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medical_bert_tiny_asymmetric_old_dataset_v3 – AI Model by AI-ML-Research | AlphaNeural AI
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AI-ML-Research
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medical_bert_tiny_asymmetric_old_dataset_v3
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transformers
safetensors
bert
text-classification
generated_from_trainer
prajjwal1/bert-tiny
finetune
mit
text-embeddings-inference
endpoints_compatible
us
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medical_bert_tiny_asymmetric_old_dataset_v3
This model is a fine-tuned version of
prajjwal1/bert-tiny
on an unknown dataset. It achieves the following results on the evaluation set:
eval_loss: 0.0062
eval_accuracy: 0.9947
eval_recall_weighted: 0.9947
eval_precision_weighted: 0.9948
eval_f1_weighted: 0.9947
eval_f1_macro: 0.9926
eval_f1_prescription: 0.9889
eval_f1_lab_report: 0.9894
eval_f1_others: 0.9996
eval_false_alarm_rate: 0.0009
eval_cross_class_error: 0.0103
eval_runtime: 2.6043
eval_samples_per_second: 801.365
eval_steps_per_second: 12.671
epoch: 2.0
step: 1784
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
Framework versions
Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.8.5
Tokenizers 0.22.2
