ner_ANAT_DISO – AI Model by roscazo | AlphaNeural AI

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roscazo/
ner_ANAT_DISO

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token-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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ner_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: 0.0746
Anat Precision: 0.6512
Anat Recall: 0.6573
Anat F1: 0.6542
Anat Number: 534
Diso Precision: 0.8727
Diso Recall: 0.8844
Diso F1: 0.8785
Diso Number: 2915
Overall Precision: 0.8385
Overall Recall: 0.8492
Overall F1: 0.8438
Overall Accuracy: 0.9838

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: 2e-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: 4

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
0.0625	1.0	1682	0.0591	0.5407	0.6723	0.5993	534	0.8516	0.8624	0.8570	2915	0.7945	0.8330	0.8133	0.9808
0.0397	2.0	3364	0.0633	0.6237	0.6798	0.6505	534	0.8576	0.8820	0.8696	2915	0.8196	0.8507	0.8348	0.9826
0.0181	3.0	5046	0.0698	0.6452	0.6948	0.6691	534	0.8670	0.8878	0.8773	2915	0.8312	0.8579	0.8443	0.9833
0.0121	4.0	6728	0.0746	0.6512	0.6573	0.6542	534	0.8727	0.8844	0.8785	2915	0.8385	0.8492	0.8438	0.9838

Framework versions

Transformers 4.25.1
Pytorch 1.13.0+cu116
Datasets 2.8.0
Tokenizers 0.13.2