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training.log

@@ -0,0 +1,511 @@
+2023-10-24 10:02:59,640 ----------------------------------------------------------------------------------------------------
+2023-10-24 10:02:59,641 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=21, bias=True)
+  (loss_function): CrossEntropyLoss()
+)"
+2023-10-24 10:02:59,641 ----------------------------------------------------------------------------------------------------
+2023-10-24 10:02:59,641 MultiCorpus: 5901 train + 1287 dev + 1505 test sentences
+ - NER_HIPE_2022 Corpus: 5901 train + 1287 dev + 1505 test sentences - /home/ubuntu/.flair/datasets/ner_hipe_2022/v2.1/hipe2020/fr/with_doc_seperator
+2023-10-24 10:02:59,641 ----------------------------------------------------------------------------------------------------
+2023-10-24 10:02:59,641 Train:  5901 sentences
+2023-10-24 10:02:59,641         (train_with_dev=False, train_with_test=False)
+2023-10-24 10:02:59,641 ----------------------------------------------------------------------------------------------------
+2023-10-24 10:02:59,641 Training Params:
+2023-10-24 10:02:59,641  - learning_rate: "3e-05" 
+2023-10-24 10:02:59,641  - mini_batch_size: "8"
+2023-10-24 10:02:59,641  - max_epochs: "10"
+2023-10-24 10:02:59,641  - shuffle: "True"
+2023-10-24 10:02:59,641 ----------------------------------------------------------------------------------------------------
+2023-10-24 10:02:59,641 Plugins:
+2023-10-24 10:02:59,641  - TensorboardLogger
+2023-10-24 10:02:59,642  - LinearScheduler | warmup_fraction: '0.1'
+2023-10-24 10:02:59,642 ----------------------------------------------------------------------------------------------------
+2023-10-24 10:02:59,642 Final evaluation on model from best epoch (best-model.pt)
+2023-10-24 10:02:59,642  - metric: "('micro avg', 'f1-score')"
+2023-10-24 10:02:59,642 ----------------------------------------------------------------------------------------------------
+2023-10-24 10:02:59,642 Computation:
+2023-10-24 10:02:59,642  - compute on device: cuda:0
+2023-10-24 10:02:59,642  - embedding storage: none
+2023-10-24 10:02:59,642 ----------------------------------------------------------------------------------------------------
+2023-10-24 10:02:59,642 Model training base path: "hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-64k-td-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2"
+2023-10-24 10:02:59,642 ----------------------------------------------------------------------------------------------------
+2023-10-24 10:02:59,642 ----------------------------------------------------------------------------------------------------
+2023-10-24 10:02:59,642 Logging anything other than scalars to TensorBoard is currently not supported.
+2023-10-24 10:03:05,895 epoch 1 - iter 73/738 - loss 2.42550890 - time (sec): 6.25 - samples/sec: 2471.21 - lr: 0.000003 - momentum: 0.000000
+2023-10-24 10:03:12,507 epoch 1 - iter 146/738 - loss 1.50600625 - time (sec): 12.86 - samples/sec: 2418.68 - lr: 0.000006 - momentum: 0.000000
+2023-10-24 10:03:19,533 epoch 1 - iter 219/738 - loss 1.12767070 - time (sec): 19.89 - samples/sec: 2397.52 - lr: 0.000009 - momentum: 0.000000
