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results/results_E6.txt

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+***** Test results *****
+Thu Sep 22 06:41:21 2022
+Task: ner
+Model path: bert-base-uncased
+Data path: ./data/ud/
+Tokenizer: bert-base-uncased
+Batch size: 32
+Epoch: 6
+Learning rate: 2e-05
+LR Decay End Factor: 0.3LR Decay End Epoch: 5Sequence length: 96
+Training: True
+Num Threads: 24
+Num Sentences: 0
+Max Grad Norm: 0.0
+Use GNN: False
+Syntax graph style: dep
+Use label weights: False
+Clip value: 50
+              precision    recall  f1-score   support
+
+    CARDINAL     0.7133    0.6503    0.6803       612
+        DATE     0.6922    0.7254    0.7084      1045
+       EVENT     0.4429    0.3875    0.4133        80
+         FAC     0.3390    0.3974    0.3659       151
+         GPE     0.8456    0.8714    0.8583      1936
+    LANGUAGE     0.5135    0.2468    0.3333        77
+         LAW     0.4130    0.3333    0.3689        57
+         LOC     0.5934    0.4977    0.5414       217
+       MONEY     0.5370    0.4754    0.5043        61
+        NORP     0.6211    0.7536    0.6809       422
+     ORDINAL     0.8208    0.8304    0.8256       171
+         ORG     0.5289    0.5869    0.5564       857
+     PERCENT     0.3333    0.4722    0.3908        36
+      PERSON     0.7192    0.7885    0.7523      1371
+     PRODUCT     0.2705    0.3367    0.3000        98
+    QUANTITY     0.3485    0.4340    0.3866        53
+        SEP]     0.0000    0.0000    0.0000         0
+        TIME     0.6071    0.6355    0.6210       214
+ WORK_OF_ART     0.3000    0.2538    0.2750       130
+
+   micro avg     0.6487    0.7110    0.6784      7588
+   macro avg     0.5073    0.5093    0.5033      7588
+weighted avg     0.6821    0.7110    0.6946      7588
+
+ Special token predictions: 0

results/results_E7.txt

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+***** Test results *****
+Thu Sep 22 07:44:16 2022
+Task: ner
+Model path: bert-base-uncased
+Data path: ./data/ud/
+Tokenizer: bert-base-uncased
+Batch size: 32
+Epoch: 7
+Learning rate: 2e-05
+LR Decay End Factor: 0.3LR Decay End Epoch: 5Sequence length: 96
+Training: True
+Num Threads: 24
+Num Sentences: 0
+Max Grad Norm: 0.0
+Use GNN: False
+Syntax graph style: dep
+Use label weights: False
+Clip value: 50
+              precision    recall  f1-score   support
+
+    CARDINAL     0.7030    0.6225    0.6603       612
+        DATE     0.7036    0.7177    0.7106      1045
+       EVENT     0.4133    0.3875    0.4000        80
+         FAC     0.3661    0.4437    0.4012       151
+         GPE     0.8721    0.8667    0.8694      1936
+    LANGUAGE     0.5758    0.2468    0.3455        77
+         LAW     0.3621    0.3684    0.3652        57
+         LOC     0.4978    0.5115    0.5045       217
+       MONEY     0.5849    0.5082    0.5439        61
+        NORP     0.6927    0.7156    0.7040       422
+     ORDINAL     0.8035    0.8129    0.8081       171
+         ORG     0.5158    0.5893    0.5501       857
+     PERCENT     0.3878    0.5278    0.4471        36
+      PERSON     0.7476    0.7994    0.7726      1371
+     PRODUCT     0.2742    0.3469    0.3063        98
+    QUANTITY     0.3443    0.3962    0.3684        53
+        SEP]     0.0000    0.0000    0.0000         0
+        TIME     0.5816    0.6495    0.6137       214
