biobert-finetuned-pico

This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.2 on the pico-breast-cancer dataset. It achieves the following results on the evaluation set:

Loss: 0.4646
Precision: 0.6466
Recall: 0.7182
F1: 0.6805
Accuracy: 0.9329
Total-participants: 0.8914
Intervention-participants: 0.8140
Control-participants: 0.8424
Age: 0.5357
Eligibility: 0.4859
Ethinicity: 0.5116
Condition: 0.6049
Location: 0.4932
Intervention: 0.6078
Control: 0.5150
Outcome: 0.6102
Outcome-measure: 0.6638
Iv-bin-abs: 0.7895
Cv-bin-abs: 0.8343
Iv-bin-percent: 0.7873
Cv-bin-percent: 0.7810
Iv-cont-mean: 0.7125
Cv-cont-mean: 0.7383
Iv-cont-median: 0.7126
Cv-cont-median: 0.7838
Iv-cont-sd: 0.7333
Cv-cont-sd: 0.8197

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: 20
eval_batch_size: 32
seed: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy	Total-participants	Intervention-participants	Control-participants	Age	Eligibility	Ethinicity	Condition	Location	Intervention	Control	Outcome	Outcome-measure	Iv-bin-abs	Cv-bin-abs	Iv-bin-percent	Cv-bin-percent	Iv-cont-mean	Cv-cont-mean	Iv-cont-median	Cv-cont-median	Iv-cont-sd	Cv-cont-sd
No log	1.0	479	0.2514	0.4795	0.6284	0.5439	0.9130	0.8151	0.5783	0.6369	0.1926	0.3725	0.0	0.5443	0.4444	0.5976	0.4429	0.5423	0.6059	0.6033	0.5987	0.6287	0.5293	0.4231	0.3378	0.5476	0.6087	0.2857	0.0755
0.4045	2.0	958	0.2136	0.5636	0.6598	0.6079	0.9281	0.8406	0.6455	0.7101	0.4727	0.4483	0.3571	0.5359	0.4828	0.6085	0.5204	0.5662	0.6434	0.6479	0.6667	0.6784	0.6052	0.6329	0.6027	0.5400	0.5974	0.5574	0.6286
0.1933	3.0	1437	0.2236	0.5753	0.6632	0.6161	0.9259	0.8889	0.7453	0.7888	0.4068	0.4578	0.4286	0.6154	0.4918	0.5642	0.5035	0.5618	0.6491	0.6471	0.6569	0.6844	0.7284	0.5328	0.5906	0.5051	0.6	0.6667	0.6129
0.1374	4.0	1916	0.2507	0.6199	0.6859	0.6512	0.9308	0.8899	0.7754	0.8141	0.4094	0.4444	0.5714	0.6341	0.5455	0.6177	0.5191	0.5986	0.6738	0.7319	0.7439	0.7080	0.7193	0.6620	0.6338	0.6047	0.6923	0.6000	0.6429
0.0964	5.0	2395	0.2615	0.6143	0.7106	0.6590	0.9283	0.9009	0.7916	0.8125	0.4950	0.4250	0.5641	0.6623	0.6301	0.6025	0.5109	0.5905	0.6740	0.7586	0.7514	0.7788	0.7798	0.6627	0.6803	0.6316	0.6207	0.6667	0.7302
0.0694	6.0	2874	0.2939	0.6146	0.7123	0.6598	0.9287	0.9051	0.8170	0.8519	0.5246	0.4679	0.4889	0.5714	0.5205	0.5704	0.5047	0.5824	0.6826	0.7748	0.7821	0.7599	0.7692	0.6835	0.7403	0.5859	0.6835	0.7077	0.7869
0.0503	7.0	3353	0.3304	0.6323	0.6854	0.6578	0.9323	0.8952	0.7839	0.8312	0.5263	0.3831	0.4681	0.5780	0.5067	0.6122	0.5442	0.5850	0.6904	0.7364	0.7515	0.7379	0.7389	0.6901	0.6986	0.7021	0.7297	0.7541	0.7667
0.0336	8.0	3832	0.3486	0.6251	0.7089	0.6644	0.9293	0.9002	0.7948	0.8415	0.5217	0.4167	0.5238	0.6038	0.5641	0.5816	0.4969	0.5965	0.6798	0.7598	0.7861	0.7740	0.7719	0.725	0.7308	0.6744	0.6829	0.7333	0.8065
0.025	9.0	4311	0.3752	0.6387	0.7227	0.6781	0.9314	0.8875	0.8164	0.8378	0.5333	0.4936	0.6190	0.5952	0.6000	0.6080	0.5504	0.5952	0.6696	0.7387	0.7650	0.7840	0.7984	0.6829	0.7550	0.7126	0.7632	0.7812	0.7813
0.0176	10.0	4790	0.3921	0.6376	0.7126	0.6730	0.9317	0.8865	0.8229	0.8562	0.5437	0.4653	0.5641	0.6024	0.4225	0.6157	0.5011	0.5990	0.6681	0.7534	0.8156	0.7746	0.78	0.6748	0.7195	0.7059	0.7397	0.7719	0.8358
0.0125	11.0	5269	0.4271	0.6283	0.7154	0.6690	0.9299	0.8962	0.8097	0.8318	0.5254	0.4573	0.5238	0.5963	0.4722	0.6189	0.5219	0.6046	0.6553	0.7911	0.8046	0.7603	0.7547	0.6627	0.6933	0.6809	0.7467	0.7333	0.8000
0.009	12.0	5748	0.4350	0.6399	0.7142	0.6751	0.9317	0.8914	0.8076	0.8519	0.5577	0.4726	0.5217	0.6173	0.4722	0.5890	0.5088	0.6139	0.6509	0.7725	0.7976	0.7708	0.7722	0.7081	0.7297	0.7333	0.7945	0.7541	0.8136
0.0066	13.0	6227	0.4629	0.6537	0.7126	0.6819	0.9333	0.8923	0.8120	0.8466	0.5714	0.4718	0.5238	0.6154	0.5263	0.6062	0.5265	0.6096	0.6755	0.7841	0.8202	0.7847	0.775	0.7170	0.7248	0.7500	0.7838	0.7213	0.8197
0.0049	14.0	6706	0.4581	0.6520	0.7140	0.6816	0.9333	0.8923	0.8120	0.8450	0.5439	0.4948	0.5641	0.6087	0.4507	0.6037	0.5079	0.6164	0.6581	0.7822	0.8202	0.7698	0.7778	0.7215	0.7383	0.7273	0.8333	0.7333	0.8197
0.0041	15.0	7185	0.4646	0.6466	0.7182	0.6805	0.9329	0.8914	0.8140	0.8424	0.5357	0.4859	0.5116	0.6049	0.4932	0.6078	0.5150	0.6102	0.6638	0.7895	0.8343	0.7873	0.7810	0.7125	0.7383	0.7126	0.7838	0.7333	0.8197

Framework versions

Transformers 4.29.2
Pytorch 2.0.1
Datasets 2.12.0
Tokenizers 0.13.2

biobert-finetuned-pico

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

NSDT 3DConvert

UnrealSynth

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