+2023-10-24 10:03:26,301 epoch 1 - iter 292/738 - loss 0.93406611 - time (sec): 26.66 - samples/sec: 2376.48 - lr: 0.000012 - momentum: 0.000000
+2023-10-24 10:03:33,259 epoch 1 - iter 365/738 - loss 0.80134282 - time (sec): 33.62 - samples/sec: 2375.26 - lr: 0.000015 - momentum: 0.000000
+2023-10-24 10:03:39,853 epoch 1 - iter 438/738 - loss 0.71149145 - time (sec): 40.21 - samples/sec: 2361.86 - lr: 0.000018 - momentum: 0.000000
+2023-10-24 10:03:47,300 epoch 1 - iter 511/738 - loss 0.63540461 - time (sec): 47.66 - samples/sec: 2356.51 - lr: 0.000021 - momentum: 0.000000
+2023-10-24 10:03:53,988 epoch 1 - iter 584/738 - loss 0.57914697 - time (sec): 54.35 - samples/sec: 2358.43 - lr: 0.000024 - momentum: 0.000000
+2023-10-24 10:04:01,580 epoch 1 - iter 657/738 - loss 0.53176914 - time (sec): 61.94 - samples/sec: 2360.80 - lr: 0.000027 - momentum: 0.000000
+2023-10-24 10:04:09,449 epoch 1 - iter 730/738 - loss 0.49158401 - time (sec): 69.81 - samples/sec: 2357.65 - lr: 0.000030 - momentum: 0.000000
+2023-10-24 10:04:10,192 ----------------------------------------------------------------------------------------------------
+2023-10-24 10:04:10,192 EPOCH 1 done: loss 0.4878 - lr: 0.000030
+2023-10-24 10:04:16,415 DEV : loss 0.10594037920236588 - f1-score (micro avg)  0.7283
+2023-10-24 10:04:16,436 saving best model
+2023-10-24 10:04:16,986 ----------------------------------------------------------------------------------------------------
+2023-10-24 10:04:23,926 epoch 2 - iter 73/738 - loss 0.14091494 - time (sec): 6.94 - samples/sec: 2335.41 - lr: 0.000030 - momentum: 0.000000
+2023-10-24 10:04:31,241 epoch 2 - iter 146/738 - loss 0.12231896 - time (sec): 14.25 - samples/sec: 2342.68 - lr: 0.000029 - momentum: 0.000000
+2023-10-24 10:04:37,881 epoch 2 - iter 219/738 - loss 0.12111484 - time (sec): 20.89 - samples/sec: 2339.29 - lr: 0.000029 - momentum: 0.000000
+2023-10-24 10:04:45,345 epoch 2 - iter 292/738 - loss 0.12184837 - time (sec): 28.36 - samples/sec: 2317.62 - lr: 0.000029 - momentum: 0.000000
+2023-10-24 10:04:52,383 epoch 2 - iter 365/738 - loss 0.12080821 - time (sec): 35.40 - samples/sec: 2342.50 - lr: 0.000028 - momentum: 0.000000
+2023-10-24 10:04:59,079 epoch 2 - iter 438/738 - loss 0.11532571 - time (sec): 42.09 - samples/sec: 2347.84 - lr: 0.000028 - momentum: 0.000000
+2023-10-24 10:05:06,120 epoch 2 - iter 511/738 - loss 0.11512762 - time (sec): 49.13 - samples/sec: 2336.44 - lr: 0.000028 - momentum: 0.000000
+2023-10-24 10:05:13,584 epoch 2 - iter 584/738 - loss 0.11551858 - time (sec): 56.60 - samples/sec: 2347.44 - lr: 0.000027 - momentum: 0.000000
+2023-10-24 10:05:20,545 epoch 2 - iter 657/738 - loss 0.11451103 - time (sec): 63.56 - samples/sec: 2343.30 - lr: 0.000027 - momentum: 0.000000
+2023-10-24 10:05:27,102 epoch 2 - iter 730/738 - loss 0.11268099 - time (sec): 70.11 - samples/sec: 2353.65 - lr: 0.000027 - momentum: 0.000000
+2023-10-24 10:05:27,733 ----------------------------------------------------------------------------------------------------
+2023-10-24 10:05:27,734 EPOCH 2 done: loss 0.1124 - lr: 0.000027
+2023-10-24 10:05:36,219 DEV : loss 0.1031927615404129 - f1-score (micro avg)  0.8039
+2023-10-24 10:05:36,241 saving best model
