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training.log

@@ -0,0 +1,506 @@
+2023-10-24 17:38:12,465 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:38:12,466 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 17:38:12,466 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:38:12,467 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 17:38:12,467 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:38:12,467 Train:  7936 sentences
+2023-10-24 17:38:12,467         (train_with_dev=False, train_with_test=False)
+2023-10-24 17:38:12,467 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:38:12,467 Training Params:
+2023-10-24 17:38:12,467  - learning_rate: "3e-05" 
+2023-10-24 17:38:12,467  - mini_batch_size: "8"
+2023-10-24 17:38:12,467  - max_epochs: "10"
+2023-10-24 17:38:12,467  - shuffle: "True"
+2023-10-24 17:38:12,467 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:38:12,467 Plugins:
+2023-10-24 17:38:12,467  - TensorboardLogger
+2023-10-24 17:38:12,467  - LinearScheduler | warmup_fraction: '0.1'
+2023-10-24 17:38:12,467 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:38:12,467 Final evaluation on model from best epoch (best-model.pt)
+2023-10-24 17:38:12,467  - metric: "('micro avg', 'f1-score')"
+2023-10-24 17:38:12,467 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:38:12,467 Computation:
+2023-10-24 17:38:12,467  - compute on device: cuda:0
+2023-10-24 17:38:12,467  - embedding storage: none
+2023-10-24 17:38:12,467 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:38:12,467 Model training base path: "hmbench-icdar/fr-dbmdz/bert-base-historic-multilingual-64k-td-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3"
+2023-10-24 17:38:12,467 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:38:12,467 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:38:12,467 Logging anything other than scalars to TensorBoard is currently not supported.
+2023-10-24 17:38:20,974 epoch 1 - iter 99/992 - loss 1.74525625 - time (sec): 8.51 - samples/sec: 2051.05 - lr: 0.000003 - momentum: 0.000000
+2023-10-24 17:38:29,111 epoch 1 - iter 198/992 - loss 1.08863551 - time (sec): 16.64 - samples/sec: 2023.06 - lr: 0.000006 - momentum: 0.000000
+2023-10-24 17:38:37,169 epoch 1 - iter 297/992 - loss 0.81797787 - time (sec): 24.70 - samples/sec: 1987.76 - lr: 0.000009 - momentum: 0.000000
+2023-10-24 17:38:45,550 epoch 1 - iter 396/992 - loss 0.65812865 - time (sec): 33.08 - samples/sec: 1983.87 - lr: 0.000012 - momentum: 0.000000
+2023-10-24 17:38:53,652 epoch 1 - iter 495/992 - loss 0.56334832 - time (sec): 41.18 - samples/sec: 1974.74 - lr: 0.000015 - momentum: 0.000000
+2023-10-24 17:39:01,801 epoch 1 - iter 594/992 - loss 0.49615806 - time (sec): 49.33 - samples/sec: 1968.64 - lr: 0.000018 - momentum: 0.000000
+2023-10-24 17:39:10,421 epoch 1 - iter 693/992 - loss 0.43998124 - time (sec): 57.95 - samples/sec: 1965.03 - lr: 0.000021 - momentum: 0.000000
+2023-10-24 17:39:18,883 epoch 1 - iter 792/992 - loss 0.39963914 - time (sec): 66.41 - samples/sec: 1961.65 - lr: 0.000024 - momentum: 0.000000
+2023-10-24 17:39:27,283 epoch 1 - iter 891/992 - loss 0.37071134 - time (sec): 74.82 - samples/sec: 1968.84 - lr: 0.000027 - momentum: 0.000000
+2023-10-24 17:39:35,696 epoch 1 - iter 990/992 - loss 0.34738778 - time (sec): 83.23 - samples/sec: 1965.86 - lr: 0.000030 - momentum: 0.000000