+ WORK_OF_ART     0.3544    0.2154    0.2679       130
+
+   micro avg     0.6680    0.7080    0.6874      7588
+   macro avg     0.5148    0.5119    0.5073      7588
+weighted avg     0.6955    0.7080    0.6998      7588
+
+ Special token predictions: 0

results/results_E8.txt

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+***** Test results *****
+Thu Sep 22 08:47:08 2022
+Task: ner
+Model path: bert-base-uncased
+Data path: ./data/ud/
+Tokenizer: bert-base-uncased
+Batch size: 32
+Epoch: 8
+Learning rate: 2e-05
+LR Decay End Factor: 0.3LR Decay End Epoch: 5Sequence length: 96
+Training: True
+Num Threads: 24
+Num Sentences: 0
+Max Grad Norm: 0.0
+Use GNN: False
+Syntax graph style: dep
+Use label weights: False
+Clip value: 50
+              precision    recall  f1-score   support
+
+    CARDINAL     0.7269    0.6307    0.6754       612
+        DATE     0.6856    0.7053    0.6953      1045
+       EVENT     0.4286    0.4500    0.4390        80
+         FAC     0.3454    0.4437    0.3884       151
+         GPE     0.8709    0.8574    0.8641      1936
+    LANGUAGE     0.5758    0.2468    0.3455        77
+         LAW     0.4314    0.3860    0.4074        57
+         LOC     0.5829    0.4700    0.5204       217
+       MONEY     0.5085    0.4918    0.5000        61
+        NORP     0.7023    0.7156    0.7089       422
+     ORDINAL     0.8258    0.8596    0.8424       171
+         ORG     0.5300    0.5776    0.5528       857
+     PERCENT     0.4255    0.5556    0.4819        36
+      PERSON     0.7398    0.7841    0.7613      1371
+     PRODUCT     0.2975    0.3673    0.3288        98
+    QUANTITY     0.3284    0.4151    0.3667        53
+        SEP]     0.0000    0.0000    0.0000         0
+        TIME     0.5586    0.6682    0.6085       214
+ WORK_OF_ART     0.3010    0.2385    0.2661       130
+
+   micro avg     0.6607    0.7024    0.6809      7588
+   macro avg     0.5192    0.5191    0.5133      7588
+weighted avg     0.6968    0.7024    0.6977      7588
+
+ Special token predictions: 0

results/results_E9.txt

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+***** Test results *****
+Thu Sep 22 09:50:02 2022
+Task: ner
+Model path: bert-base-uncased
+Data path: ./data/ud/
+Tokenizer: bert-base-uncased
+Batch size: 32
+Epoch: 9
+Learning rate: 2e-05
+LR Decay End Factor: 0.3LR Decay End Epoch: 5Sequence length: 96
+Training: True
+Num Threads: 24
+Num Sentences: 0
+Max Grad Norm: 0.0
+Use GNN: False
+Syntax graph style: dep
+Use label weights: False
+Clip value: 50
+              precision    recall  f1-score   support
+
+    CARDINAL     0.6809    0.6520    0.6661       612
+        DATE     0.6870    0.7225    0.7043      1045
+       EVENT     0.3977    0.4375    0.4167        80
+         FAC     0.3404    0.4238    0.3776       151
+         GPE     0.8799    0.8549    0.8672      1936
+    LANGUAGE     0.4906    0.3377    0.4000        77
+         LAW     0.4062    0.4561    0.4298        57
+         LOC     0.5000    0.5023    0.5011       217
+       MONEY     0.5161    0.5246    0.5203        61
+        NORP     0.6817    0.7512    0.7148       422
+     ORDINAL     0.8276    0.8421    0.8348       171
+         ORG     0.5455    0.5741    0.5594       857
+     PERCENT     0.5476    0.6389    0.5897        36
+      PERSON     0.7531    0.7943    0.7732      1371
+     PRODUCT     0.2937    0.4286    0.3485        98
