medical_bert_tiny_asymmetric_old_dataset_v2

This model is a fine-tuned version of prajjwal1/bert-tiny on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0096
• Accuracy: 0.9952
• Recall Weighted: 0.9952
• Precision Weighted: 0.9952
• F1 Weighted: 0.9952
• F1 Macro: 0.9931
• F1 Prescription: 0.9889
• F1 Lab Report: 0.9904
• F1 Others: 1.0
• False Alarm Rate: 0.0
• Cross Class Error: 0.0103

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: 0.0002
• train_batch_size: 8
• eval_batch_size: 64
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• 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
• lr_scheduler_warmup_steps: 0.1
• num_epochs: 5
• label_smoothing_factor: 0.1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Recall Weighted	Precision Weighted	F1 Weighted	F1 Macro	F1 Prescription	F1 Lab Report	F1 Others	False Alarm Rate	Cross Class Error
0.0543	1.0	892	0.0200	0.9904	0.9904	0.9906	0.9904	0.9865	0.9784	0.9820	0.9991	0.0	0.0186
0.0011	2.0	1784	0.0095	0.9952	0.9952	0.9952	0.9952	0.9935	0.9910	0.9904	0.9991	0.0009	0.0083
0.0005	3.0	2676	0.0068	0.9962	0.9962	0.9962	0.9962	0.9949	0.9933	0.9923	0.9991	0.0	0.0062
0.0004	4.0	3568	0.0074	0.9962	0.9962	0.9962	0.9962	0.9949	0.9933	0.9923	0.9991	0.0	0.0062
0.0003	5.0	4460	0.0096	0.9952	0.9952	0.9952	0.9952	0.9931	0.9889	0.9904	1.0	0.0	0.0103

Framework versions

• Transformers 5.0.0
• Pytorch 2.10.0+cu128
• Datasets 4.8.5
• Tokenizers 0.22.2