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# NeRUBioS_RoBERTa_Training_Testing

This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-biomedical-clinical-es](https://huggingface.co/PlanTL-GOB-ES/roberta-base-biomedical-clinical-es) on an adaptation of the Nubes dataset called NeRUBioS. Training and Testing Datasets have 13832 and 2765 samples, respectively. This is a result of the PhD dissertation of Antonio Tamayo.
It achieves the following results on the evaluation set:
- Loss: 0.3540
- Negref Precision: 0.5522
- Negref Recall: 0.6138
- Negref F1: 0.5814
- Neg Precision: 0.9530
- Neg Recall: 0.9684
- Neg F1: 0.9606
- Nsco Precision: 0.8812
- Nsco Recall: 0.9092
- Nsco F1: 0.8950
- Unc Precision: 0.8208
- Unc Recall: 0.8923
- Unc F1: 0.8550
- Usco Precision: 0.6786
- Usco Recall: 0.7815
- Usco F1: 0.7264
- Precision: 0.8223
- Recall: 0.8680
- F1: 0.8446
- Accuracy: 0.9497

## 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: 12

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Negref Precision | Negref Recall | Negref F1 | Neg Precision | Neg Recall | Neg F1 | Nsco Precision | Nsco Recall | Nsco F1 | Unc Precision | Unc Recall | Unc F1 | Usco Precision | Usco Recall | Usco F1 | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:|:---------:|:------:|:------:|:--------:|
| 0.1945        | 1.0   | 1729  | 0.2026          | 0.4356           | 0.4919        | 0.4621    | 0.9246        | 0.9558     | 0.9399 | 0.8264         | 0.9039      | 0.8634  | 0.7192        | 0.8077     | 0.7609 | 0.6125         | 0.7069      | 0.6563  | 0.7610    | 0.8276 | 0.7929 | 0.9383   |
| 0.1173        | 2.0   | 3458  | 0.2070          | 0.4961           | 0.5595        | 0.5259    | 0.9314        | 0.9635     | 0.9472 | 0.8552         | 0.9070      | 0.8803  | 0.8221        | 0.8769     | 0.8486 | 0.6472         | 0.7404      | 0.6906  | 0.7953    | 0.8516 | 0.8225 | 0.9423   |
| 0.0938        | 3.0   | 5187  | 0.2145          | 0.5006           | 0.6182        | 0.5532    | 0.9348        | 0.9670     | 0.9506 | 0.8679         | 0.9047      | 0.8859  | 0.7991        | 0.8667     | 0.8315 | 0.6316         | 0.7404      | 0.6817  | 0.7919    | 0.8607 | 0.8249 | 0.9435   |
| 0.0543        | 4.0   | 6916  | 0.2151          | 0.5267           | 0.6226        | 0.5707    | 0.9425        | 0.9677     | 0.9550 | 0.8588         | 0.9017      | 0.8797  | 0.8076        | 0.8718     | 0.8385 | 0.6733         | 0.7789      | 0.7223  | 0.8036    | 0.8647 | 0.8330 | 0.9460   |
| 0.0403        | 5.0   | 8645  | 0.2669          | 0.5627           | 0.5536        | 0.5581    | 0.9470        | 0.9670     | 0.9569 | 0.8697         | 0.9085      | 0.8886  | 0.8153        | 0.8487     | 0.8317 | 0.7009         | 0.7712      | 0.7344  | 0.8265    | 0.8526 | 0.8393 | 0.9472   |
| 0.0325        | 6.0   | 10374 | 0.2608          | 0.5207           | 0.6094        | 0.5616    | 0.9503        | 0.9677     | 0.9589 | 0.8751         | 0.9062      | 0.8904  | 0.7977        | 0.8795     | 0.8366 | 0.6771         | 0.7815      | 0.7255  | 0.8093    | 0.8650 | 0.8362 | 0.9476   |
| 0.0205        | 7.0   | 12103 | 0.3006          | 0.5646           | 0.6285        | 0.5949    | 0.9419        | 0.9684     | 0.9550 | 0.8671         | 0.9130      | 0.8895  | 0.7958        | 0.8795     | 0.8356 | 0.6594         | 0.7815      | 0.7153  | 0.8125    | 0.8704 | 0.8404 | 0.9484   |
