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# bsc-bio-ehr-es-finetuned-clinais-augmented1

This model is a fine-tuned version of [joheras/bsc-bio-ehr-es-finetuned-clinais](https://huggingface.co/joheras/bsc-bio-ehr-es-finetuned-clinais) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3581
- Precision: 0.5320
- Recall: 0.6494
- F1: 0.5849
- Accuracy: 0.8583

## 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   | 0.6246          | 0.2759    | 0.3485 | 0.3080 | 0.8286   |
| No log        | 2.0   | 180  | 0.5890          | 0.3511    | 0.4520 | 0.3952 | 0.8402   |
| No log        | 3.0   | 270  | 0.5973          | 0.3690    | 0.5322 | 0.4358 | 0.8539   |
| No log        | 4.0   | 360  | 0.6676          | 0.3693    | 0.5713 | 0.4486 | 0.8554   |
| No log        | 5.0   | 450  | 0.7173          | 0.4227    | 0.6061 | 0.4980 | 0.8544   |
| 0.3579        | 6.0   | 540  | 0.7854          | 0.4438    | 0.6082 | 0.5131 | 0.8552   |
| 0.3579        | 7.0   | 630  | 0.8437          | 0.4530    | 0.6103 | 0.5200 | 0.8522   |
| 0.3579        | 8.0   | 720  | 0.8716          | 0.4349    | 0.6103 | 0.5079 | 0.8513   |
| 0.3579        | 9.0   | 810  | 0.8868          | 0.4500    | 0.6030 | 0.5153 | 0.8544   |
| 0.3579        | 10.0  | 900  | 0.8917          | 0.4680    | 0.6251 | 0.5353 | 0.8574   |
| 0.3579        | 11.0  | 990  | 0.9175          | 0.4769    | 0.6336 | 0.5442 | 0.8548   |
| 0.0229        | 12.0  | 1080 | 0.9081          | 0.4767    | 0.6473 | 0.5490 | 0.8651   |
| 0.0229        | 13.0  | 1170 | 0.9692          | 0.4854    | 0.6336 | 0.5497 | 0.8532   |
| 0.0229        | 14.0  | 1260 | 0.9568          | 0.4947    | 0.6431 | 0.5592 | 0.8592   |
| 0.0229        | 15.0  | 1350 | 1.0028          | 0.4848    | 0.6241 | 0.5457 | 0.8505   |
| 0.0229        | 16.0  | 1440 | 1.0302          | 0.4821    | 0.6251 | 0.5444 | 0.8557   |
| 0.0076        | 17.0  | 1530 | 0.9892          | 0.4918    | 0.6325 | 0.5533 | 0.8584   |
| 0.0076        | 18.0  | 1620 | 1.0339          | 0.4755    | 0.6135 | 0.5357 | 0.8480   |
| 0.0076        | 19.0  | 1710 | 1.0066          | 0.4935    | 0.6399 | 0.5572 | 0.8570   |
| 0.0076        | 20.0  | 1800 | 1.0403          | 0.4959    | 0.6410 | 0.5592 | 0.8564   |
| 0.0076        | 21.0  | 1890 | 1.0374          | 0.4979    | 0.6336 | 0.5576 | 0.8561   |
| 0.0076        | 22.0  | 1980 | 1.0758          | 0.4821    | 0.6262 | 0.5448 | 0.8528   |
| 0.0044        | 23.0  | 2070 | 1.0818          | 0.4876    | 0.6230 | 0.5471 | 0.8524   |
| 0.0044        | 24.0  | 2160 | 1.0668          | 0.5096    | 0.6431 | 0.5686 | 0.8569   |
| 0.0044        | 25.0  | 2250 | 1.1033          | 0.4873    | 0.6294 | 0.5493 | 0.8541   |
| 0.0044        | 26.0  | 2340 | 1.0936          | 0.4880    | 0.6209 | 0.5465 | 0.8544   |
| 0.0044        | 27.0  | 2430 | 1.0802          | 0.4856    | 0.6399 | 0.5522 | 0.8583   |
| 0.0028        | 28.0  | 2520 | 1.1245          | 0.5034    | 0.6346 | 0.5614 | 0.8542   |
| 0.0028        | 29.0  | 2610 | 1.1293          | 0.4874    | 0.6336 | 0.5510 | 0.8521   |
| 0.0028        | 30.0  | 2700 | 1.0883          | 0.4984    | 0.6494 | 0.5640 | 0.8591   |
| 0.0028        | 31.0  | 2790 | 1.1434          | 0.5055    | 0.6315 | 0.5615 | 0.8565   |
| 0.0028        | 32.0  | 2880 | 1.1394          | 0.5041    | 0.6505 | 0.5680 | 0.8558   |
