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final-model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d36b2ad45e1159e1fa953433b4d913a4af9c8103fc1ef8012d9f32645ff00f45
+size 1140158181

loss.tsv

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+EPOCH	TIMESTAMP	BAD_EPOCHS	LEARNING_RATE	TRAIN_LOSS	DEV_LOSS	DEV_PRECISION	DEV_RECALL	DEV_F1	DEV_ACCURACY
+1	15:50:17	0	0.0100	0.15425708817024494	0.060714565217494965	0.7787	0.8033	0.7908	0.7707
+2	16:12:13	0	0.0100	0.08213652963048304	0.05180404335260391	0.8249	0.8572	0.8408	0.8224
+3	16:34:11	0	0.0100	0.07016307590417552	0.04575943946838379	0.8564	0.8825	0.8693	0.8513
+4	16:55:45	1	0.0100	0.06056221985322536	0.04747875779867172	0.8472	0.8787	0.8627	0.8453
+5	17:17:21	0	0.0100	0.052649540430491894	0.03879784420132637	0.8726	0.9006	0.8864	0.8699
+6	17:39:07	0	0.0100	0.047476279502849085	0.03874654322862625	0.8813	0.9006	0.8908	0.873
+7	18:00:59	0	0.0100	0.04204823572638501	0.04413652420043945	0.8832	0.9108	0.8968	0.8809
+8	18:22:46	0	0.0100	0.03738312710290932	0.03726610541343689	0.9065	0.917	0.9117	0.8934
+9	18:44:24	1	0.0100	0.03297667390843091	0.04557322338223457	0.8949	0.9224	0.9084	0.8937
+10	19:06:06	0	0.0100	0.0303017038951756	0.03892701491713524	0.905	0.9216	0.9132	0.8958

training.log

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+2022-10-26 15:28:10,168 ----------------------------------------------------------------------------------------------------
+2022-10-26 15:28:10,173 Model: "SequenceTagger(
+  (embeddings): TransformerWordEmbeddings(
+    (model): XLMRobertaModel(
+      (embeddings): RobertaEmbeddings(
+        (word_embeddings): Embedding(250002, 768, padding_idx=1)
+        (position_embeddings): Embedding(514, 768, padding_idx=1)
+        (token_type_embeddings): Embedding(1, 768)
+        (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (encoder): RobertaEncoder(
+        (layer): ModuleList(
+          (0): RobertaLayer(
+            (attention): RobertaAttention(
+              (self): RobertaSelfAttention(
+                (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): RobertaSelfOutput(
+                (dense): Linear(in_features=768, out_features=768, bias=True)
+                (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+            )
+            (intermediate): RobertaIntermediate(
+              (dense): Linear(in_features=768, out_features=3072, bias=True)
+              (intermediate_act_fn): GELUActivation()
+            )
+            (output): RobertaOutput(
+              (dense): Linear(in_features=3072, out_features=768, bias=True)
+              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+          )
+          (1): RobertaLayer(
+            (attention): RobertaAttention(
+              (self): RobertaSelfAttention(
+                (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): RobertaSelfOutput(
+                (dense): Linear(in_features=768, out_features=768, bias=True)
+                (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+            )
+            (intermediate): RobertaIntermediate(
+              (dense): Linear(in_features=768, out_features=3072, bias=True)
+              (intermediate_act_fn): GELUActivation()
+            )
+            (output): RobertaOutput(
+              (dense): Linear(in_features=3072, out_features=768, bias=True)
+              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+          )
+          (2): RobertaLayer(
+            (attention): RobertaAttention(
+              (self): RobertaSelfAttention(
+                (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): RobertaSelfOutput(
+                (dense): Linear(in_features=768, out_features=768, bias=True)
