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# clinico-bsc-bio-ehr-es

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: 0.9988
- Precision: 0.4916
- Recall: 0.6526
- F1: 0.5608
- Accuracy: 0.8586

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 25   | 1.2185          | 0.0189    | 0.0359 | 0.0247 | 0.6197   |
| No log        | 2.0   | 50   | 0.7442          | 0.1562    | 0.1975 | 0.1744 | 0.7996   |
| No log        | 3.0   | 75   | 0.6502          | 0.2108    | 0.2640 | 0.2344 | 0.8180   |
| No log        | 4.0   | 100  | 0.6404          | 0.3453    | 0.4572 | 0.3935 | 0.8258   |
| No log        | 5.0   | 125  | 0.6131          | 0.3639    | 0.4657 | 0.4085 | 0.8303   |
| No log        | 6.0   | 150  | 0.6123          | 0.3356    | 0.4256 | 0.3752 | 0.8341   |
| No log        | 7.0   | 175  | 0.6093          | 0.3411    | 0.4498 | 0.3880 | 0.8370   |
| No log        | 8.0   | 200  | 0.6198          | 0.3840    | 0.4931 | 0.4318 | 0.8379   |
| No log        | 9.0   | 225  | 0.6490          | 0.3878    | 0.5037 | 0.4382 | 0.8378   |
| No log        | 10.0  | 250  | 0.6653          | 0.3810    | 0.5005 | 0.4327 | 0.8371   |
| No log        | 11.0  | 275  | 0.6456          | 0.3223    | 0.4847 | 0.3872 | 0.8387   |
| No log        | 12.0  | 300  | 0.6475          | 0.3377    | 0.4847 | 0.3981 | 0.8474   |
| No log        | 13.0  | 325  | 0.6620          | 0.4004    | 0.5734 | 0.4716 | 0.8506   |
| No log        | 14.0  | 350  | 0.6798          | 0.3914    | 0.5649 | 0.4624 | 0.8533   |
| No log        | 15.0  | 375  | 0.6880          | 0.3969    | 0.5671 | 0.4670 | 0.8520   |
| No log        | 16.0  | 400  | 0.7012          | 0.4192    | 0.5913 | 0.4906 | 0.8551   |
| No log        | 17.0  | 425  | 0.7224          | 0.4143    | 0.5924 | 0.4876 | 0.8517   |
| No log        | 18.0  | 450  | 0.7510          | 0.4302    | 0.6051 | 0.5029 | 0.8553   |
| No log        | 19.0  | 475  | 0.7388          | 0.4271    | 0.6030 | 0.5    | 0.8532   |
| 0.3652        | 20.0  | 500  | 0.7524          | 0.4374    | 0.6125 | 0.5103 | 0.8569   |
| 0.3652        | 21.0  | 525  | 0.7408          | 0.4427    | 0.6082 | 0.5125 | 0.8580   |
| 0.3652        | 22.0  | 550  | 0.7430          | 0.4448    | 0.6125 | 0.5153 | 0.8610   |
| 0.3652        | 23.0  | 575  | 0.7726          | 0.4193    | 0.6093 | 0.4968 | 0.8582   |
| 0.3652        | 24.0  | 600  | 0.7876          | 0.4316    | 0.6061 | 0.5042 | 0.8562   |
| 0.3652        | 25.0  | 625  | 0.7777          | 0.4620    | 0.6294 | 0.5329 | 0.8595   |
| 0.3652        | 26.0  | 650  | 0.8009          | 0.4521    | 0.6272 | 0.5254 | 0.8570   |
| 0.3652        | 27.0  | 675  | 0.8153          | 0.4583    | 0.6378 | 0.5333 | 0.8572   |
| 0.3652        | 28.0  | 700  | 0.8215          | 0.4611    | 0.6262 | 0.5311 | 0.8580   |
| 0.3652        | 29.0  | 725  | 0.8296          | 0.4699    | 0.6336 | 0.5396 | 0.8595   |
| 0.3652        | 30.0  | 750  | 0.8174          | 0.4597    | 0.6378 | 0.5343 | 0.8603   |
| 0.3652        | 31.0  | 775  | 0.8442          | 0.4765    | 0.6410 | 0.5466 | 0.8599   |
| 0.3652        | 32.0  | 800  | 0.8281          | 0.4646    | 0.6315 | 0.5354 | 0.8610   |
| 0.3652        | 33.0  | 825  | 0.8322          | 0.4583    | 0.6389 | 0.5337 | 0.8591   |
| 0.3652        | 34.0  | 850  | 0.8153          | 0.4559    | 0.6272 | 0.528  | 0.8623   |
| 0.3652        | 35.0  | 875  | 0.8529          | 0.4861    | 0.6294 | 0.5486 | 0.8589   |
