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FineTune_16All1265068.out

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+
+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
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+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])
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+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])
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+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
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+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])
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+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])
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+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:
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+input_ids max value: 15945
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+input_ids max value: 15973
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+attention_mask shape: torch.Size([16, 256])
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+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])
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+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])
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+Batch 700:
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+Batch 800:
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+input_ids max value: 15973
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+Batch 900:
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+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

FineTune_withPlots32k1115474.out

@@ -0,0 +1,1071 @@
+Loading pytorch-gpu/py3/2.1.1
+  Loading requirement: cuda/11.8.0 nccl/2.18.5-1-cuda cudnn/8.7.0.84-cuda
+    gcc/8.5.0 openmpi/4.1.5-cuda intel-mkl/2020.4 magma/2.7.1-cuda sox/14.4.2
+    sparsehash/2.0.3 libjpeg-turbo/2.1.3 ffmpeg/4.4.4
++ HF_DATASETS_OFFLINE=1
++ TRANSFORMERS_OFFLINE=1
++ python3 FIneTune_withPlots.py
+
+Checking label assignment:
+
+Domain: Mathematics
+Categories: math.OA math.PR
+Abstract: we study the distributional behavior for products and for sums of boolean independent random variabl...
+
+Domain: Computer Science
+Categories: cs.CL physics.soc-ph
+Abstract: zipfs law states that if words of language are ranked in the order of decreasing frequency in texts ...
+
+Domain: Physics
+Categories: physics.atom-ph
+Abstract: the effects of parity and time reversal violating potential in particular the tensorpseudotensor ele...
+
+Domain: Chemistry
+Categories: nlin.AO
+Abstract: over a period of approximately five years pankaj ghemawat of harvard business school and daniel levi...
+
+Domain: Statistics
+Categories: stat.AP
+Abstract: we consider data consisting of photon counts of diffracted xray radiation as a function of the angle...
+
+Domain: Biology
+Categories: q-bio.PE q-bio.GN
+Abstract: this paper develops simplified mathematical models describing the mutationselection balance for the ...
+/linkhome/rech/genrug01/uft12cr/.local/lib/python3.11/site-packages/transformers/tokenization_utils_base.py:2057: FutureWarning: Calling BertTokenizer.from_pretrained() with the path to a single file or url is deprecated and won't be possible anymore in v5. Use a model identifier or the path to a directory instead.
+  warnings.warn(
+
+Training with All Cluster tokenizer:
+Vocabulary size: 29376
+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: 29376
+/gpfsdswork/projects/rech/fmr/uft12cr/finetuneAli/FIneTune_withPlots.py:173: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.
+  scaler = amp.GradScaler()
+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: 29374
+Vocab size: 29376
+/gpfsdswork/projects/rech/fmr/uft12cr/finetuneAli/FIneTune_withPlots.py:202: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with amp.autocast():
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+Epoch 1/5:
+Train Loss: 0.8540, Train Accuracy: 0.7226
+Val Loss: 0.6542, Val Accuracy: 0.7833, Val F1: 0.7250
+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: 29374
+Vocab size: 29376
+/gpfsdswork/projects/rech/fmr/uft12cr/finetuneAli/FIneTune_withPlots.py:202: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with amp.autocast():
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+Epoch 2/5:
+Train Loss: 0.6120, Train Accuracy: 0.8040
+Val Loss: 0.6541, Val Accuracy: 0.7765, Val F1: 0.7610
+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: 29374
+Vocab size: 29376
+/gpfsdswork/projects/rech/fmr/uft12cr/finetuneAli/FIneTune_withPlots.py:202: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with amp.autocast():
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+Epoch 3/5:
+Train Loss: 0.5221, Train Accuracy: 0.8347
+Val Loss: 0.6959, Val Accuracy: 0.7582, Val F1: 0.7540
+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: 29374
+Vocab size: 29376
+/gpfsdswork/projects/rech/fmr/uft12cr/finetuneAli/FIneTune_withPlots.py:202: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with amp.autocast():
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+Epoch 4/5:
+Train Loss: 0.4214, Train Accuracy: 0.8676
+Val Loss: 0.5618, Val Accuracy: 0.8204, Val F1: 0.7935
+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: 29374
+Vocab size: 29376
+/gpfsdswork/projects/rech/fmr/uft12cr/finetuneAli/FIneTune_withPlots.py:202: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with amp.autocast():
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+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: 29374
+Vocab size: 29376
+Epoch 5/5:
+Train Loss: 0.3263, Train Accuracy: 0.8953
+Val Loss: 0.5990, Val Accuracy: 0.8125, Val F1: 0.8073
+
+Test Results for All Cluster tokenizer:
+Accuracy: 0.8125
+F1 Score: 0.8071
+AUC-ROC: 0.8733
+
+Training with Final tokenizer:
+Vocabulary size: 27998
+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: 27998
+/gpfsdswork/projects/rech/fmr/uft12cr/finetuneAli/FIneTune_withPlots.py:173: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.