+                (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+            )
+            (intermediate): RobertaIntermediate(
+              (dense): Linear(in_features=768, out_features=3072, bias=True)
+              (intermediate_act_fn): GELUActivation()
+            )
+            (output): RobertaOutput(
+              (dense): Linear(in_features=3072, out_features=768, bias=True)
+              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+          )
+          (3): RobertaLayer(
+            (attention): RobertaAttention(
+              (self): RobertaSelfAttention(
+                (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): RobertaSelfOutput(
+                (dense): Linear(in_features=768, out_features=768, bias=True)
+                (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+            )
+            (intermediate): RobertaIntermediate(
+              (dense): Linear(in_features=768, out_features=3072, bias=True)
+              (intermediate_act_fn): GELUActivation()
+            )
+            (output): RobertaOutput(
+              (dense): Linear(in_features=3072, out_features=768, bias=True)
+              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+          )
+          (4): RobertaLayer(
+            (attention): RobertaAttention(
+              (self): RobertaSelfAttention(
+                (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): RobertaSelfOutput(
+                (dense): Linear(in_features=768, out_features=768, bias=True)
+                (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+            )
+            (intermediate): RobertaIntermediate(
+              (dense): Linear(in_features=768, out_features=3072, bias=True)
+              (intermediate_act_fn): GELUActivation()
+            )
+            (output): RobertaOutput(
+              (dense): Linear(in_features=3072, out_features=768, bias=True)
+              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+          )
+          (5): RobertaLayer(
+            (attention): RobertaAttention(
+              (self): RobertaSelfAttention(
+                (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): RobertaSelfOutput(
+                (dense): Linear(in_features=768, out_features=768, bias=True)
+                (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+            )
+            (intermediate): RobertaIntermediate(
+              (dense): Linear(in_features=768, out_features=3072, bias=True)
+              (intermediate_act_fn): GELUActivation()
+            )
+            (output): RobertaOutput(
+              (dense): Linear(in_features=3072, out_features=768, bias=True)
+              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+          )
+          (6): RobertaLayer(
+            (attention): RobertaAttention(
+              (self): RobertaSelfAttention(
+                (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): RobertaSelfOutput(
+                (dense): Linear(in_features=768, out_features=768, bias=True)
+                (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+            )
+            (intermediate): RobertaIntermediate(
+              (dense): Linear(in_features=768, out_features=3072, bias=True)
+              (intermediate_act_fn): GELUActivation()
+            )
+            (output): RobertaOutput(
+              (dense): Linear(in_features=3072, out_features=768, bias=True)
+              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+          )
+          (7): RobertaLayer(
+            (attention): RobertaAttention(
+              (self): RobertaSelfAttention(
+                (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): RobertaSelfOutput(