+2023-10-24 10:05:36,964 ----------------------------------------------------------------------------------------------------
+2023-10-24 10:05:43,681 epoch 3 - iter 73/738 - loss 0.06142961 - time (sec): 6.72 - samples/sec: 2392.58 - lr: 0.000026 - momentum: 0.000000
+2023-10-24 10:05:50,369 epoch 3 - iter 146/738 - loss 0.06320856 - time (sec): 13.40 - samples/sec: 2398.13 - lr: 0.000026 - momentum: 0.000000
+2023-10-24 10:05:57,239 epoch 3 - iter 219/738 - loss 0.06406728 - time (sec): 20.27 - samples/sec: 2350.76 - lr: 0.000026 - momentum: 0.000000
+2023-10-24 10:06:04,920 epoch 3 - iter 292/738 - loss 0.06949115 - time (sec): 27.95 - samples/sec: 2363.73 - lr: 0.000025 - momentum: 0.000000
+2023-10-24 10:06:12,095 epoch 3 - iter 365/738 - loss 0.06948464 - time (sec): 35.13 - samples/sec: 2368.81 - lr: 0.000025 - momentum: 0.000000
+2023-10-24 10:06:18,669 epoch 3 - iter 438/738 - loss 0.06584312 - time (sec): 41.70 - samples/sec: 2376.09 - lr: 0.000025 - momentum: 0.000000
+2023-10-24 10:06:25,135 epoch 3 - iter 511/738 - loss 0.06518111 - time (sec): 48.17 - samples/sec: 2382.47 - lr: 0.000024 - momentum: 0.000000
+2023-10-24 10:06:32,856 epoch 3 - iter 584/738 - loss 0.06469238 - time (sec): 55.89 - samples/sec: 2372.12 - lr: 0.000024 - momentum: 0.000000
+2023-10-24 10:06:39,410 epoch 3 - iter 657/738 - loss 0.06589124 - time (sec): 62.45 - samples/sec: 2374.79 - lr: 0.000024 - momentum: 0.000000
+2023-10-24 10:06:46,704 epoch 3 - iter 730/738 - loss 0.06610678 - time (sec): 69.74 - samples/sec: 2361.48 - lr: 0.000023 - momentum: 0.000000
+2023-10-24 10:06:47,394 ----------------------------------------------------------------------------------------------------
+2023-10-24 10:06:47,394 EPOCH 3 done: loss 0.0660 - lr: 0.000023
+2023-10-24 10:06:55,870 DEV : loss 0.10477666556835175 - f1-score (micro avg)  0.822
+2023-10-24 10:06:55,892 saving best model
+2023-10-24 10:06:56,591 ----------------------------------------------------------------------------------------------------
+2023-10-24 10:07:03,333 epoch 4 - iter 73/738 - loss 0.04080442 - time (sec): 6.74 - samples/sec: 2324.73 - lr: 0.000023 - momentum: 0.000000
+2023-10-24 10:07:11,372 epoch 4 - iter 146/738 - loss 0.04335989 - time (sec): 14.78 - samples/sec: 2266.77 - lr: 0.000023 - momentum: 0.000000
+2023-10-24 10:07:18,506 epoch 4 - iter 219/738 - loss 0.04233301 - time (sec): 21.91 - samples/sec: 2373.52 - lr: 0.000022 - momentum: 0.000000
+2023-10-24 10:07:25,615 epoch 4 - iter 292/738 - loss 0.04153088 - time (sec): 29.02 - samples/sec: 2358.09 - lr: 0.000022 - momentum: 0.000000
+2023-10-24 10:07:32,101 epoch 4 - iter 365/738 - loss 0.04133152 - time (sec): 35.51 - samples/sec: 2370.06 - lr: 0.000022 - momentum: 0.000000
+2023-10-24 10:07:39,193 epoch 4 - iter 438/738 - loss 0.04256243 - time (sec): 42.60 - samples/sec: 2370.12 - lr: 0.000021 - momentum: 0.000000
+2023-10-24 10:07:46,147 epoch 4 - iter 511/738 - loss 0.04221785 - time (sec): 49.56 - samples/sec: 2352.83 - lr: 0.000021 - momentum: 0.000000
+2023-10-24 10:07:53,003 epoch 4 - iter 584/738 - loss 0.04300245 - time (sec): 56.41 - samples/sec: 2352.17 - lr: 0.000021 - momentum: 0.000000
+2023-10-24 10:08:00,219 epoch 4 - iter 657/738 - loss 0.04276968 - time (sec): 63.63 - samples/sec: 2342.79 - lr: 0.000020 - momentum: 0.000000
