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# Regression_Albert

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: 0.0459
- Mse: 0.0459
- Mae: 0.1675
- R2: 0.9763
- Accuracy: 1.0

## 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    | 1.4379          | 1.4379 | 1.1107 | -0.3492 | 0.0      |
| No log        | 2.0   | 14   | 1.2159          | 1.2159 | 1.0476 | -0.1409 | 0.1429   |
| No log        | 3.0   | 21   | 1.7679          | 1.7679 | 1.1233 | -0.6588 | 0.4286   |
| No log        | 4.0   | 28   | 1.7069          | 1.7069 | 1.1072 | -0.6015 | 0.1429   |
| No log        | 5.0   | 35   | 1.4438          | 1.4438 | 0.9771 | -0.3547 | 0.5714   |
| No log        | 6.0   | 42   | 1.0275          | 1.0275 | 0.7910 | 0.0359  | 0.4286   |
| No log        | 7.0   | 49   | 0.7649          | 0.7649 | 0.7080 | 0.2823  | 0.4286   |
| No log        | 8.0   | 56   | 0.6584          | 0.6584 | 0.7083 | 0.3823  | 0.2857   |
| No log        | 9.0   | 63   | 0.5064          | 0.5064 | 0.6108 | 0.5248  | 0.4286   |
| No log        | 10.0  | 70   | 0.3638          | 0.3638 | 0.5078 | 0.6586  | 0.4286   |
| No log        | 11.0  | 77   | 0.2660          | 0.2660 | 0.4352 | 0.7504  | 0.5714   |
| No log        | 12.0  | 84   | 0.1570          | 0.1570 | 0.3323 | 0.8527  | 0.7143   |
| No log        | 13.0  | 91   | 0.1953          | 0.1953 | 0.3863 | 0.8168  | 0.4286   |
| No log        | 14.0  | 98   | 0.2230          | 0.2230 | 0.4033 | 0.7908  | 0.7143   |
| No log        | 15.0  | 105  | 0.0578          | 0.0578 | 0.1935 | 0.9458  | 1.0      |
| No log        | 16.0  | 112  | 0.0504          | 0.0504 | 0.1701 | 0.9527  | 1.0      |
| No log        | 17.0  | 119  | 0.0466          | 0.0466 | 0.1713 | 0.9563  | 1.0      |
| No log        | 18.0  | 126  | 0.0173          | 0.0173 | 0.1148 | 0.9837  | 1.0      |
| No log        | 19.0  | 133  | 0.0417          | 0.0417 | 0.1811 | 0.9609  | 1.0      |
| No log        | 20.0  | 140  | 0.0899          | 0.0899 | 0.1895 | 0.9156  | 0.8571   |
| No log        | 21.0  | 147  | 0.0571          | 0.0571 | 0.1599 | 0.9465  | 0.8571   |
| No log        | 22.0  | 154  | 0.0247          | 0.0247 | 0.1478 | 0.9768  | 1.0      |
| No log        | 23.0  | 161  | 0.0201          | 0.0201 | 0.1261 | 0.9812  | 1.0      |
| No log        | 24.0  | 168  | 0.0178          | 0.0178 | 0.1262 | 0.9833  | 1.0      |
| No log        | 25.0  | 175  | 0.0172          | 0.0172 | 0.1208 | 0.9838  | 1.0      |


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


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: 0.0459
* Mse: 0.0459
* Mae: 0.1675
* R2: 0.9763
* Accuracy: 1.0


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 #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: 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 #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: 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