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# Regression_bert_10

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: 0.0535
- Train Mae: 0.2673
- Train Mse: 0.1031
- Train R2-score: 0.6896
- Validation Loss: 0.1142
- Validation Mae: 0.3549
- Validation Mse: 0.1957
- Validation R2-score: 0.9230
- 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 |
|:----------:|:---------:|:---------:|:--------------:|:---------------:|:--------------:|:--------------:|:-------------------:|:-----:|
| 0.2988     | 0.4759    | 0.3361    | 0.6079         | 0.1967          | 0.3939         | 0.2542         | 0.9026              | 0     |
| 0.1715     | 0.4010    | 0.2357    | 0.6812         | 0.1680          | 0.4014         | 0.2478         | 0.9049              | 1     |
| 0.0903     | 0.3374    | 0.1532    | 0.8384         | 0.1354          | 0.3432         | 0.1971         | 0.9210              | 2     |
| 0.0636     | 0.3139    | 0.1272    | 0.4117         | 0.1538          | 0.4066         | 0.2304         | 0.9034              | 3     |
| 0.0746     | 0.3142    | 0.1294    | 0.9220         | 0.1184          | 0.3589         | 0.2015         | 0.9224              | 4     |
| 0.0604     | 0.2837    | 0.1119    | 0.9439         | 0.1268          | 0.3450         | 0.1994         | 0.9209              | 5     |
| 0.0556     | 0.2660    | 0.1049    | 0.6002         | 0.1193          | 0.3037         | 0.1704         | 0.9265              | 6     |
| 0.0541     | 0.2581    | 0.1007    | 0.8081         | 0.1125          | 0.3350         | 0.1743         | 0.9229              | 7     |
| 0.0532     | 0.2679    | 0.1044    | 0.8917         | 0.1109          | 0.3131         | 0.1757         | 0.9311              | 8     |
| 0.0535     | 0.2673    | 0.1031    | 0.6896         | 0.1142          | 0.3549         | 0.1957         | 0.9230              | 9     |


### Framework versions

- Transformers 4.27.4
- TensorFlow 2.12.0
- Datasets 2.11.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Regression_bert_10", "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\_10
====================


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: 0.0535
* Train Mae: 0.2673
* Train Mse: 0.1031
* Train R2-score: 0.6896
* Validation Loss: 0.1142
* Validation Mae: 0.3549
* Validation Mse: 0.1957
* Validation R2-score: 0.9230
* 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 4.27.4
* TensorFlow 2.12.0
* Datasets 2.11.0
* 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 4.27.4
* TensorFlow 2.12.0
* Datasets 2.11.0
* 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 4.27.4
* TensorFlow 2.12.0
* Datasets 2.11.0
* 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 4.27.4
* TensorFlow 2.12.0
* Datasets 2.11.0
* Tokenizers 0.13.2