+2023-10-24 17:39:35,876 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:39:35,876 EPOCH 1 done: loss 0.3469 - lr: 0.000030
+2023-10-24 17:39:38,923 DEV : loss 0.09140600264072418 - f1-score (micro avg)  0.7223
+2023-10-24 17:39:38,938 saving best model
+2023-10-24 17:39:39,407 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:39:47,548 epoch 2 - iter 99/992 - loss 0.09680147 - time (sec): 8.14 - samples/sec: 2002.81 - lr: 0.000030 - momentum: 0.000000
+2023-10-24 17:39:55,858 epoch 2 - iter 198/992 - loss 0.09397801 - time (sec): 16.45 - samples/sec: 1974.74 - lr: 0.000029 - momentum: 0.000000
+2023-10-24 17:40:04,370 epoch 2 - iter 297/992 - loss 0.09651916 - time (sec): 24.96 - samples/sec: 1957.39 - lr: 0.000029 - momentum: 0.000000
+2023-10-24 17:40:12,929 epoch 2 - iter 396/992 - loss 0.09944484 - time (sec): 33.52 - samples/sec: 1957.00 - lr: 0.000029 - momentum: 0.000000
+2023-10-24 17:40:21,301 epoch 2 - iter 495/992 - loss 0.09736309 - time (sec): 41.89 - samples/sec: 1967.03 - lr: 0.000028 - momentum: 0.000000
+2023-10-24 17:40:29,549 epoch 2 - iter 594/992 - loss 0.09763263 - time (sec): 50.14 - samples/sec: 1968.89 - lr: 0.000028 - momentum: 0.000000
+2023-10-24 17:40:38,016 epoch 2 - iter 693/992 - loss 0.09689609 - time (sec): 58.61 - samples/sec: 1971.33 - lr: 0.000028 - momentum: 0.000000
+2023-10-24 17:40:46,350 epoch 2 - iter 792/992 - loss 0.09551141 - time (sec): 66.94 - samples/sec: 1960.12 - lr: 0.000027 - momentum: 0.000000
+2023-10-24 17:40:54,691 epoch 2 - iter 891/992 - loss 0.09603742 - time (sec): 75.28 - samples/sec: 1955.14 - lr: 0.000027 - momentum: 0.000000
+2023-10-24 17:41:03,174 epoch 2 - iter 990/992 - loss 0.09714532 - time (sec): 83.77 - samples/sec: 1954.54 - lr: 0.000027 - momentum: 0.000000
+2023-10-24 17:41:03,320 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:41:03,320 EPOCH 2 done: loss 0.0971 - lr: 0.000027
+2023-10-24 17:41:06,418 DEV : loss 0.08006458729505539 - f1-score (micro avg)  0.753
+2023-10-24 17:41:06,433 saving best model
+2023-10-24 17:41:07,035 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:41:15,236 epoch 3 - iter 99/992 - loss 0.06139682 - time (sec): 8.20 - samples/sec: 1973.44 - lr: 0.000026 - momentum: 0.000000
+2023-10-24 17:41:23,724 epoch 3 - iter 198/992 - loss 0.06703090 - time (sec): 16.69 - samples/sec: 1974.09 - lr: 0.000026 - momentum: 0.000000
+2023-10-24 17:41:31,896 epoch 3 - iter 297/992 - loss 0.07066772 - time (sec): 24.86 - samples/sec: 1965.86 - lr: 0.000026 - momentum: 0.000000
+2023-10-24 17:41:39,983 epoch 3 - iter 396/992 - loss 0.06888834 - time (sec): 32.95 - samples/sec: 1966.40 - lr: 0.000025 - momentum: 0.000000
+2023-10-24 17:41:48,706 epoch 3 - iter 495/992 - loss 0.06680337 - time (sec): 41.67 - samples/sec: 1977.56 - lr: 0.000025 - momentum: 0.000000
+2023-10-24 17:41:57,109 epoch 3 - iter 594/992 - loss 0.06827427 - time (sec): 50.07 - samples/sec: 1972.82 - lr: 0.000025 - momentum: 0.000000
+2023-10-24 17:42:05,256 epoch 3 - iter 693/992 - loss 0.06841488 - time (sec): 58.22 - samples/sec: 1970.99 - lr: 0.000024 - momentum: 0.000000
+2023-10-24 17:42:13,637 epoch 3 - iter 792/992 - loss 0.06732059 - time (sec): 66.60 - samples/sec: 1972.16 - lr: 0.000024 - momentum: 0.000000
+2023-10-24 17:42:22,045 epoch 3 - iter 891/992 - loss 0.06623660 - time (sec): 75.01 - samples/sec: 1970.81 - lr: 0.000024 - momentum: 0.000000
