medbert-512-finetuned-grasc

This model is a fine-tuned version of GerMedBERT/medbert-512 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0060
• Accuracy: 1.0
• F1-weighted: 1.0
• F1-micro: 1.0
• F1-macro: 1.0
• Precision-weighted: 1.0
• Precision-micro: 1.0
• Precision-macro: 1.0
• Recall-weighted: 1.0
• Recall-micro: 1.0
• Recall-macro: 1.0
• Ballanced-accuracy: 1.0

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: 3e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1-weighted	F1-micro	F1-macro	Precision-weighted	Precision-micro	Precision-macro	Recall-weighted	Recall-micro	Recall-macro	Ballanced-accuracy
3.7258	1.0	16	2.4052	0.4355	0.2642	0.4355	0.0758	0.1896	0.4355	0.0544	0.4355	0.4355	0.125	0.125
2.2185	2.0	32	1.8621	0.6452	0.5636	0.6452	0.1873	0.5625	0.6452	0.1939	0.6452	0.6452	0.2062	0.2062
1.8431	3.0	48	1.5623	0.5323	0.4322	0.5323	0.1390	0.5325	0.5323	0.1853	0.5323	0.5323	0.1625	0.1625
1.6836	4.0	64	1.1638	0.7742	0.7250	0.7742	0.2820	0.6922	0.7742	0.2812	0.7742	0.7742	0.2917	0.2917
1.1477	5.0	80	1.0214	0.7258	0.6300	0.7258	0.2110	0.5597	0.7258	0.1888	0.7258	0.7258	0.2407	0.2407
1.3781	6.0	96	0.6523	0.8710	0.8281	0.8710	0.3360	0.7902	0.8710	0.3183	0.8710	0.8710	0.3565	0.3565
0.5876	7.0	112	0.5180	0.9194	0.8907	0.9194	0.4745	0.8677	0.9194	0.4582	0.9194	0.9194	0.4954	0.4954
0.8002	8.0	128	0.3721	0.9194	0.8900	0.9194	0.4545	0.8651	0.9194	0.4319	0.9194	0.9194	0.4861	0.4861
0.3814	9.0	144	0.2805	0.9516	0.9292	0.9516	0.6102	0.9097	0.9516	0.5978	0.9516	0.9516	0.625	0.625
0.3762	10.0	160	0.2354	0.9516	0.9306	0.9516	0.5875	0.9145	0.9516	0.5606	0.9516	0.9516	0.625	0.625
0.2225	11.0	176	0.2019	0.9839	0.9762	0.9839	0.8684	0.9694	0.9839	0.8625	0.9839	0.9839	0.875	0.875
0.236	12.0	192	0.1627	0.9839	0.9762	0.9839	0.8684	0.9694	0.9839	0.8625	0.9839	0.9839	0.875	0.875
0.2791	13.0	208	0.1400	0.9839	0.9762	0.9839	0.8684	0.9694	0.9839	0.8625	0.9839	0.9839	0.875	0.875
0.0915	14.0	224	0.1219	0.9839	0.9762	0.9839	0.8684	0.9694	0.9839	0.8625	0.9839	0.9839	0.875	0.875
0.1426	15.0	240	0.1118	0.9839	0.9762	0.9839	0.8684	0.9694	0.9839	0.8625	0.9839	0.9839	0.875	0.875
0.1103	16.0	256	0.1005	0.9839	0.9762	0.9839	0.8684	0.9694	0.9839	0.8625	0.9839	0.9839	0.875	0.875
0.1324	17.0	272	0.0916	0.9839	0.9762	0.9839	0.8684	0.9694	0.9839	0.8625	0.9839	0.9839	0.875	0.875
0.034	18.0	288	0.0880	0.9839	0.9762	0.9839	0.8684	0.9694	0.9839	0.8625	0.9839	0.9839	0.875	0.875
0.1798	19.0	304	0.0592	0.9839	0.9762	0.9839	0.8684	0.9694	0.9839	0.8625	0.9839	0.9839	0.875	0.875
0.0274	20.0	320	0.0627	0.9839	0.9762	0.9839	0.8684	0.9694	0.9839	0.8625	0.9839	0.9839	0.875	0.875
0.0768	21.0	336	0.0726	0.9839	0.9762	0.9839	0.8684	0.9694	0.9839	0.8625	0.9839	0.9839	0.875	0.875
0.1617	22.0	352	0.0624	0.9839	0.9762	0.9839	0.8684	0.9694	0.9839	0.8625	0.9839	0.9839	0.875	0.875
0.0706	23.0	368	0.0618	0.9839	0.9762	0.9839	0.8684	0.9694	0.9839	0.8625	0.9839	0.9839	0.875	0.875
0.0198	24.0	384	0.0651	0.9839	0.9762	0.9839	0.8684	0.9694	0.9839	0.8625	0.9839	0.9839	0.875	0.875
0.0762	25.0	400	0.0443	0.9839	0.9762	0.9839	0.8684	0.9694	0.9839	0.8625	0.9839	0.9839	0.875	0.875
0.0641	26.0	416	0.0319	0.9839	0.9762	0.9839	0.8684	0.9694	0.9839	0.8625	0.9839	0.9839	0.875	0.875
0.0468	27.0	432	0.0334	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0357	28.0	448	0.0207	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0386	29.0	464	0.0347	0.9839	0.9762	0.9839	0.8684	0.9694	0.9839	0.8625	0.9839	0.9839	0.875	0.875
0.0361	30.0	480	0.0214	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0207	31.0	496	0.0152	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0197	32.0	512	0.0126	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0153	33.0	528	0.0116	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0101	34.0	544	0.0108	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0185	35.0	560	0.0093	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0101	36.0	576	0.0088	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0069	37.0	592	0.0084	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0135	38.0	608	0.0077	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0068	39.0	624	0.0074	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0103	40.0	640	0.0072	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0112	41.0	656	0.0069	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0096	42.0	672	0.0067	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0098	43.0	688	0.0065	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0104	44.0	704	0.0063	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.008	45.0	720	0.0062	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0081	46.0	736	0.0061	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0079	47.0	752	0.0061	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0081	48.0	768	0.0061	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0068	49.0	784	0.0060	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0089	50.0	800	0.0060	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0

Framework versions

• Transformers 4.56.1
• Pytorch 2.8.0+cu126
• Datasets 4.0.0
• Tokenizers 0.22.0