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# clinico-xlm-roberta-large-finetuned-augmented1

This model is a fine-tuned version of [joheras/xlm-roberta-base-finetuned-clinais](https://huggingface.co/joheras/xlm-roberta-base-finetuned-clinais) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4632
- Precision: 0.5074
- Recall: 0.6293
- F1: 0.5618
- Accuracy: 0.8560

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 179  | 0.6074          | 0.3003    | 0.5057 | 0.3768 | 0.8147   |
| No log        | 2.0   | 358  | 0.5758          | 0.3091    | 0.5263 | 0.3895 | 0.8415   |
| 0.4921        | 3.0   | 537  | 0.7089          | 0.3357    | 0.5503 | 0.4170 | 0.8438   |
| 0.4921        | 4.0   | 716  | 0.8219          | 0.3541    | 0.5789 | 0.4394 | 0.8391   |
| 0.4921        | 5.0   | 895  | 0.8857          | 0.4050    | 0.5950 | 0.4819 | 0.8507   |
| 0.0628        | 6.0   | 1074 | 0.9386          | 0.3888    | 0.5961 | 0.4706 | 0.8485   |
| 0.0628        | 7.0   | 1253 | 1.0302          | 0.4072    | 0.6201 | 0.4916 | 0.8458   |
| 0.0628        | 8.0   | 1432 | 0.9960          | 0.4352    | 0.6144 | 0.5095 | 0.8535   |
| 0.0195        | 9.0   | 1611 | 1.0593          | 0.4349    | 0.6190 | 0.5109 | 0.8546   |
| 0.0195        | 10.0  | 1790 | 1.1262          | 0.4366    | 0.6190 | 0.5121 | 0.8512   |
| 0.0195        | 11.0  | 1969 | 1.1968          | 0.4675    | 0.6178 | 0.5323 | 0.8498   |
| 0.0085        | 12.0  | 2148 | 1.1940          | 0.4368    | 0.6167 | 0.5114 | 0.8472   |
| 0.0085        | 13.0  | 2327 | 1.1795          | 0.4670    | 0.6224 | 0.5336 | 0.8588   |
| 0.0043        | 14.0  | 2506 | 1.2596          | 0.4791    | 0.6281 | 0.5436 | 0.8534   |
| 0.0043        | 15.0  | 2685 | 1.2958          | 0.4580    | 0.6236 | 0.5281 | 0.8504   |
| 0.0043        | 16.0  | 2864 | 1.2891          | 0.4977    | 0.6270 | 0.5549 | 0.8533   |
| 0.0022        | 17.0  | 3043 | 1.2992          | 0.4615    | 0.6178 | 0.5284 | 0.8504   |
| 0.0022        | 18.0  | 3222 | 1.3102          | 0.4681    | 0.5961 | 0.5244 | 0.8549   |
| 0.0022        | 19.0  | 3401 | 1.2654          | 0.4485    | 0.6178 | 0.5197 | 0.8602   |
| 0.0027        | 20.0  | 3580 | 1.3123          | 0.4420    | 0.6064 | 0.5113 | 0.8479   |
| 0.0027        | 21.0  | 3759 | 1.3100          | 0.4877    | 0.6350 | 0.5517 | 0.8554   |
| 0.0027        | 22.0  | 3938 | 1.3696          | 0.4683    | 0.6247 | 0.5353 | 0.8532   |
| 0.0013        | 23.0  | 4117 | 1.3392          | 0.4851    | 0.5973 | 0.5354 | 0.8536   |
| 0.0013        | 24.0  | 4296 | 1.3680          | 0.4946    | 0.6270 | 0.5530 | 0.8509   |
| 0.0013        | 25.0  | 4475 | 1.4145          | 0.4914    | 0.6236 | 0.5497 | 0.8512   |
| 0.0014        | 26.0  | 4654 | 1.2962          | 0.5       | 0.6327 | 0.5586 | 0.8634   |
| 0.0014        | 27.0  | 4833 | 1.3871          | 0.4743    | 0.6327 | 0.5422 | 0.8532   |
| 0.0014        | 28.0  | 5012 | 1.3761          | 0.4982    | 0.6430 | 0.5614 | 0.8562   |
| 0.0014        | 29.0  | 5191 | 1.3929          | 0.4764    | 0.6224 | 0.5397 | 0.8501   |
| 0.0014        | 30.0  | 5370 | 1.3716          | 0.4862    | 0.6270 | 0.5477 | 0.8558   |