| 0.0028        | 33.0  | 2970 | 1.1473          | 0.5083    | 0.6452 | 0.5686 | 0.8550   |
| 0.0026        | 34.0  | 3060 | 1.2286          | 0.4996    | 0.6177 | 0.5524 | 0.8437   |
| 0.0026        | 35.0  | 3150 | 1.1982          | 0.4996    | 0.6251 | 0.5553 | 0.8521   |
| 0.0026        | 36.0  | 3240 | 1.1878          | 0.4987    | 0.6294 | 0.5565 | 0.8491   |
| 0.0026        | 37.0  | 3330 | 1.1633          | 0.4935    | 0.6399 | 0.5572 | 0.8511   |
| 0.0026        | 38.0  | 3420 | 1.1619          | 0.5097    | 0.6410 | 0.5678 | 0.8587   |
| 0.0021        | 39.0  | 3510 | 1.1438          | 0.5021    | 0.6420 | 0.5635 | 0.8575   |
| 0.0021        | 40.0  | 3600 | 1.1511          | 0.5087    | 0.6494 | 0.5705 | 0.8575   |
| 0.0021        | 41.0  | 3690 | 1.1631          | 0.5128    | 0.6558 | 0.5755 | 0.8576   |
| 0.0021        | 42.0  | 3780 | 1.1639          | 0.5137    | 0.6526 | 0.5749 | 0.8612   |
| 0.0021        | 43.0  | 3870 | 1.1946          | 0.5174    | 0.6452 | 0.5742 | 0.8568   |
| 0.0021        | 44.0  | 3960 | 1.1822          | 0.5132    | 0.6378 | 0.5687 | 0.8556   |
| 0.0012        | 45.0  | 4050 | 1.1533          | 0.5379    | 0.6441 | 0.5863 | 0.8617   |
| 0.0012        | 46.0  | 4140 | 1.1584          | 0.5242    | 0.6410 | 0.5767 | 0.8602   |
| 0.0012        | 47.0  | 4230 | 1.2217          | 0.5159    | 0.6357 | 0.5695 | 0.8567   |
| 0.0012        | 48.0  | 4320 | 1.2451          | 0.5265    | 0.6399 | 0.5777 | 0.8533   |
| 0.0012        | 49.0  | 4410 | 1.2191          | 0.5281    | 0.6357 | 0.5769 | 0.8563   |
| 0.0009        | 50.0  | 4500 | 1.2092          | 0.5320    | 0.6505 | 0.5853 | 0.8548   |
| 0.0009        | 51.0  | 4590 | 1.2168          | 0.5310    | 0.6431 | 0.5817 | 0.8607   |
| 0.0009        | 52.0  | 4680 | 1.2273          | 0.5068    | 0.6251 | 0.5598 | 0.8530   |
| 0.0009        | 53.0  | 4770 | 1.1903          | 0.5254    | 0.6441 | 0.5787 | 0.8618   |
| 0.0009        | 54.0  | 4860 | 1.1939          | 0.5354    | 0.6473 | 0.5860 | 0.8635   |
| 0.0009        | 55.0  | 4950 | 1.2311          | 0.5025    | 0.6357 | 0.5613 | 0.8581   |
| 0.001         | 56.0  | 5040 | 1.2224          | 0.5097    | 0.6389 | 0.5670 | 0.8606   |
| 0.001         | 57.0  | 5130 | 1.2298          | 0.5017    | 0.6410 | 0.5628 | 0.8586   |
| 0.001         | 58.0  | 5220 | 1.2278          | 0.5114    | 0.6389 | 0.5681 | 0.8584   |
| 0.001         | 59.0  | 5310 | 1.2703          | 0.5146    | 0.6505 | 0.5746 | 0.8586   |
| 0.001         | 60.0  | 5400 | 1.2709          | 0.5445    | 0.6336 | 0.5857 | 0.8549   |
| 0.001         | 61.0  | 5490 | 1.2691          | 0.5094    | 0.6283 | 0.5626 | 0.8554   |
| 0.0006        | 62.0  | 5580 | 1.2777          | 0.5076    | 0.6315 | 0.5628 | 0.8523   |
| 0.0006        | 63.0  | 5670 | 1.2472          | 0.5271    | 0.6357 | 0.5764 | 0.8563   |
| 0.0006        | 64.0  | 5760 | 1.2709          | 0.5220    | 0.6515 | 0.5796 | 0.8572   |
| 0.0006        | 65.0  | 5850 | 1.2792          | 0.5306    | 0.6410 | 0.5806 | 0.8613   |
| 0.0006        | 66.0  | 5940 | 1.2403          | 0.5058    | 0.6399 | 0.5650 | 0.8583   |
| 0.0005        | 67.0  | 6030 | 1.2778          | 0.5219    | 0.6410 | 0.5754 | 0.8564   |
| 0.0005        | 68.0  | 6120 | 1.3046          | 0.5431    | 0.6515 | 0.5924 | 0.8595   |
| 0.0005        | 69.0  | 6210 | 1.3002          | 0.5236    | 0.6452 | 0.5781 | 0.8547   |