+2023-10-24 10:08:06,859 epoch 4 - iter 730/738 - loss 0.04317653 - time (sec): 70.27 - samples/sec: 2342.56 - lr: 0.000020 - momentum: 0.000000
+2023-10-24 10:08:07,592 ----------------------------------------------------------------------------------------------------
+2023-10-24 10:08:07,592 EPOCH 4 done: loss 0.0432 - lr: 0.000020
+2023-10-24 10:08:16,095 DEV : loss 0.152599036693573 - f1-score (micro avg)  0.8181
+2023-10-24 10:08:16,116 ----------------------------------------------------------------------------------------------------
+2023-10-24 10:08:23,270 epoch 5 - iter 73/738 - loss 0.03851342 - time (sec): 7.15 - samples/sec: 2259.95 - lr: 0.000020 - momentum: 0.000000
+2023-10-24 10:08:30,725 epoch 5 - iter 146/738 - loss 0.02771039 - time (sec): 14.61 - samples/sec: 2337.46 - lr: 0.000019 - momentum: 0.000000
+2023-10-24 10:08:37,294 epoch 5 - iter 219/738 - loss 0.03124182 - time (sec): 21.18 - samples/sec: 2370.01 - lr: 0.000019 - momentum: 0.000000
+2023-10-24 10:08:44,328 epoch 5 - iter 292/738 - loss 0.02900780 - time (sec): 28.21 - samples/sec: 2371.67 - lr: 0.000019 - momentum: 0.000000
+2023-10-24 10:08:51,054 epoch 5 - iter 365/738 - loss 0.02939080 - time (sec): 34.94 - samples/sec: 2388.23 - lr: 0.000018 - momentum: 0.000000
+2023-10-24 10:08:58,572 epoch 5 - iter 438/738 - loss 0.03158563 - time (sec): 42.45 - samples/sec: 2390.55 - lr: 0.000018 - momentum: 0.000000
+2023-10-24 10:09:05,161 epoch 5 - iter 511/738 - loss 0.03343792 - time (sec): 49.04 - samples/sec: 2377.13 - lr: 0.000018 - momentum: 0.000000
+2023-10-24 10:09:12,032 epoch 5 - iter 584/738 - loss 0.03263845 - time (sec): 55.91 - samples/sec: 2365.33 - lr: 0.000017 - momentum: 0.000000
+2023-10-24 10:09:18,863 epoch 5 - iter 657/738 - loss 0.03330939 - time (sec): 62.75 - samples/sec: 2355.58 - lr: 0.000017 - momentum: 0.000000
+2023-10-24 10:09:26,361 epoch 5 - iter 730/738 - loss 0.03263123 - time (sec): 70.24 - samples/sec: 2343.37 - lr: 0.000017 - momentum: 0.000000
+2023-10-24 10:09:27,040 ----------------------------------------------------------------------------------------------------
+2023-10-24 10:09:27,041 EPOCH 5 done: loss 0.0325 - lr: 0.000017
+2023-10-24 10:09:35,593 DEV : loss 0.16575849056243896 - f1-score (micro avg)  0.8262
+2023-10-24 10:09:35,615 saving best model
+2023-10-24 10:09:36,323 ----------------------------------------------------------------------------------------------------
+2023-10-24 10:09:44,530 epoch 6 - iter 73/738 - loss 0.02678751 - time (sec): 8.21 - samples/sec: 2432.33 - lr: 0.000016 - momentum: 0.000000
+2023-10-24 10:09:50,761 epoch 6 - iter 146/738 - loss 0.02291770 - time (sec): 14.44 - samples/sec: 2413.80 - lr: 0.000016 - momentum: 0.000000
+2023-10-24 10:09:58,454 epoch 6 - iter 219/738 - loss 0.02553640 - time (sec): 22.13 - samples/sec: 2340.90 - lr: 0.000016 - momentum: 0.000000
+2023-10-24 10:10:04,889 epoch 6 - iter 292/738 - loss 0.02480548 - time (sec): 28.56 - samples/sec: 2332.14 - lr: 0.000015 - momentum: 0.000000
+2023-10-24 10:10:11,608 epoch 6 - iter 365/738 - loss 0.02547611 - time (sec): 35.28 - samples/sec: 2353.19 - lr: 0.000015 - momentum: 0.000000
