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

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: 0.9890
- Precision: 0.4656
- Recall: 0.6294
- F1: 0.5352
- Accuracy: 0.8600

## 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   | 1.1489          | 0.0399    | 0.0623 | 0.0486 | 0.6528   |
| No log        | 2.0   | 50   | 0.7106          | 0.1739    | 0.2070 | 0.1890 | 0.8063   |
| No log        | 3.0   | 75   | 0.6518          | 0.2380    | 0.2724 | 0.2541 | 0.8202   |
| No log        | 4.0   | 100  | 0.6281          | 0.3515    | 0.4562 | 0.3971 | 0.8288   |
| No log        | 5.0   | 125  | 0.6029          | 0.3424    | 0.4372 | 0.3840 | 0.8357   |
| No log        | 6.0   | 150  | 0.5911          | 0.3479    | 0.4446 | 0.3904 | 0.8344   |
| No log        | 7.0   | 175  | 0.5809          | 0.3363    | 0.4382 | 0.3806 | 0.8429   |
| No log        | 8.0   | 200  | 0.5792          | 0.3338    | 0.4593 | 0.3867 | 0.8442   |
| No log        | 9.0   | 225  | 0.5980          | 0.3465    | 0.4984 | 0.4088 | 0.8531   |
| No log        | 10.0  | 250  | 0.6144          | 0.3700    | 0.5333 | 0.4369 | 0.8511   |
| No log        | 11.0  | 275  | 0.6376          | 0.3634    | 0.5280 | 0.4305 | 0.8445   |
| No log        | 12.0  | 300  | 0.6668          | 0.3802    | 0.5396 | 0.4461 | 0.8454   |
| No log        | 13.0  | 325  | 0.6618          | 0.3957    | 0.5692 | 0.4669 | 0.8506   |
| No log        | 14.0  | 350  | 0.6622          | 0.3906    | 0.5713 | 0.4640 | 0.8563   |
| No log        | 15.0  | 375  | 0.6637          | 0.4241    | 0.5808 | 0.4902 | 0.8565   |
| No log        | 16.0  | 400  | 0.6884          | 0.4251    | 0.5903 | 0.4943 | 0.8594   |
| No log        | 17.0  | 425  | 0.7183          | 0.4213    | 0.6051 | 0.4967 | 0.8517   |
| No log        | 18.0  | 450  | 0.7387          | 0.4104    | 0.5977 | 0.4867 | 0.8545   |
| No log        | 19.0  | 475  | 0.7256          | 0.4261    | 0.5998 | 0.4982 | 0.8563   |
| 0.346         | 20.0  | 500  | 0.7427          | 0.4178    | 0.6040 | 0.4940 | 0.8534   |
| 0.346         | 21.0  | 525  | 0.7562          | 0.4240    | 0.6008 | 0.4972 | 0.8569   |
| 0.346         | 22.0  | 550  | 0.7590          | 0.4038    | 0.6051 | 0.4844 | 0.8517   |
| 0.346         | 23.0  | 575  | 0.7677          | 0.4163    | 0.6146 | 0.4964 | 0.8573   |
| 0.346         | 24.0  | 600  | 0.8148          | 0.4127    | 0.6040 | 0.4904 | 0.8483   |
| 0.346         | 25.0  | 625  | 0.7992          | 0.4230    | 0.6030 | 0.4972 | 0.8533   |
| 0.346         | 26.0  | 650  | 0.8156          | 0.4203    | 0.6072 | 0.4968 | 0.8537   |
| 0.346         | 27.0  | 675  | 0.7999          | 0.4356    | 0.6103 | 0.5084 | 0.8562   |
| 0.346         | 28.0  | 700  | 0.8326          | 0.4379    | 0.6146 | 0.5114 | 0.8508   |
| 0.346         | 29.0  | 725  | 0.8394          | 0.4441    | 0.6209 | 0.5178 | 0.8542   |
| 0.346         | 30.0  | 750  | 0.8414          | 0.4373    | 0.6072 | 0.5084 | 0.8535   |
| 0.346         | 31.0  | 775  | 0.8363          | 0.4394    | 0.6082 | 0.5102 | 0.8566   |
| 0.346         | 32.0  | 800  | 0.8442          | 0.4536    | 0.6188 | 0.5234 | 0.8574   |
| 0.346         | 33.0  | 825  | 0.8470          | 0.4655    | 0.6199 | 0.5317 | 0.8608   |