| 0.0005        | 70.0  | 6300 | 1.3068          | 0.5179    | 0.6410 | 0.5729 | 0.8575   |
| 0.0005        | 71.0  | 6390 | 1.3123          | 0.5259    | 0.6431 | 0.5786 | 0.8572   |
| 0.0005        | 72.0  | 6480 | 1.3205          | 0.5395    | 0.6484 | 0.5890 | 0.8576   |
| 0.0004        | 73.0  | 6570 | 1.3281          | 0.5420    | 0.6473 | 0.5900 | 0.8578   |
| 0.0004        | 74.0  | 6660 | 1.3326          | 0.5381    | 0.6484 | 0.5881 | 0.8575   |
| 0.0004        | 75.0  | 6750 | 1.3532          | 0.5393    | 0.6452 | 0.5875 | 0.8553   |
| 0.0004        | 76.0  | 6840 | 1.3562          | 0.5215    | 0.6283 | 0.5699 | 0.8537   |
| 0.0004        | 77.0  | 6930 | 1.3385          | 0.5144    | 0.6420 | 0.5712 | 0.8569   |
| 0.0003        | 78.0  | 7020 | 1.3435          | 0.5303    | 0.6463 | 0.5826 | 0.8570   |
| 0.0003        | 79.0  | 7110 | 1.3402          | 0.5366    | 0.6505 | 0.5881 | 0.8568   |
| 0.0003        | 80.0  | 7200 | 1.3415          | 0.5469    | 0.6526 | 0.5951 | 0.8569   |
| 0.0003        | 81.0  | 7290 | 1.3335          | 0.5181    | 0.6505 | 0.5768 | 0.8578   |
| 0.0003        | 82.0  | 7380 | 1.3433          | 0.5258    | 0.6452 | 0.5794 | 0.8569   |
| 0.0003        | 83.0  | 7470 | 1.3351          | 0.5247    | 0.6515 | 0.5813 | 0.8566   |
| 0.0002        | 84.0  | 7560 | 1.3912          | 0.5187    | 0.6431 | 0.5743 | 0.8515   |
| 0.0002        | 85.0  | 7650 | 1.3507          | 0.5147    | 0.6463 | 0.5730 | 0.8566   |
| 0.0002        | 86.0  | 7740 | 1.3594          | 0.5221    | 0.6494 | 0.5788 | 0.8556   |
| 0.0002        | 87.0  | 7830 | 1.3647          | 0.5262    | 0.6463 | 0.5801 | 0.8547   |
| 0.0002        | 88.0  | 7920 | 1.3629          | 0.5263    | 0.6441 | 0.5793 | 0.8550   |
| 0.0002        | 89.0  | 8010 | 1.3769          | 0.5277    | 0.6441 | 0.5801 | 0.8535   |
| 0.0002        | 90.0  | 8100 | 1.3733          | 0.5268    | 0.6431 | 0.5792 | 0.8556   |
| 0.0002        | 91.0  | 8190 | 1.3648          | 0.5240    | 0.6452 | 0.5783 | 0.8562   |
| 0.0002        | 92.0  | 8280 | 1.3666          | 0.5228    | 0.6410 | 0.5759 | 0.8561   |
| 0.0002        | 93.0  | 8370 | 1.3577          | 0.5231    | 0.6452 | 0.5778 | 0.8580   |
| 0.0002        | 94.0  | 8460 | 1.3514          | 0.5340    | 0.6547 | 0.5882 | 0.8580   |
| 0.0002        | 95.0  | 8550 | 1.3564          | 0.5328    | 0.6526 | 0.5866 | 0.8582   |
| 0.0002        | 96.0  | 8640 | 1.3563          | 0.5342    | 0.6515 | 0.5871 | 0.8584   |
| 0.0002        | 97.0  | 8730 | 1.3567          | 0.5347    | 0.6505 | 0.5869 | 0.8584   |
| 0.0002        | 98.0  | 8820 | 1.3576          | 0.5347    | 0.6505 | 0.5869 | 0.8583   |
| 0.0002        | 99.0  | 8910 | 1.3583          | 0.5339    | 0.6494 | 0.5860 | 0.8582   |
| 0.0001        | 100.0 | 9000 | 1.3581          | 0.5320    | 0.6494 | 0.5849 | 0.8583   |


### 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": "bsc-bio-ehr-es-finetuned-clinais-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 

bsc-bio-ehr-es-finetuned-clinais-augmented1
===========================================


This model is a fine-tuned version of joheras/bsc-bio-ehr-es-finetuned-clinais on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.3581
* Precision: 0.5320
* Recall: 0.6494
* F1: 0.5849
* Accuracy: 0.8583


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