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# modelBsc62

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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1964
- Precision: 0.6650
- Recall: 0.6422
- F1: 0.6534
- Accuracy: 0.9693

## 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: 6e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 32

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 29   | 0.3017          | 0.0       | 0.0    | 0.0    | 0.9362   |
| No log        | 2.0   | 58   | 0.2840          | 0.0       | 0.0    | 0.0    | 0.9362   |
| No log        | 3.0   | 87   | 0.2159          | 0.15      | 0.0147 | 0.0268 | 0.9421   |
| No log        | 4.0   | 116  | 0.1681          | 0.4189    | 0.1520 | 0.2230 | 0.9504   |
| No log        | 5.0   | 145  | 0.1433          | 0.4872    | 0.2794 | 0.3551 | 0.9578   |
| No log        | 6.0   | 174  | 0.1490          | 0.5532    | 0.3824 | 0.4522 | 0.9585   |
| No log        | 7.0   | 203  | 0.1397          | 0.5093    | 0.5392 | 0.5238 | 0.9605   |
| No log        | 8.0   | 232  | 0.1473          | 0.5289    | 0.5833 | 0.5548 | 0.9631   |
| No log        | 9.0   | 261  | 0.1499          | 0.5888    | 0.5686 | 0.5786 | 0.9656   |
| No log        | 10.0  | 290  | 0.1638          | 0.6728    | 0.5343 | 0.5956 | 0.9674   |
| No log        | 11.0  | 319  | 0.1507          | 0.5893    | 0.6471 | 0.6168 | 0.9658   |
| No log        | 12.0  | 348  | 0.1765          | 0.6550    | 0.5490 | 0.5973 | 0.9678   |
| No log        | 13.0  | 377  | 0.1671          | 0.6294    | 0.6078 | 0.6185 | 0.9678   |
| No log        | 14.0  | 406  | 0.1616          | 0.6154    | 0.6275 | 0.6214 | 0.9674   |
| No log        | 15.0  | 435  | 0.1860          | 0.6078    | 0.6078 | 0.6078 | 0.9669   |
| No log        | 16.0  | 464  | 0.1697          | 0.6906    | 0.6127 | 0.6494 | 0.9691   |
| No log        | 17.0  | 493  | 0.1861          | 0.6667    | 0.6373 | 0.6516 | 0.9685   |
| 0.089         | 18.0  | 522  | 0.1790          | 0.6279    | 0.6618 | 0.6444 | 0.9674   |
| 0.089         | 19.0  | 551  | 0.1811          | 0.6685    | 0.5833 | 0.6230 | 0.9681   |
| 0.089         | 20.0  | 580  | 0.1805          | 0.6505    | 0.6569 | 0.6537 | 0.9688   |
| 0.089         | 21.0  | 609  | 0.1863          | 0.6578    | 0.6029 | 0.6292 | 0.9686   |
| 0.089         | 22.0  | 638  | 0.1881          | 0.6811    | 0.6176 | 0.6478 | 0.9691   |
| 0.089         | 23.0  | 667  | 0.1896          | 0.6502    | 0.6471 | 0.6486 | 0.9690   |
| 0.089         | 24.0  | 696  | 0.1910          | 0.6717    | 0.6520 | 0.6617 | 0.9693   |
| 0.089         | 25.0  | 725  | 0.1944          | 0.6471    | 0.6471 | 0.6471 | 0.9690   |
| 0.089         | 26.0  | 754  | 0.1946          | 0.6633    | 0.6373 | 0.6500 | 0.9693   |
| 0.089         | 27.0  | 783  | 0.1950          | 0.6599    | 0.6373 | 0.6484 | 0.9690   |
| 0.089         | 28.0  | 812  | 0.1950          | 0.6667    | 0.6373 | 0.6516 | 0.9693   |
| 0.089         | 29.0  | 841  | 0.1961          | 0.6520    | 0.6520 | 0.6520 | 0.9693   |
| 0.089         | 30.0  | 870  | 0.1968          | 0.6633    | 0.6471 | 0.6551 | 0.9693   |
| 0.089         | 31.0  | 899  | 0.1963          | 0.6650    | 0.6422 | 0.6534 | 0.9693   |
| 0.089         | 32.0  | 928  | 0.1964          | 0.6650    | 0.6422 | 0.6534 | 0.9693   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "modelBsc62", "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 

modelBsc62
==========


This model is a fine-tuned version of PlanTL-GOB-ES/bsc-bio-ehr-es on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1964
* Precision: 0.6650
* Recall: 0.6422
* F1: 0.6534
* Accuracy: 0.9693


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: 6e-05
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 32


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-05
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 32 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* 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: 6e-05
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 32 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3