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# roberta-finetuned-solvencia-v1

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4141
- Accuracy: 0.8919
- F1: 0.8919
- Precision: 0.8919
- Recall: 0.8919

## 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: 8
- eval_batch_size: 8
- 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     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log        | 1.0   | 333  | 0.3781          | 0.8544   | 0.8544 | 0.8544    | 0.8544 |
| 0.4429        | 2.0   | 666  | 0.3295          | 0.8679   | 0.8679 | 0.8679    | 0.8679 |
| 0.4429        | 3.0   | 999  | 0.3664          | 0.8784   | 0.8784 | 0.8784    | 0.8784 |
| 0.3512        | 4.0   | 1332 | 0.4602          | 0.8649   | 0.8649 | 0.8649    | 0.8649 |
| 0.2975        | 5.0   | 1665 | 0.4721          | 0.8889   | 0.8889 | 0.8889    | 0.8889 |
| 0.2975        | 6.0   | 1998 | 0.4141          | 0.8919   | 0.8919 | 0.8919    | 0.8919 |
| 0.2499        | 7.0   | 2331 | 0.4054          | 0.8889   | 0.8889 | 0.8889    | 0.8889 |
| 0.2132        | 8.0   | 2664 | 0.4878          | 0.8829   | 0.8829 | 0.8829    | 0.8829 |
| 0.2132        | 9.0   | 2997 | 0.4867          | 0.8904   | 0.8904 | 0.8904    | 0.8904 |
| 0.1812        | 10.0  | 3330 | 0.5339          | 0.8889   | 0.8889 | 0.8889    | 0.8889 |


### Framework versions

- Transformers 4.16.2
- Pytorch 1.9.1
- Datasets 1.18.4
- Tokenizers 0.11.6

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "roberta-finetuned-solvencia-v1", "results": []}]}
transformers pytorch tensorboard roberta text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

roberta-finetuned-solvencia-v1
==============================


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


* Loss: 0.4141
* Accuracy: 0.8919
* F1: 0.8919
* Precision: 0.8919
* Recall: 0.8919


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: 8
* eval\_batch\_size: 8
* 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 4.16.2
* Pytorch 1.9.1
* Datasets 1.18.4
* Tokenizers 0.11.6



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* 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 4.16.2
* Pytorch 1.9.1
* Datasets 1.18.4
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #tensorboard #roberta #text-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: 8
* eval\_batch\_size: 8
* 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 4.16.2
* Pytorch 1.9.1
* Datasets 1.18.4
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #tensorboard #roberta #text-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: 8
* eval\_batch\_size: 8
* 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 4.16.2
* Pytorch 1.9.1
* Datasets 1.18.4
* Tokenizers 0.11.6