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# Regression_bert_1500

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: 0.3665
- Train Mae: 0.5651
- Train Mse: 0.4539
- Train R2-score: 0.5632
- Validation Loss: 0.3640
- Validation Mae: 0.6123
- Validation Mse: 0.4470
- Validation R2-score: 0.5765
- Epoch: 22

## 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 |
|:----------:|:---------:|:---------:|:--------------:|:---------------:|:--------------:|:--------------:|:-------------------:|:-----:|
| 0.3911     | 0.5811    | 0.4875    | 0.5636         | 0.3808          | 0.6393         | 0.4778         | 0.4775              | 0     |
| 0.3669     | 0.5644    | 0.4527    | 0.6196         | 0.3524          | 0.5673         | 0.4286         | 0.6944              | 1     |
| 0.3652     | 0.5606    | 0.4457    | 0.6645         | 0.3711          | 0.6253         | 0.4600         | 0.5315              | 2     |
| 0.3669     | 0.5642    | 0.4490    | 0.5194         | 0.3525          | 0.5695         | 0.4286         | 0.6901              | 3     |
| 0.3693     | 0.5693    | 0.4580    | 0.6646         | 0.3558          | 0.5904         | 0.4329         | 0.6414              | 4     |
| 0.3682     | 0.5633    | 0.4540    | 0.7464         | 0.3602          | 0.5255         | 0.4485         | 0.7509              | 5     |
| 0.3712     | 0.5632    | 0.4527    | 0.6645         | 0.3650          | 0.6145         | 0.4489         | 0.5693              | 6     |
| 0.3781     | 0.5720    | 0.4661    | 0.5801         | 0.3545          | 0.5849         | 0.4309         | 0.6553              | 7     |
| 0.3659     | 0.5673    | 0.4564    | 0.1693         | 0.3723          | 0.6271         | 0.4621         | 0.5247              | 8     |
| 0.3693     | 0.5642    | 0.4487    | 0.7048         | 0.3524          | 0.5641         | 0.4289         | 0.7006              | 9     |
| 0.3656     | 0.5655    | 0.4495    | 0.6565         | 0.3575          | 0.5328         | 0.4425         | 0.7448              | 10    |
| 0.3685     | 0.5632    | 0.4540    | 0.7202         | 0.3551          | 0.5878         | 0.4319         | 0.6482              | 11    |
| 0.3702     | 0.5646    | 0.4543    | 0.7295         | 0.3528          | 0.5557         | 0.4306         | 0.7152              | 12    |
| 0.3661     | 0.5615    | 0.4450    | 0.6631         | 0.3683          | 0.5240         | 0.4664         | 0.7592              | 13    |
| 0.3835     | 0.5742    | 0.4757    | 0.7335         | 0.3531          | 0.5523         | 0.4316         | 0.7206              | 14    |
| 0.3641     | 0.5628    | 0.4472    | 0.7325         | 0.3559          | 0.5909         | 0.4331         | 0.6399              | 15    |
| 0.3764     | 0.5633    | 0.4566    | 0.7291         | 0.3549          | 0.5867         | 0.4315         | 0.6508              | 16    |
| 0.3625     | 0.5594    | 0.4443    | 0.5555         | 0.3648          | 0.6141         | 0.4486         | 0.5707              | 17    |
| 0.3816     | 0.5743    | 0.4693    | 0.6649         | 0.3559          | 0.5385         | 0.4385         | 0.7389              | 18    |
| 0.3721     | 0.5721    | 0.4618    | 0.6791         | 0.3529          | 0.5745         | 0.4288         | 0.6795              | 19    |
| 0.3711     | 0.5659    | 0.4586    | 0.2709         | 0.3610          | 0.5234         | 0.4505         | 0.7525              | 20    |
| 0.3693     | 0.5641    | 0.4501    | 0.7400         | 0.3525          | 0.5607         | 0.4294         | 0.7068              | 21    |
| 0.3665     | 0.5651    | 0.4539    | 0.5632         | 0.3640          | 0.6123         | 0.4470         | 0.5765              | 22    |


### Framework versions

- Transformers 4.27.4
- TensorFlow 2.12.0
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Regression_bert_1500", "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\_bert\_1500
======================


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: 0.3665
* Train Mae: 0.5651
* Train Mse: 0.4539
* Train R2-score: 0.5632
* Validation Loss: 0.3640
* Validation Mae: 0.6123
* Validation Mse: 0.4470
* Validation R2-score: 0.5765
* Epoch: 22


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 4.27.4
* TensorFlow 2.12.0
* Datasets 2.11.0
* 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 4.27.4
* TensorFlow 2.12.0
* Datasets 2.11.0
* 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 4.27.4
* TensorFlow 2.12.0
* Datasets 2.11.0
* 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 4.27.4
* TensorFlow 2.12.0
* Datasets 2.11.0
* Tokenizers 0.13.3