samples/sec: 1959.03 - lr: 0.000014 - momentum: 0.000000
+2023-10-24 16:34:19,905 epoch 6 - iter 792/992 - loss 0.02835216 - time (sec): 66.69 - samples/sec: 1955.74 - lr: 0.000014 - momentum: 0.000000
+2023-10-24 16:34:28,428 epoch 6 - iter 891/992 - loss 0.02822978 - time (sec): 75.21 - samples/sec: 1950.65 - lr: 0.000014 - momentum: 0.000000
+2023-10-24 16:34:36,703 epoch 6 - iter 990/992 - loss 0.02826272 - time (sec): 83.49 - samples/sec: 1960.26 - lr: 0.000013 - momentum: 0.000000
+2023-10-24 16:34:36,863 ----------------------------------------------------------------------------------------------------
+2023-10-24 16:34:36,863 EPOCH 6 done: loss 0.0282 - lr: 0.000013
+2023-10-24 16:34:39,974 DEV : loss 0.1790362298488617 - f1-score (micro avg)  0.7511
+2023-10-24 16:34:39,989 ----------------------------------------------------------------------------------------------------
+2023-10-24 16:34:48,498 epoch 7 - iter 99/992 - loss 0.01644419 - time (sec): 8.51 - samples/sec: 1981.82 - lr: 0.000013 - momentum: 0.000000
+2023-10-24 16:34:56,792 epoch 7 - iter 198/992 - loss 0.02013642 - time (sec): 16.80 - samples/sec: 2028.95 - lr: 0.000013 - momentum: 0.000000
+2023-10-24 16:35:05,121 epoch 7 - iter 297/992 - loss 0.02125966 - time (sec): 25.13 - samples/sec: 1985.38 - lr: 0.000012 - momentum: 0.000000
+2023-10-24 16:35:13,293 epoch 7 - iter 396/992 - loss 0.02244887 - time (sec): 33.30 - samples/sec: 1971.50 - lr: 0.000012 - momentum: 0.000000
+2023-10-24 16:35:21,764 epoch 7 - iter 495/992 - loss 0.02220930 - time (sec): 41.77 - samples/sec: 1972.33 - lr: 0.000012 - momentum: 0.000000
+2023-10-24 16:35:29,832 epoch 7 - iter 594/992 - loss 0.02281129 - time (sec): 49.84 - samples/sec: 1973.45 - lr: 0.000011 - momentum: 0.000000
+2023-10-24 16:35:38,344 epoch 7 - iter 693/992 - loss 0.02207634 - time (sec): 58.35 - samples/sec: 1975.07 - lr: 0.000011 - momentum: 0.000000
+2023-10-24 16:35:46,894 epoch 7 - iter 792/992 - loss 0.02167285 - time (sec): 66.90 - samples/sec: 1970.54 - lr: 0.000011 - momentum: 0.000000
+2023-10-24 16:35:55,536 epoch 7 - iter 891/992 - loss 0.02151418 - time (sec): 75.55 - samples/sec: 1963.93 - lr: 0.000010 - momentum: 0.000000
+2023-10-24 16:36:03,450 epoch 7 - iter 990/992 - loss 0.02174271 - time (sec): 83.46 - samples/sec: 1960.72 - lr: 0.000010 - momentum: 0.000000
+2023-10-24 16:36:03,610 ----------------------------------------------------------------------------------------------------
+2023-10-24 16:36:03,610 EPOCH 7 done: loss 0.0219 - lr: 0.000010
+2023-10-24 16:36:07,061 DEV : loss 0.21934953331947327 - f1-score (micro avg)  0.7551
+2023-10-24 16:36:07,077 ----------------------------------------------------------------------------------------------------
+2023-10-24 16:36:15,500 epoch 8 - iter 99/992 - loss 0.01765916 - time (sec): 8.42 - samples/sec: 1960.11 - lr: 0.000010 - momentum: 0.000000
+2023-10-24 16:36:24,237 epoch 8 - iter 198/992 - loss 0.01884836 - time (sec): 17.16 - samples/sec: 1946.40 - lr: 0.000009 - momentum: 0.000000
