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# Regression_albert_3

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: 0.7092
- Mse: 0.7092
- Mae: 0.6931
- R2: -0.3058
- Accuracy: 0.4737

## 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   | 0.3632          | 0.3632 | 0.5672 | -0.0851 | 0.2703   |
| No log        | 2.0   | 66   | 0.3855          | 0.3855 | 0.5860 | -0.1518 | 0.2703   |
| No log        | 3.0   | 99   | 0.4619          | 0.4619 | 0.5229 | -0.3801 | 0.5405   |
| No log        | 4.0   | 132  | 0.4573          | 0.4573 | 0.5791 | -0.3665 | 0.4324   |
| No log        | 5.0   | 165  | 0.3254          | 0.3254 | 0.4284 | 0.0277  | 0.7297   |
| No log        | 6.0   | 198  | 0.3139          | 0.3139 | 0.4078 | 0.0622  | 0.6757   |
| No log        | 7.0   | 231  | 0.3489          | 0.3489 | 0.4370 | -0.0424 | 0.5946   |
| No log        | 8.0   | 264  | 0.3933          | 0.3933 | 0.4113 | -0.1753 | 0.6757   |
| No log        | 9.0   | 297  | 0.3219          | 0.3219 | 0.3611 | 0.0381  | 0.7027   |
| No log        | 10.0  | 330  | 0.3228          | 0.3228 | 0.3423 | 0.0356  | 0.7568   |
| No log        | 11.0  | 363  | 0.3289          | 0.3289 | 0.3964 | 0.0173  | 0.6757   |
| No log        | 12.0  | 396  | 0.3717          | 0.3717 | 0.3917 | -0.1107 | 0.6757   |
| No log        | 13.0  | 429  | 0.4160          | 0.4160 | 0.4238 | -0.2430 | 0.6486   |
| No log        | 14.0  | 462  | 0.3691          | 0.3691 | 0.3781 | -0.1027 | 0.6486   |
| No log        | 15.0  | 495  | 0.4483          | 0.4483 | 0.4233 | -0.3394 | 0.7027   |
| 0.1519        | 16.0  | 528  | 0.4205          | 0.4205 | 0.3878 | -0.2563 | 0.7027   |
| 0.1519        | 17.0  | 561  | 0.3750          | 0.3750 | 0.4112 | -0.1205 | 0.6216   |
| 0.1519        | 18.0  | 594  | 0.3895          | 0.3895 | 0.4010 | -0.1639 | 0.6486   |
| 0.1519        | 19.0  | 627  | 0.3884          | 0.3884 | 0.3933 | -0.1605 | 0.6757   |
| 0.1519        | 20.0  | 660  | 0.3907          | 0.3907 | 0.3871 | -0.1674 | 0.6757   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "Regression_albert_3", "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\_3
=====================


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: 0.7092
* Mse: 0.7092
* Mae: 0.6931
* R2: -0.3058
* Accuracy: 0.4737


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 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.0
* Tokenizers 0.13.2



### 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 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.0
* Tokenizers 0.13.2
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 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.0
* Tokenizers 0.13.2
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 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.0
* Tokenizers 0.13.2