
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# roberta-base-bne-finetuned-detests

This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1686
- Accuracy: 0.8494
- F1-score: 0.7869
- Precision: 0.7855
- Recall: 0.7883
- Auc: 0.7883

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score | Precision | Recall | Auc    |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:------:|:------:|
| 0.0238        | 1.0   | 174  | 0.6262          | 0.8543   | 0.7656   | 0.8161    | 0.7382 | 0.7382 |
| 0.0269        | 2.0   | 348  | 1.1233          | 0.8298   | 0.6964   | 0.7997    | 0.6665 | 0.6665 |
| 0.0003        | 3.0   | 522  | 0.9814          | 0.8429   | 0.7600   | 0.7839    | 0.7435 | 0.7435 |
| 0.0001        | 4.0   | 696  | 1.1054          | 0.8445   | 0.7794   | 0.7787    | 0.7801 | 0.7801 |
| 0.0001        | 5.0   | 870  | 1.1088          | 0.8511   | 0.7948   | 0.7865    | 0.8046 | 0.8046 |
| 0.0001        | 6.0   | 1044 | 1.1265          | 0.8511   | 0.7908   | 0.7873    | 0.7945 | 0.7945 |
| 0.0001        | 7.0   | 1218 | 1.1441          | 0.8494   | 0.7879   | 0.7852    | 0.7909 | 0.7909 |
| 0.0           | 8.0   | 1392 | 1.1574          | 0.8494   | 0.7869   | 0.7855    | 0.7883 | 0.7883 |
| 0.0           | 9.0   | 1566 | 1.1657          | 0.8494   | 0.7869   | 0.7855    | 0.7883 | 0.7883 |
| 0.0           | 10.0  | 1740 | 1.1686          | 0.8494   | 0.7869   | 0.7855    | 0.7883 | 0.7883 |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall"], "base_model": "BSC-TeMU/roberta-base-bne", "model-index": [{"name": "roberta-base-bne-finetuned-detests", "results": []}]}
transformers pytorch tensorboard roberta text-classification generated_from_trainer base_model:BSC-TeMU/roberta-base-bne license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #base_model-BSC-TeMU/roberta-base-bne #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

roberta-base-bne-finetuned-detests
==================================


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


* Loss: 1.1686
* Accuracy: 0.8494
* F1-score: 0.7869
* Precision: 0.7855
* Recall: 0.7883
* Auc: 0.7883


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: 10


### Training results



### Framework versions


* Transformers 4.33.1
* Pytorch 2.0.1+cu118
* Datasets 2.14.5
* Tokenizers 0.13.3



### 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: 10 ### Training results ### Framework versions


* Transformers 4.33.1
* Pytorch 2.0.1+cu118
* Datasets 2.14.5
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #base_model-BSC-TeMU/roberta-base-bne #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: 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: 10 ### Training results ### Framework versions


* Transformers 4.33.1
* Pytorch 2.0.1+cu118
* Datasets 2.14.5
* Tokenizers 0.13.3
passage: TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #base_model-BSC-TeMU/roberta-base-bne #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: 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: 10### Training results### Framework versions


* Transformers 4.33.1
* Pytorch 2.0.1+cu118
* Datasets 2.14.5
* Tokenizers 0.13.3