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# Regression_albert_12_NO_aug

This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6997
- Mse: 0.6997
- Mae: 0.7013
- R2: -0.2883
- Accuracy: 0.4211

## 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: 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mse    | Mae    | R2      | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:--------:|
| No log        | 1.0   | 33   | 0.3797          | 0.3797 | 0.5648 | -0.1345 | 0.3514   |
| No log        | 2.0   | 66   | 0.4018          | 0.4018 | 0.5029 | -0.2005 | 0.4865   |
| No log        | 3.0   | 99   | 0.4384          | 0.4384 | 0.5738 | -0.3100 | 0.4054   |
| No log        | 4.0   | 132  | 0.6817          | 0.6817 | 0.6523 | -1.0370 | 0.5405   |
| No log        | 5.0   | 165  | 0.4155          | 0.4155 | 0.4750 | -0.2415 | 0.5676   |
| No log        | 6.0   | 198  | 0.5695          | 0.5695 | 0.5599 | -0.7017 | 0.5405   |
| No log        | 7.0   | 231  | 0.5646          | 0.5646 | 0.5588 | -0.6869 | 0.5405   |
| No log        | 8.0   | 264  | 0.5240          | 0.5240 | 0.5330 | -0.5656 | 0.5676   |
| No log        | 9.0   | 297  | 0.4613          | 0.4613 | 0.4798 | -0.3783 | 0.5676   |
| No log        | 10.0  | 330  | 0.6285          | 0.6285 | 0.6172 | -0.8778 | 0.5135   |
| No log        | 11.0  | 363  | 0.6012          | 0.6012 | 0.5600 | -0.7964 | 0.5676   |
| No log        | 12.0  | 396  | 0.4417          | 0.4417 | 0.4767 | -0.3198 | 0.5405   |
| No log        | 13.0  | 429  | 0.5486          | 0.5486 | 0.5349 | -0.6392 | 0.5676   |
| No log        | 14.0  | 462  | 0.5328          | 0.5328 | 0.5174 | -0.5919 | 0.5676   |
| No log        | 15.0  | 495  | 0.5442          | 0.5442 | 0.5165 | -0.6259 | 0.5405   |
| 0.2088        | 16.0  | 528  | 0.4587          | 0.4587 | 0.4619 | -0.3705 | 0.5405   |
| 0.2088        | 17.0  | 561  | 0.5056          | 0.5056 | 0.4970 | -0.5107 | 0.5405   |
| 0.2088        | 18.0  | 594  | 0.4787          | 0.4787 | 0.4744 | -0.4304 | 0.5405   |
| 0.2088        | 19.0  | 627  | 0.4349          | 0.4349 | 0.4531 | -0.2995 | 0.5676   |
| 0.2088        | 20.0  | 660  | 0.4605          | 0.4605 | 0.4642 | -0.3759 | 0.5676   |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "Regression_albert_12_NO_aug", "results": []}]}
transformers pytorch tensorboard albert text-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

Regression\_albert\_12\_NO\_aug
===============================


This model is a fine-tuned version of albert-base-v2 on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.6997
* Mse: 0.6997
* Mae: 0.7013
* R2: -0.2883
* Accuracy: 0.4211


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: 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: 20


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 20 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #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: 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: 20 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #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: 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: 20### Training results### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3