| 0.0006        | 31.0  | 5549 | 1.3881          | 0.4573    | 0.6247 | 0.5280 | 0.8566   |
| 0.0006        | 32.0  | 5728 | 1.3783          | 0.4688    | 0.6350 | 0.5394 | 0.8591   |
| 0.0006        | 33.0  | 5907 | 1.4211          | 0.5060    | 0.6293 | 0.5609 | 0.8585   |
| 0.001         | 34.0  | 6086 | 1.4034          | 0.4819    | 0.6236 | 0.5436 | 0.8566   |
| 0.001         | 35.0  | 6265 | 1.4026          | 0.5168    | 0.6339 | 0.5694 | 0.8592   |
| 0.001         | 36.0  | 6444 | 1.4153          | 0.4904    | 0.6419 | 0.5560 | 0.8586   |
| 0.0003        | 37.0  | 6623 | 1.4071          | 0.5161    | 0.6224 | 0.5643 | 0.8569   |
| 0.0003        | 38.0  | 6802 | 1.4308          | 0.5046    | 0.6293 | 0.5601 | 0.8562   |
| 0.0003        | 39.0  | 6981 | 1.4089          | 0.5083    | 0.6339 | 0.5642 | 0.8597   |
| 0.0001        | 40.0  | 7160 | 1.4522          | 0.5093    | 0.6293 | 0.5629 | 0.8543   |
| 0.0001        | 41.0  | 7339 | 1.4427          | 0.4964    | 0.6224 | 0.5523 | 0.8545   |
| 0.0001        | 42.0  | 7518 | 1.4641          | 0.4759    | 0.6213 | 0.5390 | 0.8513   |
| 0.0001        | 43.0  | 7697 | 1.4405          | 0.5014    | 0.6293 | 0.5581 | 0.8595   |
| 0.0001        | 44.0  | 7876 | 1.4584          | 0.5060    | 0.6281 | 0.5605 | 0.8555   |
| 0.0002        | 45.0  | 8055 | 1.4847          | 0.5157    | 0.6201 | 0.5631 | 0.8526   |
| 0.0002        | 46.0  | 8234 | 1.4545          | 0.4977    | 0.6270 | 0.5549 | 0.8577   |
| 0.0002        | 47.0  | 8413 | 1.4482          | 0.5106    | 0.6339 | 0.5656 | 0.8570   |
| 0.0001        | 48.0  | 8592 | 1.4639          | 0.5074    | 0.6281 | 0.5613 | 0.8563   |
| 0.0001        | 49.0  | 8771 | 1.4643          | 0.5088    | 0.6270 | 0.5618 | 0.8563   |
| 0.0001        | 50.0  | 8950 | 1.4632          | 0.5074    | 0.6293 | 0.5618 | 0.8560   |


### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.0
- Datasets 2.8.0
- Tokenizers 0.12.1

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "clinico-xlm-roberta-large-finetuned-augmented1", "results": []}]}
transformers pytorch tensorboard xlm-roberta token-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

clinico-xlm-roberta-large-finetuned-augmented1
==============================================


This model is a fine-tuned version of joheras/xlm-roberta-base-finetuned-clinais on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.4632
* Precision: 0.5074
* Recall: 0.6293
* F1: 0.5618
* Accuracy: 0.8560


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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50
* mixed\_precision\_training: Native AMP


### 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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50
* mixed\_precision\_training: Native AMP ### 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 #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50
* mixed\_precision\_training: Native AMP ### 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 #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.0
* Datasets 2.8.0
* Tokenizers 0.12.1