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bioBert-without_frezze – AI Model by adity12345 | AlphaNeural AI
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bioBert-without_frezze
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transformers
tensorboard
safetensors
bert
text-classification
generated_from_trainer
dmis-lab/biobert-base-cased-v1.2
finetune
autotrain_compatible
endpoints_compatible
us
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bioBert-without_frezze
This model is a fine-tuned version of
dmis-lab/biobert-base-cased-v1.2
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.0337
Accuracy: 0.996
Auc: 0.999
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
learning_rate: 0.0001
train_batch_size: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 5
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
Auc
0.2846
1.0
452
0.0514
0.993
1.0
0.3325
2.0
904
0.0902
0.996
0.996
0.3149
3.0
1356
0.0728
0.997
0.996
0.245
4.0
1808
0.1013
0.981
0.984
0.158
5.0
2260
0.0337
0.996
0.999
Framework versions
Transformers 4.52.4
Pytorch 2.6.0+cu124
Datasets 2.14.4
Tokenizers 0.21.2