+                (dense): Linear(in_features=768, out_features=768, bias=True)
+                (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+            )
+            (intermediate): RobertaIntermediate(
+              (dense): Linear(in_features=768, out_features=3072, bias=True)
+              (intermediate_act_fn): GELUActivation()
+            )
+            (output): RobertaOutput(
+              (dense): Linear(in_features=3072, out_features=768, bias=True)
+              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+          )
+          (8): RobertaLayer(
+            (attention): RobertaAttention(
+              (self): RobertaSelfAttention(
+                (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): RobertaSelfOutput(
+                (dense): Linear(in_features=768, out_features=768, bias=True)
+                (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+            )
+            (intermediate): RobertaIntermediate(
+              (dense): Linear(in_features=768, out_features=3072, bias=True)
+              (intermediate_act_fn): GELUActivation()
+            )
+            (output): RobertaOutput(
+              (dense): Linear(in_features=3072, out_features=768, bias=True)
+              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+          )
+          (9): RobertaLayer(
+            (attention): RobertaAttention(
+              (self): RobertaSelfAttention(
+                (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): RobertaSelfOutput(
+                (dense): Linear(in_features=768, out_features=768, bias=True)
+                (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+            )
+            (intermediate): RobertaIntermediate(
+              (dense): Linear(in_features=768, out_features=3072, bias=True)
+              (intermediate_act_fn): GELUActivation()
+            )
+            (output): RobertaOutput(
+              (dense): Linear(in_features=3072, out_features=768, bias=True)
+              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+          )
+          (10): RobertaLayer(
+            (attention): RobertaAttention(
+              (self): RobertaSelfAttention(
+                (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): RobertaSelfOutput(
+                (dense): Linear(in_features=768, out_features=768, bias=True)
+                (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+            )
+            (intermediate): RobertaIntermediate(
+              (dense): Linear(in_features=768, out_features=3072, bias=True)
+              (intermediate_act_fn): GELUActivation()
+            )
+            (output): RobertaOutput(
+              (dense): Linear(in_features=3072, out_features=768, bias=True)
+              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+          )
+          (11): RobertaLayer(
+            (attention): RobertaAttention(
+              (self): RobertaSelfAttention(
+                (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): RobertaSelfOutput(
+                (dense): Linear(in_features=768, out_features=768, bias=True)
+                (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+            )
+            (intermediate): RobertaIntermediate(
+              (dense): Linear(in_features=768, out_features=3072, bias=True)
+              (intermediate_act_fn): GELUActivation()
+            )
+            (output): RobertaOutput(
+              (dense): Linear(in_features=3072, out_features=768, bias=True)
+              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+          )
+        )
+      )
+      (pooler): RobertaPooler(
