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training.log

@@ -0,0 +1,505 @@
+2023-10-24 15:13:44,762 ----------------------------------------------------------------------------------------------------
+2023-10-24 15:13:44,763 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 15:13:44,763 ----------------------------------------------------------------------------------------------------
+2023-10-24 15:13:44,763 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 15:13:44,763 ----------------------------------------------------------------------------------------------------
+2023-10-24 15:13:44,763 Train:  7936 sentences
+2023-10-24 15:13:44,763         (train_with_dev=False, train_with_test=False)
+2023-10-24 15:13:44,764 ----------------------------------------------------------------------------------------------------
+2023-10-24 15:13:44,764 Training Params:
+2023-10-24 15:13:44,764  - learning_rate: "3e-05" 
+2023-10-24 15:13:44,764  - mini_batch_size: "8"
+2023-10-24 15:13:44,764  - max_epochs: "10"
+2023-10-24 15:13:44,764  - shuffle: "True"
+2023-10-24 15:13:44,764 ----------------------------------------------------------------------------------------------------
+2023-10-24 15:13:44,764 Plugins:
+2023-10-24 15:13:44,764  - TensorboardLogger
+2023-10-24 15:13:44,764  - LinearScheduler | warmup_fraction: '0.1'
+2023-10-24 15:13:44,764 ----------------------------------------------------------------------------------------------------
+2023-10-24 15:13:44,764 Final evaluation on model from best epoch (best-model.pt)
+2023-10-24 15:13:44,764  - metric: "('micro avg', 'f1-score')"
+2023-10-24 15:13:44,764 ----------------------------------------------------------------------------------------------------
+2023-10-24 15:13:44,764 Computation:
+2023-10-24 15:13:44,764  - compute on device: cuda:0
+2023-10-24 15:13:44,764  - embedding storage: none
+2023-10-24 15:13:44,764 ----------------------------------------------------------------------------------------------------
+2023-10-24 15:13:44,764 Model training base path: "hmbench-icdar/fr-dbmdz/bert-base-historic-multilingual-64k-td-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1"
+2023-10-24 15:13:44,764 ----------------------------------------------------------------------------------------------------
+2023-10-24 15:13:44,764 ----------------------------------------------------------------------------------------------------
+2023-10-24 15:13:44,764 Logging anything other than scalars to TensorBoard is currently not supported.
+2023-10-24 15:13:52,870 epoch 1 - iter 99/992 - loss 2.00138499 - time (sec): 8.11 - samples/sec: 1940.03 - lr: 0.000003 - momentum: 0.000000
+2023-10-24 15:14:00,756 epoch 1 - iter 198/992 - loss 1.19486555 - time (sec): 15.99 - samples/sec: 1939.90 - lr: 0.000006 - momentum: 0.000000
+2023-10-24 15:14:08,909 epoch 1 - iter 297/992 - loss 0.87346403 - time (sec): 24.14 - samples/sec: 1963.76 - lr: 0.000009 - momentum: 0.000000
+2023-10-24 15:14:17,064 epoch 1 - iter 396/992 - loss 0.69769156 - time (sec): 32.30 - samples/sec: 1961.48 - lr: 0.000012 - momentum: 0.000000
