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# clinico-roberta-biomedical-finetuned

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: 0.9272
- Precision: 0.5095
- Recall: 0.6463
- F1: 0.5698
- Accuracy: 0.8623

## 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   | 1.2199          | 0.0033    | 0.0053 | 0.0040 | 0.5756   |
| No log        | 2.0   | 50   | 0.7306          | 0.2031    | 0.2642 | 0.2296 | 0.8021   |
| No log        | 3.0   | 75   | 0.6366          | 0.2967    | 0.3811 | 0.3336 | 0.8235   |
| No log        | 4.0   | 100  | 0.6135          | 0.3497    | 0.4653 | 0.3993 | 0.8304   |
| No log        | 5.0   | 125  | 0.5845          | 0.3421    | 0.4537 | 0.3900 | 0.8331   |
| No log        | 6.0   | 150  | 0.5697          | 0.3307    | 0.4421 | 0.3784 | 0.8390   |
| No log        | 7.0   | 175  | 0.5415          | 0.3211    | 0.4495 | 0.3746 | 0.8471   |
| No log        | 8.0   | 200  | 0.5430          | 0.3589    | 0.5179 | 0.4240 | 0.8567   |
| No log        | 9.0   | 225  | 0.5513          | 0.3342    | 0.5474 | 0.4150 | 0.8604   |
| No log        | 10.0  | 250  | 0.5681          | 0.3769    | 0.5768 | 0.4559 | 0.8582   |
| No log        | 11.0  | 275  | 0.5813          | 0.3756    | 0.5863 | 0.4579 | 0.8553   |
| No log        | 12.0  | 300  | 0.6096          | 0.4181    | 0.5968 | 0.4918 | 0.8574   |
| No log        | 13.0  | 325  | 0.6318          | 0.3978    | 0.6042 | 0.4797 | 0.8539   |
| No log        | 14.0  | 350  | 0.6309          | 0.3892    | 0.5968 | 0.4711 | 0.8553   |
| No log        | 15.0  | 375  | 0.6559          | 0.3987    | 0.5968 | 0.4781 | 0.8565   |
| No log        | 16.0  | 400  | 0.6391          | 0.4275    | 0.6021 | 0.5    | 0.8560   |
| No log        | 17.0  | 425  | 0.6812          | 0.4388    | 0.6074 | 0.5095 | 0.8584   |
| No log        | 18.0  | 450  | 0.6901          | 0.4287    | 0.6137 | 0.5048 | 0.8563   |
| No log        | 19.0  | 475  | 0.6834          | 0.4572    | 0.6074 | 0.5217 | 0.8581   |
| 0.3478        | 20.0  | 500  | 0.7050          | 0.4397    | 0.6179 | 0.5138 | 0.8573   |
| 0.3478        | 21.0  | 525  | 0.7004          | 0.4462    | 0.6242 | 0.5204 | 0.8591   |
| 0.3478        | 22.0  | 550  | 0.7038          | 0.4264    | 0.6126 | 0.5028 | 0.8599   |
| 0.3478        | 23.0  | 575  | 0.7384          | 0.4416    | 0.6284 | 0.5187 | 0.8576   |
| 0.3478        | 24.0  | 600  | 0.7197          | 0.4479    | 0.62   | 0.5201 | 0.8619   |
| 0.3478        | 25.0  | 625  | 0.7412          | 0.4381    | 0.6221 | 0.5141 | 0.8559   |
| 0.3478        | 26.0  | 650  | 0.7535          | 0.4489    | 0.6242 | 0.5222 | 0.8566   |
| 0.3478        | 27.0  | 675  | 0.7534          | 0.4657    | 0.6432 | 0.5402 | 0.8586   |
| 0.3478        | 28.0  | 700  | 0.7672          | 0.4525    | 0.6168 | 0.5220 | 0.8567   |
| 0.3478        | 29.0  | 725  | 0.7680          | 0.4637    | 0.6316 | 0.5348 | 0.8599   |
| 0.3478        | 30.0  | 750  | 0.7590          | 0.4611    | 0.6242 | 0.5304 | 0.8607   |
| 0.3478        | 31.0  | 775  | 0.7671          | 0.4732    | 0.6326 | 0.5414 | 0.8625   |
| 0.3478        | 32.0  | 800  | 0.7921          | 0.4674    | 0.6337 | 0.5380 | 0.8590   |
| 0.3478        | 33.0  | 825  | 0.8037          | 0.4828    | 0.6358 | 0.5488 | 0.8574   |
