metadata

language:
  - es
license: openrail
tags:
  - generated_from_trainer
model-index:
  - name: nominal-groups-recognition-medical-disease-competencia2-bert-medical-ner
    results: []

nominal-groups-recognition-medical-disease-competencia2-bert-medical-ner

This model is a fine-tuned version of ukkendane/bert-medical-ner on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.3607
Body Part Precision: 0.6555
Body Part Recall: 0.7094
Body Part F1: 0.6814
Body Part Number: 413
Disease Precision: 0.6835
Disease Recall: 0.7067
Disease F1: 0.6949
Disease Number: 975
Family Member Precision: 1.0
Family Member Recall: 0.6
Family Member F1: 0.7500
Family Member Number: 30
Medication Precision: 0.7647
Medication Recall: 0.6989
Medication F1: 0.7303
Medication Number: 93
Procedure Precision: 0.5385
Procedure Recall: 0.5402
Procedure F1: 0.5393
Procedure Number: 311
Overall Precision: 0.6594
Overall Recall: 0.6767
Overall F1: 0.6679
Overall Accuracy: 0.9079

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: 13
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Body Part Precision	Body Part Recall	Body Part F1	Body Part Number	Disease Precision	Disease Recall	Disease F1	Disease Number	Family Member Precision	Family Member Recall	Family Member F1	Family Member Number	Medication Precision	Medication Recall	Medication F1	Medication Number	Procedure Precision	Procedure Recall	Procedure F1	Procedure Number	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.4541	1.0	8025	0.3607	0.6555	0.7094	0.6814	413	0.6835	0.7067	0.6949	975	1.0	0.6	0.7500	30	0.7647	0.6989	0.7303	93	0.5385	0.5402	0.5393	311	0.6594	0.6767	0.6679	0.9079
0.3149	2.0	16050	0.3607	0.6555	0.7094	0.6814	413	0.6835	0.7067	0.6949	975	1.0	0.6	0.7500	30	0.7647	0.6989	0.7303	93	0.5385	0.5402	0.5393	311	0.6594	0.6767	0.6679	0.9079
0.3161	3.0	24075	0.3607	0.6555	0.7094	0.6814	413	0.6835	0.7067	0.6949	975	1.0	0.6	0.7500	30	0.7647	0.6989	0.7303	93	0.5385	0.5402	0.5393	311	0.6594	0.6767	0.6679	0.9079
0.3181	4.0	32100	0.3607	0.6555	0.7094	0.6814	413	0.6835	0.7067	0.6949	975	1.0	0.6	0.7500	30	0.7647	0.6989	0.7303	93	0.5385	0.5402	0.5393	311	0.6594	0.6767	0.6679	0.9079
0.3164	5.0	40125	0.3607	0.6555	0.7094	0.6814	413	0.6835	0.7067	0.6949	975	1.0	0.6	0.7500	30	0.7647	0.6989	0.7303	93	0.5385	0.5402	0.5393	311	0.6594	0.6767	0.6679	0.9079

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu117
Datasets 2.13.1
Tokenizers 0.13.3