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bert-finetuned-non – AI Model by mrpks | AlphaNeural AI
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mrpks
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bert-finetuned-non
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transformers
tensorboard
safetensors
bert
token-classification
generated_from_trainer
dmis-lab/biobert-v1.1
finetune
autotrain_compatible
endpoints_compatible
us
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bert-finetuned-non
This model is a fine-tuned version of
dmis-lab/biobert-v1.1
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.2190
Precision: 0.5143
Recall: 0.5455
F1: 0.5294
Accuracy: 0.9135
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: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
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: 3
Training results
Training Loss
Epoch
Step
Validation Loss
Precision
Recall
F1
Accuracy
No log
1.0
25
0.2802
0.3846
0.4545
0.4167
0.9077
No log
2.0
50
0.2265
0.5588
0.5758
0.5672
0.9231
No log
3.0
75
0.2190
0.5143
0.5455
0.5294
0.9135
Framework versions
Transformers 4.48.3
Pytorch 2.5.1+cu124
Datasets 3.3.2
Tokenizers 0.21.0