| 0.346         | 34.0  | 850  | 0.8323          | 0.4647    | 0.6177 | 0.5304 | 0.8587   |
| 0.346         | 35.0  | 875  | 0.8590          | 0.4495    | 0.6199 | 0.5211 | 0.8573   |
| 0.346         | 36.0  | 900  | 0.8457          | 0.4542    | 0.6230 | 0.5254 | 0.8589   |
| 0.346         | 37.0  | 925  | 0.8720          | 0.4543    | 0.6251 | 0.5262 | 0.8552   |
| 0.346         | 38.0  | 950  | 0.8736          | 0.4562    | 0.6167 | 0.5245 | 0.8562   |
| 0.346         | 39.0  | 975  | 0.8710          | 0.4384    | 0.6199 | 0.5136 | 0.8543   |
| 0.0257        | 40.0  | 1000 | 0.8805          | 0.4416    | 0.6230 | 0.5169 | 0.8569   |
| 0.0257        | 41.0  | 1025 | 0.8963          | 0.4634    | 0.6209 | 0.5307 | 0.8536   |
| 0.0257        | 42.0  | 1050 | 0.8973          | 0.4619    | 0.6146 | 0.5274 | 0.8546   |
| 0.0257        | 43.0  | 1075 | 0.9123          | 0.4733    | 0.6188 | 0.5364 | 0.8571   |
| 0.0257        | 44.0  | 1100 | 0.9169          | 0.4570    | 0.6230 | 0.5273 | 0.8532   |
| 0.0257        | 45.0  | 1125 | 0.9094          | 0.4847    | 0.6357 | 0.5500 | 0.8592   |
| 0.0257        | 46.0  | 1150 | 0.9096          | 0.4761    | 0.6304 | 0.5425 | 0.8611   |
| 0.0257        | 47.0  | 1175 | 0.9074          | 0.4622    | 0.6262 | 0.5318 | 0.8590   |
| 0.0257        | 48.0  | 1200 | 0.9087          | 0.4536    | 0.6199 | 0.5239 | 0.8582   |
| 0.0257        | 49.0  | 1225 | 0.9412          | 0.4426    | 0.6103 | 0.5131 | 0.8580   |
| 0.0257        | 50.0  | 1250 | 0.9221          | 0.4435    | 0.6262 | 0.5193 | 0.8587   |
| 0.0257        | 51.0  | 1275 | 0.9232          | 0.4608    | 0.6199 | 0.5286 | 0.8578   |
| 0.0257        | 52.0  | 1300 | 0.9313          | 0.4696    | 0.6199 | 0.5344 | 0.8592   |
| 0.0257        | 53.0  | 1325 | 0.9340          | 0.4529    | 0.6241 | 0.5249 | 0.8603   |
| 0.0257        | 54.0  | 1350 | 0.9418          | 0.4599    | 0.6241 | 0.5296 | 0.8561   |
| 0.0257        | 55.0  | 1375 | 0.9428          | 0.4608    | 0.6146 | 0.5267 | 0.8579   |
| 0.0257        | 56.0  | 1400 | 0.9386          | 0.4728    | 0.6230 | 0.5376 | 0.8608   |
| 0.0257        | 57.0  | 1425 | 0.9467          | 0.4641    | 0.6209 | 0.5312 | 0.8579   |
| 0.0257        | 58.0  | 1450 | 0.9402          | 0.4639    | 0.6167 | 0.5295 | 0.8614   |
| 0.0257        | 59.0  | 1475 | 0.9389          | 0.4667    | 0.6220 | 0.5333 | 0.8601   |
| 0.0095        | 60.0  | 1500 | 0.9363          | 0.4633    | 0.6262 | 0.5326 | 0.8597   |
| 0.0095        | 61.0  | 1525 | 0.9302          | 0.4706    | 0.6251 | 0.5370 | 0.8604   |
| 0.0095        | 62.0  | 1550 | 0.9456          | 0.4707    | 0.6272 | 0.5378 | 0.8609   |
| 0.0095        | 63.0  | 1575 | 0.9470          | 0.4700    | 0.6283 | 0.5377 | 0.8602   |
| 0.0095        | 64.0  | 1600 | 0.9706          | 0.4609    | 0.6230 | 0.5299 | 0.8562   |
| 0.0095        | 65.0  | 1625 | 0.9710          | 0.4785    | 0.6230 | 0.5413 | 0.8567   |
| 0.0095        | 66.0  | 1650 | 0.9715          | 0.4806    | 0.6283 | 0.5446 | 0.8568   |
| 0.0095        | 67.0  | 1675 | 0.9638          | 0.4621    | 0.6177 | 0.5287 | 0.8586   |
| 0.0095        | 68.0  | 1700 | 0.9750          | 0.4754    | 0.6230 | 0.5393 | 0.8568   |