| 0.0146        | 8.0   | 13832 | 0.3124          | 0.5653           | 0.5844        | 0.5747    | 0.9569        | 0.9677     | 0.9623 | 0.8896         | 0.9085      | 0.8990  | 0.8085        | 0.8769     | 0.8413 | 0.6966         | 0.7969      | 0.7434  | 0.8320    | 0.8628 | 0.8471 | 0.9496   |
| 0.0095        | 9.0   | 15561 | 0.3160          | 0.5459           | 0.6285        | 0.5843    | 0.9544        | 0.9705     | 0.9624 | 0.8770         | 0.9115      | 0.8939  | 0.8255        | 0.8974     | 0.8600 | 0.6868         | 0.7892      | 0.7344  | 0.8202    | 0.8730 | 0.8458 | 0.9511   |
| 0.0069        | 10.0  | 17290 | 0.3415          | 0.5461           | 0.6094        | 0.5760    | 0.9536        | 0.9677     | 0.9606 | 0.8810         | 0.9070      | 0.8938  | 0.8182        | 0.9        | 0.8571 | 0.6785         | 0.7866      | 0.7286  | 0.8207    | 0.8676 | 0.8435 | 0.9505   |
| 0.0047        | 11.0  | 19019 | 0.3483          | 0.5481           | 0.6197        | 0.5817    | 0.9517        | 0.9684     | 0.9600 | 0.8776         | 0.9107      | 0.8938  | 0.8160        | 0.8872     | 0.8501 | 0.6909         | 0.7815      | 0.7334  | 0.8204    | 0.8690 | 0.8440 | 0.9490   |
| 0.0018        | 12.0  | 20748 | 0.3540          | 0.5522           | 0.6138        | 0.5814    | 0.9530        | 0.9684     | 0.9606 | 0.8812         | 0.9092      | 0.8950  | 0.8208        | 0.8923     | 0.8550 | 0.6786         | 0.7815      | 0.7264  | 0.8223    | 0.8680 | 0.8446 | 0.9497   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "Desde entoces refiere que ha tomado varias tandas de antibioticos sin terminar de ceder la cl\u00ednica (a\u00fan presenta cierta disuria y polaquiuria)."}, {"text": "Dato rese\u00f1able: en los primeros d\u00edas a nuestro cuidado present\u00f3 de nuevo anemizaci\u00f3n (se adjuntan controles anal\u00edticos) que ha ido recuperando sin que haya precisado hemotransfusi\u00f3n (al alta de Had presenta Hgb de 10.2g/dl)."}, {"text": "Paciente con diagn\u00f3stico de ELA en abril de 2015 que presenta desde hace m\u00e1s de dos meses disfagia progresiva, para l\u00edquidos preferentemente, con dos neumon\u00edas por aspiraci\u00f3n, por lo que se programa ingreso para colocaci\u00f3n de sonda de gastrostom\u00eda, realiz\u00e1ndose el d\u00eda 31 de diciembre, sin complicaciones y tolerando posteriormente la dieta por gastrostom\u00eda."}, {"text": "El ecocardiograma doppler color no muestra patologia que justifique los s\u00edntomas y la paciente evoluciona completamente asintom\u00e1tica y estable."}, {"text": "Ultima deposici\u00f3n, esa ma\u00f1ana, de escasa cuantia, sin emisi\u00f3n de gases."}, {"text": "Al alta presentaba hemipresia derecha con actividad en hombro hasta 90\u00ba de AF, mano esp\u00e1stica en flexi\u00f3n sin actividad."}, {"text": "Bases pulmonares, cavidades pleurales, h\u00edgado, bazo, p\u00e1ncreas y suprarrenales sin hallazgos."}, {"text": "Paciente con los antecedentes rese\u00f1ados que ingresa por cuadro de escasas horas de evoluci\u00f3n consistente en exacerbacion de su temblor habitual, que parece haberse hecho generalizado y cuya descripcion es incapaz de precisar."}, {"text": "Resumen de Historia clinica: Paciente ingresado por incremento de la disnea a los esfuerzos, en la urgencia se detecta ACxFA antes no conocida."}, {"text": "Mujer con 5 meses de gestaci\u00f3n que ingresa por cuadro grave debido a bloqueo AV completo, que parece haber ocurrido en la madrugada y cuya sintomatolog\u00eda es incapaz de precisar."