+    QUANTITY     0.3492    0.4151    0.3793        53
+        SEP]     0.0000    0.0000    0.0000         0
+        TIME     0.5748    0.6822    0.6239       214
+ WORK_OF_ART     0.2963    0.2462    0.2689       130
+
+   micro avg     0.6736    0.7127    0.6926      7588
+   macro avg     0.5141    0.5413    0.5250      7588
+weighted avg     0.6958    0.7127    0.7033      7588
+
+ Special token predictions: 0

results/syngnn_main.log

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+Loading model from path bert-base-uncased
+Task: ner
+Model path: bert-base-uncased
+Data path: ./data/ud/
+Tokenizer: bert-base-uncased
+Batch size: 32
+Epochs: 10
+Learning rate: 2e-05
+LR Decay: 0.3
+LR Decay End Epoch: 5
+Sequence length: 96
+Training: True
+Num Threads: 24
+Num Sentences: 0
+Max Norm: 0.0
+Use GNN: False
+Use label weights: False
+PID: 3523179, PGID: 3523174
+ATen/Parallel:
+	at::get_num_threads() : 24
+	at::get_num_interop_threads() : 36
+OpenMP 201511 (a.k.a. OpenMP 4.5)
+	omp_get_max_threads() : 24
+Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
+	mkl_get_max_threads() : 24
+Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)
+std::thread::hardware_concurrency() : 72
+Environment variables:
+	OMP_NUM_THREADS : 24
+	MKL_NUM_THREADS : 24
+ATen parallel backend: OpenMP
+
+Training model
+Loading Training Data
+Loading NER labels from ./data/ud/**/*-train-orig.ner
+en_atis-ud-train-orig.ner
+num sentences: 4274
+en_cesl-ud-train-orig.ner
+num sentences: 4124
+en_ewt-ud-train-orig.ner
+num sentences: 11649
+en_gum-ud-train-orig.ner
+num sentences: 5344
+en_lines-ud-train-orig.ner
+num sentences: 3010
+en_partut-ud-train-orig.ner
+num sentences: 1739
+Example of NER labels: [[['what', 'O'], ['is', 'O'], ['the', 'O'], ['cost', 'O'], ['of', 'O'], ['a', 'O'], ['round', 'O'], ['trip', 'O'], ['flight', 'O'], ['from', 'O'], ['pittsburgh', 'S-GPE'], ['to', 'O'], ['atlanta', 'S-GPE'], ['beginning', 'O'], ['on', 'O'], ['april', 'B-DATE'], ['twenty', 'I-DATE'], ['fifth', 'E-DATE'], ['and', 'O'], ['returning', 'O'], ['on', 'O'], ['may', 'B-DATE'], ['sixth', 'E-DATE']], [['now', 'O'], ['i', 'O'], ['need', 'O'], ['a', 'O'], ['flight', 'O'], ['leaving', 'O'], ['fort', 'B-GPE'], ['worth', 'E-GPE'], ['and', 'O'], ['arriving', 'O'], ['in', 'O'], ['denver', 'S-GPE'], ['no', 'O'], ['later', 'O'], ['than', 'O'], ['2', 'B-TIME'], ['pm', 'E-TIME'], ['next', 'B-DATE'], ['monday', 'E-DATE']]]
+30140 sentences, 942 batches of size 32
+
+Control example of InputFeatures
+Input Ids: [101, 2085, 1045, 2342, 1037, 3462, 2975, 3481, 4276, 1998, 7194, 1999, 7573, 2053, 2101, 2084, 1016, 7610, 2279, 6928, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+Input Mask: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+Label Ids: [77, 1, 1, 1, 1, 1, 1, 31, 32, 1, 1, 1, 16, 1, 1, 1, 28, 30, 17, 19, 78, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+Valid Ids: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
+Label Mask: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+Segment Ids: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+Loading Validation Data
+Loading NER labels from ./data/ud/**/*-dev-orig.ner
+en_atis-ud-dev-orig.ner
+num sentences: 572
+en_cesl-ud-dev-orig.ner
+num sentences: 500
+en_ewt-ud-dev-orig.ner
+num sentences: 1875
+en_gum-ud-dev-orig.ner
+num sentences: 788
+en_lines-ud-dev-orig.ner
+num sentences: 986
+en_partut-ud-dev-orig.ner