+        (dense): Linear(in_features=768, out_features=768, bias=True)
+        (activation): Tanh()
+      )
+    )
+  )
+  (word_dropout): WordDropout(p=0.05)
+  (locked_dropout): LockedDropout(p=0.5)
+  (embedding2nn): Linear(in_features=768, out_features=768, bias=True)
+  (rnn): LSTM(768, 256, batch_first=True, bidirectional=True)
+  (linear): Linear(in_features=512, out_features=15, bias=True)
+  (loss_function): ViterbiLoss()
+  (crf): CRF()
+)"
+2022-10-26 15:28:10,176 ----------------------------------------------------------------------------------------------------
+2022-10-26 15:28:10,180 Corpus: "Corpus: 8551 train + 1425 dev + 1425 test sentences"
+2022-10-26 15:28:10,182 ----------------------------------------------------------------------------------------------------
+2022-10-26 15:28:10,184 Parameters:
+2022-10-26 15:28:10,186  - learning_rate: "0.010000"
+2022-10-26 15:28:10,187  - mini_batch_size: "8"
+2022-10-26 15:28:10,188  - patience: "3"
+2022-10-26 15:28:10,189  - anneal_factor: "0.5"
+2022-10-26 15:28:10,191  - max_epochs: "10"
+2022-10-26 15:28:10,192  - shuffle: "True"
+2022-10-26 15:28:10,193  - train_with_dev: "False"
+2022-10-26 15:28:10,194  - batch_growth_annealing: "False"
+2022-10-26 15:28:10,196 ----------------------------------------------------------------------------------------------------
+2022-10-26 15:28:10,197 Model training base path: "/content/model/xlmr_ner"
+2022-10-26 15:28:10,198 ----------------------------------------------------------------------------------------------------
+2022-10-26 15:28:10,199 Device: cuda:0
+2022-10-26 15:28:10,201 ----------------------------------------------------------------------------------------------------
+2022-10-26 15:28:10,202 Embeddings storage mode: none
+2022-10-26 15:28:10,203 ----------------------------------------------------------------------------------------------------
+2022-10-26 15:30:29,962 epoch 1 - iter 106/1069 - loss 0.55101171 - samples/sec: 6.07 - lr: 0.010000
+2022-10-26 15:32:28,714 epoch 1 - iter 212/1069 - loss 0.35636418 - samples/sec: 7.14 - lr: 0.010000
+2022-10-26 15:34:23,625 epoch 1 - iter 318/1069 - loss 0.28047260 - samples/sec: 7.38 - lr: 0.010000
+2022-10-26 15:36:24,015 epoch 1 - iter 424/1069 - loss 0.23890211 - samples/sec: 7.04 - lr: 0.010000
+2022-10-26 15:38:21,987 epoch 1 - iter 530/1069 - loss 0.21322222 - samples/sec: 7.19 - lr: 0.010000
+2022-10-26 15:40:22,521 epoch 1 - iter 636/1069 - loss 0.19431796 - samples/sec: 7.04 - lr: 0.010000
+2022-10-26 15:42:18,754 epoch 1 - iter 742/1069 - loss 0.18084010 - samples/sec: 7.30 - lr: 0.010000
+2022-10-26 15:44:18,344 epoch 1 - iter 848/1069 - loss 0.16975329 - samples/sec: 7.09 - lr: 0.010000
+2022-10-26 15:46:14,738 epoch 1 - iter 954/1069 - loss 0.16158584 - samples/sec: 7.29 - lr: 0.010000
+2022-10-26 15:48:14,067 epoch 1 - iter 1060/1069 - loss 0.15491697 - samples/sec: 7.11 - lr: 0.010000
+2022-10-26 15:48:24,569 ----------------------------------------------------------------------------------------------------
+2022-10-26 15:48:24,577 EPOCH 1 done: loss 0.1543 - lr 0.010000
+2022-10-26 15:50:17,480 Evaluating as a multi-label problem: False
+2022-10-26 15:50:17,512 DEV : loss 0.060714565217494965 - f1-score (micro avg)  0.7908
+2022-10-26 15:50:17,553 BAD EPOCHS (no improvement): 0
+2022-10-26 15:50:17,554 saving best model
+2022-10-26 15:50:23,470 ----------------------------------------------------------------------------------------------------
+2022-10-26 15:52:24,219 epoch 2 - iter 106/1069 - loss 0.08869057 - samples/sec: 7.02 - lr: 0.010000
+2022-10-26 15:54:21,594 epoch 2 - iter 212/1069 - loss 0.08600343 - samples/sec: 7.23 - lr: 0.010000
+2022-10-26 15:56:19,809 epoch 2 - iter 318/1069 - loss 0.08546665 - samples/sec: 7.17 - lr: 0.010000
