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# clinico-roberta-biomedical-finetuned-augmented1

This model is a fine-tuned version of [joheras/roberta-base-biomedical-clinical-es-finetuned-clinais](https://huggingface.co/joheras/roberta-base-biomedical-clinical-es-finetuned-clinais) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2547
- Precision: 0.5202
- Recall: 0.6368
- F1: 0.5726
- Accuracy: 0.8678

## 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   | 90   | 0.5952          | 0.3894    | 0.4968 | 0.4366 | 0.8389   |
| No log        | 2.0   | 180  | 0.5302          | 0.3351    | 0.4653 | 0.3896 | 0.8506   |
| No log        | 3.0   | 270  | 0.5888          | 0.3925    | 0.5863 | 0.4702 | 0.8587   |
| No log        | 4.0   | 360  | 0.6183          | 0.4100    | 0.6116 | 0.4909 | 0.8644   |
| No log        | 5.0   | 450  | 0.6412          | 0.4702    | 0.6232 | 0.5360 | 0.8696   |
| 0.3362        | 6.0   | 540  | 0.7014          | 0.4808    | 0.6326 | 0.5464 | 0.8693   |
| 0.3362        | 7.0   | 630  | 0.7379          | 0.4500    | 0.6305 | 0.5252 | 0.8682   |
| 0.3362        | 8.0   | 720  | 0.7744          | 0.4722    | 0.6358 | 0.5419 | 0.8653   |
| 0.3362        | 9.0   | 810  | 0.7712          | 0.4896    | 0.6432 | 0.5560 | 0.8716   |
| 0.3362        | 10.0  | 900  | 0.7924          | 0.4904    | 0.6484 | 0.5585 | 0.8687   |
| 0.3362        | 11.0  | 990  | 0.8283          | 0.4984    | 0.6463 | 0.5628 | 0.8691   |
| 0.0237        | 12.0  | 1080 | 0.8286          | 0.5131    | 0.64   | 0.5696 | 0.8710   |
| 0.0237        | 13.0  | 1170 | 0.8492          | 0.5098    | 0.6558 | 0.5737 | 0.8687   |
| 0.0237        | 14.0  | 1260 | 0.8649          | 0.5137    | 0.6516 | 0.5745 | 0.8676   |
| 0.0237        | 15.0  | 1350 | 0.8748          | 0.5232    | 0.6526 | 0.5808 | 0.8702   |
| 0.0237        | 16.0  | 1440 | 0.8653          | 0.5183    | 0.6421 | 0.5736 | 0.8685   |
| 0.0086        | 17.0  | 1530 | 0.8938          | 0.5219    | 0.6526 | 0.5800 | 0.8722   |
| 0.0086        | 18.0  | 1620 | 0.9006          | 0.5083    | 0.6432 | 0.5678 | 0.8682   |
| 0.0086        | 19.0  | 1710 | 0.9220          | 0.5238    | 0.6484 | 0.5795 | 0.8693   |
| 0.0086        | 20.0  | 1800 | 0.8676          | 0.5151    | 0.6463 | 0.5733 | 0.8724   |
| 0.0086        | 21.0  | 1890 | 0.9404          | 0.5185    | 0.6495 | 0.5766 | 0.8643   |
| 0.0086        | 22.0  | 1980 | 0.9477          | 0.5409    | 0.6537 | 0.5920 | 0.8678   |
| 0.0052        | 23.0  | 2070 | 0.9441          | 0.5342    | 0.6568 | 0.5892 | 0.8736   |
| 0.0052        | 24.0  | 2160 | 0.9786          | 0.5373    | 0.6368 | 0.5829 | 0.8685   |
| 0.0052        | 25.0  | 2250 | 0.9510          | 0.5243    | 0.6463 | 0.5790 | 0.8722   |
| 0.0052        | 26.0  | 2340 | 0.9876          | 0.5261    | 0.6463 | 0.5801 | 0.8683   |
| 0.0052        | 27.0  | 2430 | 1.0049          | 0.5265    | 0.6484 | 0.5811 | 0.8652   |
| 0.0033        | 28.0  | 2520 | 1.0204          | 0.5347    | 0.6495 | 0.5865 | 0.8630   |
| 0.0033        | 29.0  | 2610 | 1.0027          | 0.5101    | 0.6411 | 0.5681 | 0.8647   |
| 0.0033        | 30.0  | 2700 | 1.0345          | 0.5243    | 0.6347 | 0.5743 | 0.8649   |
| 0.0033        | 31.0  | 2790 | 1.0199          | 0.5222    | 0.6316 | 0.5717 | 0.8663   |
| 0.0033        | 32.0  | 2880 | 1.0424          | 0.5243    | 0.6368 | 0.5751 | 0.8669   |
| 0.0033        | 33.0  | 2970 | 1.0341          | 0.5294    | 0.6453 | 0.5816 | 0.8662   |
