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# bsc-bio-ehr-es-finetuned-clinais-augmented2

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: 1.3136
- Precision: 0.4988
- Recall: 0.6452
- F1: 0.5626
- Accuracy: 0.8593

## 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   | 114   | 0.7550          | 0.1166    | 0.1605 | 0.1351 | 0.7937   |
| No log        | 2.0   | 228   | 0.6577          | 0.2096    | 0.2904 | 0.2435 | 0.8123   |
| No log        | 3.0   | 342   | 0.5928          | 0.2167    | 0.3157 | 0.2570 | 0.8197   |
| No log        | 4.0   | 456   | 0.5837          | 0.2532    | 0.3791 | 0.3036 | 0.8374   |
| 0.5045        | 5.0   | 570   | 0.5758          | 0.3309    | 0.5206 | 0.4046 | 0.8479   |
| 0.5045        | 6.0   | 684   | 0.6266          | 0.3506    | 0.5216 | 0.4194 | 0.8384   |
| 0.5045        | 7.0   | 798   | 0.6329          | 0.3503    | 0.5586 | 0.4306 | 0.8486   |
| 0.5045        | 8.0   | 912   | 0.6251          | 0.3769    | 0.5639 | 0.4518 | 0.8565   |
| 0.1043        | 9.0   | 1026  | 0.6509          | 0.3999    | 0.5945 | 0.4781 | 0.8582   |
| 0.1043        | 10.0  | 1140  | 0.7431          | 0.3755    | 0.5734 | 0.4538 | 0.8453   |
| 0.1043        | 11.0  | 1254  | 0.7000          | 0.4181    | 0.6061 | 0.4948 | 0.8598   |
| 0.1043        | 12.0  | 1368  | 0.7232          | 0.4375    | 0.6315 | 0.5169 | 0.8574   |
| 0.1043        | 13.0  | 1482  | 0.7741          | 0.4568    | 0.6199 | 0.5260 | 0.8596   |
| 0.0344        | 14.0  | 1596  | 0.8108          | 0.4661    | 0.6251 | 0.5341 | 0.8528   |
| 0.0344        | 15.0  | 1710  | 0.7922          | 0.4516    | 0.6209 | 0.5229 | 0.8613   |
| 0.0344        | 16.0  | 1824  | 0.8318          | 0.4727    | 0.6315 | 0.5407 | 0.8566   |
| 0.0344        | 17.0  | 1938  | 0.8579          | 0.4887    | 0.6378 | 0.5534 | 0.8558   |
| 0.0158        | 18.0  | 2052  | 0.8745          | 0.4626    | 0.6209 | 0.5302 | 0.8539   |
| 0.0158        | 19.0  | 2166  | 0.8959          | 0.4461    | 0.6167 | 0.5177 | 0.8530   |
| 0.0158        | 20.0  | 2280  | 0.9600          | 0.4442    | 0.6135 | 0.5153 | 0.8490   |
| 0.0158        | 21.0  | 2394  | 0.8769          | 0.4578    | 0.6294 | 0.5300 | 0.8601   |
| 0.0095        | 22.0  | 2508  | 0.9427          | 0.4503    | 0.6125 | 0.5190 | 0.8516   |
| 0.0095        | 23.0  | 2622  | 0.9188          | 0.4793    | 0.6367 | 0.5469 | 0.8582   |
| 0.0095        | 24.0  | 2736  | 0.9538          | 0.4790    | 0.6367 | 0.5467 | 0.8556   |
| 0.0095        | 25.0  | 2850  | 0.9785          | 0.4775    | 0.6262 | 0.5418 | 0.8495   |
| 0.0095        | 26.0  | 2964  | 0.9745          | 0.4785    | 0.6463 | 0.5499 | 0.8514   |
| 0.0066        | 27.0  | 3078  | 1.0168          | 0.4909    | 0.6262 | 0.5503 | 0.8541   |
| 0.0066        | 28.0  | 3192  | 0.9852          | 0.4796    | 0.6452 | 0.5502 | 0.8515   |
| 0.0066        | 29.0  | 3306  | 1.0372          | 0.4711    | 0.6367 | 0.5415 | 0.8526   |
| 0.0066        | 30.0  | 3420  | 1.0234          | 0.5012    | 0.6399 | 0.5622 | 0.8530   |
| 0.0045        | 31.0  | 3534  | 1.0058          | 0.4944    | 0.6484 | 0.5610 | 0.8582   |
| 0.0045        | 32.0  | 3648  | 1.0212          | 0.4776    | 0.6420 | 0.5477 | 0.8559   |
| 0.0045        | 33.0  | 3762  | 1.0301          | 0.4643    | 0.6378 | 0.5374 | 0.8533   |