+  scaler = amp.GradScaler()
+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: 27997
+Vocab size: 27998
+/gpfsdswork/projects/rech/fmr/uft12cr/finetuneAli/FIneTune_withPlots.py:202: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with amp.autocast():
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+Epoch 1/5:
+Train Loss: 0.8917, Train Accuracy: 0.7102
+Val Loss: 0.7550, Val Accuracy: 0.7533, Val F1: 0.7130
+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: 27997
+Vocab size: 27998
+/gpfsdswork/projects/rech/fmr/uft12cr/finetuneAli/FIneTune_withPlots.py:202: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with amp.autocast():
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+Epoch 2/5:
+Train Loss: 0.6483, Train Accuracy: 0.7855
+Val Loss: 0.6702, Val Accuracy: 0.7822, Val F1: 0.7506
+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: 27997
+Vocab size: 27998
+/gpfsdswork/projects/rech/fmr/uft12cr/finetuneAli/FIneTune_withPlots.py:202: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with amp.autocast():
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+Epoch 3/5:
+Train Loss: 0.5660, Train Accuracy: 0.8135
+Val Loss: 0.6397, Val Accuracy: 0.7983, Val F1: 0.7548
+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: 27997
+Vocab size: 27998
+/gpfsdswork/projects/rech/fmr/uft12cr/finetuneAli/FIneTune_withPlots.py:202: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with amp.autocast():
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+Epoch 4/5:
+Train Loss: 0.4725, Train Accuracy: 0.8545
+Val Loss: 0.7259, Val Accuracy: 0.7707, Val F1: 0.7672
+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: 27997
+Vocab size: 27998
+/gpfsdswork/projects/rech/fmr/uft12cr/finetuneAli/FIneTune_withPlots.py:202: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with amp.autocast():
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+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: 27997
+Vocab size: 27998
+Epoch 5/5:
+Train Loss: 0.3889, Train Accuracy: 0.8792
+Val Loss: 0.5967, Val Accuracy: 0.8174, Val F1: 0.7926
+
+Test Results for Final tokenizer:
+Accuracy: 0.8174
+F1 Score: 0.7925
+AUC-ROC: 0.8663
+
+Training with General tokenizer:
+Vocabulary size: 30522
+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: 30522
+/gpfsdswork/projects/rech/fmr/uft12cr/finetuneAli/FIneTune_withPlots.py:173: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.