+2023-10-24 17:42:30,186 epoch 3 - iter 990/992 - loss 0.06618561 - time (sec): 83.15 - samples/sec: 1969.27 - lr: 0.000023 - momentum: 0.000000
+2023-10-24 17:42:30,341 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:42:30,341 EPOCH 3 done: loss 0.0661 - lr: 0.000023
+2023-10-24 17:42:33,454 DEV : loss 0.09770625084638596 - f1-score (micro avg)  0.7664
+2023-10-24 17:42:33,469 saving best model
+2023-10-24 17:42:34,044 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:42:42,469 epoch 4 - iter 99/992 - loss 0.04066720 - time (sec): 8.42 - samples/sec: 1876.58 - lr: 0.000023 - momentum: 0.000000
+2023-10-24 17:42:51,202 epoch 4 - iter 198/992 - loss 0.04839658 - time (sec): 17.16 - samples/sec: 1910.97 - lr: 0.000023 - momentum: 0.000000
+2023-10-24 17:42:59,645 epoch 4 - iter 297/992 - loss 0.04694213 - time (sec): 25.60 - samples/sec: 1923.25 - lr: 0.000022 - momentum: 0.000000
+2023-10-24 17:43:07,788 epoch 4 - iter 396/992 - loss 0.04727140 - time (sec): 33.74 - samples/sec: 1930.44 - lr: 0.000022 - momentum: 0.000000
+2023-10-24 17:43:15,990 epoch 4 - iter 495/992 - loss 0.04760580 - time (sec): 41.95 - samples/sec: 1945.69 - lr: 0.000022 - momentum: 0.000000
+2023-10-24 17:43:23,619 epoch 4 - iter 594/992 - loss 0.04584277 - time (sec): 49.57 - samples/sec: 1943.44 - lr: 0.000021 - momentum: 0.000000
+2023-10-24 17:43:32,148 epoch 4 - iter 693/992 - loss 0.04678695 - time (sec): 58.10 - samples/sec: 1952.99 - lr: 0.000021 - momentum: 0.000000
+2023-10-24 17:43:40,536 epoch 4 - iter 792/992 - loss 0.04681106 - time (sec): 66.49 - samples/sec: 1951.01 - lr: 0.000021 - momentum: 0.000000
+2023-10-24 17:43:48,623 epoch 4 - iter 891/992 - loss 0.04740672 - time (sec): 74.58 - samples/sec: 1961.24 - lr: 0.000020 - momentum: 0.000000
+2023-10-24 17:43:57,554 epoch 4 - iter 990/992 - loss 0.04725284 - time (sec): 83.51 - samples/sec: 1959.72 - lr: 0.000020 - momentum: 0.000000
+2023-10-24 17:43:57,705 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:43:57,705 EPOCH 4 done: loss 0.0472 - lr: 0.000020
+2023-10-24 17:44:00,820 DEV : loss 0.1370622217655182 - f1-score (micro avg)  0.7684
+2023-10-24 17:44:00,835 saving best model
+2023-10-24 17:44:01,508 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:44:10,012 epoch 5 - iter 99/992 - loss 0.02951998 - time (sec): 8.50 - samples/sec: 1998.04 - lr: 0.000020 - momentum: 0.000000
+2023-10-24 17:44:18,292 epoch 5 - iter 198/992 - loss 0.03452251 - time (sec): 16.78 - samples/sec: 1967.20 - lr: 0.000019 - momentum: 0.000000
+2023-10-24 17:44:26,827 epoch 5 - iter 297/992 - loss 0.03477441 - time (sec): 25.32 - samples/sec: 1958.56 - lr: 0.000019 - momentum: 0.000000
+2023-10-24 17:44:35,053 epoch 5 - iter 396/992 - loss 0.03442328 - time (sec): 33.54 - samples/sec: 1945.10 - lr: 0.000019 - momentum: 0.000000
+2023-10-24 17:44:43,298 epoch 5 - iter 495/992 - loss 0.03659471 - time (sec): 41.79 - samples/sec: 1960.49 - lr: 0.000018 - momentum: 0.000000
+2023-10-24 17:44:51,314 epoch 5 - iter 594/992 - loss 0.03569550 - time (sec): 49.80 - samples/sec: 1964.23 - lr: 0.000018 - momentum: 0.000000
+2023-10-24 17:45:00,024 epoch 5 - iter 693/992 - loss 0.03585171 - time (sec): 58.51 - samples/sec: 1961.05 - lr: 0.000018 - momentum: 0.000000
+2023-10-24 17:45:08,378 epoch 5 - iter 792/992 - loss 0.03741357 - time (sec): 66.87 - samples/sec: 1960.60 - lr: 0.000017 - momentum: 0.000000