| 0.3652        | 36.0  | 900  | 0.8826          | 0.4699    | 0.6272 | 0.5373 | 0.8559   |
| 0.3652        | 37.0  | 925  | 0.8856          | 0.4654    | 0.6325 | 0.5363 | 0.8571   |
| 0.3652        | 38.0  | 950  | 0.8983          | 0.4819    | 0.6315 | 0.5466 | 0.8560   |
| 0.3652        | 39.0  | 975  | 0.8723          | 0.4641    | 0.6272 | 0.5335 | 0.8556   |
| 0.0269        | 40.0  | 1000 | 0.8788          | 0.4662    | 0.6399 | 0.5394 | 0.8550   |
| 0.0269        | 41.0  | 1025 | 0.8952          | 0.4805    | 0.6378 | 0.5481 | 0.8611   |
| 0.0269        | 42.0  | 1050 | 0.8901          | 0.4657    | 0.6304 | 0.5357 | 0.8574   |
| 0.0269        | 43.0  | 1075 | 0.9015          | 0.4746    | 0.6410 | 0.5454 | 0.8574   |
| 0.0269        | 44.0  | 1100 | 0.8838          | 0.4655    | 0.6420 | 0.5397 | 0.8591   |
| 0.0269        | 45.0  | 1125 | 0.9093          | 0.4718    | 0.6441 | 0.5446 | 0.8598   |
| 0.0269        | 46.0  | 1150 | 0.9154          | 0.4826    | 0.6441 | 0.5518 | 0.8553   |
| 0.0269        | 47.0  | 1175 | 0.9214          | 0.4614    | 0.6315 | 0.5332 | 0.8538   |
| 0.0269        | 48.0  | 1200 | 0.9313          | 0.4639    | 0.6315 | 0.5349 | 0.8546   |
| 0.0269        | 49.0  | 1225 | 0.9137          | 0.4807    | 0.6431 | 0.5501 | 0.8582   |
| 0.0269        | 50.0  | 1250 | 0.9235          | 0.4939    | 0.6463 | 0.5599 | 0.8571   |
| 0.0269        | 51.0  | 1275 | 0.9263          | 0.4900    | 0.6441 | 0.5566 | 0.8580   |
| 0.0269        | 52.0  | 1300 | 0.9190          | 0.4787    | 0.6420 | 0.5485 | 0.8613   |
| 0.0269        | 53.0  | 1325 | 0.9159          | 0.4700    | 0.6441 | 0.5434 | 0.8616   |
| 0.0269        | 54.0  | 1350 | 0.9302          | 0.4806    | 0.6399 | 0.5489 | 0.8614   |
| 0.0269        | 55.0  | 1375 | 0.9391          | 0.4877    | 0.6515 | 0.5579 | 0.8581   |
| 0.0269        | 56.0  | 1400 | 0.9392          | 0.4959    | 0.6452 | 0.5608 | 0.8580   |
| 0.0269        | 57.0  | 1425 | 0.9444          | 0.4798    | 0.6410 | 0.5488 | 0.8570   |
| 0.0269        | 58.0  | 1450 | 0.9394          | 0.4777    | 0.6441 | 0.5486 | 0.8596   |
| 0.0269        | 59.0  | 1475 | 0.9562          | 0.4833    | 0.6420 | 0.5515 | 0.8586   |
| 0.0098        | 60.0  | 1500 | 0.9485          | 0.4801    | 0.6484 | 0.5517 | 0.8582   |
| 0.0098        | 61.0  | 1525 | 0.9521          | 0.4679    | 0.6463 | 0.5428 | 0.8582   |
| 0.0098        | 62.0  | 1550 | 0.9603          | 0.4759    | 0.6463 | 0.5481 | 0.8563   |
| 0.0098        | 63.0  | 1575 | 0.9663          | 0.4831    | 0.6473 | 0.5532 | 0.8561   |
| 0.0098        | 64.0  | 1600 | 0.9641          | 0.4780    | 0.6526 | 0.5518 | 0.8580   |
| 0.0098        | 65.0  | 1625 | 0.9607          | 0.4767    | 0.6494 | 0.5498 | 0.8606   |
| 0.0098        | 66.0  | 1650 | 0.9782          | 0.4849    | 0.6463 | 0.5541 | 0.8563   |
| 0.0098        | 67.0  | 1675 | 0.9806          | 0.4916    | 0.6484 | 0.5592 | 0.8562   |
| 0.0098        | 68.0  | 1700 | 0.9728          | 0.4889    | 0.6494 | 0.5578 | 0.8578   |
| 0.0098        | 69.0  | 1725 | 0.9766          | 0.4885    | 0.6494 | 0.5576 | 0.8584   |
| 0.0098        | 70.0  | 1750 | 0.9738          | 0.4862    | 0.6526 | 0.5573 | 0.8575   |
| 0.0098        | 71.0  | 1775 | 0.9788          | 0.4916    | 0.6505 | 0.56   | 0.8571   |
| 0.0098        | 72.0  | 1800 | 0.9845          | 0.4845    | 0.6452 | 0.5534 | 0.8563   |
| 0.0098        | 73.0  | 1825 | 0.9729          | 0.4876    | 0.6463 | 0.5559 | 0.8573   |