| 0.0025        | 34.0  | 3060 | 1.0367          | 0.5419    | 0.6474 | 0.5899 | 0.8667   |
| 0.0025        | 35.0  | 3150 | 1.0629          | 0.5225    | 0.6484 | 0.5787 | 0.8660   |
| 0.0025        | 36.0  | 3240 | 1.0406          | 0.5227    | 0.6432 | 0.5767 | 0.8672   |
| 0.0025        | 37.0  | 3330 | 1.0168          | 0.5324    | 0.6495 | 0.5851 | 0.8701   |
| 0.0025        | 38.0  | 3420 | 1.0375          | 0.5332    | 0.6505 | 0.5861 | 0.8693   |
| 0.0015        | 39.0  | 3510 | 1.0921          | 0.5378    | 0.6442 | 0.5862 | 0.8649   |
| 0.0015        | 40.0  | 3600 | 1.0742          | 0.5330    | 0.6453 | 0.5838 | 0.8657   |
| 0.0015        | 41.0  | 3690 | 1.1234          | 0.5189    | 0.6347 | 0.5710 | 0.8619   |
| 0.0015        | 42.0  | 3780 | 1.0940          | 0.5407    | 0.6505 | 0.5905 | 0.8659   |
| 0.0015        | 43.0  | 3870 | 1.0612          | 0.5493    | 0.6505 | 0.5957 | 0.8704   |
| 0.0015        | 44.0  | 3960 | 1.0730          | 0.5445    | 0.6505 | 0.5928 | 0.8696   |
| 0.0008        | 45.0  | 4050 | 1.0834          | 0.5484    | 0.6558 | 0.5973 | 0.8675   |
| 0.0008        | 46.0  | 4140 | 1.1115          | 0.5487    | 0.6463 | 0.5935 | 0.8688   |
| 0.0008        | 47.0  | 4230 | 1.1153          | 0.5491    | 0.6474 | 0.5942 | 0.8661   |
| 0.0008        | 48.0  | 4320 | 1.1142          | 0.5456    | 0.6421 | 0.5899 | 0.8674   |
| 0.0008        | 49.0  | 4410 | 1.0922          | 0.5285    | 0.6537 | 0.5845 | 0.8704   |
| 0.0007        | 50.0  | 4500 | 1.0873          | 0.5448    | 0.6463 | 0.5912 | 0.8700   |
| 0.0007        | 51.0  | 4590 | 1.1141          | 0.5342    | 0.6337 | 0.5797 | 0.8686   |
| 0.0007        | 52.0  | 4680 | 1.2066          | 0.5116    | 0.6263 | 0.5632 | 0.8617   |
| 0.0007        | 53.0  | 4770 | 1.0850          | 0.5224    | 0.6379 | 0.5744 | 0.8704   |
| 0.0007        | 54.0  | 4860 | 1.1132          | 0.5308    | 0.6526 | 0.5855 | 0.8728   |
| 0.0007        | 55.0  | 4950 | 1.1540          | 0.5118    | 0.64   | 0.5688 | 0.8667   |
| 0.001         | 56.0  | 5040 | 1.1314          | 0.5314    | 0.6495 | 0.5846 | 0.8683   |
| 0.001         | 57.0  | 5130 | 1.0893          | 0.5456    | 0.6547 | 0.5952 | 0.8713   |
| 0.001         | 58.0  | 5220 | 1.0910          | 0.5354    | 0.6453 | 0.5852 | 0.8685   |
| 0.001         | 59.0  | 5310 | 1.1131          | 0.5527    | 0.6568 | 0.6003 | 0.8742   |
| 0.001         | 60.0  | 5400 | 1.1434          | 0.5339    | 0.6463 | 0.5848 | 0.8694   |
| 0.001         | 61.0  | 5490 | 1.1186          | 0.5313    | 0.6516 | 0.5853 | 0.8731   |
| 0.0007        | 62.0  | 5580 | 1.1584          | 0.5381    | 0.6474 | 0.5877 | 0.8684   |
| 0.0007        | 63.0  | 5670 | 1.1687          | 0.5429    | 0.6463 | 0.5901 | 0.8662   |
| 0.0007        | 64.0  | 5760 | 1.1296          | 0.5223    | 0.6421 | 0.5760 | 0.8756   |
| 0.0007        | 65.0  | 5850 | 1.1499          | 0.5345    | 0.6516 | 0.5873 | 0.8710   |
| 0.0007        | 66.0  | 5940 | 1.1771          | 0.5318    | 0.6516 | 0.5856 | 0.8713   |
| 0.0005        | 67.0  | 6030 | 1.1531          | 0.5219    | 0.6526 | 0.5800 | 0.8741   |
| 0.0005        | 68.0  | 6120 | 1.1781          | 0.5383    | 0.6358 | 0.5830 | 0.8713   |
| 0.0005        | 69.0  | 6210 | 1.1989          | 0.5164    | 0.6316 | 0.5682 | 0.8684   |
| 0.0005        | 70.0  | 6300 | 1.1986          | 0.5389    | 0.6495 | 0.5890 | 0.8695   |
| 0.0005        | 71.0  | 6390 | 1.1720          | 0.5603    | 0.6411 | 0.5979 | 0.8720   |