| 0.3478        | 34.0  | 850  | 0.8376          | 0.4644    | 0.6242 | 0.5326 | 0.8534   |
| 0.3478        | 35.0  | 875  | 0.8346          | 0.4815    | 0.6284 | 0.5452 | 0.8552   |
| 0.3478        | 36.0  | 900  | 0.8249          | 0.4750    | 0.6305 | 0.5418 | 0.8567   |
| 0.3478        | 37.0  | 925  | 0.8420          | 0.4580    | 0.6305 | 0.5306 | 0.8548   |
| 0.3478        | 38.0  | 950  | 0.8341          | 0.4773    | 0.6305 | 0.5433 | 0.8550   |
| 0.3478        | 39.0  | 975  | 0.8085          | 0.4792    | 0.6316 | 0.5450 | 0.8653   |
| 0.0274        | 40.0  | 1000 | 0.7954          | 0.4992    | 0.6474 | 0.5637 | 0.8651   |
| 0.0274        | 41.0  | 1025 | 0.8145          | 0.4923    | 0.6421 | 0.5573 | 0.8635   |
| 0.0274        | 42.0  | 1050 | 0.8290          | 0.4911    | 0.6368 | 0.5545 | 0.8610   |
| 0.0274        | 43.0  | 1075 | 0.8468          | 0.4821    | 0.6379 | 0.5492 | 0.8571   |
| 0.0274        | 44.0  | 1100 | 0.8274          | 0.4791    | 0.6389 | 0.5476 | 0.8625   |
| 0.0274        | 45.0  | 1125 | 0.8583          | 0.4831    | 0.6305 | 0.5470 | 0.8551   |
| 0.0274        | 46.0  | 1150 | 0.8420          | 0.4726    | 0.6347 | 0.5418 | 0.8589   |
| 0.0274        | 47.0  | 1175 | 0.8631          | 0.5029    | 0.64   | 0.5632 | 0.8564   |
| 0.0274        | 48.0  | 1200 | 0.8421          | 0.4911    | 0.64   | 0.5558 | 0.8617   |
| 0.0274        | 49.0  | 1225 | 0.8564          | 0.5071    | 0.6411 | 0.5662 | 0.8631   |
| 0.0274        | 50.0  | 1250 | 0.8659          | 0.4845    | 0.6263 | 0.5464 | 0.8603   |
| 0.0274        | 51.0  | 1275 | 0.8596          | 0.4860    | 0.64   | 0.5525 | 0.8632   |
| 0.0274        | 52.0  | 1300 | 0.8713          | 0.4856    | 0.6368 | 0.5510 | 0.8593   |
| 0.0274        | 53.0  | 1325 | 0.8888          | 0.4868    | 0.64   | 0.5530 | 0.8585   |
| 0.0274        | 54.0  | 1350 | 0.8591          | 0.4816    | 0.6337 | 0.5473 | 0.8610   |
| 0.0274        | 55.0  | 1375 | 0.8755          | 0.4996    | 0.64   | 0.5611 | 0.8615   |
| 0.0274        | 56.0  | 1400 | 0.8749          | 0.5095    | 0.6484 | 0.5706 | 0.8583   |
| 0.0274        | 57.0  | 1425 | 0.8867          | 0.5025    | 0.6453 | 0.5650 | 0.8580   |
| 0.0274        | 58.0  | 1450 | 0.8905          | 0.4947    | 0.6337 | 0.5556 | 0.8579   |
| 0.0274        | 59.0  | 1475 | 0.8911          | 0.4881    | 0.6495 | 0.5574 | 0.8596   |
| 0.0099        | 60.0  | 1500 | 0.9220          | 0.4914    | 0.6347 | 0.5540 | 0.8570   |
| 0.0099        | 61.0  | 1525 | 0.8687          | 0.4786    | 0.6368 | 0.5465 | 0.8594   |
| 0.0099        | 62.0  | 1550 | 0.9080          | 0.4906    | 0.6337 | 0.5531 | 0.8575   |
| 0.0099        | 63.0  | 1575 | 0.9004          | 0.4831    | 0.6337 | 0.5483 | 0.8583   |
| 0.0099        | 64.0  | 1600 | 0.8906          | 0.4778    | 0.6337 | 0.5448 | 0.8619   |
| 0.0099        | 65.0  | 1625 | 0.8870          | 0.4959    | 0.6368 | 0.5576 | 0.8618   |
| 0.0099        | 66.0  | 1650 | 0.8843          | 0.4851    | 0.6358 | 0.5503 | 0.8611   |
| 0.0099        | 67.0  | 1675 | 0.8923          | 0.4912    | 0.6453 | 0.5578 | 0.8618   |
| 0.0099        | 68.0  | 1700 | 0.8864          | 0.4898    | 0.6337 | 0.5525 | 0.8615   |
