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pineiden/
nominal-groups-recognition-medical-disease-beto-cmm-competencia2-beto-prescripciones-medicas

Name: nominal-groups-recognition-medical-disease-beto-cmm-competencia2-beto-prescripciones-medicas
Availability: InStock

generated_from_trainer

autotrain_compatible

endpoints_compatible

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nominal-groups-recognition-medical-disease-beto-cmm-competencia2-beto-prescripciones-medicas

This model is a fine-tuned version of ccarvajal/beto-prescripciones-medicas on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.4614
Body Part Precision: 0.3804
Body Part Recall: 0.4697
Body Part F1: 0.4204
Body Part Number: 413
Disease Precision: 0.4680
Disease Recall: 0.5477
Disease F1: 0.5047
Disease Number: 975
Family Member Precision: 1.0
Family Member Recall: 0.6
Family Member F1: 0.7500
Family Member Number: 30
Medication Precision: 0.6364
Medication Recall: 0.0753
Medication F1: 0.1346
Medication Number: 93
Procedure Precision: 0.4139
Procedure Recall: 0.3248
Procedure F1: 0.3640
Procedure Number: 311
Overall Precision: 0.4439
Overall Recall: 0.4687
Overall F1: 0.4560
Overall Accuracy: 0.8689

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
0.5675	1.0	8025	0.4614	0.3804	0.4697	0.4204	413	0.4680	0.5477	0.5047	975	1.0	0.6	0.7500	30	0.6364	0.0753	0.1346	93	0.4139	0.3248	0.3640	311	0.4439	0.4687	0.4560	0.8689
0.4162	2.0	16050	0.4614	0.3804	0.4697	0.4204	413	0.4680	0.5477	0.5047	975	1.0	0.6	0.7500	30	0.6364	0.0753	0.1346	93	0.4139	0.3248	0.3640	311	0.4439	0.4687	0.4560	0.8689
0.4146	3.0	24075	0.4614	0.3804	0.4697	0.4204	413	0.4680	0.5477	0.5047	975	1.0	0.6	0.7500	30	0.6364	0.0753	0.1346	93	0.4139	0.3248	0.3640	311	0.4439	0.4687	0.4560	0.8689
0.4163	4.0	32100	0.4614	0.3804	0.4697	0.4204	413	0.4680	0.5477	0.5047	975	1.0	0.6	0.7500	30	0.6364	0.0753	0.1346	93	0.4139	0.3248	0.3640	311	0.4439	0.4687	0.4560	0.8689
0.4139	5.0	40125	0.4614	0.3804	0.4697	0.4204	413	0.4680	0.5477	0.5047	975	1.0	0.6	0.7500	30	0.6364	0.0753	0.1346	93	0.4139	0.3248	0.3640	311	0.4439	0.4687	0.4560	0.8689

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu117
Datasets 2.13.1
Tokenizers 0.13.3

pineiden/nominal-groups-recognition-medical-disease-beto-cmm-competencia2-beto-prescripciones-medicas

nominal-groups-recognition-medical-disease-beto-cmm-competencia2-beto-prescripciones-medicas

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

pineiden/
nominal-groups-recognition-medical-disease-beto-cmm-competencia2-beto-prescripciones-medicas