| 0.0045        | 34.0  | 3876  | 1.0545          | 0.4740    | 0.6346 | 0.5427 | 0.8529   |
| 0.0045        | 35.0  | 3990  | 1.0632          | 0.4703    | 0.6272 | 0.5376 | 0.8524   |
| 0.0059        | 36.0  | 4104  | 1.0351          | 0.4923    | 0.6389 | 0.5561 | 0.8568   |
| 0.0059        | 37.0  | 4218  | 1.0584          | 0.4665    | 0.6177 | 0.5316 | 0.8545   |
| 0.0059        | 38.0  | 4332  | 1.0513          | 0.4695    | 0.6251 | 0.5362 | 0.8566   |
| 0.0059        | 39.0  | 4446  | 1.0482          | 0.4740    | 0.6357 | 0.5431 | 0.8555   |
| 0.0028        | 40.0  | 4560  | 1.0777          | 0.4899    | 0.6378 | 0.5541 | 0.8590   |
| 0.0028        | 41.0  | 4674  | 1.1045          | 0.5066    | 0.6505 | 0.5696 | 0.8589   |
| 0.0028        | 42.0  | 4788  | 1.0910          | 0.5       | 0.6441 | 0.5630 | 0.8556   |
| 0.0028        | 43.0  | 4902  | 1.1249          | 0.4809    | 0.6389 | 0.5488 | 0.8546   |
| 0.0021        | 44.0  | 5016  | 1.0668          | 0.4806    | 0.6283 | 0.5446 | 0.8591   |
| 0.0021        | 45.0  | 5130  | 1.0699          | 0.5033    | 0.6536 | 0.5687 | 0.8586   |
| 0.0021        | 46.0  | 5244  | 1.1457          | 0.5004    | 0.6410 | 0.5620 | 0.8526   |
| 0.0021        | 47.0  | 5358  | 1.1226          | 0.4858    | 0.6336 | 0.5500 | 0.8501   |
| 0.0021        | 48.0  | 5472  | 1.1459          | 0.4963    | 0.6399 | 0.5590 | 0.8532   |
| 0.0045        | 49.0  | 5586  | 1.1515          | 0.4919    | 0.6441 | 0.5578 | 0.8508   |
| 0.0045        | 50.0  | 5700  | 1.1952          | 0.4887    | 0.6420 | 0.5550 | 0.8561   |
| 0.0045        | 51.0  | 5814  | 1.1382          | 0.5042    | 0.6294 | 0.5599 | 0.8601   |
| 0.0045        | 52.0  | 5928  | 1.1547          | 0.4757    | 0.6315 | 0.5426 | 0.8585   |
| 0.0017        | 53.0  | 6042  | 1.2089          | 0.4996    | 0.6188 | 0.5528 | 0.8511   |
| 0.0017        | 54.0  | 6156  | 1.1838          | 0.4910    | 0.6357 | 0.5541 | 0.8555   |
| 0.0017        | 55.0  | 6270  | 1.2072          | 0.4907    | 0.6378 | 0.5546 | 0.8572   |
| 0.0017        | 56.0  | 6384  | 1.1894          | 0.5012    | 0.6505 | 0.5662 | 0.8605   |
| 0.0017        | 57.0  | 6498  | 1.2195          | 0.4992    | 0.6441 | 0.5625 | 0.8543   |
| 0.002         | 58.0  | 6612  | 1.1838          | 0.5008    | 0.6484 | 0.5651 | 0.8579   |
| 0.002         | 59.0  | 6726  | 1.2107          | 0.5029    | 0.6484 | 0.5664 | 0.8561   |
| 0.002         | 60.0  | 6840  | 1.1850          | 0.5       | 0.6505 | 0.5654 | 0.8581   |
| 0.002         | 61.0  | 6954  | 1.1926          | 0.5004    | 0.6431 | 0.5628 | 0.8571   |
| 0.002         | 62.0  | 7068  | 1.2789          | 0.5062    | 0.6431 | 0.5665 | 0.8524   |
| 0.002         | 63.0  | 7182  | 1.2268          | 0.4939    | 0.6452 | 0.5595 | 0.8562   |
| 0.002         | 64.0  | 7296  | 1.2729          | 0.5103    | 0.6515 | 0.5724 | 0.8531   |
| 0.002         | 65.0  | 7410  | 1.2667          | 0.5127    | 0.6410 | 0.5697 | 0.8530   |
| 0.0016        | 66.0  | 7524  | 1.2580          | 0.5142    | 0.6484 | 0.5736 | 0.8528   |
| 0.0016        | 67.0  | 7638  | 1.2323          | 0.4839    | 0.6494 | 0.5546 | 0.8567   |
| 0.0016        | 68.0  | 7752  | 1.2647          | 0.4951    | 0.6399 | 0.5583 | 0.8514   |