+2023-10-24 17:45:16,464 epoch 5 - iter 891/992 - loss 0.03829687 - time (sec): 74.96 - samples/sec: 1960.70 - lr: 0.000017 - momentum: 0.000000
+2023-10-24 17:45:24,955 epoch 5 - iter 990/992 - loss 0.03723549 - time (sec): 83.45 - samples/sec: 1961.01 - lr: 0.000017 - momentum: 0.000000
+2023-10-24 17:45:25,122 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:45:25,122 EPOCH 5 done: loss 0.0372 - lr: 0.000017
+2023-10-24 17:45:28,550 DEV : loss 0.16291803121566772 - f1-score (micro avg)  0.7765
+2023-10-24 17:45:28,566 saving best model
+2023-10-24 17:45:29,156 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:45:37,741 epoch 6 - iter 99/992 - loss 0.02033797 - time (sec): 8.58 - samples/sec: 1891.50 - lr: 0.000016 - momentum: 0.000000
+2023-10-24 17:45:46,162 epoch 6 - iter 198/992 - loss 0.02093798 - time (sec): 17.01 - samples/sec: 1941.89 - lr: 0.000016 - momentum: 0.000000
+2023-10-24 17:45:54,439 epoch 6 - iter 297/992 - loss 0.02177729 - time (sec): 25.28 - samples/sec: 1960.95 - lr: 0.000016 - momentum: 0.000000
+2023-10-24 17:46:02,552 epoch 6 - iter 396/992 - loss 0.02426201 - time (sec): 33.40 - samples/sec: 1971.88 - lr: 0.000015 - momentum: 0.000000
+2023-10-24 17:46:11,069 epoch 6 - iter 495/992 - loss 0.02614459 - time (sec): 41.91 - samples/sec: 1972.42 - lr: 0.000015 - momentum: 0.000000
+2023-10-24 17:46:19,360 epoch 6 - iter 594/992 - loss 0.02643793 - time (sec): 50.20 - samples/sec: 1964.61 - lr: 0.000015 - momentum: 0.000000
+2023-10-24 17:46:27,558 epoch 6 - iter 693/992 - loss 0.02736029 - time (sec): 58.40 - samples/sec: 1959.09 - lr: 0.000014 - momentum: 0.000000
+2023-10-24 17:46:35,923 epoch 6 - iter 792/992 - loss 0.02667042 - time (sec): 66.77 - samples/sec: 1958.07 - lr: 0.000014 - momentum: 0.000000
+2023-10-24 17:46:44,180 epoch 6 - iter 891/992 - loss 0.02785199 - time (sec): 75.02 - samples/sec: 1949.51 - lr: 0.000014 - momentum: 0.000000
+2023-10-24 17:46:52,411 epoch 6 - iter 990/992 - loss 0.02840540 - time (sec): 83.25 - samples/sec: 1966.07 - lr: 0.000013 - momentum: 0.000000
+2023-10-24 17:46:52,574 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:46:52,574 EPOCH 6 done: loss 0.0284 - lr: 0.000013
+2023-10-24 17:46:55,693 DEV : loss 0.1790854036808014 - f1-score (micro avg)  0.7681
+2023-10-24 17:46:55,708 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:47:04,452 epoch 7 - iter 99/992 - loss 0.02418540 - time (sec): 8.74 - samples/sec: 1919.47 - lr: 0.000013 - momentum: 0.000000
+2023-10-24 17:47:12,542 epoch 7 - iter 198/992 - loss 0.02557997 - time (sec): 16.83 - samples/sec: 1928.44 - lr: 0.000013 - momentum: 0.000000
+2023-10-24 17:47:20,873 epoch 7 - iter 297/992 - loss 0.02329917 - time (sec): 25.16 - samples/sec: 1936.67 - lr: 0.000012 - momentum: 0.000000
+2023-10-24 17:47:29,301 epoch 7 - iter 396/992 - loss 0.02067675 - time (sec): 33.59 - samples/sec: 1918.15 - lr: 0.000012 - momentum: 0.000000
+2023-10-24 17:47:37,478 epoch 7 - iter 495/992 - loss 0.02044117 - time (sec): 41.77 - samples/sec: 1924.13 - lr: 0.000012 - momentum: 0.000000
+2023-10-24 17:47:46,176 epoch 7 - iter 594/992 - loss 0.02024929 - time (sec): 50.47 - samples/sec: 1938.10 - lr: 0.000011 - momentum: 0.000000
+2023-10-24 17:47:54,710 epoch 7 - iter 693/992 - loss 0.01948114 - time (sec): 59.00 - samples/sec: 1944.74 - lr: 0.000011 - momentum: 0.000000