+2022-10-26 15:58:17,214 epoch 2 - iter 424/1069 - loss 0.08476718 - samples/sec: 7.22 - lr: 0.010000
+2022-10-26 16:00:16,114 epoch 2 - iter 530/1069 - loss 0.08542624 - samples/sec: 7.13 - lr: 0.010000
+2022-10-26 16:02:13,540 epoch 2 - iter 636/1069 - loss 0.08522910 - samples/sec: 7.22 - lr: 0.010000
+2022-10-26 16:04:12,854 epoch 2 - iter 742/1069 - loss 0.08502467 - samples/sec: 7.11 - lr: 0.010000
+2022-10-26 16:06:13,219 epoch 2 - iter 848/1069 - loss 0.08373459 - samples/sec: 7.05 - lr: 0.010000
+2022-10-26 16:08:09,808 epoch 2 - iter 954/1069 - loss 0.08316639 - samples/sec: 7.27 - lr: 0.010000
+2022-10-26 16:10:11,036 epoch 2 - iter 1060/1069 - loss 0.08215396 - samples/sec: 7.00 - lr: 0.010000
+2022-10-26 16:10:21,246 ----------------------------------------------------------------------------------------------------
+2022-10-26 16:10:21,249 EPOCH 2 done: loss 0.0821 - lr 0.010000
+2022-10-26 16:12:13,875 Evaluating as a multi-label problem: False
+2022-10-26 16:12:13,905 DEV : loss 0.05180404335260391 - f1-score (micro avg)  0.8408
+2022-10-26 16:12:13,947 BAD EPOCHS (no improvement): 0
+2022-10-26 16:12:13,948 saving best model
+2022-10-26 16:12:19,344 ----------------------------------------------------------------------------------------------------
+2022-10-26 16:14:19,879 epoch 3 - iter 106/1069 - loss 0.06627178 - samples/sec: 7.04 - lr: 0.010000
+2022-10-26 16:16:18,272 epoch 3 - iter 212/1069 - loss 0.07094348 - samples/sec: 7.16 - lr: 0.010000
+2022-10-26 16:18:18,453 epoch 3 - iter 318/1069 - loss 0.07194093 - samples/sec: 7.06 - lr: 0.010000
+2022-10-26 16:20:15,802 epoch 3 - iter 424/1069 - loss 0.07242840 - samples/sec: 7.23 - lr: 0.010000
+2022-10-26 16:22:12,248 epoch 3 - iter 530/1069 - loss 0.07171872 - samples/sec: 7.28 - lr: 0.010000
+2022-10-26 16:24:12,231 epoch 3 - iter 636/1069 - loss 0.07162092 - samples/sec: 7.07 - lr: 0.010000
+2022-10-26 16:26:10,382 epoch 3 - iter 742/1069 - loss 0.07130310 - samples/sec: 7.18 - lr: 0.010000
+2022-10-26 16:28:08,953 epoch 3 - iter 848/1069 - loss 0.07050136 - samples/sec: 7.15 - lr: 0.010000
+2022-10-26 16:30:09,728 epoch 3 - iter 954/1069 - loss 0.07070517 - samples/sec: 7.02 - lr: 0.010000
+2022-10-26 16:32:08,721 epoch 3 - iter 1060/1069 - loss 0.07033198 - samples/sec: 7.13 - lr: 0.010000
+2022-10-26 16:32:18,654 ----------------------------------------------------------------------------------------------------
+2022-10-26 16:32:18,656 EPOCH 3 done: loss 0.0702 - lr 0.010000
+2022-10-26 16:34:10,956 Evaluating as a multi-label problem: False
+2022-10-26 16:34:10,986 DEV : loss 0.04575943946838379 - f1-score (micro avg)  0.8693
+2022-10-26 16:34:11,026 BAD EPOCHS (no improvement): 0
+2022-10-26 16:34:11,029 saving best model
+2022-10-26 16:34:16,564 ----------------------------------------------------------------------------------------------------
+2022-10-26 16:36:12,350 epoch 4 - iter 106/1069 - loss 0.06432601 - samples/sec: 7.32 - lr: 0.010000
+2022-10-26 16:38:08,474 epoch 4 - iter 212/1069 - loss 0.06376094 - samples/sec: 7.30 - lr: 0.010000
+2022-10-26 16:40:03,219 epoch 4 - iter 318/1069 - loss 0.06273795 - samples/sec: 7.39 - lr: 0.010000
+2022-10-26 16:41:59,110 epoch 4 - iter 424/1069 - loss 0.06153989 - samples/sec: 7.32 - lr: 0.010000
+2022-10-26 16:43:57,347 epoch 4 - iter 530/1069 - loss 0.06137878 - samples/sec: 7.17 - lr: 0.010000
+2022-10-26 16:45:55,146 epoch 4 - iter 636/1069 - loss 0.06072772 - samples/sec: 7.20 - lr: 0.010000
+2022-10-26 16:47:53,049 epoch 4 - iter 742/1069 - loss 0.06031769 - samples/sec: 7.19 - lr: 0.010000
+2022-10-26 16:49:50,705 epoch 4 - iter 848/1069 - loss 0.06084099 - samples/sec: 7.21 - lr: 0.010000