+num sentences: 149
+Example of NER labels: [[['i', 'O'], ['would', 'O'], ['like', 'O'], ['the', 'O'], ['cheapest', 'O'], ['flight', 'O'], ['from', 'O'], ['pittsburgh', 'S-GPE'], ['to', 'O'], ['atlanta', 'S-GPE'], ['leaving', 'O'], ['april', 'B-DATE'], ['twenty', 'I-DATE'], ['fifth', 'E-DATE'], ['and', 'O'], ['returning', 'O'], ['may', 'B-DATE'], ['sixth', 'E-DATE']], [['i', 'O'], ['want', 'O'], ['a', 'O'], ['flight', 'O'], ['from', 'O'], ['memphis', 'S-LOC'], ['to', 'O'], ['seattle', 'S-FAC'], ['that', 'O'], ['arrives', 'O'], ['no', 'O'], ['later', 'O'], ['than', 'O'], ['3', 'B-TIME'], ['pm', 'E-TIME']]]
+4870 sentences, 153 batches of size 32
+
+Control example of InputFeatures
+Input Ids: [101, 1045, 2215, 1037, 3462, 2013, 9774, 2000, 5862, 2008, 8480, 2053, 2101, 2084, 1017, 7610, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+Input Mask: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+Label Ids: [77, 1, 1, 1, 1, 1, 59, 1, 60, 1, 1, 1, 1, 1, 28, 30, 78, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+Valid Ids: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
+Label Mask: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+Segment Ids: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+Test Data
+Loading NER labels from ./data/ud/**/*-test-orig.ner
+en_atis-ud-test-orig.ner
+num sentences: 586
+en_cesl-ud-test-orig.ner
+num sentences: 500
+en_ewt-ud-test-orig.ner
+num sentences: 1955
+en_gum-ud-test-orig.ner
+num sentences: 851
+en_lines-ud-test-orig.ner
+num sentences: 988
+en_pud-ud-test-orig.ner
+num sentences: 973
+en_partut-ud-test-orig.ner
+num sentences: 149
+en_pronouns-ud-test-orig.ner
+Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForNer: ['cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight']
+- This IS expected if you are initializing BertForNer from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
+- This IS NOT expected if you are initializing BertForNer from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
+Some weights of BertForNer were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']
+You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
+num sentences: 265
+Example of NER labels: [[['what', 'O'], ['are', 'O'], ['the', 'O'], ['coach', 'O'], ['flights', 'O'], ['between', 'O'], ['dallas', 'S-GPE'], ['and', 'O'], ['baltimore', 'S-GPE'], ['leaving', 'O'], ['august', 'B-DATE'], ['tenth', 'E-DATE'], ['and', 'O'], ['returning', 'O'], ['august', 'B-DATE'], ['twelve', 'E-DATE']], [['i', 'O'], ['want', 'O'], ['a', 'O'], ['flight', 'O'], ['from', 'O'], ['nashville', 'S-GPE'], ['to', 'O'], ['seattle', 'S-GPE'], ['that', 'O'], ['arrives', 'O'], ['no', 'O'], ['later', 'O'], ['than', 'O'], ['3', 'B-TIME'], ['pm', 'E-TIME']]]
+6267 sentences, 196 batches of size 32
+
+Control example of InputFeatures
+Input Ids: [101, 1045, 2215, 1037, 3462, 2013, 8423, 2000, 5862, 2008, 8480, 2053, 2101, 2084, 1017, 7610, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+Input Mask: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+Label Ids: [77, 1, 1, 1, 1, 1, 16, 1, 16, 1, 1, 1, 1, 1, 28, 30, 78, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+Valid Ids: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
+Label Mask: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+Segment Ids: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+Adjusting learning rate of group 0 to 2.0000e-05.
