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ajtamayoh/
RE_NegREF_NSD_Nubes_Training_Test_dataset_roberta-base-biomedical-clinical-es_fine_tuned_v3

Name: RE_NegREF_NSD_Nubes_Training_Test_dataset_roberta-base-biomedical-clinical-es_fine_tuned_v3
Availability: InStock

generated_from_trainer

apache-2.0

autotrain_compatible

endpoints_compatible

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RE_NegREF_NSD_Nubes_Training_Test_dataset_roberta-base-biomedical-clinical-es_fine_tuned_v3

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 (For this model, uncertainty labels were not considered). 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: 0.3617
Negref Precision: 0.5916
Negref Recall: 0.6021
Negref F1: 0.5968
Neg Precision: 0.9531
Neg Recall: 0.9698
Neg F1: 0.9614
Nsco Precision: 0.8976
Nsco Recall: 0.9145
Nsco F1: 0.9060
Precision: 0.8598
Recall: 0.8754
F1: 0.8676

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	Precision	Recall	F1
0.0026	1.0	1729	0.3442	0.5689	0.5639	0.5664	0.9602	0.9663	0.9632	0.8765	0.9017	0.8889	0.8512	0.8614	0.8563
0.0098	2.0	3458	0.2580	0.5198	0.5771	0.5470	0.9254	0.9761	0.9501	0.8796	0.9123	0.8957	0.8236	0.8722	0.8472
0.0172	3.0	5187	0.2335	0.5618	0.6344	0.5959	0.9524	0.9698	0.9610	0.8908	0.9070	0.8988	0.8449	0.8789	0.8616
0.0082	4.0	6916	0.2568	0.5819	0.6520	0.6150	0.9563	0.9670	0.9616	0.8896	0.9085	0.8990	0.8505	0.8818	0.8659
0.0054	5.0	8645	0.3267	0.5882	0.6123	0.6000	0.9601	0.9628	0.9614	0.9048	0.9062	0.9055	0.8628	0.8713	0.8670
0.0069	6.0	10374	0.3017	0.5559	0.6138	0.5834	0.9556	0.9677	0.9616	0.8945	0.9107	0.9025	0.8475	0.8754	0.8612
0.0035	7.0	12103	0.3325	0.5541	0.6241	0.5870	0.9448	0.9740	0.9592	0.8859	0.9107	0.8982	0.8392	0.8801	0.8591
0.0016	8.0	13832	0.3345	0.5851	0.6109	0.5977	0.9537	0.9691	0.9613	0.8981	0.9138	0.9059	0.8576	0.8766	0.8670
0.0031	9.0	15561	0.3414	0.5974	0.6035	0.6004	0.9575	0.9642	0.9608	0.9094	0.9107	0.9101	0.8671	0.8719	0.8695
0.0014	10.0	17290	0.3479	0.5977	0.6153	0.6064	0.9518	0.9698	0.9607	0.8901	0.9130	0.9014	0.8572	0.8774	0.8672
0.0005	11.0	19019	0.3542	0.5892	0.6065	0.5977	0.9524	0.9698	0.9610	0.8970	0.9153	0.9060	0.8583	0.8766	0.8673
0.0002	12.0	20748	0.3617	0.5916	0.6021	0.5968	0.9531	0.9698	0.9614	0.8976	0.9145	0.9060	0.8598	0.8754	0.8676

Framework versions

Transformers 4.28.1
Pytorch 2.0.0+cu118
Datasets 2.11.0
Tokenizers 0.13.3

ajtamayoh/RE_NegREF_NSD_Nubes_Training_Test_dataset_roberta-base-biomedical-clinical-es_fine_tuned_v3

RE_NegREF_NSD_Nubes_Training_Test_dataset_roberta-base-biomedical-clinical-es_fine_tuned_v3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

ajtamayoh/
RE_NegREF_NSD_Nubes_Training_Test_dataset_roberta-base-biomedical-clinical-es_fine_tuned_v3