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# Regression_bert_7

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: 0.1702
- Train Mae: 0.2696
- Train Mse: 0.1221
- Train R2-score: 0.7766
- Validation Loss: 0.3290
- Validation Mae: 0.2756
- Validation Mse: 0.1076
- Validation R2-score: 0.8214
- 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 |
|:----------:|:---------:|:---------:|:--------------:|:---------------:|:--------------:|:--------------:|:-------------------:|:-----:|
| 0.5303     | 0.3176    | 0.1540    | 0.7493         | 0.6752          | 0.3537         | 0.1857         | 0.6758              | 0     |
| 0.2316     | 0.2775    | 0.1261    | 0.7746         | 0.2451          | 0.3060         | 0.1466         | 0.7473              | 1     |
| 0.2780     | 0.2930    | 0.1373    | 0.8061         | 0.1807          | 0.2593         | 0.1127         | 0.8102              | 2     |
| 0.1776     | 0.2673    | 0.1177    | 0.6536         | 0.1407          | 0.2617         | 0.1181         | 0.7975              | 3     |
| 0.2248     | 0.2906    | 0.1349    | 0.7639         | 0.1896          | 0.2915         | 0.1364         | 0.7665              | 4     |
| 0.2295     | 0.2718    | 0.1196    | 0.7991         | 0.2038          | 0.2757         | 0.1248         | 0.7882              | 5     |
| 0.2443     | 0.2460    | 0.0975    | 0.7298         | 0.1509          | 0.2779         | 0.1301         | 0.7783              | 6     |
| 0.2538     | 0.2907    | 0.1343    | 0.7783         | 0.1930          | 0.2984         | 0.1426         | 0.7559              | 7     |
| 0.2067     | 0.2777    | 0.1281    | 0.7605         | 0.1537          | 0.2809         | 0.1318         | 0.7756              | 8     |
| 0.1702     | 0.2696    | 0.1221    | 0.7766         | 0.3290          | 0.2756         | 0.1076         | 0.8214              | 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_7", "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\_7
===================


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: 0.1702
* Train Mae: 0.2696
* Train Mse: 0.1221
* Train R2-score: 0.7766
* Validation Loss: 0.3290
* Validation Mae: 0.2756
* Validation Mse: 0.1076
* Validation R2-score: 0.8214
* 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