+
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+/home/9_QuAnTuM_6/cdaniel/venv_syntrans/lib/python3.8/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: [CLS] seems not to be NE tag.
+  warnings.warn('{} seems not to be NE tag.'.format(chunk))
+/home/9_QuAnTuM_6/cdaniel/venv_syntrans/lib/python3.8/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: <unk> seems not to be NE tag.
+  warnings.warn('{} seems not to be NE tag.'.format(chunk))
+/home/9_QuAnTuM_6/cdaniel/venv_syntrans/lib/python3.8/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: <START> seems not to be NE tag.
+  warnings.warn('{} seems not to be NE tag.'.format(chunk))
+/home/9_QuAnTuM_6/cdaniel/venv_syntrans/lib/python3.8/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: X seems not to be NE tag.
+  warnings.warn('{} seems not to be NE tag.'.format(chunk))
+/home/9_QuAnTuM_6/cdaniel/venv_syntrans/lib/python3.8/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: [SEP] seems not to be NE tag.
+  warnings.warn('{} seems not to be NE tag.'.format(chunk))
+/home/9_QuAnTuM_6/cdaniel/venv_syntrans/lib/python3.8/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: <STOP> seems not to be NE tag.
+  warnings.warn('{} seems not to be NE tag.'.format(chunk))
+O Token Predictions: 471383, NER token predictions: 32921
+loss: 0.4831624427086608 w prec: 0.48333733331137896 w recall: 0.32263332619404583 w f1: 0.3743943894083346
+
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+O Token Predictions: 68594, NER token predictions: 6306
+loss: 0.24858878246125052 w prec: 0.5736847431314509 w recall: 0.5198167695678152 w f1: 0.5394522935165895
+Adjusting learning rate of group 0 to 1.7200e-05.
+
  0%|          | 0/942 [00:00<?, ?it/s]
+O Token Predictions: 457692, NER token predictions: 46666
+loss: 0.23743267873828072 w prec: 0.642246843284797 w recall: 0.5536785178839152 w f1: 0.5869654887673703
+
  0%|          | 0/153 [00:00<?, ?it/s]
+O Token Predictions: 67004, NER token predictions: 7896
+loss: 0.19031139838150124 w prec: 0.6106517236837183 w recall: 0.6608245369448317 w f1: 0.6317247629054747
+Adjusting learning rate of group 0 to 1.4400e-05.
+
  0%|          | 0/942 [00:00<?, ?it/s]
+O Token Predictions: 452542, NER token predictions: 51816
+loss: 0.17240141229723796 w prec: 0.6995943496786741 w recall: 0.6429374598415079 w f1: 0.6678698690045989
+
  0%|          | 0/153 [00:00<?, ?it/s]
+O Token Predictions: 67555, NER token predictions: 7345
+loss: 0.16576223912971472 w prec: 0.6909975902827813 w recall: 0.6671977693686517 w f1: 0.6759967434078691
+Adjusting learning rate of group 0 to 1.1600e-05.
+
  0%|          | 0/942 [00:00<?, ?it/s]
+O Token Predictions: 450026, NER token predictions: 54332
+loss: 0.13777431711601984 w prec: 0.7394564960771575 w recall: 0.7012208181623474 w f1: 0.718659603222537
+
  0%|          | 0/153 [00:00<?, ?it/s]
+O Token Predictions: 67201, NER token predictions: 7699
+loss: 0.16132921111934326 w prec: 0.6829530578688375 w recall: 0.7128062139016133 w f1: 0.6956742392256572
+Adjusting learning rate of group 0 to 8.8000e-06.
