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# Regression_albert_4

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: 0.1353
- Mse: 0.1353
- Mae: 0.3311
- R2: 0.0037
- Accuracy: 0.8421

## 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   | 0.0644          | 0.0644 | 0.1871 | 0.2305  | 0.9459   |
| No log        | 2.0   | 66   | 0.1220          | 0.1220 | 0.2936 | -0.4587 | 0.8919   |
| No log        | 3.0   | 99   | 0.0755          | 0.0755 | 0.2180 | 0.0979  | 0.9459   |
| No log        | 4.0   | 132  | 0.0662          | 0.0662 | 0.1757 | 0.2086  | 0.9189   |
| No log        | 5.0   | 165  | 0.0827          | 0.0827 | 0.1978 | 0.0121  | 0.8919   |
| No log        | 6.0   | 198  | 0.0962          | 0.0962 | 0.2147 | -0.1498 | 0.9189   |
| No log        | 7.0   | 231  | 0.0918          | 0.0918 | 0.1867 | -0.0973 | 0.8919   |
| No log        | 8.0   | 264  | 0.0955          | 0.0955 | 0.2075 | -0.1419 | 0.8378   |
| No log        | 9.0   | 297  | 0.0950          | 0.0950 | 0.2361 | -0.1358 | 0.8649   |
| No log        | 10.0  | 330  | 0.0875          | 0.0875 | 0.1819 | -0.0455 | 0.8108   |
| No log        | 11.0  | 363  | 0.0922          | 0.0922 | 0.2030 | -0.1020 | 0.8649   |
| No log        | 12.0  | 396  | 0.0976          | 0.0976 | 0.2194 | -0.1666 | 0.8378   |
| No log        | 13.0  | 429  | 0.0872          | 0.0872 | 0.2206 | -0.0416 | 0.8649   |
| No log        | 14.0  | 462  | 0.0810          | 0.0810 | 0.1818 | 0.0315  | 0.8919   |
| No log        | 15.0  | 495  | 0.0877          | 0.0877 | 0.1861 | -0.0485 | 0.9189   |
| 0.0535        | 16.0  | 528  | 0.0882          | 0.0882 | 0.1963 | -0.0541 | 0.8919   |
| 0.0535        | 17.0  | 561  | 0.0814          | 0.0814 | 0.1869 | 0.0268  | 0.9189   |
| 0.0535        | 18.0  | 594  | 0.0902          | 0.0902 | 0.1953 | -0.0775 | 0.8649   |
| 0.0535        | 19.0  | 627  | 0.0934          | 0.0934 | 0.1957 | -0.1169 | 0.8649   |
| 0.0535        | 20.0  | 660  | 0.0923          | 0.0923 | 0.1928 | -0.1027 | 0.8649   |


### Framework versions

- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "Regression_albert_4", "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\_4
=====================


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: 0.1353
* Mse: 0.1353
* Mae: 0.3311
* R2: 0.0037
* Accuracy: 0.8421


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 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* 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 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* 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 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* 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 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2