
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->

# Regression_bert_1500

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: 0.2224
- Train Mae: 0.4296
- Train Mse: 0.2717
- Train R2-score: 0.8508
- Validation Loss: 0.1846
- Validation Mae: 0.4542
- Validation Mse: 0.2649
- Validation R2-score: 0.7458
- Epoch: 39

## 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 |
|:----------:|:---------:|:---------:|:--------------:|:---------------:|:--------------:|:--------------:|:-------------------:|:-----:|
| 0.2245     | 0.4557    | 0.2970    | 0.6980         | 0.1019          | 0.3869         | 0.1805         | 0.6789              | 0     |
| 0.1102     | 0.3741    | 0.1848    | 0.8480         | 0.0839          | 0.2912         | 0.1209         | 0.8311              | 1     |
| 0.0845     | 0.3575    | 0.1689    | 0.2888         | 0.0620          | 0.3294         | 0.1463         | 0.8421              | 2     |
| 0.0554     | 0.3231    | 0.1366    | 0.7118         | 0.0613          | 0.2950         | 0.1305         | 0.7960              | 3     |
| 0.0481     | 0.3071    | 0.1276    | 0.8265         | 0.0516          | 0.3112         | 0.1357         | 0.8145              | 4     |
| 0.0461     | 0.2876    | 0.1168    | 0.8131         | 0.0524          | 0.2770         | 0.1097         | 0.8434              | 5     |
| 0.0454     | 0.2809    | 0.1140    | 0.7029         | 0.0502          | 0.3041         | 0.1340         | 0.8063              | 6     |
| 0.0448     | 0.2930    | 0.1212    | 0.6336         | 0.0514          | 0.2761         | 0.1143         | 0.8154              | 7     |
| 0.0446     | 0.2728    | 0.1079    | 0.8086         | 0.0532          | 0.2696         | 0.1145         | 0.8033              | 8     |
| 0.0450     | 0.2733    | 0.1086    | 0.6564         | 0.0504          | 0.2590         | 0.1033         | 0.8335              | 9     |
| 0.0792     | 0.3267    | 0.1471    | 0.5533         | 0.0848          | 0.3585         | 0.1709         | 0.7364              | 10    |
| 0.0673     | 0.3235    | 0.1407    | 0.6299         | 0.0621          | 0.3478         | 0.1549         | 0.7915              | 11    |
| 0.0571     | 0.3151    | 0.1347    | 0.6762         | 0.0729          | 0.2784         | 0.1203         | 0.8483              | 12    |
| 0.0528     | 0.2797    | 0.1104    | 0.7393         | 0.0642          | 0.2901         | 0.1291         | 0.7939              | 13    |
| 0.0490     | 0.2930    | 0.1187    | 0.6853         | 0.0821          | 0.2995         | 0.1446         | 0.7879              | 14    |
| 0.0566     | 0.2789    | 0.1120    | 0.7938         | 0.0878          | 0.3242         | 0.1589         | 0.8498              | 15    |
| 0.1926     | 0.4228    | 0.2638    | 0.6635         | 0.2201          | 0.4528         | 0.2705         | 0.6836              | 16    |
| 0.1921     | 0.4231    | 0.2458    | 0.7452         | 0.1018          | 0.3940         | 0.1900         | 0.7454              | 17    |
| 0.1288     | 0.4126    | 0.2154    | 0.6279         | 0.1035          | 0.3943         | 0.1991         | 0.7491              | 18    |
| 0.1897     | 0.4375    | 0.2617    | 0.8354         | 0.2664          | 0.4703         | 0.3447         | 0.7632              | 19    |
| 0.2095     | 0.4337    | 0.2676    | 0.8187         | 0.2730          | 0.4652         | 0.3425         | 0.8093              | 20    |
| 0.2682     | 0.4555    | 0.3358    | 0.7789         | 0.2851          | 0.4590         | 0.3518         | 0.8013              | 21    |
| 0.2416     | 0.4616    | 0.3213    | 0.7117         | 0.2466          | 0.4999         | 0.3667         | 0.6966              | 22    |
| 0.1798     | 0.4569    | 0.2737    | 0.5086         | 0.0945          | 0.4097         | 0.2023         | 0.7475              | 23    |
| 0.1361     | 0.4186    | 0.2140    | 0.5820         | 0.1181          | 0.4135         | 0.2075         | 0.7951              | 24    |
| 0.1660     | 0.4334    | 0.2322    | 0.4041         | 0.2522          | 0.4329         | 0.3019         | 0.7785              | 25    |
| 0.3046     | 0.4603    | 0.3555    | 0.7796         | 0.2886          | 0.4894         | 0.3587         | 0.7229              | 26    |
| 0.2843     | 0.4723    | 0.3491    | 0.7267         | 0.2937          | 0.4712         | 0.3616         | 0.7308              | 27    |
| 0.2793     | 0.4531    | 0.3379    | 0.7893         | 0.2952          | 0.4762         | 0.3738         | 0.7540              | 28    |
| 0.2410     | 0.4286    | 0.2899    | 0.8189         | 0.2358          | 0.4379         | 0.2932         | 0.7727              | 29    |
| 0.2270     | 0.4203    | 0.2691    | 0.8224         | 0.2361          | 0.4466         | 0.2992         | 0.7742              | 30    |
| 0.2260     | 0.4341    | 0.2776    | 0.8716         | 0.2290          | 0.4345         | 0.2885         | 0.7809              | 31    |
| 0.2198     | 0.4225    | 0.2629    | 0.7666         | 0.2303          | 0.4392         | 0.2895         | 0.7775              | 32    |
| 0.2247     | 0.4245    | 0.2709    | 0.8193         | 0.2291          | 0.4424         | 0.2921         | 0.7813              | 33    |
| 0.2241     | 0.4276    | 0.2704    | 0.6859         | 0.2280          | 0.4430         | 0.2960         | 0.7879              | 34    |
| 0.2239     | 0.4292    | 0.2709    | 0.8308         | 0.2314          | 0.4445         | 0.2893         | 0.7641              | 35    |
| 0.2174     | 0.4251    | 0.2637    | 0.7179         | 0.2354          | 0.4565         | 0.3017         | 0.7566              | 36    |
| 0.2211     | 0.4257    | 0.2686    | 0.7896         | 0.2409          | 0.4467         | 0.3014         | 0.7683              | 37    |
| 0.2321     | 0.4218    | 0.2745    | 0.8021         | 0.2413          | 0.4525         | 0.3062         | 0.7663              | 38    |
| 0.2224     | 0.4296    | 0.2717    | 0.8508         | 0.1846          | 0.4542         | 0.2649         | 0.7458              | 39    |


### Framework versions

- Transformers 4.28.1
- 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 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\_1500
======================


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: 0.2224
* Train Mae: 0.4296
* Train Mse: 0.2717
* Train R2-score: 0.8508
* Validation Loss: 0.1846
* Validation Mae: 0.4542
* Validation Mse: 0.2649
* Validation R2-score: 0.7458
* Epoch: 39


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 4.28.1
* 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 4.28.1
* TensorFlow 2.12.0
* Datasets 2.11.0
* Tokenizers 0.13.3
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 4.28.1
* TensorFlow 2.12.0
* Datasets 2.11.0
* Tokenizers 0.13.3
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 4.28.1
* TensorFlow 2.12.0
* Datasets 2.11.0
* Tokenizers 0.13.3