+2023-10-24 15:14:25,604 epoch 1 - iter 495/992 - loss 0.58166806 - time (sec): 40.84 - samples/sec: 1973.97 - lr: 0.000015 - momentum: 0.000000
+2023-10-24 15:14:33,994 epoch 1 - iter 594/992 - loss 0.50752231 - time (sec): 49.23 - samples/sec: 1971.55 - lr: 0.000018 - momentum: 0.000000
+2023-10-24 15:14:42,580 epoch 1 - iter 693/992 - loss 0.45336096 - time (sec): 57.82 - samples/sec: 1963.72 - lr: 0.000021 - momentum: 0.000000
+2023-10-24 15:14:51,243 epoch 1 - iter 792/992 - loss 0.41245068 - time (sec): 66.48 - samples/sec: 1965.17 - lr: 0.000024 - momentum: 0.000000
+2023-10-24 15:14:59,630 epoch 1 - iter 891/992 - loss 0.38042237 - time (sec): 74.87 - samples/sec: 1966.67 - lr: 0.000027 - momentum: 0.000000
+2023-10-24 15:15:07,967 epoch 1 - iter 990/992 - loss 0.35462009 - time (sec): 83.20 - samples/sec: 1967.35 - lr: 0.000030 - momentum: 0.000000
+2023-10-24 15:15:08,132 ----------------------------------------------------------------------------------------------------
+2023-10-24 15:15:08,132 EPOCH 1 done: loss 0.3542 - lr: 0.000030
+2023-10-24 15:15:11,185 DEV : loss 0.0961548462510109 - f1-score (micro avg)  0.7105
+2023-10-24 15:15:11,200 saving best model
+2023-10-24 15:15:11,755 ----------------------------------------------------------------------------------------------------
+2023-10-24 15:15:20,111 epoch 2 - iter 99/992 - loss 0.11640822 - time (sec): 8.35 - samples/sec: 2042.78 - lr: 0.000030 - momentum: 0.000000
+2023-10-24 15:15:28,447 epoch 2 - iter 198/992 - loss 0.11616878 - time (sec): 16.69 - samples/sec: 2002.25 - lr: 0.000029 - momentum: 0.000000
+2023-10-24 15:15:37,122 epoch 2 - iter 297/992 - loss 0.11115161 - time (sec): 25.37 - samples/sec: 2003.37 - lr: 0.000029 - momentum: 0.000000
+2023-10-24 15:15:45,186 epoch 2 - iter 396/992 - loss 0.10710185 - time (sec): 33.43 - samples/sec: 2000.29 - lr: 0.000029 - momentum: 0.000000
+2023-10-24 15:15:53,527 epoch 2 - iter 495/992 - loss 0.10512998 - time (sec): 41.77 - samples/sec: 1994.14 - lr: 0.000028 - momentum: 0.000000
+2023-10-24 15:16:01,850 epoch 2 - iter 594/992 - loss 0.10552659 - time (sec): 50.09 - samples/sec: 1965.95 - lr: 0.000028 - momentum: 0.000000
+2023-10-24 15:16:09,917 epoch 2 - iter 693/992 - loss 0.10493309 - time (sec): 58.16 - samples/sec: 1962.81 - lr: 0.000028 - momentum: 0.000000
+2023-10-24 15:16:18,183 epoch 2 - iter 792/992 - loss 0.10434383 - time (sec): 66.43 - samples/sec: 1962.00 - lr: 0.000027 - momentum: 0.000000
+2023-10-24 15:16:26,821 epoch 2 - iter 891/992 - loss 0.10270095 - time (sec): 75.07 - samples/sec: 1961.30 - lr: 0.000027 - momentum: 0.000000
+2023-10-24 15:16:35,036 epoch 2 - iter 990/992 - loss 0.10121394 - time (sec): 83.28 - samples/sec: 1965.27 - lr: 0.000027 - momentum: 0.000000
+2023-10-24 15:16:35,186 ----------------------------------------------------------------------------------------------------
+2023-10-24 15:16:35,186 EPOCH 2 done: loss 0.1011 - lr: 0.000027
+2023-10-24 15:16:38,599 DEV : loss 0.09430491924285889 - f1-score (micro avg)  0.7529
+2023-10-24 15:16:38,614 saving best model
+2023-10-24 15:16:39,338 ----------------------------------------------------------------------------------------------------
