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# CTEBMSP_ANAT_DISO

This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-ehr-es](https://huggingface.co/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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "CTEBMSP_ANAT_DISO", "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 

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



### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2



### 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 ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2
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: 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 ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2
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: 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### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2