+2023-10-24 16:36:32,496 epoch 8 - iter 297/992 - loss 0.01792273 - time (sec): 25.42 - samples/sec: 1946.76 - lr: 0.000009 - momentum: 0.000000
+2023-10-24 16:36:40,614 epoch 8 - iter 396/992 - loss 0.01573405 - time (sec): 33.54 - samples/sec: 1946.65 - lr: 0.000009 - momentum: 0.000000
+2023-10-24 16:36:48,919 epoch 8 - iter 495/992 - loss 0.01514155 - time (sec): 41.84 - samples/sec: 1953.36 - lr: 0.000008 - momentum: 0.000000
+2023-10-24 16:36:57,378 epoch 8 - iter 594/992 - loss 0.01537701 - time (sec): 50.30 - samples/sec: 1952.83 - lr: 0.000008 - momentum: 0.000000
+2023-10-24 16:37:05,565 epoch 8 - iter 693/992 - loss 0.01515308 - time (sec): 58.49 - samples/sec: 1957.64 - lr: 0.000008 - momentum: 0.000000
+2023-10-24 16:37:13,947 epoch 8 - iter 792/992 - loss 0.01497357 - time (sec): 66.87 - samples/sec: 1960.07 - lr: 0.000007 - momentum: 0.000000
+2023-10-24 16:37:22,132 epoch 8 - iter 891/992 - loss 0.01482836 - time (sec): 75.05 - samples/sec: 1968.70 - lr: 0.000007 - momentum: 0.000000
+2023-10-24 16:37:30,512 epoch 8 - iter 990/992 - loss 0.01497237 - time (sec): 83.43 - samples/sec: 1962.21 - lr: 0.000007 - momentum: 0.000000
+2023-10-24 16:37:30,659 ----------------------------------------------------------------------------------------------------
+2023-10-24 16:37:30,660 EPOCH 8 done: loss 0.0150 - lr: 0.000007
+2023-10-24 16:37:33,778 DEV : loss 0.2332099825143814 - f1-score (micro avg)  0.7553
+2023-10-24 16:37:33,794 ----------------------------------------------------------------------------------------------------
+2023-10-24 16:37:41,933 epoch 9 - iter 99/992 - loss 0.01078508 - time (sec): 8.14 - samples/sec: 1968.47 - lr: 0.000006 - momentum: 0.000000
+2023-10-24 16:37:50,232 epoch 9 - iter 198/992 - loss 0.01190655 - time (sec): 16.44 - samples/sec: 1970.40 - lr: 0.000006 - momentum: 0.000000
+2023-10-24 16:37:58,290 epoch 9 - iter 297/992 - loss 0.01143782 - time (sec): 24.50 - samples/sec: 1969.81 - lr: 0.000006 - momentum: 0.000000
+2023-10-24 16:38:06,384 epoch 9 - iter 396/992 - loss 0.01071707 - time (sec): 32.59 - samples/sec: 1984.83 - lr: 0.000005 - momentum: 0.000000
+2023-10-24 16:38:15,150 epoch 9 - iter 495/992 - loss 0.01063424 - time (sec): 41.36 - samples/sec: 1971.48 - lr: 0.000005 - momentum: 0.000000
+2023-10-24 16:38:23,284 epoch 9 - iter 594/992 - loss 0.01044319 - time (sec): 49.49 - samples/sec: 1967.33 - lr: 0.000005 - momentum: 0.000000
+2023-10-24 16:38:31,709 epoch 9 - iter 693/992 - loss 0.01001900 - time (sec): 57.91 - samples/sec: 1959.44 - lr: 0.000004 - momentum: 0.000000
+2023-10-24 16:38:40,254 epoch 9 - iter 792/992 - loss 0.00985411 - time (sec): 66.46 - samples/sec: 1969.84 - lr: 0.000004 - momentum: 0.000000
+2023-10-24 16:38:48,649 epoch 9 - iter 891/992 - loss 0.01079216 - time (sec): 74.85 - samples/sec: 1977.22 - lr: 0.000004 - momentum: 0.000000
+2023-10-24 16:38:57,185 epoch 9 - iter 990/992 - loss 0.01138071 - time (sec): 83.39 - samples/sec: 1963.27 - lr: 0.000003 - momentum: 0.000000