+  scaler = amp.GradScaler()
+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: 29605
+Vocab size: 30522
+/gpfsdswork/projects/rech/fmr/uft12cr/finetuneAli/FIneTune_withPlots.py:202: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with amp.autocast():
+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: 29438
+Vocab size: 30522
+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: 29300
+Vocab size: 30522
+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: 29464
+Vocab size: 30522
+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: 29494
+Vocab size: 30522
+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: 29464
+Vocab size: 30522
+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: 29464
+Vocab size: 30522
+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: 29464
+Vocab size: 30522
+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: 29340
+Vocab size: 30522
+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: 29454
+Vocab size: 30522
+Epoch 1/5:
+Train Loss: 0.8557, Train Accuracy: 0.7257
+Val Loss: 0.6864, Val Accuracy: 0.7724, Val F1: 0.7309
+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: 29300
+Vocab size: 30522
+/gpfsdswork/projects/rech/fmr/uft12cr/finetuneAli/FIneTune_withPlots.py:202: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with amp.autocast():
+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: 29464
+Vocab size: 30522
+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: 29494
+Vocab size: 30522
+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: 29474
+Vocab size: 30522
+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: 29535
+Vocab size: 30522
+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: 29577
+Vocab size: 30522
+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: 29598
+Vocab size: 30522
+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: 29605
+Vocab size: 30522
+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: 29160
+Vocab size: 30522
+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: 29532
+Vocab size: 30522
+Epoch 2/5:
+Train Loss: 0.5995, Train Accuracy: 0.8029
+Val Loss: 0.6449, Val Accuracy: 0.7882, Val F1: 0.7366
+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: 29536
+Vocab size: 30522
+/gpfsdswork/projects/rech/fmr/uft12cr/finetuneAli/FIneTune_withPlots.py:202: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with amp.autocast():
+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: 29464
+Vocab size: 30522
+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: 29536
+Vocab size: 30522
+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: 29464
+Vocab size: 30522
+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: 29464
+Vocab size: 30522
+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: 29464
+Vocab size: 30522
+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: 29413
+Vocab size: 30522
+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: 29346
+Vocab size: 30522
+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: 29451
+Vocab size: 30522
+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: 29280
+Vocab size: 30522
+Epoch 3/5:
+Train Loss: 0.5332, Train Accuracy: 0.8291
+Val Loss: 0.6577, Val Accuracy: 0.7942, Val F1: 0.7687
+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: 29464
+Vocab size: 30522
+/gpfsdswork/projects/rech/fmr/uft12cr/finetuneAli/FIneTune_withPlots.py:202: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with amp.autocast():
+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: 29464
+Vocab size: 30522
+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: 29535
+Vocab size: 30522
+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: 29413
+Vocab size: 30522
+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: 29461
+Vocab size: 30522
+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: 29536
+Vocab size: 30522
+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: 29300
+Vocab size: 30522
+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: 29536
+Vocab size: 30522
+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: 29513
+Vocab size: 30522
+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: 29536
+Vocab size: 30522
+Epoch 4/5:
+Train Loss: 0.4665, Train Accuracy: 0.8555
+Val Loss: 0.6495, Val Accuracy: 0.7931, Val F1: 0.7709
+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: 29454
+Vocab size: 30522
+/gpfsdswork/projects/rech/fmr/uft12cr/finetuneAli/FIneTune_withPlots.py:202: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with amp.autocast():
+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: 29598
+Vocab size: 30522
+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: 29336
+Vocab size: 30522
+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: 29602
+Vocab size: 30522
+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: 29598
+Vocab size: 30522
+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: 29464
+Vocab size: 30522
+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: 29513
+Vocab size: 30522
+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: 29464
+Vocab size: 30522
+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: 29536
+Vocab size: 30522
+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: 29535
+Vocab size: 30522
+Epoch 5/5:
+Train Loss: 0.3991, Train Accuracy: 0.8781
+Val Loss: 0.6572, Val Accuracy: 0.7948, Val F1: 0.7804
+
+Test Results for General tokenizer:
+Accuracy: 0.7945
+F1 Score: 0.7802
+AUC-ROC: 0.8825
+
+Summary of Results:
+
+All Cluster Tokenizer:
+Accuracy: 0.8125
+F1 Score: 0.8071
+AUC-ROC: 0.8733
+
+Final Tokenizer:
+Accuracy: 0.8174
+F1 Score: 0.7925
+AUC-ROC: 0.8663
+
+General Tokenizer:
+Accuracy: 0.7945
+F1 Score: 0.7802
+AUC-ROC: 0.8825
+
+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