prueba2

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: 0.1829
• Precision: 0.7232
• Recall: 0.6454
• F1: 0.6821
• Accuracy: 0.9744

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: 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: 32

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	29	0.1726	0.7014	0.5896	0.6407	0.9720
No log	2.0	58	0.1712	0.6090	0.6454	0.6267	0.9679
No log	3.0	87	0.1665	0.6746	0.6773	0.6759	0.9720
No log	4.0	116	0.1945	0.7042	0.5976	0.6466	0.9719
No log	5.0	145	0.1850	0.6927	0.6016	0.6439	0.9724
No log	6.0	174	0.1872	0.6570	0.6335	0.6450	0.9697
No log	7.0	203	0.2014	0.7527	0.5578	0.6407	0.9730
No log	8.0	232	0.1696	0.6706	0.6733	0.6720	0.9727
No log	9.0	261	0.1743	0.6820	0.6494	0.6653	0.9730
No log	10.0	290	0.1686	0.6735	0.6574	0.6653	0.9730
No log	11.0	319	0.1868	0.6934	0.5857	0.6350	0.9712
No log	12.0	348	0.1930	0.7089	0.6016	0.6509	0.9727
No log	13.0	377	0.1826	0.7087	0.6494	0.6778	0.9730
No log	14.0	406	0.1920	0.7103	0.6056	0.6538	0.9722
No log	15.0	435	0.1848	0.6402	0.6733	0.6563	0.9712
No log	16.0	464	0.1843	0.6822	0.6414	0.6612	0.9734
No log	17.0	493	0.1874	0.7009	0.6255	0.6611	0.9730
0.0016	18.0	522	0.1844	0.6736	0.6494	0.6613	0.9730
0.0016	19.0	551	0.1850	0.7273	0.6375	0.6794	0.9744
0.0016	20.0	580	0.1737	0.7179	0.6693	0.6928	0.9749
0.0016	21.0	609	0.1798	0.7376	0.6494	0.6907	0.9747
0.0016	22.0	638	0.1797	0.7174	0.6574	0.6861	0.9739
0.0016	23.0	667	0.1783	0.7046	0.6653	0.6844	0.9742
0.0016	24.0	696	0.1784	0.7301	0.6574	0.6918	0.9745
0.0016	25.0	725	0.1818	0.7352	0.6414	0.6851	0.9745
0.0016	26.0	754	0.1823	0.7419	0.6414	0.6880	0.9745
0.0016	27.0	783	0.1786	0.7205	0.6574	0.6875	0.9749
0.0016	28.0	812	0.1781	0.7051	0.6574	0.6804	0.9734
0.0016	29.0	841	0.1802	0.7181	0.6494	0.6820	0.9744
0.0016	30.0	870	0.1801	0.7174	0.6574	0.6861	0.9749
0.0016	31.0	899	0.1824	0.7232	0.6454	0.6821	0.9745
0.0016	32.0	928	0.1829	0.7232	0.6454	0.6821	0.9744

Framework versions

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