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# Regression_Albert_2

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: 3.4677
- Mse: 3.4677
- Mae: 1.6443
- R2: -0.7892
- Accuracy: 0.1429

## 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   | 3    | 1.5252          | 1.5252 | 1.1112 | -0.4311 | 0.1429   |
| No log        | 2.0   | 6    | 1.3872          | 1.3872 | 1.0182 | -0.3016 | 0.2857   |
| No log        | 3.0   | 9    | 2.5294          | 2.5294 | 1.2286 | -1.3733 | 0.4286   |
| No log        | 4.0   | 12   | 3.8938          | 3.8938 | 1.5973 | -2.6535 | 0.1429   |
| No log        | 5.0   | 15   | 5.5535          | 5.5535 | 2.0657 | -4.2108 | 0.0      |
| No log        | 6.0   | 18   | 7.0814          | 7.0814 | 2.3965 | -5.6444 | 0.0      |
| No log        | 7.0   | 21   | 7.5510          | 7.5510 | 2.4797 | -6.0850 | 0.0      |
| No log        | 8.0   | 24   | 6.6578          | 6.6578 | 2.2618 | -5.2469 | 0.0      |
| No log        | 9.0   | 27   | 5.7320          | 5.7320 | 2.0266 | -4.3783 | 0.1429   |
| No log        | 10.0  | 30   | 6.9700          | 6.9700 | 2.2615 | -5.5398 | 0.1429   |
| No log        | 11.0  | 33   | 7.9965          | 7.9965 | 2.4660 | -6.5030 | 0.0      |
| No log        | 12.0  | 36   | 7.7483          | 7.7483 | 2.4191 | -6.2700 | 0.0      |
| No log        | 13.0  | 39   | 7.6652          | 7.6652 | 2.3991 | -6.1921 | 0.0      |
| No log        | 14.0  | 42   | 7.7858          | 7.7858 | 2.4436 | -6.3053 | 0.0      |
| No log        | 15.0  | 45   | 7.8747          | 7.8747 | 2.4527 | -6.3886 | 0.0      |
| No log        | 16.0  | 48   | 7.6498          | 7.6498 | 2.4028 | -6.1776 | 0.0      |
| No log        | 17.0  | 51   | 7.3543          | 7.3543 | 2.3428 | -5.9004 | 0.0      |
| No log        | 18.0  | 54   | 7.3656          | 7.3656 | 2.3428 | -5.9110 | 0.0      |
| No log        | 19.0  | 57   | 7.3185          | 7.3185 | 2.3360 | -5.8668 | 0.0      |
| No log        | 20.0  | 60   | 7.3529          | 7.3529 | 2.3417 | -5.8990 | 0.0      |


### Framework versions

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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "Regression_Albert_2", "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\_2
=====================


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: 3.4677
* Mse: 3.4677
* Mae: 1.6443
* R2: -0.7892
* Accuracy: 0.1429


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 2.9.0
* 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 2.9.0
* 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 2.9.0
* 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 2.9.0
* Tokenizers 0.13.2