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# Regression_albert_5

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1548
- Train Mae: 0.2765
- Train Mse: 0.1336
- Train R2-score: 0.7547
- Train Accuracy: 0.7462
- Validation Loss: 0.1908
- Validation Mae: 0.3787
- Validation Mse: 0.1894
- Validation R2-score: 0.8458
- Validation Accuracy: 0.4595
- Epoch: 9

## 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': 2e-05, '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 | Train Accuracy | Validation Loss | Validation Mae | Validation Mse | Validation R2-score | Validation Accuracy | Epoch |
|:----------:|:---------:|:---------:|:--------------:|:--------------:|:---------------:|:--------------:|:--------------:|:-------------------:|:-------------------:|:-----:|
| 0.5723     | 0.3984    | 0.2343    | 0.4755         | 0.5923         | 0.1856          | 0.3686         | 0.1843         | 0.8559              | 0.4324              | 0     |
| 0.1822     | 0.2906    | 0.1403    | 0.7246         | 0.6538         | 0.1577          | 0.3485         | 0.1561         | 0.8714              | 0.9459              | 1     |
| 0.1765     | 0.2865    | 0.1376    | 0.6770         | 0.6538         | 0.1356          | 0.3325         | 0.1337         | 0.8808              | 0.9459              | 2     |
| 0.1959     | 0.2945    | 0.1383    | 0.6806         | 0.7308         | 0.2115          | 0.4054         | 0.2104         | 0.8366              | 0.3243              | 3     |
| 0.1698     | 0.2906    | 0.1408    | 0.7195         | 0.6231         | 0.1489          | 0.3371         | 0.1472         | 0.8726              | 0.9459              | 4     |
| 0.2081     | 0.2687    | 0.1178    | 0.7632         | 0.8385         | 0.2547          | 0.4572         | 0.2539         | 0.8046              | 0.3243              | 5     |
| 0.1806     | 0.3087    | 0.1554    | 0.7168         | 0.6462         | 0.1477          | 0.3401         | 0.1460         | 0.8757              | 0.9459              | 6     |
| 0.1910     | 0.3102    | 0.1559    | 0.7295         | 0.6308         | 0.1726          | 0.3544         | 0.1711         | 0.8602              | 0.8919              | 7     |
| 0.1697     | 0.2609    | 0.1132    | 0.7876         | 0.8538         | 0.1856          | 0.3694         | 0.1843         | 0.8537              | 0.5946              | 8     |
| 0.1548     | 0.2765    | 0.1336    | 0.7547         | 0.7462         | 0.1908          | 0.3787         | 0.1894         | 0.8458              | 0.4595              | 9     |


### Framework versions

- Transformers 4.27.2
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Regression_albert_5", "results": []}]}
transformers tf distilbert text-classification generated_from_keras_callback license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

Regression\_albert\_5
=====================


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


* Train Loss: 0.1548
* Train Mae: 0.2765
* Train Mse: 0.1336
* Train R2-score: 0.7547
* Train Accuracy: 0.7462
* Validation Loss: 0.1908
* Validation Mae: 0.3787
* Validation Mse: 0.1894
* Validation R2-score: 0.8458
* Validation Accuracy: 0.4595
* Epoch: 9


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': 2e-05, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32


### Training results



### Framework versions


* Transformers 4.27.2
* TensorFlow 2.11.0
* Datasets 2.10.1
* Tokenizers 0.13.2



### 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': 2e-05, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.27.2
* TensorFlow 2.11.0
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #tf #distilbert #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': 2e-05, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.27.2
* TensorFlow 2.11.0
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #tf #distilbert #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': 2e-05, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32### Training results### Framework versions


* Transformers 4.27.2
* TensorFlow 2.11.0
* Datasets 2.10.1
* Tokenizers 0.13.2