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# Regression_albert_8

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: 0.0710
- Mse: 0.0710
- Mae: 0.1978
- R2: 0.0202
- Accuracy: 0.9259

## 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   | 49   | 0.0777          | 0.0777 | 0.2323 | 0.2804 | 0.9464   |
| No log        | 2.0   | 98   | 0.0649          | 0.0649 | 0.2176 | 0.3990 | 0.9464   |
| No log        | 3.0   | 147  | 0.0885          | 0.0885 | 0.2354 | 0.1799 | 0.8571   |
| No log        | 4.0   | 196  | 0.0620          | 0.0620 | 0.1971 | 0.4252 | 0.9643   |
| No log        | 5.0   | 245  | 0.0605          | 0.0605 | 0.2071 | 0.4394 | 0.9821   |
| No log        | 6.0   | 294  | 0.0523          | 0.0523 | 0.1714 | 0.5155 | 0.9821   |
| No log        | 7.0   | 343  | 0.1047          | 0.1047 | 0.2598 | 0.0301 | 0.8393   |
| No log        | 8.0   | 392  | 0.0421          | 0.0421 | 0.1543 | 0.6103 | 0.9643   |
| No log        | 9.0   | 441  | 0.0445          | 0.0445 | 0.1612 | 0.5875 | 0.9643   |
| No log        | 10.0  | 490  | 0.0438          | 0.0438 | 0.1608 | 0.5939 | 0.9821   |
| 0.0478        | 11.0  | 539  | 0.0529          | 0.0529 | 0.1816 | 0.5095 | 0.9464   |
| 0.0478        | 12.0  | 588  | 0.0401          | 0.0401 | 0.1495 | 0.6288 | 0.9643   |
| 0.0478        | 13.0  | 637  | 0.0471          | 0.0471 | 0.1637 | 0.5639 | 0.9643   |
| 0.0478        | 14.0  | 686  | 0.0454          | 0.0454 | 0.1632 | 0.5797 | 0.9643   |
| 0.0478        | 15.0  | 735  | 0.0436          | 0.0436 | 0.1526 | 0.5957 | 0.9643   |
| 0.0478        | 16.0  | 784  | 0.0520          | 0.0520 | 0.1764 | 0.5178 | 0.9643   |
| 0.0478        | 17.0  | 833  | 0.0414          | 0.0414 | 0.1536 | 0.6166 | 0.9821   |
| 0.0478        | 18.0  | 882  | 0.0413          | 0.0413 | 0.1490 | 0.6176 | 0.9643   |
| 0.0478        | 19.0  | 931  | 0.0413          | 0.0413 | 0.1514 | 0.6174 | 0.9821   |
| 0.0478        | 20.0  | 980  | 0.0429          | 0.0429 | 0.1537 | 0.6023 | 0.9821   |


### Framework versions

- Transformers 4.27.3
- 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_8", "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\_8
=====================


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: 0.0710
* Mse: 0.0710
* Mae: 0.1978
* R2: 0.0202
* Accuracy: 0.9259


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 4.27.3
* 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 4.27.3
* 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 4.27.3
* 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 4.27.3
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2