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# clinico-bsc-bio-ehr-es-finetuned-v2

This model is a fine-tuned version of [joheras/bsc-bio-ehr-es-finetuned-clinais-v2](https://huggingface.co/joheras/bsc-bio-ehr-es-finetuned-clinais-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9998
- Precision: 0.4800
- Recall: 0.6452
- F1: 0.5505
- Accuracy: 0.8566

## 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   | 1.2290          | 0.0070    | 0.0137 | 0.0093 | 0.5710   |
| No log        | 2.0   | 50   | 0.7436          | 0.1540    | 0.1943 | 0.1718 | 0.7956   |
| No log        | 3.0   | 75   | 0.6477          | 0.2407    | 0.3126 | 0.2719 | 0.8209   |
| No log        | 4.0   | 100  | 0.6309          | 0.3249    | 0.4467 | 0.3762 | 0.8267   |
| No log        | 5.0   | 125  | 0.6076          | 0.3437    | 0.4572 | 0.3924 | 0.8286   |
| No log        | 6.0   | 150  | 0.6041          | 0.3445    | 0.4551 | 0.3922 | 0.8328   |
| No log        | 7.0   | 175  | 0.5999          | 0.3242    | 0.4382 | 0.3727 | 0.8376   |
| No log        | 8.0   | 200  | 0.6016          | 0.3551    | 0.5005 | 0.4154 | 0.8355   |
| No log        | 9.0   | 225  | 0.6019          | 0.3628    | 0.5375 | 0.4332 | 0.8523   |
| No log        | 10.0  | 250  | 0.6052          | 0.3340    | 0.5259 | 0.4085 | 0.8463   |
| No log        | 11.0  | 275  | 0.6518          | 0.3405    | 0.5354 | 0.4163 | 0.8449   |
| No log        | 12.0  | 300  | 0.6613          | 0.3851    | 0.5660 | 0.4583 | 0.8500   |
| No log        | 13.0  | 325  | 0.6725          | 0.3737    | 0.5702 | 0.4515 | 0.8488   |
| No log        | 14.0  | 350  | 0.6770          | 0.3889    | 0.5892 | 0.4685 | 0.8528   |
| No log        | 15.0  | 375  | 0.6816          | 0.3742    | 0.5702 | 0.4519 | 0.8471   |
| No log        | 16.0  | 400  | 0.6998          | 0.3830    | 0.5808 | 0.4616 | 0.8498   |
| No log        | 17.0  | 425  | 0.7434          | 0.3789    | 0.5882 | 0.4609 | 0.8474   |
| No log        | 18.0  | 450  | 0.7543          | 0.4176    | 0.6051 | 0.4942 | 0.8504   |
| No log        | 19.0  | 475  | 0.7537          | 0.4196    | 0.6114 | 0.4976 | 0.8521   |
| 0.3481        | 20.0  | 500  | 0.7482          | 0.4102    | 0.6199 | 0.4937 | 0.8489   |
| 0.3481        | 21.0  | 525  | 0.7488          | 0.4203    | 0.6241 | 0.5023 | 0.8545   |
| 0.3481        | 22.0  | 550  | 0.7603          | 0.4097    | 0.6325 | 0.4973 | 0.8548   |
| 0.3481        | 23.0  | 575  | 0.7809          | 0.4333    | 0.6177 | 0.5094 | 0.8522   |
| 0.3481        | 24.0  | 600  | 0.7873          | 0.4317    | 0.6272 | 0.5114 | 0.8545   |
| 0.3481        | 25.0  | 625  | 0.7914          | 0.4368    | 0.6241 | 0.5139 | 0.8581   |
| 0.3481        | 26.0  | 650  | 0.8077          | 0.4508    | 0.6241 | 0.5235 | 0.8526   |
| 0.3481        | 27.0  | 675  | 0.8091          | 0.4639    | 0.6304 | 0.5345 | 0.8589   |
| 0.3481        | 28.0  | 700  | 0.8218          | 0.4543    | 0.6346 | 0.5295 | 0.8558   |
| 0.3481        | 29.0  | 725  | 0.8316          | 0.4522    | 0.6241 | 0.5244 | 0.8545   |
| 0.3481        | 30.0  | 750  | 0.8444          | 0.4434    | 0.6325 | 0.5213 | 0.8535   |
| 0.3481        | 31.0  | 775  | 0.8208          | 0.4506    | 0.6209 | 0.5222 | 0.8559   |
| 0.3481        | 32.0  | 800  | 0.8425          | 0.4572    | 0.6262 | 0.5285 | 0.8554   |