+2023-10-24 15:16:47,529 epoch 3 - iter 99/992 - loss 0.06719236 - time (sec): 8.19 - samples/sec: 1940.21 - lr: 0.000026 - momentum: 0.000000
+2023-10-24 15:16:55,855 epoch 3 - iter 198/992 - loss 0.06613133 - time (sec): 16.52 - samples/sec: 1993.65 - lr: 0.000026 - momentum: 0.000000
+2023-10-24 15:17:04,381 epoch 3 - iter 297/992 - loss 0.06330398 - time (sec): 25.04 - samples/sec: 1985.65 - lr: 0.000026 - momentum: 0.000000
+2023-10-24 15:17:12,671 epoch 3 - iter 396/992 - loss 0.06632455 - time (sec): 33.33 - samples/sec: 1960.17 - lr: 0.000025 - momentum: 0.000000
+2023-10-24 15:17:20,731 epoch 3 - iter 495/992 - loss 0.07066014 - time (sec): 41.39 - samples/sec: 1959.36 - lr: 0.000025 - momentum: 0.000000
+2023-10-24 15:17:29,327 epoch 3 - iter 594/992 - loss 0.07098366 - time (sec): 49.99 - samples/sec: 1951.79 - lr: 0.000025 - momentum: 0.000000
+2023-10-24 15:17:37,244 epoch 3 - iter 693/992 - loss 0.07039656 - time (sec): 57.91 - samples/sec: 1953.94 - lr: 0.000024 - momentum: 0.000000
+2023-10-24 15:17:45,665 epoch 3 - iter 792/992 - loss 0.06956794 - time (sec): 66.33 - samples/sec: 1960.93 - lr: 0.000024 - momentum: 0.000000
+2023-10-24 15:17:53,989 epoch 3 - iter 891/992 - loss 0.06922567 - time (sec): 74.65 - samples/sec: 1958.25 - lr: 0.000024 - momentum: 0.000000
+2023-10-24 15:18:02,727 epoch 3 - iter 990/992 - loss 0.06916264 - time (sec): 83.39 - samples/sec: 1961.66 - lr: 0.000023 - momentum: 0.000000
+2023-10-24 15:18:02,917 ----------------------------------------------------------------------------------------------------
+2023-10-24 15:18:02,917 EPOCH 3 done: loss 0.0693 - lr: 0.000023
+2023-10-24 15:18:06,033 DEV : loss 0.0983629822731018 - f1-score (micro avg)  0.7488
+2023-10-24 15:18:06,049 ----------------------------------------------------------------------------------------------------
+2023-10-24 15:18:14,336 epoch 4 - iter 99/992 - loss 0.04434730 - time (sec): 8.29 - samples/sec: 2012.20 - lr: 0.000023 - momentum: 0.000000
+2023-10-24 15:18:22,782 epoch 4 - iter 198/992 - loss 0.04085860 - time (sec): 16.73 - samples/sec: 1966.74 - lr: 0.000023 - momentum: 0.000000
+2023-10-24 15:18:30,640 epoch 4 - iter 297/992 - loss 0.04207446 - time (sec): 24.59 - samples/sec: 1954.17 - lr: 0.000022 - momentum: 0.000000
+2023-10-24 15:18:38,789 epoch 4 - iter 396/992 - loss 0.04681766 - time (sec): 32.74 - samples/sec: 1963.82 - lr: 0.000022 - momentum: 0.000000
+2023-10-24 15:18:47,334 epoch 4 - iter 495/992 - loss 0.04857690 - time (sec): 41.29 - samples/sec: 1962.60 - lr: 0.000022 - momentum: 0.000000
+2023-10-24 15:18:56,652 epoch 4 - iter 594/992 - loss 0.04806280 - time (sec): 50.60 - samples/sec: 1963.48 - lr: 0.000021 - momentum: 0.000000
+2023-10-24 15:19:04,615 epoch 4 - iter 693/992 - loss 0.04657590 - time (sec): 58.57 - samples/sec: 1966.76 - lr: 0.000021 - momentum: 0.000000
+2023-10-24 15:19:13,077 epoch 4 - iter 792/992 - loss 0.04774712 - time (sec): 67.03 - samples/sec: 1966.19 - lr: 0.000021 - momentum: 0.000000
+2023-10-24 15:19:21,518 epoch 4 - iter 891/992 - loss 0.04815205 - time (sec): 75.47 - samples/sec: 1961.15 - lr: 0.000020 - momentum: 0.000000
