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# Regression_AlBERT_aug_MSEloss

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: 0.2216
- Mse: 0.2216
- Mae: 0.3302
- R2: 0.5082
- Accuracy: 0.7533

## 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  | 0.2554          | 0.2554 | 0.3841 | 0.4041 | 0.6867   |
| 0.2379        | 2.0   | 526  | 0.1471          | 0.1471 | 0.2426 | 0.6568 | 0.86     |
| 0.2379        | 3.0   | 789  | 0.1189          | 0.1189 | 0.2212 | 0.7226 | 0.8633   |
| 0.0845        | 4.0   | 1052 | 0.1052          | 0.1052 | 0.2121 | 0.7546 | 0.8767   |
| 0.0845        | 5.0   | 1315 | 0.0913          | 0.0913 | 0.1642 | 0.7869 | 0.9067   |
| 0.0441        | 6.0   | 1578 | 0.0610          | 0.0610 | 0.1272 | 0.8577 | 0.9467   |
| 0.0441        | 7.0   | 1841 | 0.0594          | 0.0594 | 0.0965 | 0.8614 | 0.9433   |
| 0.0269        | 8.0   | 2104 | 0.0750          | 0.0750 | 0.1312 | 0.8249 | 0.93     |
| 0.0269        | 9.0   | 2367 | 0.0544          | 0.0544 | 0.0998 | 0.8731 | 0.95     |
| 0.0218        | 10.0  | 2630 | 0.0680          | 0.0680 | 0.1031 | 0.8413 | 0.93     |
| 0.0218        | 11.0  | 2893 | 0.0486          | 0.0486 | 0.1015 | 0.8867 | 0.9533   |
| 0.0141        | 12.0  | 3156 | 0.0582          | 0.0582 | 0.1056 | 0.8643 | 0.95     |
| 0.0141        | 13.0  | 3419 | 0.0494          | 0.0494 | 0.0832 | 0.8847 | 0.9533   |
| 0.0112        | 14.0  | 3682 | 0.0561          | 0.0561 | 0.0927 | 0.8690 | 0.9467   |
| 0.0112        | 15.0  | 3945 | 0.0576          | 0.0576 | 0.0833 | 0.8656 | 0.9467   |
| 0.0064        | 16.0  | 4208 | 0.0639          | 0.0639 | 0.0972 | 0.8509 | 0.9467   |
| 0.0064        | 17.0  | 4471 | 0.0657          | 0.0657 | 0.0971 | 0.8467 | 0.9367   |
| 0.0052        | 18.0  | 4734 | 0.0718          | 0.0718 | 0.0912 | 0.8325 | 0.94     |
| 0.0052        | 19.0  | 4997 | 0.0689          | 0.0689 | 0.0883 | 0.8392 | 0.94     |
| 0.0031        | 20.0  | 5260 | 0.0601          | 0.0601 | 0.0816 | 0.8598 | 0.9433   |


### Framework versions

- Transformers 4.28.1
- 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_aug_MSEloss", "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\_aug\_MSEloss
================================


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: 0.2216
* Mse: 0.2216
* Mae: 0.3302
* R2: 0.5082
* Accuracy: 0.7533


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 4.28.1
* 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 4.28.1
* 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 4.28.1
* 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 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3