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# Regression_albert_11_aug

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: 0.2285
- Mse: 0.2285
- Mae: 0.3670
- R2: 0.4927
- Accuracy: 0.7067

## 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   | 263  | 0.2010          | 0.2010 | 0.3575 | 0.5311 | 0.7367   |
| 0.2435        | 2.0   | 526  | 0.1490          | 0.1490 | 0.2495 | 0.6523 | 0.8733   |
| 0.2435        | 3.0   | 789  | 0.0972          | 0.0972 | 0.2068 | 0.7732 | 0.9067   |
| 0.0906        | 4.0   | 1052 | 0.1115          | 0.1115 | 0.2082 | 0.7399 | 0.9067   |
| 0.0906        | 5.0   | 1315 | 0.0904          | 0.0904 | 0.1684 | 0.7890 | 0.9      |
| 0.0421        | 6.0   | 1578 | 0.0791          | 0.0791 | 0.1542 | 0.8153 | 0.93     |
| 0.0421        | 7.0   | 1841 | 0.0843          | 0.0843 | 0.1415 | 0.8034 | 0.9133   |
| 0.0274        | 8.0   | 2104 | 0.0694          | 0.0694 | 0.1152 | 0.8380 | 0.9333   |
| 0.0274        | 9.0   | 2367 | 0.0742          | 0.0742 | 0.1435 | 0.8269 | 0.93     |
| 0.0213        | 10.0  | 2630 | 0.0659          | 0.0659 | 0.1022 | 0.8463 | 0.9367   |
| 0.0213        | 11.0  | 2893 | 0.0600          | 0.0600 | 0.1054 | 0.8599 | 0.9433   |
| 0.0127        | 12.0  | 3156 | 0.0540          | 0.0540 | 0.0988 | 0.8739 | 0.9433   |
| 0.0127        | 13.0  | 3419 | 0.0479          | 0.0479 | 0.0854 | 0.8883 | 0.9567   |
| 0.0077        | 14.0  | 3682 | 0.0517          | 0.0517 | 0.0848 | 0.8793 | 0.95     |
| 0.0077        | 15.0  | 3945 | 0.0405          | 0.0405 | 0.0851 | 0.9054 | 0.9633   |
| 0.0051        | 16.0  | 4208 | 0.0430          | 0.0430 | 0.0742 | 0.8996 | 0.9533   |
| 0.0051        | 17.0  | 4471 | 0.0368          | 0.0368 | 0.0721 | 0.9142 | 0.96     |
| 0.0036        | 18.0  | 4734 | 0.0352          | 0.0352 | 0.0709 | 0.9180 | 0.96     |
| 0.0036        | 19.0  | 4997 | 0.0345          | 0.0345 | 0.0654 | 0.9195 | 0.9567   |
| 0.0029        | 20.0  | 5260 | 0.0366          | 0.0366 | 0.0671 | 0.9146 | 0.96     |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "Regression_albert_11_aug", "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\_11\_aug
===========================


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: 0.2285
* Mse: 0.2285
* Mae: 0.3670
* R2: 0.4927
* Accuracy: 0.7067


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 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### 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 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
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 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
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 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3