+2022-10-26 16:51:49,833 epoch 4 - iter 954/1069 - loss 0.06096388 - samples/sec: 7.12 - lr: 0.010000
+2022-10-26 16:53:45,640 epoch 4 - iter 1060/1069 - loss 0.06061743 - samples/sec: 7.32 - lr: 0.010000
+2022-10-26 16:53:54,974 ----------------------------------------------------------------------------------------------------
+2022-10-26 16:53:54,976 EPOCH 4 done: loss 0.0606 - lr 0.010000
+2022-10-26 16:55:45,518 Evaluating as a multi-label problem: False
+2022-10-26 16:55:45,548 DEV : loss 0.04747875779867172 - f1-score (micro avg)  0.8627
+2022-10-26 16:55:45,589 BAD EPOCHS (no improvement): 1
+2022-10-26 16:55:45,590 ----------------------------------------------------------------------------------------------------
+2022-10-26 16:57:41,259 epoch 5 - iter 106/1069 - loss 0.05285565 - samples/sec: 7.33 - lr: 0.010000
+2022-10-26 16:59:40,296 epoch 5 - iter 212/1069 - loss 0.05049977 - samples/sec: 7.12 - lr: 0.010000
+2022-10-26 17:01:35,184 epoch 5 - iter 318/1069 - loss 0.05297933 - samples/sec: 7.38 - lr: 0.010000
+2022-10-26 17:03:34,028 epoch 5 - iter 424/1069 - loss 0.05293744 - samples/sec: 7.14 - lr: 0.010000
+2022-10-26 17:05:29,295 epoch 5 - iter 530/1069 - loss 0.05359386 - samples/sec: 7.36 - lr: 0.010000
+2022-10-26 17:07:25,593 epoch 5 - iter 636/1069 - loss 0.05307424 - samples/sec: 7.29 - lr: 0.010000
+2022-10-26 17:09:22,893 epoch 5 - iter 742/1069 - loss 0.05323355 - samples/sec: 7.23 - lr: 0.010000
+2022-10-26 17:11:22,602 epoch 5 - iter 848/1069 - loss 0.05272547 - samples/sec: 7.08 - lr: 0.010000
+2022-10-26 17:13:22,960 epoch 5 - iter 954/1069 - loss 0.05280553 - samples/sec: 7.05 - lr: 0.010000
+2022-10-26 17:15:20,527 epoch 5 - iter 1060/1069 - loss 0.05265360 - samples/sec: 7.21 - lr: 0.010000
+2022-10-26 17:15:29,931 ----------------------------------------------------------------------------------------------------
+2022-10-26 17:15:29,932 EPOCH 5 done: loss 0.0526 - lr 0.010000
+2022-10-26 17:17:21,728 Evaluating as a multi-label problem: False
+2022-10-26 17:17:21,760 DEV : loss 0.03879784420132637 - f1-score (micro avg)  0.8864
+2022-10-26 17:17:21,803 BAD EPOCHS (no improvement): 0
+2022-10-26 17:17:21,804 saving best model
+2022-10-26 17:17:27,330 ----------------------------------------------------------------------------------------------------
+2022-10-26 17:19:26,401 epoch 6 - iter 106/1069 - loss 0.04801558 - samples/sec: 7.12 - lr: 0.010000
+2022-10-26 17:21:22,988 epoch 6 - iter 212/1069 - loss 0.05008290 - samples/sec: 7.27 - lr: 0.010000
+2022-10-26 17:23:16,794 epoch 6 - iter 318/1069 - loss 0.04925649 - samples/sec: 7.45 - lr: 0.010000
+2022-10-26 17:25:15,532 epoch 6 - iter 424/1069 - loss 0.04786643 - samples/sec: 7.14 - lr: 0.010000
+2022-10-26 17:27:13,913 epoch 6 - iter 530/1069 - loss 0.04879792 - samples/sec: 7.16 - lr: 0.010000
+2022-10-26 17:29:10,114 epoch 6 - iter 636/1069 - loss 0.04800786 - samples/sec: 7.30 - lr: 0.010000
+2022-10-26 17:31:07,810 epoch 6 - iter 742/1069 - loss 0.04755361 - samples/sec: 7.21 - lr: 0.010000
+2022-10-26 17:33:04,496 epoch 6 - iter 848/1069 - loss 0.04782375 - samples/sec: 7.27 - lr: 0.010000
+2022-10-26 17:35:05,834 epoch 6 - iter 954/1069 - loss 0.04776160 - samples/sec: 6.99 - lr: 0.010000
+2022-10-26 17:37:03,878 epoch 6 - iter 1060/1069 - loss 0.04743945 - samples/sec: 7.18 - lr: 0.010000
+2022-10-26 17:37:14,466 ----------------------------------------------------------------------------------------------------
+2022-10-26 17:37:14,468 EPOCH 6 done: loss 0.0475 - lr 0.010000
+2022-10-26 17:39:07,562 Evaluating as a multi-label problem: False
+2022-10-26 17:39:07,592 DEV : loss 0.03874654322862625 - f1-score (micro avg)  0.8908
+2022-10-26 17:39:07,633 BAD EPOCHS (no improvement): 0
