FineTune_16All1265068.out

Checking label assignment:

Domain: Mathematics
Categories: cs.IT math.IT
Abstract: information embedding ie is the transmission of information within a host signal subject to a distor...

Domain: Computer Science
Categories: cs.CY
Abstract: according to socioconstructivism approach collective situations are promoted to favor learning in cl...

Domain: Physics
Categories: physics.pop-ph physics.optics
Abstract: a method is presented for generation of a subwavelength lambda longitudinally polarized beam which p...

Domain: Chemistry
Categories: nlin.PS
Abstract: rolls in finite prandtl number rotating convection with freeslip top and bottom boundary conditions ...

Domain: Statistics
Categories: stat.ME stat.CO
Abstract: in this paper we introduce a novel particle filter scheme for a class of partiallyobserved multivari...

Domain: Biology
Categories: q-bio.PE q-bio.CB quant-ph
Abstract: this is a supplement to the paper arxivqbio containing the text of correspondence sent to nature in...

Training with All Cluster tokenizer:
Vocabulary size: 16005
Could not load pretrained weights from /gpfswork/rech/fmr/uft12cr/finetuneAli/Bert_Model. Starting with random weights. Error: Error while deserializing header: HeaderTooLarge
Initialized model with vocabulary size: 16005
Batch 0:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 16003
Vocab size: 16005
Batch 100:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 16003
Vocab size: 16005
Batch 200:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 16003
Vocab size: 16005
Batch 300:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 16003
Vocab size: 16005
Batch 400:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 16003
Vocab size: 16005
Batch 500:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 16003
Vocab size: 16005
Batch 600:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 16003
Vocab size: 16005
Batch 700:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 16003
Vocab size: 16005
Batch 800:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 16003
Vocab size: 16005
Batch 900:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 16003
Vocab size: 16005
Epoch 1/3:
Train Loss: 0.9143, Train Accuracy: 0.6955
Val Loss: 0.6986, Val Accuracy: 0.7743, Val F1: 0.7502
Batch 0:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 16003
Vocab size: 16005
Batch 100:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 16003
Vocab size: 16005
Batch 200:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 16003
Vocab size: 16005
Batch 300:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 16003
Vocab size: 16005
Batch 400:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 16003
Vocab size: 16005
Batch 500:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 16003
Vocab size: 16005
Batch 600:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 16003
Vocab size: 16005
Batch 700:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 16003
Vocab size: 16005
Batch 800:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 16003
Vocab size: 16005
Batch 900:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 16003
Vocab size: 16005
Epoch 2/3:
Train Loss: 0.6277, Train Accuracy: 0.7987
Val Loss: 0.6150, Val Accuracy: 0.8002, Val F1: 0.7753
Batch 0:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 16003
Vocab size: 16005
Batch 100:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 16003
Vocab size: 16005
Batch 200:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 16003
Vocab size: 16005
Batch 300:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 16003
Vocab size: 16005
Batch 400:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 16003
Vocab size: 16005
Batch 500:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 16003
Vocab size: 16005
Batch 600:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 16003
Vocab size: 16005
Batch 700:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 16003
Vocab size: 16005
Batch 800:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 16003
Vocab size: 16005
Batch 900:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 16003
Vocab size: 16005
Epoch 3/3:
Train Loss: 0.5085, Train Accuracy: 0.8373
Val Loss: 0.6998, Val Accuracy: 0.7784, Val F1: 0.7468

Test Results for All Cluster tokenizer:
Accuracy: 0.7781
F1 Score: 0.7465
AUC-ROC: 0.8821