| 0.0098        | 74.0  | 1850 | 0.9854          | 0.4846    | 0.6494 | 0.5551 | 0.8569   |
| 0.0098        | 75.0  | 1875 | 0.9903          | 0.4885    | 0.6505 | 0.5580 | 0.8562   |
| 0.0098        | 76.0  | 1900 | 0.9825          | 0.4886    | 0.6558 | 0.5600 | 0.8568   |
| 0.0098        | 77.0  | 1925 | 0.9994          | 0.4876    | 0.6463 | 0.5559 | 0.8554   |
| 0.0098        | 78.0  | 1950 | 0.9922          | 0.4905    | 0.6515 | 0.5596 | 0.8546   |
| 0.0098        | 79.0  | 1975 | 1.0084          | 0.4928    | 0.6484 | 0.5600 | 0.8578   |
| 0.0057        | 80.0  | 2000 | 0.9931          | 0.4976    | 0.6526 | 0.5646 | 0.8580   |
| 0.0057        | 81.0  | 2025 | 0.9864          | 0.4826    | 0.6452 | 0.5522 | 0.8595   |
| 0.0057        | 82.0  | 2050 | 0.9929          | 0.4900    | 0.6484 | 0.5582 | 0.8595   |
| 0.0057        | 83.0  | 2075 | 0.9902          | 0.4916    | 0.6473 | 0.5588 | 0.8588   |
| 0.0057        | 84.0  | 2100 | 1.0021          | 0.4872    | 0.6431 | 0.5544 | 0.8573   |
| 0.0057        | 85.0  | 2125 | 1.0013          | 0.4964    | 0.6473 | 0.5619 | 0.8582   |
| 0.0057        | 86.0  | 2150 | 0.9814          | 0.4865    | 0.6484 | 0.5559 | 0.8625   |
| 0.0057        | 87.0  | 2175 | 0.9841          | 0.4932    | 0.6558 | 0.5630 | 0.8622   |
| 0.0057        | 88.0  | 2200 | 0.9888          | 0.4866    | 0.6515 | 0.5571 | 0.8610   |
| 0.0057        | 89.0  | 2225 | 0.9898          | 0.4924    | 0.6515 | 0.5609 | 0.8610   |
| 0.0057        | 90.0  | 2250 | 0.9860          | 0.4870    | 0.6526 | 0.5578 | 0.8607   |
| 0.0057        | 91.0  | 2275 | 0.9925          | 0.4912    | 0.6484 | 0.5589 | 0.8589   |
| 0.0057        | 92.0  | 2300 | 0.9904          | 0.4956    | 0.6536 | 0.5638 | 0.8599   |
| 0.0057        | 93.0  | 2325 | 0.9902          | 0.4980    | 0.6526 | 0.5649 | 0.8602   |
| 0.0057        | 94.0  | 2350 | 0.9925          | 0.5041    | 0.6547 | 0.5696 | 0.8602   |
| 0.0057        | 95.0  | 2375 | 0.9959          | 0.4897    | 0.6515 | 0.5591 | 0.8589   |
| 0.0057        | 96.0  | 2400 | 0.9951          | 0.4901    | 0.6505 | 0.5590 | 0.8591   |
| 0.0057        | 97.0  | 2425 | 0.9962          | 0.4924    | 0.6505 | 0.5605 | 0.8588   |
| 0.0057        | 98.0  | 2450 | 0.9972          | 0.5008    | 0.6505 | 0.5659 | 0.8585   |
| 0.0057        | 99.0  | 2475 | 0.9988          | 0.4920    | 0.6526 | 0.5611 | 0.8588   |
| 0.0045        | 100.0 | 2500 | 0.9988          | 0.4916    | 0.6526 | 0.5608 | 0.8586   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.0
- Datasets 2.8.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "clinico-bsc-bio-ehr-es", "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 

clinico-bsc-bio-ehr-es
======================


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: 0.9988
* Precision: 0.4916
* Recall: 0.6526
* F1: 0.5608
* Accuracy: 0.8586


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: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100


### Training results



### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0
* Datasets 2.8.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0
* Datasets 2.8.0
* Tokenizers 0.12.1
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: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0
* Datasets 2.8.0
* Tokenizers 0.12.1
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: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100### Training results### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0
* Datasets 2.8.0
* Tokenizers 0.12.1