| 0.0099        | 69.0  | 1725 | 0.8974          | 0.4943    | 0.6411 | 0.5582 | 0.8615   |
| 0.0099        | 70.0  | 1750 | 0.8851          | 0.4821    | 0.6379 | 0.5492 | 0.8611   |
| 0.0099        | 71.0  | 1775 | 0.8958          | 0.4920    | 0.6453 | 0.5583 | 0.8593   |
| 0.0099        | 72.0  | 1800 | 0.8880          | 0.4988    | 0.6411 | 0.5610 | 0.8618   |
| 0.0099        | 73.0  | 1825 | 0.8959          | 0.4852    | 0.6379 | 0.5512 | 0.8606   |
| 0.0099        | 74.0  | 1850 | 0.9036          | 0.4773    | 0.6305 | 0.5433 | 0.8598   |
| 0.0099        | 75.0  | 1875 | 0.9031          | 0.4864    | 0.6389 | 0.5523 | 0.8615   |
| 0.0099        | 76.0  | 1900 | 0.9243          | 0.4907    | 0.6368 | 0.5543 | 0.8590   |
| 0.0099        | 77.0  | 1925 | 0.9285          | 0.4877    | 0.6453 | 0.5555 | 0.8590   |
| 0.0099        | 78.0  | 1950 | 0.9261          | 0.5074    | 0.6516 | 0.5705 | 0.8598   |
| 0.0099        | 79.0  | 1975 | 0.9374          | 0.5037    | 0.64   | 0.5637 | 0.8580   |
| 0.0061        | 80.0  | 2000 | 0.9165          | 0.5021    | 0.6316 | 0.5594 | 0.8621   |
| 0.0061        | 81.0  | 2025 | 0.9307          | 0.5162    | 0.6368 | 0.5702 | 0.8582   |
| 0.0061        | 82.0  | 2050 | 0.9369          | 0.4911    | 0.6358 | 0.5541 | 0.8574   |
| 0.0061        | 83.0  | 2075 | 0.9293          | 0.5191    | 0.6421 | 0.5741 | 0.8584   |
| 0.0061        | 84.0  | 2100 | 0.9187          | 0.5004    | 0.6453 | 0.5637 | 0.8629   |
| 0.0061        | 85.0  | 2125 | 0.9293          | 0.4927    | 0.6379 | 0.5560 | 0.8623   |
| 0.0061        | 86.0  | 2150 | 0.9200          | 0.5041    | 0.6453 | 0.5660 | 0.8634   |
| 0.0061        | 87.0  | 2175 | 0.9273          | 0.4992    | 0.6421 | 0.5617 | 0.8631   |
| 0.0061        | 88.0  | 2200 | 0.9325          | 0.5021    | 0.6442 | 0.5643 | 0.8623   |
| 0.0061        | 89.0  | 2225 | 0.9245          | 0.4844    | 0.6389 | 0.5511 | 0.8630   |
| 0.0061        | 90.0  | 2250 | 0.9291          | 0.4979    | 0.6368 | 0.5589 | 0.8593   |
| 0.0061        | 91.0  | 2275 | 0.9264          | 0.5083    | 0.6432 | 0.5678 | 0.8622   |
| 0.0061        | 92.0  | 2300 | 0.9283          | 0.5025    | 0.6411 | 0.5634 | 0.8619   |
| 0.0061        | 93.0  | 2325 | 0.9264          | 0.5008    | 0.6442 | 0.5635 | 0.8613   |
| 0.0061        | 94.0  | 2350 | 0.9205          | 0.5079    | 0.6463 | 0.5688 | 0.8626   |
| 0.0061        | 95.0  | 2375 | 0.9223          | 0.5121    | 0.6484 | 0.5722 | 0.8625   |
| 0.0061        | 96.0  | 2400 | 0.9244          | 0.5045    | 0.6421 | 0.5651 | 0.8620   |
| 0.0061        | 97.0  | 2425 | 0.9248          | 0.5062    | 0.6463 | 0.5677 | 0.8622   |
| 0.0061        | 98.0  | 2450 | 0.9277          | 0.5037    | 0.6453 | 0.5658 | 0.8621   |
| 0.0061        | 99.0  | 2475 | 0.9272          | 0.5083    | 0.6463 | 0.5690 | 0.8623   |
| 0.0046        | 100.0 | 2500 | 0.9272          | 0.5095    | 0.6463 | 0.5698 | 0.8623   |


### 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", "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
====================================


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: 0.9272
* Precision: 0.5095
* Recall: 0.6463
* F1: 0.5698
* Accuracy: 0.8623


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