| 0.0005        | 72.0  | 6480 | 1.1699          | 0.5308    | 0.6432 | 0.5816 | 0.8725   |
| 0.0005        | 73.0  | 6570 | 1.1781          | 0.5541    | 0.6411 | 0.5944 | 0.8708   |
| 0.0005        | 74.0  | 6660 | 1.2327          | 0.5304    | 0.6337 | 0.5775 | 0.8664   |
| 0.0005        | 75.0  | 6750 | 1.2070          | 0.5537    | 0.6463 | 0.5964 | 0.8718   |
| 0.0005        | 76.0  | 6840 | 1.2032          | 0.5502    | 0.6463 | 0.5944 | 0.8728   |
| 0.0005        | 77.0  | 6930 | 1.2100          | 0.5525    | 0.6484 | 0.5966 | 0.8713   |
| 0.0003        | 78.0  | 7020 | 1.2171          | 0.5336    | 0.6442 | 0.5837 | 0.8715   |
| 0.0003        | 79.0  | 7110 | 1.2256          | 0.5241    | 0.64   | 0.5763 | 0.8704   |
| 0.0003        | 80.0  | 7200 | 1.2238          | 0.5323    | 0.6421 | 0.5821 | 0.8696   |
| 0.0003        | 81.0  | 7290 | 1.2219          | 0.5342    | 0.6326 | 0.5793 | 0.8693   |
| 0.0003        | 82.0  | 7380 | 1.2251          | 0.5325    | 0.6379 | 0.5805 | 0.8694   |
| 0.0003        | 83.0  | 7470 | 1.2187          | 0.5468    | 0.6389 | 0.5893 | 0.8681   |
| 0.0003        | 84.0  | 7560 | 1.2309          | 0.5365    | 0.6421 | 0.5846 | 0.8683   |
| 0.0003        | 85.0  | 7650 | 1.2445          | 0.5350    | 0.6432 | 0.5841 | 0.8676   |
| 0.0003        | 86.0  | 7740 | 1.2561          | 0.5288    | 0.6474 | 0.5821 | 0.8680   |
| 0.0003        | 87.0  | 7830 | 1.2567          | 0.5263    | 0.6421 | 0.5785 | 0.8678   |
| 0.0003        | 88.0  | 7920 | 1.2470          | 0.5346    | 0.6421 | 0.5835 | 0.8679   |
| 0.0002        | 89.0  | 8010 | 1.2458          | 0.5468    | 0.6453 | 0.5920 | 0.8684   |
| 0.0002        | 90.0  | 8100 | 1.2448          | 0.5484    | 0.6442 | 0.5924 | 0.8689   |
| 0.0002        | 91.0  | 8190 | 1.2439          | 0.5469    | 0.6379 | 0.5889 | 0.8684   |
| 0.0002        | 92.0  | 8280 | 1.2453          | 0.5338    | 0.64   | 0.5821 | 0.8695   |
| 0.0002        | 93.0  | 8370 | 1.2462          | 0.5315    | 0.64   | 0.5807 | 0.8692   |
| 0.0002        | 94.0  | 8460 | 1.2472          | 0.5328    | 0.6411 | 0.5819 | 0.8691   |
| 0.0002        | 95.0  | 8550 | 1.2502          | 0.5311    | 0.6379 | 0.5796 | 0.8686   |
| 0.0002        | 96.0  | 8640 | 1.2464          | 0.5330    | 0.6368 | 0.5803 | 0.8691   |
| 0.0002        | 97.0  | 8730 | 1.2526          | 0.5185    | 0.6337 | 0.5703 | 0.8681   |
| 0.0002        | 98.0  | 8820 | 1.2543          | 0.5167    | 0.6347 | 0.5697 | 0.8679   |
| 0.0002        | 99.0  | 8910 | 1.2544          | 0.5211    | 0.6358 | 0.5728 | 0.8680   |
| 0.0002        | 100.0 | 9000 | 1.2547          | 0.5202    | 0.6368 | 0.5726 | 0.8678   |


### Framework versions

- Transformers 4.27.0.dev0
- 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-roberta-biomedical-finetuned-augmented1", "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-roberta-biomedical-finetuned-augmented1
===============================================


This model is a fine-tuned version of joheras/roberta-base-biomedical-clinical-es-finetuned-clinais on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.2547
* Precision: 0.5202
* Recall: 0.6368
* F1: 0.5726
* Accuracy: 0.8678


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 4.27.0.dev0
* 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 4.27.0.dev0
* 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 4.27.0.dev0
* 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 4.27.0.dev0
* Pytorch 1.13.0
* Datasets 2.8.0
* Tokenizers 0.12.1