CTEBMSP_ANAT_DISO – AI Model by roscazo | AlphaNeural AI

You can deploy this model and start earning money today!

roscazo/
CTEBMSP_ANAT_DISO

like

0

token-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

Views

No views yet

CTEBMSP_ANAT_DISO

This model is a fine-tuned version of PlanTL-GOB-ES/bsc-bio-ehr-es on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0909
Anat Precision: 0.7522
Anat Recall: 0.7147
Anat F1: 0.7330
Anat Number: 361
Diso Precision: 0.8915
Diso Recall: 0.8919
Diso F1: 0.8917
Diso Number: 2645
Overall Precision: 0.8755
Overall Recall: 0.8706
Overall F1: 0.8731
Overall Accuracy: 0.9873

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

Training results

Training Loss	Epoch	Step	Validation Loss	Anat Precision	Anat Recall	Anat F1	Anat Number	Diso Precision	Diso Recall	Diso F1	Diso Number	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.0592	1.0	2133	0.0506	0.6950	0.4986	0.5806	361	0.8635	0.8609	0.8622	2645	0.8484	0.8174	0.8326	0.9843
0.0323	2.0	4266	0.0583	0.7899	0.6039	0.6845	361	0.8780	0.8817	0.8798	2645	0.8697	0.8483	0.8589	0.9858
0.0201	3.0	6399	0.0580	0.6565	0.7147	0.6844	361	0.8598	0.8764	0.8680	2645	0.8339	0.8570	0.8453	0.9851
0.0121	4.0	8532	0.0758	0.7240	0.6759	0.6991	361	0.8976	0.8752	0.8863	2645	0.8776	0.8513	0.8642	0.9863
0.0078	5.0	10665	0.0814	0.7219	0.7119	0.7169	361	0.8776	0.8975	0.8875	2645	0.8595	0.8752	0.8673	0.9862
0.0031	6.0	12798	0.0974	0.7599	0.6399	0.6947	361	0.8895	0.8915	0.8905	2645	0.8761	0.8613	0.8686	0.9867
0.002	7.0	14931	0.0980	0.7143	0.6787	0.6960	361	0.8813	0.8957	0.8884	2645	0.8624	0.8696	0.8660	0.9860
0.0005	8.0	17064	0.0909	0.7522	0.7147	0.7330	361	0.8915	0.8919	0.8917	2645	0.8755	0.8706	0.8731	0.9873

Framework versions

Transformers 4.25.1
Pytorch 1.13.0+cu116
Datasets 2.8.0
Tokenizers 0.13.2