prueba3

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: 0.2158
• Precision: 0.7162
• Recall: 0.6335
• F1: 0.6723
• Accuracy: 0.9737

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: 32
• 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	29	0.2562	0.7732	0.5976	0.6742	0.9719
No log	2.0	58	0.2526	0.705	0.5618	0.6253	0.9704
No log	3.0	87	0.2187	0.6833	0.6534	0.6680	0.9705
No log	4.0	116	0.2205	0.6583	0.6295	0.6436	0.9715
No log	5.0	145	0.2161	0.7162	0.6534	0.6833	0.9712
No log	6.0	174	0.2293	0.6977	0.5976	0.6438	0.9722
No log	7.0	203	0.2207	0.6972	0.6056	0.6482	0.9724
No log	8.0	232	0.2343	0.6781	0.6295	0.6529	0.9707
No log	9.0	261	0.2212	0.7115	0.5896	0.6449	0.9730
No log	10.0	290	0.2171	0.7260	0.6016	0.6580	0.9734
No log	11.0	319	0.2191	0.6851	0.6414	0.6626	0.9725
No log	12.0	348	0.2101	0.7056	0.6494	0.6763	0.9740
No log	13.0	377	0.2227	0.7240	0.6375	0.6780	0.9732
No log	14.0	406	0.2226	0.7442	0.6375	0.6867	0.9739
No log	15.0	435	0.2247	0.7339	0.6375	0.6823	0.9739
No log	16.0	464	0.2167	0.6983	0.6454	0.6708	0.9729
No log	17.0	493	0.2220	0.7281	0.6295	0.6752	0.9732
0.0005	18.0	522	0.2294	0.7299	0.6135	0.6667	0.9725
0.0005	19.0	551	0.2104	0.6949	0.6534	0.6735	0.9722
0.0005	20.0	580	0.2103	0.7240	0.6375	0.6780	0.9730
0.0005	21.0	609	0.2092	0.7137	0.6454	0.6778	0.9735
0.0005	22.0	638	0.2091	0.7181	0.6494	0.6820	0.9737
0.0005	23.0	667	0.2081	0.7162	0.6534	0.6833	0.9735
0.0005	24.0	696	0.2198	0.7264	0.6135	0.6652	0.9722
0.0005	25.0	725	0.2206	0.7290	0.6215	0.6710	0.9725
0.0005	26.0	754	0.2194	0.7256	0.6215	0.6695	0.9735
0.0005	27.0	783	0.2220	0.7290	0.6215	0.6710	0.9739
0.0005	28.0	812	0.2230	0.7290	0.6215	0.6710	0.9735
0.0005	29.0	841	0.2163	0.7182	0.6295	0.6709	0.9737
0.0005	30.0	870	0.2158	0.7162	0.6335	0.6723	0.9737

Framework versions

• Transformers 4.27.3
• Pytorch 1.13.1+cu116
• Datasets 2.10.1
• Tokenizers 0.13.2