+2023-10-24 17:48:02,922 epoch 7 - iter 792/992 - loss 0.01956172 - time (sec): 67.21 - samples/sec: 1949.30 - lr: 0.000011 - momentum: 0.000000
+2023-10-24 17:48:11,022 epoch 7 - iter 891/992 - loss 0.01970571 - time (sec): 75.31 - samples/sec: 1956.65 - lr: 0.000010 - momentum: 0.000000
+2023-10-24 17:48:19,161 epoch 7 - iter 990/992 - loss 0.02041426 - time (sec): 83.45 - samples/sec: 1959.32 - lr: 0.000010 - momentum: 0.000000
+2023-10-24 17:48:19,336 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:48:19,336 EPOCH 7 done: loss 0.0204 - lr: 0.000010
+2023-10-24 17:48:22,769 DEV : loss 0.20467530190944672 - f1-score (micro avg)  0.7616
+2023-10-24 17:48:22,785 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:48:31,373 epoch 8 - iter 99/992 - loss 0.01338429 - time (sec): 8.59 - samples/sec: 2020.71 - lr: 0.000010 - momentum: 0.000000
+2023-10-24 17:48:40,061 epoch 8 - iter 198/992 - loss 0.01222624 - time (sec): 17.27 - samples/sec: 1977.55 - lr: 0.000009 - momentum: 0.000000
+2023-10-24 17:48:48,204 epoch 8 - iter 297/992 - loss 0.01238322 - time (sec): 25.42 - samples/sec: 1955.48 - lr: 0.000009 - momentum: 0.000000
+2023-10-24 17:48:56,609 epoch 8 - iter 396/992 - loss 0.01378072 - time (sec): 33.82 - samples/sec: 1945.84 - lr: 0.000009 - momentum: 0.000000
+2023-10-24 17:49:04,670 epoch 8 - iter 495/992 - loss 0.01464499 - time (sec): 41.88 - samples/sec: 1950.49 - lr: 0.000008 - momentum: 0.000000
+2023-10-24 17:49:13,145 epoch 8 - iter 594/992 - loss 0.01529863 - time (sec): 50.36 - samples/sec: 1962.49 - lr: 0.000008 - momentum: 0.000000
+2023-10-24 17:49:21,465 epoch 8 - iter 693/992 - loss 0.01426628 - time (sec): 58.68 - samples/sec: 1964.81 - lr: 0.000008 - momentum: 0.000000
+2023-10-24 17:49:29,282 epoch 8 - iter 792/992 - loss 0.01434806 - time (sec): 66.50 - samples/sec: 1961.40 - lr: 0.000007 - momentum: 0.000000
+2023-10-24 17:49:37,726 epoch 8 - iter 891/992 - loss 0.01444871 - time (sec): 74.94 - samples/sec: 1960.50 - lr: 0.000007 - momentum: 0.000000
+2023-10-24 17:49:46,097 epoch 8 - iter 990/992 - loss 0.01488712 - time (sec): 83.31 - samples/sec: 1964.08 - lr: 0.000007 - momentum: 0.000000
+2023-10-24 17:49:46,245 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:49:46,245 EPOCH 8 done: loss 0.0149 - lr: 0.000007
+2023-10-24 17:49:49,369 DEV : loss 0.23477818071842194 - f1-score (micro avg)  0.7571
+2023-10-24 17:49:49,384 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:49:57,571 epoch 9 - iter 99/992 - loss 0.01371693 - time (sec): 8.19 - samples/sec: 1937.71 - lr: 0.000006 - momentum: 0.000000
+2023-10-24 17:50:05,790 epoch 9 - iter 198/992 - loss 0.01100052 - time (sec): 16.40 - samples/sec: 1927.30 - lr: 0.000006 - momentum: 0.000000
+2023-10-24 17:50:13,928 epoch 9 - iter 297/992 - loss 0.01149748 - time (sec): 24.54 - samples/sec: 1925.21 - lr: 0.000006 - momentum: 0.000000
+2023-10-24 17:50:23,074 epoch 9 - iter 396/992 - loss 0.01212931 - time (sec): 33.69 - samples/sec: 1919.27 - lr: 0.000005 - momentum: 0.000000
+2023-10-24 17:50:31,771 epoch 9 - iter 495/992 - loss 0.01093322 - time (sec): 42.39 - samples/sec: 1929.30 - lr: 0.000005 - momentum: 0.000000
+2023-10-24 17:50:40,350 epoch 9 - iter 594/992 - loss 0.01065967 - time (sec): 50.97 - samples/sec: 1930.89 - lr: 0.000005 - momentum: 0.000000