| 0.0095        | 69.0  | 1725 | 0.9856          | 0.4643    | 0.6251 | 0.5329 | 0.8554   |
| 0.0095        | 70.0  | 1750 | 0.9855          | 0.4512    | 0.6199 | 0.5222 | 0.8570   |
| 0.0095        | 71.0  | 1775 | 0.9811          | 0.4756    | 0.6272 | 0.5410 | 0.8563   |
| 0.0095        | 72.0  | 1800 | 0.9858          | 0.4679    | 0.6167 | 0.5321 | 0.8569   |
| 0.0095        | 73.0  | 1825 | 0.9794          | 0.4676    | 0.6241 | 0.5346 | 0.8580   |
| 0.0095        | 74.0  | 1850 | 0.9774          | 0.4772    | 0.6199 | 0.5393 | 0.8572   |
| 0.0095        | 75.0  | 1875 | 0.9772          | 0.4810    | 0.6272 | 0.5445 | 0.8580   |
| 0.0095        | 76.0  | 1900 | 0.9805          | 0.4757    | 0.6294 | 0.5418 | 0.8584   |
| 0.0095        | 77.0  | 1925 | 0.9782          | 0.4782    | 0.6251 | 0.5419 | 0.8585   |
| 0.0095        | 78.0  | 1950 | 0.9921          | 0.4731    | 0.6315 | 0.5409 | 0.8572   |
| 0.0095        | 79.0  | 1975 | 0.9797          | 0.4684    | 0.6188 | 0.5332 | 0.8599   |
| 0.0057        | 80.0  | 2000 | 0.9844          | 0.4747    | 0.6251 | 0.5397 | 0.8585   |
| 0.0057        | 81.0  | 2025 | 0.9824          | 0.4636    | 0.6251 | 0.5324 | 0.8579   |
| 0.0057        | 82.0  | 2050 | 0.9803          | 0.4765    | 0.6220 | 0.5396 | 0.8591   |
| 0.0057        | 83.0  | 2075 | 0.9834          | 0.4742    | 0.6304 | 0.5413 | 0.8607   |
| 0.0057        | 84.0  | 2100 | 0.9897          | 0.4727    | 0.6315 | 0.5407 | 0.8585   |
| 0.0057        | 85.0  | 2125 | 0.9835          | 0.4723    | 0.6220 | 0.5369 | 0.8573   |
| 0.0057        | 86.0  | 2150 | 0.9838          | 0.4773    | 0.6230 | 0.5405 | 0.8580   |
| 0.0057        | 87.0  | 2175 | 0.9879          | 0.4663    | 0.6220 | 0.5330 | 0.8579   |
| 0.0057        | 88.0  | 2200 | 0.9844          | 0.4806    | 0.6262 | 0.5438 | 0.8595   |
| 0.0057        | 89.0  | 2225 | 0.9903          | 0.4767    | 0.6262 | 0.5413 | 0.8588   |
| 0.0057        | 90.0  | 2250 | 0.9929          | 0.4806    | 0.6283 | 0.5446 | 0.8581   |
| 0.0057        | 91.0  | 2275 | 0.9947          | 0.4873    | 0.6294 | 0.5493 | 0.8576   |
| 0.0057        | 92.0  | 2300 | 0.9888          | 0.4713    | 0.6251 | 0.5374 | 0.8581   |
| 0.0057        | 93.0  | 2325 | 0.9869          | 0.4780    | 0.6315 | 0.5441 | 0.8586   |
| 0.0057        | 94.0  | 2350 | 0.9866          | 0.4807    | 0.6315 | 0.5459 | 0.8587   |
| 0.0057        | 95.0  | 2375 | 0.9892          | 0.4850    | 0.6315 | 0.5486 | 0.8587   |
| 0.0057        | 96.0  | 2400 | 0.9891          | 0.4735    | 0.6325 | 0.5416 | 0.8589   |
| 0.0057        | 97.0  | 2425 | 0.9883          | 0.4635    | 0.6294 | 0.5338 | 0.8593   |
| 0.0057        | 98.0  | 2450 | 0.9883          | 0.4670    | 0.6283 | 0.5358 | 0.8601   |
| 0.0057        | 99.0  | 2475 | 0.9889          | 0.4671    | 0.6304 | 0.5366 | 0.8601   |
| 0.0045        | 100.0 | 2500 | 0.9890          | 0.4656    | 0.6294 | 0.5352 | 0.8600   |


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


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: 0.9890
* Precision: 0.4656
* Recall: 0.6294
* F1: 0.5352
* Accuracy: 0.8600


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