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# Regression_albert_NOaug_CustomLoss_3

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:
- Train Loss: 0.1481
- Train Mae: 0.5450
- Train Mse: 0.3746
- Train R2-score: 0.7999
- Validation Loss: 0.1364
- Validation Mae: 0.6382
- Validation Mse: 0.4728
- Validation R2-score: 0.8856
- Epoch: 14

## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 1e-04, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Train Mae | Train Mse | Train R2-score | Validation Loss | Validation Mae | Validation Mse | Validation R2-score | Epoch |
|:----------:|:---------:|:---------:|:--------------:|:---------------:|:--------------:|:--------------:|:-------------------:|:-----:|
| 0.2075     | 0.6054    | 0.4691    | 0.1331         | 0.1389          | 0.6396         | 0.4919         | 0.8859              | 0     |
| 0.1982     | 0.5741    | 0.4337    | 0.8066         | 0.1275          | 0.5890         | 0.3885         | 0.8851              | 1     |
| 0.1775     | 0.5592    | 0.3934    | 0.7398         | 0.1849          | 0.6878         | 0.5975         | 0.8749              | 2     |
| 0.1511     | 0.5350    | 0.3713    | 0.8239         | 0.1441          | 0.6497         | 0.4982         | 0.8841              | 3     |
| 0.1489     | 0.5429    | 0.3710    | 0.8262         | 0.1319          | 0.6294         | 0.4547         | 0.8862              | 4     |
| 0.1477     | 0.5429    | 0.3837    | 0.7268         | 0.1269          | 0.6120         | 0.4229         | 0.8865              | 5     |
| 0.1580     | 0.5603    | 0.3782    | 0.6256         | 0.1556          | 0.6630         | 0.5300         | 0.8817              | 6     |
| 0.1491     | 0.5482    | 0.3743    | 0.8104         | 0.1515          | 0.6586         | 0.5192         | 0.8826              | 7     |
| 0.1499     | 0.5354    | 0.3661    | 0.8207         | 0.2043          | 0.7009         | 0.6370         | 0.8702              | 8     |
| 0.1811     | 0.5516    | 0.4196    | 0.7534         | 0.1303          | 0.6252         | 0.4465         | 0.8865              | 9     |
| 0.1547     | 0.5531    | 0.3798    | 0.6862         | 0.1438          | 0.6493         | 0.4971         | 0.8842              | 10    |
| 0.1464     | 0.5429    | 0.3604    | 0.7679         | 0.1549          | 0.6622         | 0.5282         | 0.8818              | 11    |
| 0.1507     | 0.5507    | 0.3787    | 0.7918         | 0.1489          | 0.6556         | 0.5119         | 0.8831              | 12    |
| 0.1555     | 0.5530    | 0.3888    | 0.7355         | 0.1269          | 0.6126         | 0.4238         | 0.8866              | 13    |
| 0.1481     | 0.5450    | 0.3746    | 0.7999         | 0.1364          | 0.6382         | 0.4728         | 0.8856              | 14    |


### Framework versions

- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Regression_albert_NOaug_CustomLoss_3", "results": []}]}
transformers tf albert text-classification generated_from_keras_callback license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #tf #albert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

Regression\_albert\_NOaug\_CustomLoss\_3
========================================


This model is a fine-tuned version of albert-base-v2 on an unknown dataset.
It achieves the following results on the evaluation set:


* Train Loss: 0.1481
* Train Mae: 0.5450
* Train Mse: 0.3746
* Train R2-score: 0.7999
* Validation Loss: 0.1364
* Validation Mae: 0.6382
* Validation Mse: 0.4728
* Validation R2-score: 0.8856
* Epoch: 14


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:


* optimizer: {'name': 'Adam', 'weight\_decay': None, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': True, 'is\_legacy\_optimizer': False, 'learning\_rate': 1e-04, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32


### Training results



### Framework versions


* Transformers 4.28.1
* TensorFlow 2.12.0
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'weight\_decay': None, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': True, 'is\_legacy\_optimizer': False, 'learning\_rate': 1e-04, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.28.1
* TensorFlow 2.12.0
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #tf #albert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'weight\_decay': None, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': True, 'is\_legacy\_optimizer': False, 'learning\_rate': 1e-04, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.28.1
* TensorFlow 2.12.0
* Datasets 2.12.0
* Tokenizers 0.13.3