+
  0%|          | 0/942 [00:00<?, ?it/s]
+O Token Predictions: 448629, NER token predictions: 55729
+loss: 0.11438079141950405 w prec: 0.770823616445863 w recall: 0.7418344399228957 w f1: 0.7551456680915405
+
  0%|          | 0/153 [00:00<?, ?it/s]
+O Token Predictions: 67145, NER token predictions: 7755
+loss: 0.15573614080941756 w prec: 0.720899538634842 w recall: 0.7211710814578769 w f1: 0.7191736846769018
+Adjusting learning rate of group 0 to 6.0000e-06.
+
  0%|          | 0/942 [00:00<?, ?it/s]
+O Token Predictions: 447725, NER token predictions: 56633
+loss: 0.09880779274579161 w prec: 0.7914035913278309 w recall: 0.7704005140286999 w f1: 0.780219783818005
+
  0%|          | 0/153 [00:00<?, ?it/s]
+O Token Predictions: 66994, NER token predictions: 7906
+loss: 0.15097037561578688 w prec: 0.7114692952948101 w recall: 0.7382991435968931 w f1: 0.7234172615803383
+Adjusting learning rate of group 0 to 6.0000e-06.
+
  0%|          | 0/942 [00:00<?, ?it/s]
+O Token Predictions: 447405, NER token predictions: 56953
+loss: 0.0908111283029657 w prec: 0.8023543679818423 w recall: 0.7863300492610837 w f1: 0.7937841209593911
+
  0%|          | 0/153 [00:00<?, ?it/s]
+loss: 0.15317525608112026 w prec: 0.7115102285753689 w recall: 0.7444732125074687 w f1: 0.7263377157668733
+Adjusting learning rate of group 0 to 6.0000e-06.
+Model evaluation
+
+
+
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+/home/9_QuAnTuM_6/cdaniel/venv_syntrans/lib/python3.8/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.
+  _warn_prf(average, modifier, msg_start, len(result))
+***** Test results *****
+              precision    recall  f1-score   support
+
+    CARDINAL     0.7133    0.6503    0.6803       612
+        DATE     0.6922    0.7254    0.7084      1045
+       EVENT     0.4429    0.3875    0.4133        80
+         FAC     0.3390    0.3974    0.3659       151
+         GPE     0.8456    0.8714    0.8583      1936
+    LANGUAGE     0.5135    0.2468    0.3333        77
+         LAW     0.4130    0.3333    0.3689        57
+         LOC     0.5934    0.4977    0.5414       217
+       MONEY     0.5370    0.4754    0.5043        61
+        NORP     0.6211    0.7536    0.6809       422
+     ORDINAL     0.8208    0.8304    0.8256       171
+         ORG     0.5289    0.5869    0.5564       857
+     PERCENT     0.3333    0.4722    0.3908        36
+      PERSON     0.7192    0.7885    0.7523      1371
+     PRODUCT     0.2705    0.3367    0.3000        98
+    QUANTITY     0.3485    0.4340    0.3866        53
+        SEP]     0.0000    0.0000    0.0000         0
+        TIME     0.6071    0.6355    0.6210       214
+ WORK_OF_ART     0.3000    0.2538    0.2750       130
+
+   micro avg     0.6487    0.7110    0.6784      7588
+   macro avg     0.5073    0.5093    0.5033      7588
+weighted avg     0.6821    0.7110    0.6946      7588
+
+ Special token predictions: 0
+
  0%|          | 0/942 [00:00<?, ?it/s]
+O Token Predictions: 447026, NER token predictions: 57332
+loss: 0.0834173557759834 w prec: 0.815729338081138 w recall: 0.8016705932747912 w f1: 0.8082084594984448
+
  0%|          | 0/153 [00:00<?, ?it/s]
+loss: 0.15407624415249802 w prec: 0.7284351035722969 w recall: 0.7444732125074687 w f1: 0.7346851885936531
+Adjusting learning rate of group 0 to 6.0000e-06.