+2023-10-24 10:10:18,635 epoch 6 - iter 438/738 - loss 0.02484851 - time (sec): 42.31 - samples/sec: 2356.94 - lr: 0.000015 - momentum: 0.000000
+2023-10-24 10:10:25,292 epoch 6 - iter 511/738 - loss 0.02610251 - time (sec): 48.97 - samples/sec: 2361.14 - lr: 0.000014 - momentum: 0.000000
+2023-10-24 10:10:31,906 epoch 6 - iter 584/738 - loss 0.02565136 - time (sec): 55.58 - samples/sec: 2358.71 - lr: 0.000014 - momentum: 0.000000
+2023-10-24 10:10:38,251 epoch 6 - iter 657/738 - loss 0.02458488 - time (sec): 61.93 - samples/sec: 2358.32 - lr: 0.000014 - momentum: 0.000000
+2023-10-24 10:10:45,681 epoch 6 - iter 730/738 - loss 0.02431977 - time (sec): 69.36 - samples/sec: 2365.19 - lr: 0.000013 - momentum: 0.000000
+2023-10-24 10:10:46,741 ----------------------------------------------------------------------------------------------------
+2023-10-24 10:10:46,741 EPOCH 6 done: loss 0.0242 - lr: 0.000013
+2023-10-24 10:10:55,250 DEV : loss 0.19801990687847137 - f1-score (micro avg)  0.8271
+2023-10-24 10:10:55,271 saving best model
+2023-10-24 10:10:55,967 ----------------------------------------------------------------------------------------------------
+2023-10-24 10:11:02,824 epoch 7 - iter 73/738 - loss 0.01696546 - time (sec): 6.86 - samples/sec: 2413.99 - lr: 0.000013 - momentum: 0.000000
+2023-10-24 10:11:09,882 epoch 7 - iter 146/738 - loss 0.01294632 - time (sec): 13.91 - samples/sec: 2378.56 - lr: 0.000013 - momentum: 0.000000
+2023-10-24 10:11:17,288 epoch 7 - iter 219/738 - loss 0.01398777 - time (sec): 21.32 - samples/sec: 2369.26 - lr: 0.000012 - momentum: 0.000000
+2023-10-24 10:11:24,456 epoch 7 - iter 292/738 - loss 0.01547935 - time (sec): 28.49 - samples/sec: 2349.20 - lr: 0.000012 - momentum: 0.000000
+2023-10-24 10:11:31,386 epoch 7 - iter 365/738 - loss 0.01507352 - time (sec): 35.42 - samples/sec: 2339.48 - lr: 0.000012 - momentum: 0.000000
+2023-10-24 10:11:38,521 epoch 7 - iter 438/738 - loss 0.01688411 - time (sec): 42.55 - samples/sec: 2328.32 - lr: 0.000011 - momentum: 0.000000
+2023-10-24 10:11:46,033 epoch 7 - iter 511/738 - loss 0.01735857 - time (sec): 50.06 - samples/sec: 2335.27 - lr: 0.000011 - momentum: 0.000000
+2023-10-24 10:11:52,504 epoch 7 - iter 584/738 - loss 0.01711860 - time (sec): 56.54 - samples/sec: 2328.63 - lr: 0.000011 - momentum: 0.000000
+2023-10-24 10:11:58,762 epoch 7 - iter 657/738 - loss 0.01671606 - time (sec): 62.79 - samples/sec: 2345.39 - lr: 0.000010 - momentum: 0.000000
+2023-10-24 10:12:06,314 epoch 7 - iter 730/738 - loss 0.01629479 - time (sec): 70.35 - samples/sec: 2344.46 - lr: 0.000010 - momentum: 0.000000
+2023-10-24 10:12:06,944 ----------------------------------------------------------------------------------------------------
+2023-10-24 10:12:06,944 EPOCH 7 done: loss 0.0164 - lr: 0.000010
+2023-10-24 10:12:15,453 DEV : loss 0.2032857984304428 - f1-score (micro avg)  0.8268
+2023-10-24 10:12:15,475 ----------------------------------------------------------------------------------------------------
+2023-10-24 10:12:22,424 epoch 8 - iter 73/738 - loss 0.01216183 - time (sec): 6.95 - samples/sec: 2287.40 - lr: 0.000010 - momentum: 0.000000
+2023-10-24 10:12:29,512 epoch 8 - iter 146/738 - loss 0.01236948 - time (sec): 14.04 - samples/sec: 2336.90 - lr: 0.000009 - momentum: 0.000000
