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# Regression_bert_1

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: 0.1363
- Train Mae: 0.2288
- Train Mse: 0.0928
- Train R2-score: 0.8065
- Validation Loss: 0.0994
- Validation Mae: 0.2297
- Validation Mse: 0.0947
- Validation R2-score: 0.8577
- 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': 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 |
|:----------:|:---------:|:---------:|:--------------:|:---------------:|:--------------:|:--------------:|:-------------------:|:-----:|
| 0.4890     | 0.3421    | 0.1734    | 0.1995         | 0.1833          | 0.3662         | 0.1820         | 0.8574              | 0     |
| 0.1771     | 0.3049    | 0.1526    | 0.7380         | 0.1715          | 0.3553         | 0.1701         | 0.8642              | 1     |
| 0.1563     | 0.2820    | 0.1337    | 0.7278         | 0.1823          | 0.3662         | 0.1810         | 0.8577              | 2     |
| 0.1578     | 0.2739    | 0.1288    | 0.7954         | 0.1689          | 0.3535         | 0.1674         | 0.8641              | 3     |
| 0.2301     | 0.3034    | 0.1383    | 0.7186         | 0.1158          | 0.3132         | 0.1136         | 0.8866              | 4     |
| 0.2067     | 0.2768    | 0.1203    | 0.6568         | 0.1462          | 0.3375         | 0.1445         | 0.8772              | 5     |
| 0.1631     | 0.2726    | 0.1200    | 0.7418         | 0.1646          | 0.3473         | 0.1632         | 0.8659              | 6     |
| 0.1689     | 0.2843    | 0.1269    | 0.4797         | 0.0993          | 0.2797         | 0.0972         | 0.8961              | 7     |
| 0.1756     | 0.2115    | 0.0726    | 0.8657         | 0.1047          | 0.2537         | 0.1027         | 0.8923              | 8     |
| 0.1363     | 0.2288    | 0.0928    | 0.8065         | 0.0994          | 0.2297         | 0.0947         | 0.8577              | 9     |


### Framework versions

- Transformers 4.27.3
- 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_bert_1", "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\_bert\_1
===================


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: 0.1363
* Train Mae: 0.2288
* Train Mse: 0.0928
* Train R2-score: 0.8065
* Validation Loss: 0.0994
* Validation Mae: 0.2297
* Validation Mse: 0.0947
* Validation R2-score: 0.8577
* 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': 1e-04, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32


### Training results



### Framework versions


* Transformers 4.27.3
* 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': 1e-04, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.27.3
* 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': 1e-04, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.27.3
* 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': 1e-04, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32### Training results### Framework versions


* Transformers 4.27.3
* TensorFlow 2.11.0
* Datasets 2.10.1
* Tokenizers 0.13.2