| 0.3481        | 33.0  | 825  | 0.8872          | 0.4453    | 0.6272 | 0.5208 | 0.8494   |
| 0.3481        | 34.0  | 850  | 0.8533          | 0.4635    | 0.6167 | 0.5292 | 0.8579   |
| 0.3481        | 35.0  | 875  | 0.8927          | 0.4529    | 0.6188 | 0.5230 | 0.8532   |
| 0.3481        | 36.0  | 900  | 0.8919          | 0.4659    | 0.6199 | 0.5319 | 0.8517   |
| 0.3481        | 37.0  | 925  | 0.8971          | 0.4480    | 0.6272 | 0.5227 | 0.8544   |
| 0.3481        | 38.0  | 950  | 0.9032          | 0.4708    | 0.6378 | 0.5417 | 0.8541   |
| 0.3481        | 39.0  | 975  | 0.8805          | 0.4449    | 0.6315 | 0.5220 | 0.8550   |
| 0.0239        | 40.0  | 1000 | 0.8927          | 0.4580    | 0.6283 | 0.5298 | 0.8567   |
| 0.0239        | 41.0  | 1025 | 0.9016          | 0.4906    | 0.6315 | 0.5522 | 0.8587   |
| 0.0239        | 42.0  | 1050 | 0.9078          | 0.4691    | 0.6336 | 0.5391 | 0.8572   |
| 0.0239        | 43.0  | 1075 | 0.9275          | 0.4748    | 0.6272 | 0.5405 | 0.8534   |
| 0.0239        | 44.0  | 1100 | 0.9360          | 0.4559    | 0.6272 | 0.528  | 0.8508   |
| 0.0239        | 45.0  | 1125 | 0.9357          | 0.4645    | 0.6357 | 0.5368 | 0.8568   |
| 0.0239        | 46.0  | 1150 | 0.9359          | 0.4730    | 0.6294 | 0.5401 | 0.8541   |
| 0.0239        | 47.0  | 1175 | 0.9388          | 0.4730    | 0.6294 | 0.5401 | 0.8550   |
| 0.0239        | 48.0  | 1200 | 0.9322          | 0.4832    | 0.6389 | 0.5503 | 0.8593   |
| 0.0239        | 49.0  | 1225 | 0.9519          | 0.4755    | 0.6262 | 0.5406 | 0.8543   |
| 0.0239        | 50.0  | 1250 | 0.9503          | 0.4674    | 0.6283 | 0.5360 | 0.8515   |
| 0.0239        | 51.0  | 1275 | 0.9547          | 0.4773    | 0.6336 | 0.5445 | 0.8520   |
| 0.0239        | 52.0  | 1300 | 0.9460          | 0.4767    | 0.6367 | 0.5452 | 0.8550   |
| 0.0239        | 53.0  | 1325 | 0.9537          | 0.4704    | 0.6283 | 0.5380 | 0.8552   |
| 0.0239        | 54.0  | 1350 | 0.9485          | 0.4759    | 0.6367 | 0.5447 | 0.8553   |
| 0.0239        | 55.0  | 1375 | 0.9503          | 0.4737    | 0.6367 | 0.5432 | 0.8570   |
| 0.0239        | 56.0  | 1400 | 0.9585          | 0.4890    | 0.6346 | 0.5524 | 0.8542   |
| 0.0239        | 57.0  | 1425 | 0.9532          | 0.4864    | 0.6420 | 0.5535 | 0.8572   |
| 0.0239        | 58.0  | 1450 | 0.9555          | 0.4734    | 0.6399 | 0.5442 | 0.8578   |
| 0.0239        | 59.0  | 1475 | 0.9683          | 0.4704    | 0.6304 | 0.5388 | 0.8517   |
| 0.009         | 60.0  | 1500 | 0.9736          | 0.4755    | 0.6357 | 0.5441 | 0.8547   |
| 0.009         | 61.0  | 1525 | 0.9656          | 0.4871    | 0.6399 | 0.5532 | 0.8553   |
| 0.009         | 62.0  | 1550 | 0.9735          | 0.4705    | 0.6315 | 0.5392 | 0.8531   |
| 0.009         | 63.0  | 1575 | 0.9700          | 0.4757    | 0.6420 | 0.5465 | 0.8568   |
| 0.009         | 64.0  | 1600 | 0.9749          | 0.4908    | 0.6452 | 0.5575 | 0.8539   |
| 0.009         | 65.0  | 1625 | 0.9815          | 0.5021    | 0.6389 | 0.5623 | 0.8536   |
| 0.009         | 66.0  | 1650 | 0.9829          | 0.4895    | 0.6399 | 0.5547 | 0.8553   |
| 0.009         | 67.0  | 1675 | 0.9950          | 0.4845    | 0.6420 | 0.5522 | 0.8537   |
| 0.009         | 68.0  | 1700 | 0.9822          | 0.5033    | 0.6452 | 0.5655 | 0.8566   |
