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# Regression_roberta_2

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.4601
- Mse: 4.4601
- Mae: 1.5854
- R2: -1.3013
- Accuracy: 0.4286

## 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mse    | Mae    | R2      | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:--------:|
| No log        | 1.0   | 1    | 3.5793          | 3.5793 | 1.5863 | -2.3584 | 0.2857   |
| No log        | 2.0   | 2    | 3.3920          | 3.3920 | 1.5228 | -2.1826 | 0.2857   |
| No log        | 3.0   | 3    | 3.2161          | 3.2161 | 1.4599 | -2.0176 | 0.2857   |
| No log        | 4.0   | 4    | 3.0482          | 3.0482 | 1.3958 | -1.8601 | 0.4286   |
| No log        | 5.0   | 5    | 2.8803          | 2.8803 | 1.3393 | -1.7026 | 0.4286   |
| No log        | 6.0   | 6    | 2.7157          | 2.7157 | 1.2996 | -1.5481 | 0.4286   |
| No log        | 7.0   | 7    | 2.5511          | 2.5511 | 1.2760 | -1.3937 | 0.4286   |
| No log        | 8.0   | 8    | 2.3882          | 2.3882 | 1.2524 | -1.2408 | 0.4286   |
| No log        | 9.0   | 9    | 2.2328          | 2.2328 | 1.2424 | -1.0950 | 0.2857   |
| No log        | 10.0  | 10   | 2.0956          | 2.0956 | 1.2608 | -0.9662 | 0.1429   |
| No log        | 11.0  | 11   | 1.9947          | 1.9947 | 1.2837 | -0.8716 | 0.1429   |
| No log        | 12.0  | 12   | 1.9404          | 1.9404 | 1.3073 | -0.8206 | 0.0      |
| No log        | 13.0  | 13   | 1.9271          | 1.9271 | 1.3254 | -0.8082 | 0.0      |
| No log        | 14.0  | 14   | 1.9439          | 1.9439 | 1.3369 | -0.8239 | 0.0      |
| No log        | 15.0  | 15   | 1.9740          | 1.9740 | 1.3394 | -0.8522 | 0.0      |
| No log        | 16.0  | 16   | 2.0039          | 2.0039 | 1.3338 | -0.8802 | 0.0      |
| No log        | 17.0  | 17   | 2.0291          | 2.0291 | 1.3237 | -0.9039 | 0.1429   |
| No log        | 18.0  | 18   | 2.0540          | 2.0540 | 1.3152 | -0.9273 | 0.1429   |
| No log        | 19.0  | 19   | 2.0721          | 2.0721 | 1.3080 | -0.9442 | 0.1429   |
| No log        | 20.0  | 20   | 2.0814          | 2.0814 | 1.3037 | -0.9529 | 0.1429   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "Regression_roberta_2", "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 

Regression\_roberta\_2
======================


This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 4.4601
* Mse: 4.4601
* Mae: 1.5854
* R2: -1.3013
* Accuracy: 0.4286


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: 20


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2



### 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: 20 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
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: 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: 20 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
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: 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: 20### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2