}, {"text": "La funci\u00f3n renal ha sido correcta en todo momento con progresiva normalizaci\u00f3n de los indicadores infecciosos (HRF, PCR, VSG). Tras completar antibioterapia endovenosa con vancomicina el 25 de junio, inici\u00f3 el 26 de junio linezolid 600mg cada 12horas, siguiendo las recomendaciones de la unidad de Enfermedades Infecciosas, siendo bien tolerado y sin toxicidad, realiz\u00e1ndose seguimiento anal\u00edtico."}, {"text": "Presentamos un caso de convulsiones asociadas al tratamiento con L-asparaginasa, sin evidencia de eventos cerebrovasculares hemorr\u00e1gicos o tromb\u00f3ticos."}, {"text": "Paladar asim\u00e9trico con desviaci\u00f3n de uvula a la derecha, hiperemico, no abombado."}, {"text": "fecha de nacimiento 11 07 2016 niega ocntacto reicnete covid masculino de 6 anos de edad quien e straido a revision por su padre por presntar cuadro de presentacion al estar en la tarde como alas 13:00hrs en un acto religioso elmenor presenta un eventipo de mareo, un vomito gastroalimentario y afectacion visual, refiere haber ingerido alimentos 3 a 4 mnhoras previos, poero muy baja ingesta de liquidos."}, {"text": "se presneto recuperacion al 100%, el menor se encuentra neuroiologicamnete integro sin focalizaciones glasgow de 15/15 sin focalizaciones, cerebelo integro, retso normal, preocrdio normodinamico con ruidos ritmicos d eintensidad y frec7uencia dnetro d elo normal, no soplos o agregados."}, {"text": "paciente quien presnta cuadro climnico posible asociado a baj ingest ad eliquidos y someterse a evento religioso por tiempo proerfongafdo, en este momento sin afectacion c\u00f1licncia"}, {"text": "El h\u00edgado muestra tama\u00f1o, morfolog\u00eda y valores de atenuaci\u00f3n normales sin que se evidencien lesiones focales."}, {"text": "Test de las fotos: Recuerdo libre 3; Recuerdo facilitado 3; Sin recuerdo 0."}], "model-index": [{"name": "NeRUBioS_RoBERTa_Training_Testing", "results": []}]}
transformers pytorch tensorboard roberta token-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

NeRUBioS\_RoBERTa\_Training\_Testing
====================================


This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-biomedical-clinical-es on an adaptation of the Nubes dataset called NeRUBioS. Training and Testing Datasets have 13832 and 2765 samples, respectively. This is a result of the PhD dissertation of Antonio Tamayo.
It achieves the following results on the evaluation set:


* Loss: 0.3540
* Negref Precision: 0.5522
* Negref Recall: 0.6138
* Negref F1: 0.5814
* Neg Precision: 0.9530
* Neg Recall: 0.9684
* Neg F1: 0.9606
* Nsco Precision: 0.8812
* Nsco Recall: 0.9092
* Nsco F1: 0.8950
* Unc Precision: 0.8208
* Unc Recall: 0.8923
* Unc F1: 0.8550
* Usco Precision: 0.6786
* Usco Recall: 0.7815
* Usco F1: 0.7264
* Precision: 0.8223
* Recall: 0.8680
* F1: 0.8446
* Accuracy: 0.9497


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: 12


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### 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: 12 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### 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: 12 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### 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: 12### Training results### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3