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roscazo/vih_explainability

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face

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vih_explainability

This model is a fine-tuned version of PlanTL-GOB-ES/bsc-bio-ehr-es on the None dataset. It achieves the following results on the evaluation set:

—Loss: 0.3980
—Roc Auc: 0.8920
—Ap Score: 0.8575
—Precision: 0.8926
—Recall: 0.8920
—F1: 0.8919

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: 9e-06
—trainbatchsize: 8
—evalbatchsize: 8
—seed: 42
—optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
—lrschedulertype: linear
—num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Roc Auc	Ap Score	Precision	Recall	F1
0.62	0.5376	50	0.5331	0.7789	0.7389	0.7905	0.7789	0.7763
0.5106	1.0753	100	0.4343	0.7899	0.7614	0.8118	0.7899	0.7856
0.3762	1.6129	150	0.3364	0.8594	0.8075	0.8596	0.8594	0.8594
0.2878	2.1505	200	0.3582	0.8597	0.8260	0.8636	0.8597	0.8591
0.2556	2.6882	250	0.3121	0.8706	0.8440	0.8764	0.8706	0.8698
0.165	3.2258	300	0.3746	0.8652	0.8349	0.8699	0.8652	0.8645
0.2125	3.7634	350	0.3842	0.8815	0.8629	0.8898	0.8815	0.8805
0.1923	4.3011	400	0.3178	0.9080	0.8662	0.9086	0.9080	0.9081
0.1333	4.8387	450	0.3397	0.8704	0.8297	0.8709	0.8704	0.8702
0.137	5.3763	500	0.3369	0.9028	0.8718	0.9034	0.9028	0.9027
0.1103	5.9140	550	0.3493	0.9025	0.8545	0.9045	0.9025	0.9026
0.0896	6.4516	600	0.4059	0.8813	0.8507	0.8838	0.8813	0.8809
0.0573	6.9892	650	0.3956	0.8813	0.8470	0.8826	0.8813	0.8810
0.0716	7.5269	700	0.5566	0.8815	0.8674	0.8926	0.8815	0.8803
0.0893	8.0645	750	0.3980	0.8920	0.8575	0.8926	0.8920	0.8919

Framework versions

—Transformers 4.41.0
—Pytorch 2.3.0+cu121
—Datasets 2.19.1
—Tokenizers 0.19.1