prueba5

This model is a fine-tuned version of PlanTL-GOB-ES/bsc-bio-ehr-es-pharmaconer on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.2442
• Precision: 0.5258
• Recall: 0.5574
• F1: 0.5411
• Accuracy: 0.9609

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: 2.75e-05
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	57	0.2341	0.0	0.0	0.0	0.9488
No log	2.0	114	0.2411	0.0	0.0	0.0	0.9488
No log	3.0	171	0.2150	0.0385	0.0055	0.0096	0.9410
No log	4.0	228	0.1885	0.25	0.0929	0.1355	0.9500
No log	5.0	285	0.1730	0.3830	0.1967	0.2599	0.9524
No log	6.0	342	0.1591	0.5098	0.2842	0.3649	0.9581
No log	7.0	399	0.1665	0.5405	0.3279	0.4082	0.9609
No log	8.0	456	0.1856	0.5294	0.4918	0.5099	0.9604
0.1706	9.0	513	0.1727	0.5	0.5191	0.5094	0.9611
0.1706	10.0	570	0.1717	0.5669	0.4863	0.5235	0.9639
0.1706	11.0	627	0.1913	0.5024	0.5628	0.5309	0.9601
0.1706	12.0	684	0.1793	0.515	0.5628	0.5379	0.9619
0.1706	13.0	741	0.2009	0.5679	0.5027	0.5333	0.9618
0.1706	14.0	798	0.2043	0.5333	0.5683	0.5503	0.9604
0.1706	15.0	855	0.2052	0.5486	0.5246	0.5363	0.9629
0.1706	16.0	912	0.2234	0.5183	0.5410	0.5294	0.9581
0.1706	17.0	969	0.2157	0.5424	0.5246	0.5333	0.9616
0.0202	18.0	1026	0.2207	0.5025	0.5574	0.5285	0.9596
0.0202	19.0	1083	0.2297	0.5025	0.5410	0.5211	0.9573
0.0202	20.0	1140	0.2264	0.5131	0.5355	0.5241	0.9593
0.0202	21.0	1197	0.2300	0.5181	0.5464	0.5319	0.9593
0.0202	22.0	1254	0.2348	0.5241	0.5355	0.5297	0.9604
0.0202	23.0	1311	0.2372	0.5196	0.5792	0.5478	0.9588
0.0202	24.0	1368	0.2349	0.5319	0.5464	0.5391	0.9613
0.0202	25.0	1425	0.2353	0.5312	0.5574	0.544	0.9619
0.0202	26.0	1482	0.2388	0.5489	0.5519	0.5504	0.9614
0.0044	27.0	1539	0.2396	0.5243	0.5301	0.5272	0.9618
0.0044	28.0	1596	0.2442	0.5152	0.5574	0.5354	0.9603
0.0044	29.0	1653	0.2444	0.5178	0.5574	0.5368	0.9604
0.0044	30.0	1710	0.2442	0.5258	0.5574	0.5411	0.9609

Framework versions

• Transformers 4.27.4
• Pytorch 1.13.1+cu116
• Datasets 2.11.0
• Tokenizers 0.13.2