+Model evaluation
+
+
+
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+***** Test results *****
+              precision    recall  f1-score   support
+
+    CARDINAL     0.7030    0.6225    0.6603       612
+        DATE     0.7036    0.7177    0.7106      1045
+       EVENT     0.4133    0.3875    0.4000        80
+         FAC     0.3661    0.4437    0.4012       151
+         GPE     0.8721    0.8667    0.8694      1936
+    LANGUAGE     0.5758    0.2468    0.3455        77
+         LAW     0.3621    0.3684    0.3652        57
+         LOC     0.4978    0.5115    0.5045       217
+       MONEY     0.5849    0.5082    0.5439        61
+        NORP     0.6927    0.7156    0.7040       422
+     ORDINAL     0.8035    0.8129    0.8081       171
+         ORG     0.5158    0.5893    0.5501       857
+     PERCENT     0.3878    0.5278    0.4471        36
+      PERSON     0.7476    0.7994    0.7726      1371
+     PRODUCT     0.2742    0.3469    0.3063        98
+    QUANTITY     0.3443    0.3962    0.3684        53
+        SEP]     0.0000    0.0000    0.0000         0
+        TIME     0.5816    0.6495    0.6137       214
+ WORK_OF_ART     0.3544    0.2154    0.2679       130
+
+   micro avg     0.6680    0.7080    0.6874      7588
+   macro avg     0.5148    0.5119    0.5073      7588
+weighted avg     0.6955    0.7080    0.6998      7588
+
+ Special token predictions: 0
+
  0%|          | 0/942 [00:00<?, ?it/s]
+O Token Predictions: 446823, NER token predictions: 57535
+loss: 0.07700805802633807 w prec: 0.8281029278359268 w recall: 0.8139590918826302 w f1: 0.8206010130635916
+
  0%|          | 0/153 [00:00<?, ?it/s]
+loss: 0.1566143021888398 w prec: 0.7311165058086735 w recall: 0.7394941246763593 w f1: 0.7335244139037927
+Adjusting learning rate of group 0 to 6.0000e-06.
+Model evaluation
+
+
+
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+***** Test results *****
+              precision    recall  f1-score   support
+
+    CARDINAL     0.7269    0.6307    0.6754       612
+        DATE     0.6856    0.7053    0.6953      1045
+       EVENT     0.4286    0.4500    0.4390        80
+         FAC     0.3454    0.4437    0.3884       151
+         GPE     0.8709    0.8574    0.8641      1936
+    LANGUAGE     0.5758    0.2468    0.3455        77
+         LAW     0.4314    0.3860    0.4074        57
+         LOC     0.5829    0.4700    0.5204       217
+       MONEY     0.5085    0.4918    0.5000        61
+        NORP     0.7023    0.7156    0.7089       422
+     ORDINAL     0.8258    0.8596    0.8424       171
+         ORG     0.5300    0.5776    0.5528       857
+     PERCENT     0.4255    0.5556    0.4819        36
+      PERSON     0.7398    0.7841    0.7613      1371
+     PRODUCT     0.2975    0.3673    0.3288        98
+    QUANTITY     0.3284    0.4151    0.3667        53
+        SEP]     0.0000    0.0000    0.0000         0
+        TIME     0.5586    0.6682    0.6085       214
+ WORK_OF_ART     0.3010    0.2385    0.2661       130
+
+   micro avg     0.6607    0.7024    0.6809      7588
+   macro avg     0.5192    0.5191    0.5133      7588
+weighted avg     0.6968    0.7024    0.6977      7588
+
+ Special token predictions: 0
+
  0%|          | 0/942 [00:00<?, ?it/s]
+O Token Predictions: 446643, NER token predictions: 57715
+loss: 0.07239252862554131 w prec: 0.8371405381680805 w recall: 0.8260066395373742 w f1: 0.8311608710414287
+
  0%|          | 0/153 [00:00<?, ?it/s]
+loss: 0.15688133854540734 w prec: 0.7230917519740736 w recall: 0.7476598287193786 w f1: 0.7338068025133403
+Adjusting learning rate of group 0 to 6.0000e-06.
