NeRUBioS_RoBERTa_Training_Testing

This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-biomedical-clinical-es on an adaptation of the Nubes dataset called NeRUBioS. Training and Testing Datasets have 13832 and 2765 samples, respectively. This is a result of the PhD dissertation of Antonio Tamayo. It achieves the following results on the evaluation set:

Loss: 0.3540
Negref Precision: 0.5522
Negref Recall: 0.6138
Negref F1: 0.5814
Neg Precision: 0.9530
Neg Recall: 0.9684
Neg F1: 0.9606
Nsco Precision: 0.8812
Nsco Recall: 0.9092
Nsco F1: 0.8950
Unc Precision: 0.8208
Unc Recall: 0.8923
Unc F1: 0.8550
Usco Precision: 0.6786
Usco Recall: 0.7815
Usco F1: 0.7264
Precision: 0.8223
Recall: 0.8680
F1: 0.8446
Accuracy: 0.9497

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: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 12

Training results

Training Loss	Epoch	Step	Validation Loss	Negref Precision	Negref Recall	Negref F1	Neg Precision	Neg Recall	Neg F1	Nsco Precision	Nsco Recall	Nsco F1	Unc Precision	Unc Recall	Unc F1	Usco Precision	Usco Recall	Usco F1	Precision	Recall	F1	Accuracy
0.1945	1.0	1729	0.2026	0.4356	0.4919	0.4621	0.9246	0.9558	0.9399	0.8264	0.9039	0.8634	0.7192	0.8077	0.7609	0.6125	0.7069	0.6563	0.7610	0.8276	0.7929	0.9383
0.1173	2.0	3458	0.2070	0.4961	0.5595	0.5259	0.9314	0.9635	0.9472	0.8552	0.9070	0.8803	0.8221	0.8769	0.8486	0.6472	0.7404	0.6906	0.7953	0.8516	0.8225	0.9423
0.0938	3.0	5187	0.2145	0.5006	0.6182	0.5532	0.9348	0.9670	0.9506	0.8679	0.9047	0.8859	0.7991	0.8667	0.8315	0.6316	0.7404	0.6817	0.7919	0.8607	0.8249	0.9435
0.0543	4.0	6916	0.2151	0.5267	0.6226	0.5707	0.9425	0.9677	0.9550	0.8588	0.9017	0.8797	0.8076	0.8718	0.8385	0.6733	0.7789	0.7223	0.8036	0.8647	0.8330	0.9460
0.0403	5.0	8645	0.2669	0.5627	0.5536	0.5581	0.9470	0.9670	0.9569	0.8697	0.9085	0.8886	0.8153	0.8487	0.8317	0.7009	0.7712	0.7344	0.8265	0.8526	0.8393	0.9472
0.0325	6.0	10374	0.2608	0.5207	0.6094	0.5616	0.9503	0.9677	0.9589	0.8751	0.9062	0.8904	0.7977	0.8795	0.8366	0.6771	0.7815	0.7255	0.8093	0.8650	0.8362	0.9476
0.0205	7.0	12103	0.3006	0.5646	0.6285	0.5949	0.9419	0.9684	0.9550	0.8671	0.9130	0.8895	0.7958	0.8795	0.8356	0.6594	0.7815	0.7153	0.8125	0.8704	0.8404	0.9484
0.0146	8.0	13832	0.3124	0.5653	0.5844	0.5747	0.9569	0.9677	0.9623	0.8896	0.9085	0.8990	0.8085	0.8769	0.8413	0.6966	0.7969	0.7434	0.8320	0.8628	0.8471	0.9496
0.0095	9.0	15561	0.3160	0.5459	0.6285	0.5843	0.9544	0.9705	0.9624	0.8770	0.9115	0.8939	0.8255	0.8974	0.8600	0.6868	0.7892	0.7344	0.8202	0.8730	0.8458	0.9511
0.0069	10.0	17290	0.3415	0.5461	0.6094	0.5760	0.9536	0.9677	0.9606	0.8810	0.9070	0.8938	0.8182	0.9	0.8571	0.6785	0.7866	0.7286	0.8207	0.8676	0.8435	0.9505
0.0047	11.0	19019	0.3483	0.5481	0.6197	0.5817	0.9517	0.9684	0.9600	0.8776	0.9107	0.8938	0.8160	0.8872	0.8501	0.6909	0.7815	0.7334	0.8204	0.8690	0.8440	0.9490
0.0018	12.0	20748	0.3540	0.5522	0.6138	0.5814	0.9530	0.9684	0.9606	0.8812	0.9092	0.8950	0.8208	0.8923	0.8550	0.6786	0.7815	0.7264	0.8223	0.8680	0.8446	0.9497

Framework versions

Transformers 4.28.1
Pytorch 2.0.0+cu118
Datasets 2.11.0
Tokenizers 0.13.3

NeRUBioS_RoBERTa_Training_Testing

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

NSDT 3DConvert

UnrealSynth

DreamTexture.js