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# roberta-bne-fine-tuned-text-classification-SL-dss

This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5089
- F1: 0.4781
- Recall: 0.4750
- Accuracy: 0.4750
- Precision: 0.5009

## 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: 3e-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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Recall | Accuracy | Precision |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:--------:|:---------:|
| 3.235         | 1.0   | 836  | 2.4142          | 0.3995 | 0.4471 | 0.4471   | 0.4786    |
| 2.0006        | 2.0   | 1672 | 2.1013          | 0.4672 | 0.4942 | 0.4942   | 0.4867    |
| 1.2424        | 3.0   | 2508 | 2.1138          | 0.4861 | 0.4852 | 0.4852   | 0.5132    |
| 0.7242        | 4.0   | 3344 | 2.2694          | 0.4828 | 0.4747 | 0.4747   | 0.5126    |
| 0.3403        | 5.0   | 4180 | 2.5089          | 0.4781 | 0.4750 | 0.4750   | 0.5009    |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
{"language": ["es"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1", "recall", "accuracy", "precision"], "model-index": [{"name": "roberta-bne-fine-tuned-text-classification-SL-dss", "results": []}]}
transformers pytorch roberta text-classification generated_from_trainer es license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

roberta-bne-fine-tuned-text-classification-SL-dss
=================================================


This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-bne on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 2.5089
* F1: 0.4781
* Recall: 0.4750
* Accuracy: 0.4750
* Precision: 0.5009


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: 3e-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
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 5


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-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
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-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
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3