+2023-10-24 16:38:57,344 ----------------------------------------------------------------------------------------------------
+2023-10-24 16:38:57,344 EPOCH 9 done: loss 0.0114 - lr: 0.000003
+2023-10-24 16:39:00,468 DEV : loss 0.228724405169487 - f1-score (micro avg)  0.7601
+2023-10-24 16:39:00,484 ----------------------------------------------------------------------------------------------------
+2023-10-24 16:39:08,677 epoch 10 - iter 99/992 - loss 0.00781916 - time (sec): 8.19 - samples/sec: 2036.06 - lr: 0.000003 - momentum: 0.000000
+2023-10-24 16:39:16,773 epoch 10 - iter 198/992 - loss 0.00897859 - time (sec): 16.29 - samples/sec: 1997.68 - lr: 0.000003 - momentum: 0.000000
+2023-10-24 16:39:25,014 epoch 10 - iter 297/992 - loss 0.00853106 - time (sec): 24.53 - samples/sec: 1999.32 - lr: 0.000002 - momentum: 0.000000
+2023-10-24 16:39:34,307 epoch 10 - iter 396/992 - loss 0.00758245 - time (sec): 33.82 - samples/sec: 1977.32 - lr: 0.000002 - momentum: 0.000000
+2023-10-24 16:39:42,756 epoch 10 - iter 495/992 - loss 0.00749694 - time (sec): 42.27 - samples/sec: 1963.08 - lr: 0.000002 - momentum: 0.000000
+2023-10-24 16:39:51,210 epoch 10 - iter 594/992 - loss 0.00702956 - time (sec): 50.73 - samples/sec: 1972.74 - lr: 0.000001 - momentum: 0.000000
+2023-10-24 16:39:59,286 epoch 10 - iter 693/992 - loss 0.00687848 - time (sec): 58.80 - samples/sec: 1971.60 - lr: 0.000001 - momentum: 0.000000
+2023-10-24 16:40:07,435 epoch 10 - iter 792/992 - loss 0.00674550 - time (sec): 66.95 - samples/sec: 1981.65 - lr: 0.000001 - momentum: 0.000000
+2023-10-24 16:40:15,573 epoch 10 - iter 891/992 - loss 0.00706231 - time (sec): 75.09 - samples/sec: 1972.65 - lr: 0.000000 - momentum: 0.000000
+2023-10-24 16:40:23,725 epoch 10 - iter 990/992 - loss 0.00727311 - time (sec): 83.24 - samples/sec: 1964.44 - lr: 0.000000 - momentum: 0.000000
+2023-10-24 16:40:23,948 ----------------------------------------------------------------------------------------------------
+2023-10-24 16:40:23,948 EPOCH 10 done: loss 0.0073 - lr: 0.000000
+2023-10-24 16:40:27,065 DEV : loss 0.24580398201942444 - f1-score (micro avg)  0.7635
+2023-10-24 16:40:27,554 ----------------------------------------------------------------------------------------------------
+2023-10-24 16:40:27,555 Loading model from best epoch ...
+2023-10-24 16:40:29,030 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 16:40:32,100 
+Results:
+- F-score (micro) 0.7594
+- F-score (macro) 0.6798
+- Accuracy 0.633
+
+By class:
+              precision    recall  f1-score   support
+
+         LOC     0.8125    0.8137    0.8131       655
+         PER     0.7322    0.7848    0.7576       223
+         ORG     0.5000    0.4409    0.4686       127
+
+   micro avg     0.7587    0.7602    0.7594      1005
+   macro avg     0.6816    0.6798    0.6798      1005
+weighted avg     0.7552    0.7602    0.7573      1005
+
+2023-10-24 16:40:32,100 ----------------------------------------------------------------------------------------------------