+2022-10-26 17:39:07,635 saving best model
+2022-10-26 17:39:13,242 ----------------------------------------------------------------------------------------------------
+2022-10-26 17:41:11,924 epoch 7 - iter 106/1069 - loss 0.04334369 - samples/sec: 7.15 - lr: 0.010000
+2022-10-26 17:43:11,382 epoch 7 - iter 212/1069 - loss 0.04192565 - samples/sec: 7.10 - lr: 0.010000
+2022-10-26 17:45:08,087 epoch 7 - iter 318/1069 - loss 0.04115627 - samples/sec: 7.27 - lr: 0.010000
+2022-10-26 17:47:06,615 epoch 7 - iter 424/1069 - loss 0.04114928 - samples/sec: 7.16 - lr: 0.010000
+2022-10-26 17:49:03,863 epoch 7 - iter 530/1069 - loss 0.04105023 - samples/sec: 7.23 - lr: 0.010000
+2022-10-26 17:51:02,216 epoch 7 - iter 636/1069 - loss 0.04125208 - samples/sec: 7.17 - lr: 0.010000
+2022-10-26 17:53:04,293 epoch 7 - iter 742/1069 - loss 0.04151765 - samples/sec: 6.95 - lr: 0.010000
+2022-10-26 17:55:01,446 epoch 7 - iter 848/1069 - loss 0.04170200 - samples/sec: 7.24 - lr: 0.010000
+2022-10-26 17:56:59,848 epoch 7 - iter 954/1069 - loss 0.04180177 - samples/sec: 7.16 - lr: 0.010000
+2022-10-26 17:58:56,175 epoch 7 - iter 1060/1069 - loss 0.04203413 - samples/sec: 7.29 - lr: 0.010000
+2022-10-26 17:59:05,814 ----------------------------------------------------------------------------------------------------
+2022-10-26 17:59:05,816 EPOCH 7 done: loss 0.0420 - lr 0.010000
+2022-10-26 18:00:59,457 Evaluating as a multi-label problem: False
+2022-10-26 18:00:59,486 DEV : loss 0.04413652420043945 - f1-score (micro avg)  0.8968
+2022-10-26 18:00:59,527 BAD EPOCHS (no improvement): 0
+2022-10-26 18:00:59,529 saving best model
+2022-10-26 18:01:05,372 ----------------------------------------------------------------------------------------------------
+2022-10-26 18:03:03,422 epoch 8 - iter 106/1069 - loss 0.03592615 - samples/sec: 7.18 - lr: 0.010000
+2022-10-26 18:05:00,466 epoch 8 - iter 212/1069 - loss 0.03676863 - samples/sec: 7.25 - lr: 0.010000
+2022-10-26 18:06:58,178 epoch 8 - iter 318/1069 - loss 0.03702258 - samples/sec: 7.20 - lr: 0.010000
+2022-10-26 18:08:55,170 epoch 8 - iter 424/1069 - loss 0.03704658 - samples/sec: 7.25 - lr: 0.010000
+2022-10-26 18:10:52,222 epoch 8 - iter 530/1069 - loss 0.03711348 - samples/sec: 7.25 - lr: 0.010000
+2022-10-26 18:12:51,244 epoch 8 - iter 636/1069 - loss 0.03715815 - samples/sec: 7.13 - lr: 0.010000
+2022-10-26 18:14:50,229 epoch 8 - iter 742/1069 - loss 0.03708747 - samples/sec: 7.13 - lr: 0.010000
+2022-10-26 18:16:47,946 epoch 8 - iter 848/1069 - loss 0.03734575 - samples/sec: 7.20 - lr: 0.010000
+2022-10-26 18:18:45,873 epoch 8 - iter 954/1069 - loss 0.03736843 - samples/sec: 7.19 - lr: 0.010000
+2022-10-26 18:20:43,504 epoch 8 - iter 1060/1069 - loss 0.03737578 - samples/sec: 7.21 - lr: 0.010000
+2022-10-26 18:20:53,262 ----------------------------------------------------------------------------------------------------
+2022-10-26 18:20:53,265 EPOCH 8 done: loss 0.0374 - lr 0.010000
+2022-10-26 18:22:46,256 Evaluating as a multi-label problem: False
+2022-10-26 18:22:46,293 DEV : loss 0.03726610541343689 - f1-score (micro avg)  0.9117
+2022-10-26 18:22:46,336 BAD EPOCHS (no improvement): 0
+2022-10-26 18:22:46,337 saving best model
+2022-10-26 18:22:51,847 ----------------------------------------------------------------------------------------------------
+2022-10-26 18:24:50,402 epoch 9 - iter 106/1069 - loss 0.03606101 - samples/sec: 7.15 - lr: 0.010000
+2022-10-26 18:26:47,577 epoch 9 - iter 212/1069 - loss 0.03466163 - samples/sec: 7.24 - lr: 0.010000
+2022-10-26 18:28:47,029 epoch 9 - iter 318/1069 - loss 0.03420843 - samples/sec: 7.10 - lr: 0.010000
+2022-10-26 18:30:43,235 epoch 9 - iter 424/1069 - loss 0.03406325 - samples/sec: 7.30 - lr: 0.010000