+2023-10-24 17:50:48,388 epoch 9 - iter 693/992 - loss 0.01078237 - time (sec): 59.00 - samples/sec: 1939.66 - lr: 0.000004 - momentum: 0.000000
+2023-10-24 17:50:56,619 epoch 9 - iter 792/992 - loss 0.01024575 - time (sec): 67.23 - samples/sec: 1942.87 - lr: 0.000004 - momentum: 0.000000
+2023-10-24 17:51:04,644 epoch 9 - iter 891/992 - loss 0.01058774 - time (sec): 75.26 - samples/sec: 1951.24 - lr: 0.000004 - momentum: 0.000000
+2023-10-24 17:51:12,810 epoch 9 - iter 990/992 - loss 0.01091116 - time (sec): 83.43 - samples/sec: 1962.25 - lr: 0.000003 - momentum: 0.000000
+2023-10-24 17:51:12,957 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:51:12,957 EPOCH 9 done: loss 0.0109 - lr: 0.000003
+2023-10-24 17:51:16,412 DEV : loss 0.23431342840194702 - f1-score (micro avg)  0.7708
+2023-10-24 17:51:16,427 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:51:24,444 epoch 10 - iter 99/992 - loss 0.00607875 - time (sec): 8.02 - samples/sec: 2022.08 - lr: 0.000003 - momentum: 0.000000
+2023-10-24 17:51:32,702 epoch 10 - iter 198/992 - loss 0.00533387 - time (sec): 16.27 - samples/sec: 1987.29 - lr: 0.000003 - momentum: 0.000000
+2023-10-24 17:51:41,170 epoch 10 - iter 297/992 - loss 0.00635844 - time (sec): 24.74 - samples/sec: 1984.33 - lr: 0.000002 - momentum: 0.000000
+2023-10-24 17:51:49,635 epoch 10 - iter 396/992 - loss 0.00826920 - time (sec): 33.21 - samples/sec: 1992.73 - lr: 0.000002 - momentum: 0.000000
+2023-10-24 17:51:57,861 epoch 10 - iter 495/992 - loss 0.00834489 - time (sec): 41.43 - samples/sec: 1987.29 - lr: 0.000002 - momentum: 0.000000
+2023-10-24 17:52:06,239 epoch 10 - iter 594/992 - loss 0.00777999 - time (sec): 49.81 - samples/sec: 1972.40 - lr: 0.000001 - momentum: 0.000000
+2023-10-24 17:52:14,639 epoch 10 - iter 693/992 - loss 0.00812146 - time (sec): 58.21 - samples/sec: 1968.69 - lr: 0.000001 - momentum: 0.000000
+2023-10-24 17:52:22,702 epoch 10 - iter 792/992 - loss 0.00755783 - time (sec): 66.27 - samples/sec: 1964.62 - lr: 0.000001 - momentum: 0.000000
+2023-10-24 17:52:31,219 epoch 10 - iter 891/992 - loss 0.00784551 - time (sec): 74.79 - samples/sec: 1962.71 - lr: 0.000000 - momentum: 0.000000
+2023-10-24 17:52:39,702 epoch 10 - iter 990/992 - loss 0.00776579 - time (sec): 83.27 - samples/sec: 1964.99 - lr: 0.000000 - momentum: 0.000000
+2023-10-24 17:52:39,872 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:52:39,872 EPOCH 10 done: loss 0.0078 - lr: 0.000000
+2023-10-24 17:52:42,994 DEV : loss 0.2393815815448761 - f1-score (micro avg)  0.7619
+2023-10-24 17:52:43,482 ----------------------------------------------------------------------------------------------------
+2023-10-24 17:52:43,483 Loading model from best epoch ...
+2023-10-24 17:52:44,969 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 17:52:48,050 
+Results:
+- F-score (micro) 0.7854
+- F-score (macro) 0.7033
+- Accuracy 0.6628
+
+By class:
+              precision    recall  f1-score   support
+
+         LOC     0.8130    0.8565    0.8342       655
+         PER     0.7377    0.8072    0.7709       223
+         ORG     0.6386    0.4173    0.5048       127
+
+   micro avg     0.7807    0.7900    0.7854      1005
+   macro avg     0.7298    0.6937    0.7033      1005
+weighted avg     0.7743    0.7900    0.7785      1005
+
+2023-10-24 17:52:48,050 ----------------------------------------------------------------------------------------------------