ner-2

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: 0.1618
• Precision: 0.7352
• Recall: 0.6436
• F1: 0.6863
• Accuracy: 0.9712

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: 5e-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	0.3028	0.0	0.0	0.0	0.9220
No log	2.0	58	0.2800	0.0	0.0	0.0	0.9220
No log	3.0	87	0.2136	0.2105	0.0277	0.0489	0.9302
No log	4.0	116	0.1803	0.375	0.0727	0.1217	0.9391
No log	5.0	145	0.1737	0.4923	0.2215	0.3055	0.9462
No log	6.0	174	0.1354	0.6124	0.3772	0.4668	0.9584
No log	7.0	203	0.1399	0.6062	0.4048	0.4855	0.9589
No log	8.0	232	0.1444	0.6220	0.5294	0.5720	0.9623
No log	9.0	261	0.1252	0.6439	0.6194	0.6314	0.9662
No log	10.0	290	0.1757	0.7216	0.4394	0.5462	0.9604
No log	11.0	319	0.1352	0.6707	0.5779	0.6208	0.9667
No log	12.0	348	0.1276	0.6797	0.6021	0.6385	0.9677
No log	13.0	377	0.1542	0.7328	0.5882	0.6526	0.9688
No log	14.0	406	0.1418	0.7192	0.6471	0.6812	0.9712
No log	15.0	435	0.1678	0.7162	0.5502	0.6223	0.9672
No log	16.0	464	0.1559	0.7075	0.6194	0.6605	0.9689
No log	17.0	493	0.1446	0.6568	0.6886	0.6723	0.9681
0.079	18.0	522	0.1582	0.7348	0.5848	0.6513	0.9693
0.079	19.0	551	0.1519	0.6977	0.6228	0.6581	0.9705
0.079	20.0	580	0.1503	0.7251	0.6298	0.6741	0.9703
0.079	21.0	609	0.1585	0.6834	0.6125	0.6460	0.9703
0.079	22.0	638	0.1594	0.7126	0.6263	0.6667	0.9705
0.079	23.0	667	0.1558	0.7008	0.6401	0.6691	0.9703
0.079	24.0	696	0.1570	0.7273	0.6367	0.6790	0.9708
0.079	25.0	725	0.1553	0.7022	0.6609	0.6809	0.9705
0.079	26.0	754	0.1592	0.7148	0.6332	0.6716	0.9701
0.079	27.0	783	0.1579	0.7170	0.6574	0.6859	0.9710
0.079	28.0	812	0.1597	0.7148	0.6505	0.6812	0.9708
0.079	29.0	841	0.1625	0.7309	0.6298	0.6766	0.9705
0.079	30.0	870	0.1618	0.7352	0.6436	0.6863	0.9712

Framework versions

• Transformers 4.28.1
• Pytorch 2.0.0+cu118
• Datasets 2.12.0
• Tokenizers 0.13.3