+2023-10-24 10:12:37,062 epoch 8 - iter 219/738 - loss 0.01044188 - time (sec): 21.59 - samples/sec: 2309.04 - lr: 0.000009 - momentum: 0.000000
+2023-10-24 10:12:43,982 epoch 8 - iter 292/738 - loss 0.01034487 - time (sec): 28.51 - samples/sec: 2346.41 - lr: 0.000009 - momentum: 0.000000
+2023-10-24 10:12:51,543 epoch 8 - iter 365/738 - loss 0.01126341 - time (sec): 36.07 - samples/sec: 2368.99 - lr: 0.000008 - momentum: 0.000000
+2023-10-24 10:12:58,262 epoch 8 - iter 438/738 - loss 0.01076147 - time (sec): 42.79 - samples/sec: 2353.67 - lr: 0.000008 - momentum: 0.000000
+2023-10-24 10:13:05,389 epoch 8 - iter 511/738 - loss 0.01075803 - time (sec): 49.91 - samples/sec: 2354.08 - lr: 0.000008 - momentum: 0.000000
+2023-10-24 10:13:12,279 epoch 8 - iter 584/738 - loss 0.01183127 - time (sec): 56.80 - samples/sec: 2342.58 - lr: 0.000007 - momentum: 0.000000
+2023-10-24 10:13:19,173 epoch 8 - iter 657/738 - loss 0.01204421 - time (sec): 63.70 - samples/sec: 2347.40 - lr: 0.000007 - momentum: 0.000000
+2023-10-24 10:13:25,559 epoch 8 - iter 730/738 - loss 0.01168359 - time (sec): 70.08 - samples/sec: 2351.74 - lr: 0.000007 - momentum: 0.000000
+2023-10-24 10:13:26,318 ----------------------------------------------------------------------------------------------------
+2023-10-24 10:13:26,318 EPOCH 8 done: loss 0.0116 - lr: 0.000007
+2023-10-24 10:13:34,847 DEV : loss 0.19606834650039673 - f1-score (micro avg)  0.8411
+2023-10-24 10:13:34,869 saving best model
+2023-10-24 10:13:35,564 ----------------------------------------------------------------------------------------------------
+2023-10-24 10:13:42,230 epoch 9 - iter 73/738 - loss 0.00325841 - time (sec): 6.67 - samples/sec: 2350.12 - lr: 0.000006 - momentum: 0.000000
+2023-10-24 10:13:49,266 epoch 9 - iter 146/738 - loss 0.00560515 - time (sec): 13.70 - samples/sec: 2327.56 - lr: 0.000006 - momentum: 0.000000
+2023-10-24 10:13:55,847 epoch 9 - iter 219/738 - loss 0.00871536 - time (sec): 20.28 - samples/sec: 2341.82 - lr: 0.000006 - momentum: 0.000000
+2023-10-24 10:14:03,005 epoch 9 - iter 292/738 - loss 0.00745041 - time (sec): 27.44 - samples/sec: 2358.00 - lr: 0.000005 - momentum: 0.000000
+2023-10-24 10:14:10,081 epoch 9 - iter 365/738 - loss 0.00721985 - time (sec): 34.52 - samples/sec: 2337.43 - lr: 0.000005 - momentum: 0.000000
+2023-10-24 10:14:16,553 epoch 9 - iter 438/738 - loss 0.00827858 - time (sec): 40.99 - samples/sec: 2344.81 - lr: 0.000005 - momentum: 0.000000
+2023-10-24 10:14:22,960 epoch 9 - iter 511/738 - loss 0.00832065 - time (sec): 47.39 - samples/sec: 2343.01 - lr: 0.000004 - momentum: 0.000000
+2023-10-24 10:14:30,542 epoch 9 - iter 584/738 - loss 0.00748758 - time (sec): 54.98 - samples/sec: 2349.55 - lr: 0.000004 - momentum: 0.000000
+2023-10-24 10:14:37,973 epoch 9 - iter 657/738 - loss 0.00872752 - time (sec): 62.41 - samples/sec: 2360.31 - lr: 0.000004 - momentum: 0.000000
+2023-10-24 10:14:45,591 epoch 9 - iter 730/738 - loss 0.00832303 - time (sec): 70.03 - samples/sec: 2355.51 - lr: 0.000003 - momentum: 0.000000
+2023-10-24 10:14:46,236 ----------------------------------------------------------------------------------------------------