+2023-10-24 15:19:30,005 epoch 4 - iter 990/992 - loss 0.04916499 - time (sec): 83.96 - samples/sec: 1950.19 - lr: 0.000020 - momentum: 0.000000
+2023-10-24 15:19:30,145 ----------------------------------------------------------------------------------------------------
+2023-10-24 15:19:30,145 EPOCH 4 done: loss 0.0492 - lr: 0.000020
+2023-10-24 15:19:33,266 DEV : loss 0.1186017319560051 - f1-score (micro avg)  0.766
+2023-10-24 15:19:33,282 saving best model
+2023-10-24 15:19:34,070 ----------------------------------------------------------------------------------------------------
+2023-10-24 15:19:42,709 epoch 5 - iter 99/992 - loss 0.03958303 - time (sec): 8.64 - samples/sec: 1954.98 - lr: 0.000020 - momentum: 0.000000
+2023-10-24 15:19:50,877 epoch 5 - iter 198/992 - loss 0.03820388 - time (sec): 16.81 - samples/sec: 1956.19 - lr: 0.000019 - momentum: 0.000000
+2023-10-24 15:19:58,957 epoch 5 - iter 297/992 - loss 0.03759095 - time (sec): 24.89 - samples/sec: 1960.58 - lr: 0.000019 - momentum: 0.000000
+2023-10-24 15:20:07,566 epoch 5 - iter 396/992 - loss 0.03671709 - time (sec): 33.49 - samples/sec: 1974.70 - lr: 0.000019 - momentum: 0.000000
+2023-10-24 15:20:15,557 epoch 5 - iter 495/992 - loss 0.03795644 - time (sec): 41.49 - samples/sec: 1963.89 - lr: 0.000018 - momentum: 0.000000
+2023-10-24 15:20:23,714 epoch 5 - iter 594/992 - loss 0.03754771 - time (sec): 49.64 - samples/sec: 1968.60 - lr: 0.000018 - momentum: 0.000000
+2023-10-24 15:20:31,990 epoch 5 - iter 693/992 - loss 0.03701700 - time (sec): 57.92 - samples/sec: 1972.23 - lr: 0.000018 - momentum: 0.000000
+2023-10-24 15:20:40,475 epoch 5 - iter 792/992 - loss 0.03754511 - time (sec): 66.40 - samples/sec: 1975.22 - lr: 0.000017 - momentum: 0.000000
+2023-10-24 15:20:49,092 epoch 5 - iter 891/992 - loss 0.03754814 - time (sec): 75.02 - samples/sec: 1974.28 - lr: 0.000017 - momentum: 0.000000
+2023-10-24 15:20:57,344 epoch 5 - iter 990/992 - loss 0.03819911 - time (sec): 83.27 - samples/sec: 1966.82 - lr: 0.000017 - momentum: 0.000000
+2023-10-24 15:20:57,489 ----------------------------------------------------------------------------------------------------
+2023-10-24 15:20:57,489 EPOCH 5 done: loss 0.0382 - lr: 0.000017
+2023-10-24 15:21:00,612 DEV : loss 0.1500038057565689 - f1-score (micro avg)  0.7716
+2023-10-24 15:21:00,627 saving best model
+2023-10-24 15:21:01,415 ----------------------------------------------------------------------------------------------------
+2023-10-24 15:21:09,674 epoch 6 - iter 99/992 - loss 0.02489554 - time (sec): 8.26 - samples/sec: 1944.65 - lr: 0.000016 - momentum: 0.000000
+2023-10-24 15:21:17,831 epoch 6 - iter 198/992 - loss 0.02197168 - time (sec): 16.41 - samples/sec: 1969.28 - lr: 0.000016 - momentum: 0.000000
+2023-10-24 15:21:26,440 epoch 6 - iter 297/992 - loss 0.02602124 - time (sec): 25.02 - samples/sec: 1970.79 - lr: 0.000016 - momentum: 0.000000
+2023-10-24 15:21:34,700 epoch 6 - iter 396/992 - loss 0.02465442 - time (sec): 33.28 - samples/sec: 1958.95 - lr: 0.000015 - momentum: 0.000000