Training with Final tokenizer:
Vocabulary size: 15047
Could not load pretrained weights from /gpfswork/rech/fmr/uft12cr/finetuneAli/Bert_Model. Starting with random weights. Error: Error while deserializing header: HeaderTooLarge
Initialized model with vocabulary size: 15047
Batch 0:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15046
Vocab size: 15047
Batch 100:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15046
Vocab size: 15047
Batch 200:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15046
Vocab size: 15047
Batch 300:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15046
Vocab size: 15047
Batch 400:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15046
Vocab size: 15047
Batch 500:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15046
Vocab size: 15047
Batch 600:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15046
Vocab size: 15047
Batch 700:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15046
Vocab size: 15047
Batch 800:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15046
Vocab size: 15047
Batch 900:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15046
Vocab size: 15047
Epoch 1/3:
Train Loss: 0.9914, Train Accuracy: 0.6629
Val Loss: 0.8531, Val Accuracy: 0.7224, Val F1: 0.6560
Batch 0:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15046
Vocab size: 15047
Batch 100:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15046
Vocab size: 15047
Batch 200:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15046
Vocab size: 15047
Batch 300:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15046
Vocab size: 15047
Batch 400:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15046
Vocab size: 15047
Batch 500:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15046
Vocab size: 15047
Batch 600:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15046
Vocab size: 15047
Batch 700:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15046
Vocab size: 15047
Batch 800:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15046
Vocab size: 15047
Batch 900:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15046
Vocab size: 15047
Epoch 2/3:
Train Loss: 0.7899, Train Accuracy: 0.7359
Val Loss: 0.7491, Val Accuracy: 0.7516, Val F1: 0.7260
Batch 0:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15046
Vocab size: 15047
Batch 100:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15046
Vocab size: 15047
Batch 200:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15046
Vocab size: 15047
Batch 300:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15046
Vocab size: 15047
Batch 400:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15046
Vocab size: 15047
Batch 500:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15046
Vocab size: 15047
Batch 600:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15046
Vocab size: 15047
Batch 700:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15046
Vocab size: 15047
Batch 800:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15046
Vocab size: 15047
Batch 900:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15046
Vocab size: 15047
Epoch 3/3:
Train Loss: 0.6774, Train Accuracy: 0.7784
Val Loss: 0.7340, Val Accuracy: 0.7557, Val F1: 0.7386

Test Results for Final tokenizer:
Accuracy: 0.7560
F1 Score: 0.7388
AUC-ROC: 0.8423

Training with General tokenizer:
Vocabulary size: 16000
Could not load pretrained weights from /gpfswork/rech/fmr/uft12cr/finetuneAli/Bert_Model. Starting with random weights. Error: Error while deserializing header: HeaderTooLarge
Initialized model with vocabulary size: 16000
Batch 0:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15945
Vocab size: 16000
Batch 100:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15973
Vocab size: 16000
Batch 200:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15973
Vocab size: 16000
Batch 300:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15984
Vocab size: 16000
Batch 400:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15973
Vocab size: 16000
Batch 500:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15973
Vocab size: 16000
Batch 600:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15985
Vocab size: 16000
Batch 700:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15985
Vocab size: 16000
Batch 800:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15973
Vocab size: 16000
Batch 900:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15901
Vocab size: 16000
Epoch 1/3:
Train Loss: 0.8970, Train Accuracy: 0.7058
Val Loss: 0.7586, Val Accuracy: 0.7604, Val F1: 0.6892
Batch 0:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15873
Vocab size: 16000
Batch 100:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15950
Vocab size: 16000
Batch 200:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15985
Vocab size: 16000
Batch 300:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15973
Vocab size: 16000
Batch 400:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15985
Vocab size: 16000
Batch 500:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15992
Vocab size: 16000
Batch 600:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15928
Vocab size: 16000
Batch 700:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15980
Vocab size: 16000
Batch 800:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15973
Vocab size: 16000
Batch 900:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15973
Vocab size: 16000
Epoch 2/3:
Train Loss: 0.6461, Train Accuracy: 0.7883
Val Loss: 0.5972, Val Accuracy: 0.8024, Val F1: 0.7585
Batch 0:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15973
Vocab size: 16000
Batch 100:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15871
Vocab size: 16000
Batch 200:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15985
Vocab size: 16000
Batch 300:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15973
Vocab size: 16000
Batch 400:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15987
Vocab size: 16000
Batch 500:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15973
Vocab size: 16000
Batch 600:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15973
Vocab size: 16000
Batch 700:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15973
Vocab size: 16000
Batch 800:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15973
Vocab size: 16000
Batch 900:
input_ids shape: torch.Size([16, 256])
attention_mask shape: torch.Size([16, 256])
labels shape: torch.Size([16])
input_ids max value: 15956
Vocab size: 16000
Epoch 3/3:
Train Loss: 0.5426, Train Accuracy: 0.8275
Val Loss: 0.5413, Val Accuracy: 0.8275, Val F1: 0.7986

Test Results for General tokenizer:
Accuracy: 0.8281
F1 Score: 0.7992
AUC-ROC: 0.8504

Summary of Results:

All Cluster Tokenizer:
Accuracy: 0.7781
F1 Score: 0.7465
AUC-ROC: 0.8821

Final Tokenizer:
Accuracy: 0.7560
F1 Score: 0.7388
AUC-ROC: 0.8423

General Tokenizer:
Accuracy: 0.8281
F1 Score: 0.7992
AUC-ROC: 0.8504

Class distribution in training set:
Class Biology: 439 samples
Class Chemistry: 454 samples
Class Computer Science: 1358 samples
Class Mathematics: 9480 samples
Class Physics: 2733 samples
Class Statistics: 200 samples