| 0.0016        | 69.0  | 7866  | 1.2604          | 0.5178    | 0.6463 | 0.5749 | 0.8563   |
| 0.0016        | 70.0  | 7980  | 1.2154          | 0.4996    | 0.6410 | 0.5615 | 0.8596   |
| 0.0005        | 71.0  | 8094  | 1.2452          | 0.5066    | 0.6505 | 0.5696 | 0.8592   |
| 0.0005        | 72.0  | 8208  | 1.2100          | 0.4882    | 0.6357 | 0.5523 | 0.8609   |
| 0.0005        | 73.0  | 8322  | 1.2549          | 0.4936    | 0.6515 | 0.5617 | 0.8590   |
| 0.0005        | 74.0  | 8436  | 1.2600          | 0.5134    | 0.6463 | 0.5722 | 0.8617   |
| 0.0004        | 75.0  | 8550  | 1.3043          | 0.4939    | 0.6431 | 0.5587 | 0.8546   |
| 0.0004        | 76.0  | 8664  | 1.2772          | 0.4907    | 0.6389 | 0.5550 | 0.8591   |
| 0.0004        | 77.0  | 8778  | 1.3091          | 0.4955    | 0.6389 | 0.5581 | 0.8518   |
| 0.0004        | 78.0  | 8892  | 1.2821          | 0.4988    | 0.6452 | 0.5626 | 0.8565   |
| 0.0004        | 79.0  | 9006  | 1.2668          | 0.4984    | 0.6463 | 0.5628 | 0.8591   |
| 0.0004        | 80.0  | 9120  | 1.2872          | 0.5104    | 0.6463 | 0.5704 | 0.8567   |
| 0.0004        | 81.0  | 9234  | 1.2877          | 0.4919    | 0.6389 | 0.5558 | 0.8553   |
| 0.0004        | 82.0  | 9348  | 1.2701          | 0.5079    | 0.6441 | 0.5680 | 0.8587   |
| 0.0004        | 83.0  | 9462  | 1.3579          | 0.4972    | 0.6484 | 0.5628 | 0.8500   |
| 0.0004        | 84.0  | 9576  | 1.3292          | 0.5138    | 0.6494 | 0.5737 | 0.8542   |
| 0.0004        | 85.0  | 9690  | 1.3437          | 0.4963    | 0.6389 | 0.5586 | 0.8546   |
| 0.0004        | 86.0  | 9804  | 1.3193          | 0.5155    | 0.6505 | 0.5752 | 0.8577   |
| 0.0004        | 87.0  | 9918  | 1.3579          | 0.4919    | 0.6420 | 0.5570 | 0.8520   |
| 0.0003        | 88.0  | 10032 | 1.3109          | 0.5181    | 0.6494 | 0.5764 | 0.8585   |
| 0.0003        | 89.0  | 10146 | 1.2943          | 0.4876    | 0.6441 | 0.5551 | 0.8615   |
| 0.0003        | 90.0  | 10260 | 1.3132          | 0.4826    | 0.6463 | 0.5526 | 0.8574   |
| 0.0003        | 91.0  | 10374 | 1.3176          | 0.5058    | 0.6452 | 0.5671 | 0.8563   |
| 0.0003        | 92.0  | 10488 | 1.3130          | 0.4964    | 0.6463 | 0.5615 | 0.8583   |
| 0.0002        | 93.0  | 10602 | 1.3220          | 0.4992    | 0.6463 | 0.5633 | 0.8563   |
| 0.0002        | 94.0  | 10716 | 1.3160          | 0.5       | 0.6463 | 0.5638 | 0.8603   |
| 0.0002        | 95.0  | 10830 | 1.3155          | 0.4857    | 0.6473 | 0.5550 | 0.8615   |
| 0.0002        | 96.0  | 10944 | 1.3223          | 0.4912    | 0.6484 | 0.5589 | 0.8577   |
| 0.0002        | 97.0  | 11058 | 1.3138          | 0.5087    | 0.6484 | 0.5701 | 0.8593   |
| 0.0002        | 98.0  | 11172 | 1.3152          | 0.5037    | 0.6463 | 0.5661 | 0.8594   |
| 0.0002        | 99.0  | 11286 | 1.3137          | 0.4996    | 0.6452 | 0.5631 | 0.8593   |
| 0.0002        | 100.0 | 11400 | 1.3136          | 0.4988    | 0.6452 | 0.5626 | 0.8593   |


### 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-augmented2", "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-augmented2
===========================================


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: 1.3136
* Precision: 0.4988
* Recall: 0.6452
* F1: 0.5626
* Accuracy: 0.8593


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