+2023-10-24 15:21:43,130 epoch 6 - iter 495/992 - loss 0.02716845 - time (sec): 41.71 - samples/sec: 1975.40 - lr: 0.000015 - momentum: 0.000000
+2023-10-24 15:21:51,299 epoch 6 - iter 594/992 - loss 0.02699725 - time (sec): 49.88 - samples/sec: 1977.15 - lr: 0.000015 - momentum: 0.000000
+2023-10-24 15:21:59,606 epoch 6 - iter 693/992 - loss 0.02655613 - time (sec): 58.19 - samples/sec: 1976.09 - lr: 0.000014 - momentum: 0.000000
+2023-10-24 15:22:07,766 epoch 6 - iter 792/992 - loss 0.02626525 - time (sec): 66.35 - samples/sec: 1973.44 - lr: 0.000014 - momentum: 0.000000
+2023-10-24 15:22:16,581 epoch 6 - iter 891/992 - loss 0.02664702 - time (sec): 75.16 - samples/sec: 1956.18 - lr: 0.000014 - momentum: 0.000000
+2023-10-24 15:22:25,007 epoch 6 - iter 990/992 - loss 0.02687875 - time (sec): 83.59 - samples/sec: 1959.53 - lr: 0.000013 - momentum: 0.000000
+2023-10-24 15:22:25,140 ----------------------------------------------------------------------------------------------------
+2023-10-24 15:22:25,140 EPOCH 6 done: loss 0.0269 - lr: 0.000013
+2023-10-24 15:22:28,260 DEV : loss 0.18365593254566193 - f1-score (micro avg)  0.7675
+2023-10-24 15:22:28,275 ----------------------------------------------------------------------------------------------------
+2023-10-24 15:22:36,688 epoch 7 - iter 99/992 - loss 0.01924589 - time (sec): 8.41 - samples/sec: 2023.57 - lr: 0.000013 - momentum: 0.000000
+2023-10-24 15:22:44,992 epoch 7 - iter 198/992 - loss 0.02187669 - time (sec): 16.72 - samples/sec: 1959.39 - lr: 0.000013 - momentum: 0.000000
+2023-10-24 15:22:53,153 epoch 7 - iter 297/992 - loss 0.02145695 - time (sec): 24.88 - samples/sec: 1936.89 - lr: 0.000012 - momentum: 0.000000
+2023-10-24 15:23:01,187 epoch 7 - iter 396/992 - loss 0.02078135 - time (sec): 32.91 - samples/sec: 1940.16 - lr: 0.000012 - momentum: 0.000000
+2023-10-24 15:23:09,352 epoch 7 - iter 495/992 - loss 0.02110910 - time (sec): 41.08 - samples/sec: 1929.42 - lr: 0.000012 - momentum: 0.000000
+2023-10-24 15:23:17,649 epoch 7 - iter 594/992 - loss 0.02248657 - time (sec): 49.37 - samples/sec: 1948.38 - lr: 0.000011 - momentum: 0.000000
+2023-10-24 15:23:25,983 epoch 7 - iter 693/992 - loss 0.02223240 - time (sec): 57.71 - samples/sec: 1951.52 - lr: 0.000011 - momentum: 0.000000
+2023-10-24 15:23:34,185 epoch 7 - iter 792/992 - loss 0.02177442 - time (sec): 65.91 - samples/sec: 1949.07 - lr: 0.000011 - momentum: 0.000000
+2023-10-24 15:23:43,231 epoch 7 - iter 891/992 - loss 0.02152618 - time (sec): 74.95 - samples/sec: 1953.49 - lr: 0.000010 - momentum: 0.000000
+2023-10-24 15:23:51,601 epoch 7 - iter 990/992 - loss 0.02140999 - time (sec): 83.33 - samples/sec: 1965.40 - lr: 0.000010 - momentum: 0.000000
+2023-10-24 15:23:51,748 ----------------------------------------------------------------------------------------------------
+2023-10-24 15:23:51,748 EPOCH 7 done: loss 0.0214 - lr: 0.000010
+2023-10-24 15:23:54,863 DEV : loss 0.21153658628463745 - f1-score (micro avg)  0.7618
+2023-10-24 15:23:54,878 ----------------------------------------------------------------------------------------------------