| 0.009         | 69.0  | 1725 | 0.9747          | 0.5037    | 0.6526 | 0.5685 | 0.8594   |
| 0.009         | 70.0  | 1750 | 0.9825          | 0.4841    | 0.6410 | 0.5516 | 0.8560   |
| 0.009         | 71.0  | 1775 | 0.9839          | 0.4936    | 0.6484 | 0.5605 | 0.8561   |
| 0.009         | 72.0  | 1800 | 0.9855          | 0.4873    | 0.6494 | 0.5568 | 0.8570   |
| 0.009         | 73.0  | 1825 | 0.9845          | 0.4924    | 0.6494 | 0.5601 | 0.8567   |
| 0.009         | 74.0  | 1850 | 0.9842          | 0.4880    | 0.6463 | 0.5561 | 0.8561   |
| 0.009         | 75.0  | 1875 | 0.9892          | 0.4853    | 0.6441 | 0.5535 | 0.8559   |
| 0.009         | 76.0  | 1900 | 0.9899          | 0.4865    | 0.6463 | 0.5551 | 0.8568   |
| 0.009         | 77.0  | 1925 | 0.9861          | 0.4947    | 0.6420 | 0.5588 | 0.8582   |
| 0.009         | 78.0  | 1950 | 0.9875          | 0.4774    | 0.6473 | 0.5495 | 0.8596   |
| 0.009         | 79.0  | 1975 | 0.9827          | 0.5033    | 0.6473 | 0.5663 | 0.8600   |
| 0.0056        | 80.0  | 2000 | 0.9876          | 0.4734    | 0.6473 | 0.5468 | 0.8602   |
| 0.0056        | 81.0  | 2025 | 0.9957          | 0.4834    | 0.6463 | 0.5531 | 0.8574   |
| 0.0056        | 82.0  | 2050 | 0.9883          | 0.4766    | 0.6463 | 0.5486 | 0.8569   |
| 0.0056        | 83.0  | 2075 | 0.9904          | 0.4903    | 0.6420 | 0.5560 | 0.8567   |
| 0.0056        | 84.0  | 2100 | 0.9920          | 0.4829    | 0.6410 | 0.5508 | 0.8572   |
| 0.0056        | 85.0  | 2125 | 0.9817          | 0.4947    | 0.6463 | 0.5604 | 0.8600   |
| 0.0056        | 86.0  | 2150 | 0.9837          | 0.4900    | 0.6484 | 0.5582 | 0.8601   |
| 0.0056        | 87.0  | 2175 | 0.9908          | 0.4852    | 0.6420 | 0.5527 | 0.8572   |
| 0.0056        | 88.0  | 2200 | 0.9873          | 0.5049    | 0.6484 | 0.5677 | 0.8590   |
| 0.0056        | 89.0  | 2225 | 0.9924          | 0.4843    | 0.6367 | 0.5502 | 0.8560   |
| 0.0056        | 90.0  | 2250 | 0.9897          | 0.4794    | 0.6378 | 0.5473 | 0.8574   |
| 0.0056        | 91.0  | 2275 | 0.9956          | 0.4779    | 0.6389 | 0.5468 | 0.8569   |
| 0.0056        | 92.0  | 2300 | 1.0018          | 0.4829    | 0.6410 | 0.5508 | 0.8567   |
| 0.0056        | 93.0  | 2325 | 1.0021          | 0.4753    | 0.6410 | 0.5459 | 0.8562   |
| 0.0056        | 94.0  | 2350 | 0.9968          | 0.4817    | 0.6410 | 0.5501 | 0.8575   |
| 0.0056        | 95.0  | 2375 | 0.9940          | 0.4799    | 0.6441 | 0.5500 | 0.8584   |
| 0.0056        | 96.0  | 2400 | 0.9964          | 0.4780    | 0.6420 | 0.5480 | 0.8577   |
| 0.0056        | 97.0  | 2425 | 1.0001          | 0.4785    | 0.6463 | 0.5499 | 0.8578   |
| 0.0056        | 98.0  | 2450 | 0.9989          | 0.4797    | 0.6473 | 0.5510 | 0.8582   |
| 0.0056        | 99.0  | 2475 | 0.9995          | 0.4762    | 0.6452 | 0.5480 | 0.8572   |
| 0.0043        | 100.0 | 2500 | 0.9998          | 0.4800    | 0.6452 | 0.5505 | 0.8566   |


### 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-finetuned-v2", "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-finetuned-v2
===================================


This model is a fine-tuned version of joheras/bsc-bio-ehr-es-finetuned-clinais-v2 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.9998
* Precision: 0.4800
* Recall: 0.6452
* F1: 0.5505
* Accuracy: 0.8566


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