+2023-10-24 10:14:46,236 EPOCH 9 done: loss 0.0083 - lr: 0.000003
+2023-10-24 10:14:54,743 DEV : loss 0.21085196733474731 - f1-score (micro avg)  0.8465
+2023-10-24 10:14:54,765 saving best model
+2023-10-24 10:14:55,461 ----------------------------------------------------------------------------------------------------
+2023-10-24 10:15:03,114 epoch 10 - iter 73/738 - loss 0.00225549 - time (sec): 7.65 - samples/sec: 2254.22 - lr: 0.000003 - momentum: 0.000000
+2023-10-24 10:15:09,825 epoch 10 - iter 146/738 - loss 0.00248018 - time (sec): 14.36 - samples/sec: 2313.87 - lr: 0.000003 - momentum: 0.000000
+2023-10-24 10:15:16,491 epoch 10 - iter 219/738 - loss 0.00258571 - time (sec): 21.03 - samples/sec: 2308.26 - lr: 0.000002 - momentum: 0.000000
+2023-10-24 10:15:23,656 epoch 10 - iter 292/738 - loss 0.00272149 - time (sec): 28.19 - samples/sec: 2317.62 - lr: 0.000002 - momentum: 0.000000
+2023-10-24 10:15:31,083 epoch 10 - iter 365/738 - loss 0.00322358 - time (sec): 35.62 - samples/sec: 2357.62 - lr: 0.000002 - momentum: 0.000000
+2023-10-24 10:15:38,019 epoch 10 - iter 438/738 - loss 0.00349996 - time (sec): 42.56 - samples/sec: 2352.67 - lr: 0.000001 - momentum: 0.000000
+2023-10-24 10:15:45,376 epoch 10 - iter 511/738 - loss 0.00398963 - time (sec): 49.91 - samples/sec: 2355.98 - lr: 0.000001 - momentum: 0.000000
+2023-10-24 10:15:52,559 epoch 10 - iter 584/738 - loss 0.00460257 - time (sec): 57.10 - samples/sec: 2352.17 - lr: 0.000001 - momentum: 0.000000
+2023-10-24 10:15:58,908 epoch 10 - iter 657/738 - loss 0.00450873 - time (sec): 63.45 - samples/sec: 2352.91 - lr: 0.000000 - momentum: 0.000000
+2023-10-24 10:16:05,753 epoch 10 - iter 730/738 - loss 0.00478145 - time (sec): 70.29 - samples/sec: 2345.44 - lr: 0.000000 - momentum: 0.000000
+2023-10-24 10:16:06,439 ----------------------------------------------------------------------------------------------------
+2023-10-24 10:16:06,440 EPOCH 10 done: loss 0.0048 - lr: 0.000000
+2023-10-24 10:16:14,959 DEV : loss 0.21770432591438293 - f1-score (micro avg)  0.8466
+2023-10-24 10:16:14,981 saving best model
+2023-10-24 10:16:16,242 ----------------------------------------------------------------------------------------------------
+2023-10-24 10:16:16,243 Loading model from best epoch ...
+2023-10-24 10:16:18,060 SequenceTagger predicts: Dictionary with 21 tags: O, S-loc, B-loc, E-loc, I-loc, S-pers, B-pers, E-pers, I-pers, S-org, B-org, E-org, I-org, S-time, B-time, E-time, I-time, S-prod, B-prod, E-prod, I-prod
+2023-10-24 10:16:24,729 
+Results:
+- F-score (micro) 0.7923
+- F-score (macro) 0.7091
+- Accuracy 0.6784
+
+By class:
+              precision    recall  f1-score   support
+
+         loc     0.8467    0.8753    0.8607       858
+        pers     0.7404    0.7914    0.7651       537
+         org     0.5532    0.5909    0.5714       132
+        time     0.5217    0.6667    0.5854        54
+        prod     0.7895    0.7377    0.7627        61
+
+   micro avg     0.7726    0.8130    0.7923      1642
+   macro avg     0.6903    0.7324    0.7091      1642
+weighted avg     0.7755    0.8130    0.7935      1642
+
+2023-10-24 10:16:24,729 ----------------------------------------------------------------------------------------------------