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johnpaulett/
ModernRadBERT-cui-classifier

Name: ModernRadBERT-cui-classifier
Availability: InStock

generated_from_trainer

answerdotai/ModernBERT-base

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ModernRadBERT-cui-classifier

This model is a fine-tuned version of answerdotai/ModernBERT-base on the unsloth/Radiology_mini dataset. It achieves the following results on the evaluation set:

Loss: 0.1454
Precision Micro: 0.8664
Recall Micro: 0.7217
F1: 0.7874
Precision Macro: 0.6973
Recall Macro: 0.4836
F1 Macro: 0.5480
Exact Match: 0.6580
Hamming Loss: 0.0327
Label Accuracy: 0.9673

https://www.johnpaulett.com/2025/modernbert-radiology-fine-tuning-classifier/

Model description

More information needed

Intended uses & limitations

Not intended for real-world use, was an example of MLM fine-tuning on a small radiology dataset.

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 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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Precision Micro	Recall Micro	F1	Precision Macro	Recall Macro	F1 Macro	Exact Match	Hamming Loss	Label Accuracy
0.1371	1.0	205	0.1214	0.8169	0.6679	0.7350	0.4170	0.3481	0.3667	0.5681	0.0404	0.9596
0.0904	2.0	410	0.1054	0.8704	0.6833	0.7656	0.5391	0.3744	0.4106	0.6029	0.0351	0.9649
0.0458	3.0	615	0.1012	0.8316	0.7582	0.7932	0.5899	0.5157	0.5251	0.6580	0.0332	0.9668
0.0216	4.0	820	0.1134	0.8738	0.7044	0.7800	0.7129	0.4338	0.5071	0.6377	0.0333	0.9667
0.01	5.0	1025	0.1194	0.8382	0.7159	0.7723	0.6707	0.4817	0.5336	0.6290	0.0354	0.9646
0.0047	6.0	1230	0.1224	0.8721	0.7332	0.7967	0.6475	0.4692	0.5187	0.6638	0.0314	0.9686
0.0024	7.0	1435	0.1228	0.8540	0.7409	0.7934	0.7016	0.5071	0.5648	0.6725	0.0324	0.9676
0.0012	8.0	1640	0.1289	0.8744	0.7217	0.7907	0.7053	0.4852	0.5531	0.6609	0.0320	0.9680
0.0009	9.0	1845	0.1323	0.8765	0.7217	0.7916	0.7063	0.4831	0.5512	0.6667	0.0319	0.9681
0.0007	10.0	2050	0.1337	0.8765	0.7217	0.7916	0.7059	0.4809	0.5493	0.6609	0.0319	0.9681
0.0006	11.0	2255	0.1357	0.8744	0.7217	0.7907	0.7044	0.4809	0.5488	0.6609	0.0320	0.9680
0.0006	12.0	2460	0.1373	0.8701	0.7198	0.7878	0.7027	0.4805	0.5476	0.6638	0.0325	0.9675
0.0005	13.0	2665	0.1395	0.8684	0.7217	0.7883	0.6977	0.4827	0.5477	0.6638	0.0325	0.9675
0.0005	14.0	2870	0.1410	0.8701	0.7198	0.7878	0.7029	0.4815	0.5488	0.6580	0.0325	0.9675
0.0005	15.0	3075	0.1426	0.8644	0.7217	0.7866	0.6957	0.4818	0.5466	0.6551	0.0329	0.9671
0.0004	16.0	3280	0.1432	0.8670	0.7255	0.7900	0.6976	0.4872	0.5508	0.6580	0.0324	0.9676
0.0004	17.0	3485	0.1442	0.8687	0.7236	0.7895	0.6981	0.4849	0.5492	0.6580	0.0324	0.9676
0.0004	18.0	3690	0.1448	0.8670	0.7255	0.7900	0.6985	0.4872	0.5510	0.6580	0.0324	0.9676
0.0004	19.0	3895	0.1451	0.8647	0.7236	0.7879	0.6963	0.4849	0.5485	0.6580	0.0327	0.9673
0.0004	20.0	4100	0.1454	0.8664	0.7217	0.7874	0.6973	0.4836	0.5480	0.6580	0.0327	0.9673

Framework versions

Transformers 4.48.0.dev0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

johnpaulett/ModernRadBERT-cui-classifier