+2022-10-26 18:32:41,132 epoch 9 - iter 530/1069 - loss 0.03393077 - samples/sec: 7.19 - lr: 0.010000
+2022-10-26 18:34:35,953 epoch 9 - iter 636/1069 - loss 0.03438052 - samples/sec: 7.39 - lr: 0.010000
+2022-10-26 18:36:33,872 epoch 9 - iter 742/1069 - loss 0.03435922 - samples/sec: 7.19 - lr: 0.010000
+2022-10-26 18:38:30,457 epoch 9 - iter 848/1069 - loss 0.03351594 - samples/sec: 7.27 - lr: 0.010000
+2022-10-26 18:40:26,775 epoch 9 - iter 954/1069 - loss 0.03363514 - samples/sec: 7.29 - lr: 0.010000
+2022-10-26 18:42:26,040 epoch 9 - iter 1060/1069 - loss 0.03301736 - samples/sec: 7.11 - lr: 0.010000
+2022-10-26 18:42:34,477 ----------------------------------------------------------------------------------------------------
+2022-10-26 18:42:34,480 EPOCH 9 done: loss 0.0330 - lr 0.010000
+2022-10-26 18:44:24,572 Evaluating as a multi-label problem: False
+2022-10-26 18:44:24,602 DEV : loss 0.04557322338223457 - f1-score (micro avg)  0.9084
+2022-10-26 18:44:24,644 BAD EPOCHS (no improvement): 1
+2022-10-26 18:44:24,646 ----------------------------------------------------------------------------------------------------
+2022-10-26 18:46:21,774 epoch 10 - iter 106/1069 - loss 0.02992093 - samples/sec: 7.24 - lr: 0.010000
+2022-10-26 18:48:20,730 epoch 10 - iter 212/1069 - loss 0.02886380 - samples/sec: 7.13 - lr: 0.010000
+2022-10-26 18:50:20,679 epoch 10 - iter 318/1069 - loss 0.03109654 - samples/sec: 7.07 - lr: 0.010000
+2022-10-26 18:52:14,564 epoch 10 - iter 424/1069 - loss 0.03091892 - samples/sec: 7.45 - lr: 0.010000
+2022-10-26 18:54:14,888 epoch 10 - iter 530/1069 - loss 0.02977117 - samples/sec: 7.05 - lr: 0.010000
+2022-10-26 18:56:13,992 epoch 10 - iter 636/1069 - loss 0.02969566 - samples/sec: 7.12 - lr: 0.010000
+2022-10-26 18:58:12,618 epoch 10 - iter 742/1069 - loss 0.02979601 - samples/sec: 7.15 - lr: 0.010000
+2022-10-26 19:00:10,398 epoch 10 - iter 848/1069 - loss 0.03040781 - samples/sec: 7.20 - lr: 0.010000
+2022-10-26 19:02:06,063 epoch 10 - iter 954/1069 - loss 0.03029135 - samples/sec: 7.33 - lr: 0.010000
+2022-10-26 19:04:05,626 epoch 10 - iter 1060/1069 - loss 0.03035206 - samples/sec: 7.09 - lr: 0.010000
+2022-10-26 19:04:15,538 ----------------------------------------------------------------------------------------------------
+2022-10-26 19:04:15,540 EPOCH 10 done: loss 0.0303 - lr 0.010000
+2022-10-26 19:06:06,586 Evaluating as a multi-label problem: False
+2022-10-26 19:06:06,621 DEV : loss 0.03892701491713524 - f1-score (micro avg)  0.9132
+2022-10-26 19:06:06,663 BAD EPOCHS (no improvement): 0
+2022-10-26 19:06:06,665 saving best model
+2022-10-26 19:06:17,597 ----------------------------------------------------------------------------------------------------
+2022-10-26 19:06:17,723 loading file /content/model/xlmr_ner/best-model.pt
+2022-10-26 19:06:24,597 SequenceTagger predicts: Dictionary with 15 tags: O, S-PER, B-PER, E-PER, I-PER, S-MISC, B-MISC, E-MISC, I-MISC, S-LOC, B-LOC, E-LOC, I-LOC, <START>, <STOP>
+2022-10-26 19:08:17,003 Evaluating as a multi-label problem: False
+2022-10-26 19:08:17,040 0.9053	0.9316	0.9182	0.8955
+2022-10-26 19:08:17,041 
+Results:
+- F-score (micro) 0.9182
+- F-score (macro) 0.8875
+- Accuracy 0.8955
+
+By class:
+              precision    recall  f1-score   support
+
+         PER     0.9339    0.9633    0.9484      2127
+        MISC     0.8469    0.9250    0.8842       933
+         LOC     0.8955    0.7732    0.8299       388
+
+   micro avg     0.9053    0.9316    0.9182      3448
+   macro avg     0.8921    0.8872    0.8875      3448
+weighted avg     0.9060    0.9316    0.9177      3448
+
+2022-10-26 19:08:17,045 ----------------------------------------------------------------------------------------------------