+2023-10-24 15:24:03,108 epoch 8 - iter 99/992 - loss 0.01093374 - time (sec): 8.23 - samples/sec: 2004.96 - lr: 0.000010 - momentum: 0.000000
+2023-10-24 15:24:11,138 epoch 8 - iter 198/992 - loss 0.01310924 - time (sec): 16.26 - samples/sec: 1936.95 - lr: 0.000009 - momentum: 0.000000
+2023-10-24 15:24:19,358 epoch 8 - iter 297/992 - loss 0.01441408 - time (sec): 24.48 - samples/sec: 1940.68 - lr: 0.000009 - momentum: 0.000000
+2023-10-24 15:24:28,043 epoch 8 - iter 396/992 - loss 0.01461590 - time (sec): 33.16 - samples/sec: 1935.65 - lr: 0.000009 - momentum: 0.000000
+2023-10-24 15:24:36,832 epoch 8 - iter 495/992 - loss 0.01509641 - time (sec): 41.95 - samples/sec: 1945.22 - lr: 0.000008 - momentum: 0.000000
+2023-10-24 15:24:45,127 epoch 8 - iter 594/992 - loss 0.01506859 - time (sec): 50.25 - samples/sec: 1947.58 - lr: 0.000008 - momentum: 0.000000
+2023-10-24 15:24:54,112 epoch 8 - iter 693/992 - loss 0.01504122 - time (sec): 59.23 - samples/sec: 1956.47 - lr: 0.000008 - momentum: 0.000000
+2023-10-24 15:25:02,153 epoch 8 - iter 792/992 - loss 0.01598623 - time (sec): 67.27 - samples/sec: 1948.08 - lr: 0.000007 - momentum: 0.000000
+2023-10-24 15:25:10,479 epoch 8 - iter 891/992 - loss 0.01625220 - time (sec): 75.60 - samples/sec: 1952.55 - lr: 0.000007 - momentum: 0.000000
+2023-10-24 15:25:18,505 epoch 8 - iter 990/992 - loss 0.01634153 - time (sec): 83.63 - samples/sec: 1955.26 - lr: 0.000007 - momentum: 0.000000
+2023-10-24 15:25:18,705 ----------------------------------------------------------------------------------------------------
+2023-10-24 15:25:18,706 EPOCH 8 done: loss 0.0163 - lr: 0.000007
+2023-10-24 15:25:21,824 DEV : loss 0.214664027094841 - f1-score (micro avg)  0.7641
+2023-10-24 15:25:21,839 ----------------------------------------------------------------------------------------------------
+2023-10-24 15:25:30,320 epoch 9 - iter 99/992 - loss 0.00554736 - time (sec): 8.48 - samples/sec: 2033.00 - lr: 0.000006 - momentum: 0.000000
+2023-10-24 15:25:38,987 epoch 9 - iter 198/992 - loss 0.00828502 - time (sec): 17.15 - samples/sec: 1982.71 - lr: 0.000006 - momentum: 0.000000
+2023-10-24 15:25:47,155 epoch 9 - iter 297/992 - loss 0.00991386 - time (sec): 25.32 - samples/sec: 1992.59 - lr: 0.000006 - momentum: 0.000000
+2023-10-24 15:25:55,662 epoch 9 - iter 396/992 - loss 0.00972216 - time (sec): 33.82 - samples/sec: 1976.94 - lr: 0.000005 - momentum: 0.000000
+2023-10-24 15:26:03,891 epoch 9 - iter 495/992 - loss 0.00993347 - time (sec): 42.05 - samples/sec: 1975.37 - lr: 0.000005 - momentum: 0.000000
+2023-10-24 15:26:12,678 epoch 9 - iter 594/992 - loss 0.01064234 - time (sec): 50.84 - samples/sec: 1976.16 - lr: 0.000005 - momentum: 0.000000
+2023-10-24 15:26:20,744 epoch 9 - iter 693/992 - loss 0.01059840 - time (sec): 58.90 - samples/sec: 1972.26 - lr: 0.000004 - momentum: 0.000000
+2023-10-24 15:26:28,796 epoch 9 - iter 792/992 - loss 0.01094734 - time (sec): 66.96 - samples/sec: 1967.66 - lr: 0.000004 - momentum: 0.000000
+2023-10-24 15:26:36,898 epoch 9 - iter 891/992 - loss 0.01069492 - time (sec): 75.06 - samples/sec: 1964.87 - lr: 0.000004 - momentum: 0.000000
