
<!-- 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. -->

# Regression_roberta

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: 0.1625
- Mse: 0.1625
- Mae: 0.3187
- R2: 0.9161
- Accuracy: 0.5714

## 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mse    | Mae    | R2      | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:--------:|
| No log        | 1.0   | 7    | 3.0379          | 3.0379 | 1.3888 | -1.8504 | 0.4286   |
| No log        | 2.0   | 14   | 2.4610          | 2.4610 | 1.2703 | -1.3091 | 0.4286   |
| No log        | 3.0   | 21   | 1.9135          | 1.9135 | 1.3077 | -0.7954 | 0.0      |
| No log        | 4.0   | 28   | 1.7647          | 1.7647 | 1.1897 | -0.6557 | 0.1429   |
| No log        | 5.0   | 35   | 2.2432          | 2.2432 | 1.1115 | -1.1047 | 0.5714   |
| No log        | 6.0   | 42   | 2.3279          | 2.3279 | 1.1562 | -1.1842 | 0.5714   |
| No log        | 7.0   | 49   | 1.9694          | 1.9694 | 1.0216 | -0.8478 | 0.5714   |
| No log        | 8.0   | 56   | 1.6951          | 1.6951 | 0.9216 | -0.5905 | 0.5714   |
| No log        | 9.0   | 63   | 1.5986          | 1.5986 | 0.8898 | -0.4999 | 0.5714   |
| No log        | 10.0  | 70   | 1.2021          | 1.2021 | 0.7820 | -0.1279 | 0.5714   |
| No log        | 11.0  | 77   | 1.0724          | 1.0724 | 0.8114 | -0.0062 | 0.5714   |
| No log        | 12.0  | 84   | 0.7198          | 0.7198 | 0.6954 | 0.3246  | 0.4286   |
| No log        | 13.0  | 91   | 0.4436          | 0.4436 | 0.5758 | 0.5838  | 0.4286   |
| No log        | 14.0  | 98   | 0.2337          | 0.2337 | 0.4422 | 0.7807  | 0.5714   |
| No log        | 15.0  | 105  | 0.1429          | 0.1429 | 0.3187 | 0.8659  | 0.7143   |
| No log        | 16.0  | 112  | 0.1090          | 0.1090 | 0.2643 | 0.8977  | 0.8571   |
| No log        | 17.0  | 119  | 0.1228          | 0.1228 | 0.2882 | 0.8848  | 0.8571   |
| No log        | 18.0  | 126  | 0.1318          | 0.1318 | 0.2713 | 0.8763  | 0.8571   |
| No log        | 19.0  | 133  | 0.1270          | 0.1270 | 0.2451 | 0.8809  | 0.8571   |
| No log        | 20.0  | 140  | 0.1181          | 0.1181 | 0.2174 | 0.8892  | 0.8571   |
| No log        | 21.0  | 147  | 0.1441          | 0.1441 | 0.2630 | 0.8648  | 0.8571   |
| No log        | 22.0  | 154  | 0.1749          | 0.1749 | 0.3027 | 0.8359  | 0.8571   |
| No log        | 23.0  | 161  | 0.1465          | 0.1465 | 0.2596 | 0.8626  | 0.8571   |
| No log        | 24.0  | 168  | 0.1699          | 0.1699 | 0.2918 | 0.8406  | 0.7143   |
| No log        | 25.0  | 175  | 0.1877          | 0.1877 | 0.3154 | 0.8239  | 0.7143   |


### 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", "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
===================


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


* Loss: 0.1625
* Mse: 0.1625
* Mae: 0.3187
* R2: 0.9161
* Accuracy: 0.5714


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: 1e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 25


### 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: 1e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 25 ### 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: 1e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 25 ### 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: 1e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 25### Training results### Framework versions


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