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# Regression_albert_aug_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: 0.2368
- Train Mae: 0.5301
- Train Mse: 0.4296
- Train R2-score: 0.7669
- Validation Loss: 0.2410
- Validation Mae: 0.5680
- Validation Mse: 0.4286
- Validation R2-score: 0.6930
- 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 |
|:----------:|:---------:|:---------:|:--------------:|:---------------:|:--------------:|:--------------:|:-------------------:|:-----:|
| 0.2614     | 0.5480    | 0.4524    | 0.7369         | 0.2408          | 0.5194         | 0.4609         | 0.7578              | 0     |
| 0.2442     | 0.5374    | 0.4362    | 0.7109         | 0.2334          | 0.5376         | 0.4391         | 0.7399              | 1     |
| 0.2431     | 0.5349    | 0.4356    | 0.7503         | 0.2432          | 0.5234         | 0.4657         | 0.7591              | 2     |
| 0.2386     | 0.5250    | 0.4264    | 0.7926         | 0.2348          | 0.5525         | 0.4316         | 0.7203              | 3     |
| 0.2409     | 0.5342    | 0.4325    | 0.7166         | 0.2431          | 0.5233         | 0.4656         | 0.7591              | 4     |
| 0.2400     | 0.5298    | 0.4310    | 0.7553         | 0.2358          | 0.5250         | 0.4490         | 0.7513              | 5     |
| 0.2384     | 0.5274    | 0.4299    | 0.7791         | 0.2341          | 0.5491         | 0.4329         | 0.7253              | 6     |
| 0.2413     | 0.5306    | 0.4335    | 0.7593         | 0.2365          | 0.5583         | 0.4299         | 0.7109              | 7     |
| 0.2381     | 0.5299    | 0.4298    | 0.7784         | 0.2335          | 0.5452         | 0.4347         | 0.7306              | 8     |
| 0.2379     | 0.5280    | 0.4297    | 0.7575         | 0.2335          | 0.5448         | 0.4349         | 0.7312              | 9     |
| 0.2374     | 0.5306    | 0.4309    | 0.8098         | 0.2334          | 0.5441         | 0.4352         | 0.7321              | 10    |
| 0.2381     | 0.5302    | 0.4303    | 0.7428         | 0.2337          | 0.5466         | 0.4340         | 0.7288              | 11    |
| 0.2376     | 0.5323    | 0.4275    | 0.7806         | 0.2333          | 0.5411         | 0.4369         | 0.7358              | 12    |
| 0.2339     | 0.5277    | 0.4217    | 0.7986         | 0.2363          | 0.5232         | 0.4506         | 0.7525              | 13    |
| 0.2368     | 0.5301    | 0.4296    | 0.7669         | 0.2410          | 0.5680         | 0.4286         | 0.6930              | 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_aug_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\_aug\_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: 0.2368
* Train Mae: 0.5301
* Train Mse: 0.4296
* Train R2-score: 0.7669
* Validation Loss: 0.2410
* Validation Mae: 0.5680
* Validation Mse: 0.4286
* Validation R2-score: 0.6930
* 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