+Model evaluation
+
+
+
  0%|          | 0/196 [00:00<?, ?it/s]
+              precision    recall  f1-score   support
+
+    CARDINAL     0.6809    0.6520    0.6661       612
+        DATE     0.6870    0.7225    0.7043      1045
+       EVENT     0.3977    0.4375    0.4167        80
+         FAC     0.3404    0.4238    0.3776       151
+         GPE     0.8799    0.8549    0.8672      1936
+    LANGUAGE     0.4906    0.3377    0.4000        77
+         LAW     0.4062    0.4561    0.4298        57
+         LOC     0.5000    0.5023    0.5011       217
+       MONEY     0.5161    0.5246    0.5203        61
+        NORP     0.6817    0.7512    0.7148       422
+     ORDINAL     0.8276    0.8421    0.8348       171
+         ORG     0.5455    0.5741    0.5594       857
+     PERCENT     0.5476    0.6389    0.5897        36
+      PERSON     0.7531    0.7943    0.7732      1371
+     PRODUCT     0.2937    0.4286    0.3485        98
+    QUANTITY     0.3492    0.4151    0.3793        53
+        SEP]     0.0000    0.0000    0.0000         0
+        TIME     0.5748    0.6822    0.6239       214
+ WORK_OF_ART     0.2963    0.2462    0.2689       130
+
+   micro avg     0.6736    0.7127    0.6926      7588
+   macro avg     0.5141    0.5413    0.5250      7588
+weighted avg     0.6958    0.7127    0.7033      7588
+
+ Special token predictions: 0
+Test Data
+Loading NER labels from ./data/ud/**/*-test-orig.ner
+en_atis-ud-test-orig.ner
+num sentences: 586
+en_cesl-ud-test-orig.ner
+num sentences: 500
+en_ewt-ud-test-orig.ner
+num sentences: 1955
+en_gum-ud-test-orig.ner
+num sentences: 851
+en_lines-ud-test-orig.ner
+num sentences: 988
+en_pud-ud-test-orig.ner
+num sentences: 973
+en_partut-ud-test-orig.ner
+num sentences: 149
+en_pronouns-ud-test-orig.ner
+
+num sentences: 265
+Example of NER labels: [[['what', 'O'], ['are', 'O'], ['the', 'O'], ['coach', 'O'], ['flights', 'O'], ['between', 'O'], ['dallas', 'S-GPE'], ['and', 'O'], ['baltimore', 'S-GPE'], ['leaving', 'O'], ['august', 'B-DATE'], ['tenth', 'E-DATE'], ['and', 'O'], ['returning', 'O'], ['august', 'B-DATE'], ['twelve', 'E-DATE']], [['i', 'O'], ['want', 'O'], ['a', 'O'], ['flight', 'O'], ['from', 'O'], ['nashville', 'S-GPE'], ['to', 'O'], ['seattle', 'S-GPE'], ['that', 'O'], ['arrives', 'O'], ['no', 'O'], ['later', 'O'], ['than', 'O'], ['3', 'B-TIME'], ['pm', 'E-TIME']]]
+6267 sentences, 196 batches of size 32
+
+Control example of InputFeatures
+Input Ids: [101, 1045, 2215, 1037, 3462, 2013, 8423, 2000, 5862, 2008, 8480, 2053, 2101, 2084, 1017, 7610, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+Input Mask: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+Label Ids: [77, 1, 1, 1, 1, 1, 16, 1, 16, 1, 1, 1, 1, 1, 28, 30, 78, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+Valid Ids: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
+Label Mask: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+Segment Ids: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+
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