FineTune_GeneralOnly933282.out

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 OnlyGeneralTokenizer.py

Checking label assignment:

Domain: Mathematics
Categories: math.DS math.CA
Abstract: we prove an inequality for holder continuous differential forms on compact manifolds in which the in...

Domain: Computer Science
Categories: cs.NE
Abstract: when looking for a solution deterministic methods have the enormous advantage that they do find glob...

Domain: Physics
Categories: physics.hist-ph quant-ph
Abstract: maxwells demon was born in and still thrives in modern physics he plays important roles in clarifyin...

Domain: Chemistry
Categories: nlin.PS
Abstract: the modulational instability of two interacting waves in a nonlocal kerrtype medium is considered an...

Domain: Statistics
Categories: astro-ph stat.ME
Abstract: the identification of increasingly smaller signal from objects observed with a nonperfect instrument...

Domain: Biology
Categories: q-bio.MN cond-mat.stat-mech
Abstract: we find that discrete noise of inhibiting signal molecules can greatly delay the extinction of plasm...
/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 General tokenizer:
Vocabulary size: 30522
Could not load pretrained weights from /linkhome/rech/genrug01/uft12cr/bert_Model. Starting with random weights. Error: It looks like the config file at '/linkhome/rech/genrug01/uft12cr/bert_Model/config.json' is not a valid JSON file.
Initialized model with vocabulary size: 30522
/gpfsdswork/projects/rech/fmr/uft12cr/finetuneAli/OnlyGeneralTokenizer.py:172: 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: 29464
Vocab size: 30522
/gpfsdswork/projects/rech/fmr/uft12cr/finetuneAli/OnlyGeneralTokenizer.py:192: 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: 29536
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: 29536
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: 29513
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: 29413
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: 29237
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: 29586
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: 29221
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: 29514
Vocab size: 30522
Epoch 1/3:
Val Accuracy: 0.7306, Val F1: 0.6541
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: 29602
Vocab size: 30522
/gpfsdswork/projects/rech/fmr/uft12cr/finetuneAli/OnlyGeneralTokenizer.py:192: 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: 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: 29601
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: 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: 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: 29602
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: 29454
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: 29280
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: 29417
Vocab size: 30522
Epoch 2/3:
Val Accuracy: 0.7961, Val F1: 0.7582
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: 29299
Vocab size: 30522
/gpfsdswork/projects/rech/fmr/uft12cr/finetuneAli/OnlyGeneralTokenizer.py:192: 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: 29577
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: 29451
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: 29454
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: 29532
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: 29586
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: 29280
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: 29494
Vocab size: 30522
Epoch 3/3:
Val Accuracy: 0.8204, Val F1: 0.7894

Test Results for General tokenizer:
Accuracy: 0.8204
F1 Score: 0.7893
AUC-ROC: 0.8693

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