testlink-class-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: 0.2116
• Precision: 0.7516
• Recall: 0.6901
• F1: 0.7195
• Accuracy: 0.9758

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: 7.5e-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	0.2388	0.7315	0.6374	0.6813	0.9739
No log	2.0	58	0.1956	0.6169	0.7251	0.6667	0.9723
No log	3.0	87	0.1637	0.6302	0.7076	0.6667	0.9730
No log	4.0	116	0.2107	0.6810	0.6491	0.6647	0.9741
No log	5.0	145	0.1987	0.6981	0.6491	0.6727	0.9745
No log	6.0	174	0.1524	0.7355	0.6667	0.6994	0.9756
No log	7.0	203	0.1933	0.7664	0.6140	0.6818	0.9750
No log	8.0	232	0.2150	0.7836	0.6140	0.6885	0.9747
No log	9.0	261	0.1700	0.7405	0.6842	0.7112	0.9761
No log	10.0	290	0.1626	0.6142	0.7076	0.6576	0.9730
No log	11.0	319	0.1826	0.7035	0.7076	0.7055	0.9761
No log	12.0	348	0.1724	0.6802	0.6842	0.6822	0.9758
No log	13.0	377	0.1823	0.7852	0.6199	0.6928	0.9741
No log	14.0	406	0.1833	0.7284	0.6901	0.7087	0.9761
No log	15.0	435	0.1816	0.5853	0.7427	0.6546	0.9701
No log	16.0	464	0.2084	0.7770	0.6725	0.7210	0.9761
No log	17.0	493	0.2043	0.7069	0.7193	0.7130	0.9748
0.0022	18.0	522	0.1996	0.6541	0.7076	0.6798	0.9741
0.0022	19.0	551	0.2013	0.7484	0.6959	0.7212	0.9763
0.0022	20.0	580	0.1933	0.7159	0.7368	0.7262	0.9770
0.0022	21.0	609	0.1931	0.7101	0.7018	0.7059	0.9759
0.0022	22.0	638	0.1946	0.7052	0.7135	0.7093	0.9759
0.0022	23.0	667	0.1968	0.6936	0.7018	0.6977	0.9752
0.0022	24.0	696	0.2076	0.7296	0.6784	0.7030	0.9754
0.0022	25.0	725	0.2076	0.7296	0.6784	0.7030	0.9756
0.0022	26.0	754	0.2051	0.6941	0.6901	0.6921	0.9747
0.0022	27.0	783	0.2106	0.7342	0.6784	0.7052	0.9752
0.0022	28.0	812	0.2093	0.7312	0.6842	0.7069	0.9752
0.0022	29.0	841	0.2112	0.7516	0.6901	0.7195	0.9758
0.0022	30.0	870	0.2116	0.7516	0.6901	0.7195	0.9758

Framework versions

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