+2023-10-24 15:26:45,130 epoch 9 - iter 990/992 - loss 0.01088973 - time (sec): 83.29 - samples/sec: 1965.18 - lr: 0.000003 - momentum: 0.000000
+2023-10-24 15:26:45,309 ----------------------------------------------------------------------------------------------------
+2023-10-24 15:26:45,309 EPOCH 9 done: loss 0.0109 - lr: 0.000003
+2023-10-24 15:26:48,428 DEV : loss 0.22701114416122437 - f1-score (micro avg)  0.7697
+2023-10-24 15:26:48,443 ----------------------------------------------------------------------------------------------------
+2023-10-24 15:26:56,446 epoch 10 - iter 99/992 - loss 0.00704755 - time (sec): 8.00 - samples/sec: 1986.71 - lr: 0.000003 - momentum: 0.000000
+2023-10-24 15:27:04,484 epoch 10 - iter 198/992 - loss 0.00802891 - time (sec): 16.04 - samples/sec: 1989.28 - lr: 0.000003 - momentum: 0.000000
+2023-10-24 15:27:13,149 epoch 10 - iter 297/992 - loss 0.00713159 - time (sec): 24.71 - samples/sec: 2001.01 - lr: 0.000002 - momentum: 0.000000
+2023-10-24 15:27:21,521 epoch 10 - iter 396/992 - loss 0.00689762 - time (sec): 33.08 - samples/sec: 1985.90 - lr: 0.000002 - momentum: 0.000000
+2023-10-24 15:27:29,824 epoch 10 - iter 495/992 - loss 0.00714780 - time (sec): 41.38 - samples/sec: 1986.04 - lr: 0.000002 - momentum: 0.000000
+2023-10-24 15:27:38,100 epoch 10 - iter 594/992 - loss 0.00737894 - time (sec): 49.66 - samples/sec: 1992.26 - lr: 0.000001 - momentum: 0.000000
+2023-10-24 15:27:46,622 epoch 10 - iter 693/992 - loss 0.00692848 - time (sec): 58.18 - samples/sec: 1969.53 - lr: 0.000001 - momentum: 0.000000
+2023-10-24 15:27:54,981 epoch 10 - iter 792/992 - loss 0.00716748 - time (sec): 66.54 - samples/sec: 1965.85 - lr: 0.000001 - momentum: 0.000000
+2023-10-24 15:28:03,371 epoch 10 - iter 891/992 - loss 0.00737072 - time (sec): 74.93 - samples/sec: 1964.65 - lr: 0.000000 - momentum: 0.000000
+2023-10-24 15:28:11,754 epoch 10 - iter 990/992 - loss 0.00752842 - time (sec): 83.31 - samples/sec: 1962.95 - lr: 0.000000 - momentum: 0.000000
+2023-10-24 15:28:11,961 ----------------------------------------------------------------------------------------------------
+2023-10-24 15:28:11,961 EPOCH 10 done: loss 0.0076 - lr: 0.000000
+2023-10-24 15:28:15,064 DEV : loss 0.23377303779125214 - f1-score (micro avg)  0.7682
+2023-10-24 15:28:15,642 ----------------------------------------------------------------------------------------------------
+2023-10-24 15:28:15,642 Loading model from best epoch ...
+2023-10-24 15:28:17,450 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 15:28:20,523 
+Results:
+- F-score (micro) 0.7766
+- F-score (macro) 0.7019
+- Accuracy 0.6524
+
+By class:
+              precision    recall  f1-score   support
+
+         LOC     0.8214    0.8214    0.8214       655
+         PER     0.7339    0.8161    0.7728       223
+         ORG     0.6250    0.4331    0.5116       127
+
+   micro avg     0.7820    0.7711    0.7766      1005
+   macro avg     0.7267    0.6902    0.7019      1005
+weighted avg     0.7771    0.7711    0.7715      1005
+
+2023-10-24 15:28:20,523 ----------------------------------------------------------------------------------------------------