
<!-- 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. -->

# pretrained-m-bert-200

This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 5.6892
- Validation Loss: 15.9999
- Epoch: 199

## 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', 'learning_rate': 1e-04, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 10.2629    | 10.9400         | 0     |
| 7.8719     | 10.8986         | 1     |
| 6.8337     | 11.4901         | 2     |
| 6.4663     | 11.6037         | 3     |
| 6.4171     | 11.5051         | 4     |
| 6.3166     | 12.1207         | 5     |
| 6.4304     | 11.7927         | 6     |
| 6.0435     | 12.1347         | 7     |
| 5.9134     | 12.1229         | 8     |
| 6.0124     | 12.0225         | 9     |
| 5.9096     | 12.4855         | 10    |
| 5.8829     | 12.7256         | 11    |
| 5.8533     | 12.3504         | 12    |
| 5.8075     | 12.7843         | 13    |
| 6.0418     | 12.6493         | 14    |
| 5.8611     | 12.4900         | 15    |
| 5.8863     | 12.7790         | 16    |
| 5.9484     | 13.0246         | 17    |
| 5.8226     | 12.9865         | 18    |
| 5.8262     | 13.1064         | 19    |
| 5.8687     | 13.1811         | 20    |
| 5.7531     | 13.2824         | 21    |
| 5.8473     | 13.2894         | 22    |
| 5.8762     | 13.1719         | 23    |
| 5.7386     | 13.0748         | 24    |
| 5.6647     | 13.3089         | 25    |
| 5.8553     | 13.5698         | 26    |
| 5.7698     | 14.1035         | 27    |
| 5.7972     | 13.6096         | 28    |
| 5.9381     | 13.1142         | 29    |
| 5.8173     | 13.1007         | 30    |
| 5.7676     | 13.6502         | 31    |
| 5.9740     | 13.5317         | 32    |
| 5.6842     | 13.7206         | 33    |
| 5.7764     | 13.5819         | 34    |
| 5.7659     | 13.4004         | 35    |
| 5.7104     | 13.6715         | 36    |
| 5.8345     | 13.5589         | 37    |
| 5.8067     | 13.6957         | 38    |
| 5.8537     | 13.6661         | 39    |
| 5.6418     | 13.8966         | 40    |
| 5.7818     | 13.7630         | 41    |
| 5.7406     | 14.1682         | 42    |
| 5.7053     | 13.8797         | 43    |
| 5.7151     | 14.1307         | 44    |
| 5.6621     | 14.1855         | 45    |
| 5.6716     | 14.1013         | 46    |
| 5.6596     | 14.2236         | 47    |
| 5.6680     | 14.0390         | 48    |
| 5.8122     | 14.0500         | 49    |
| 5.8497     | 14.0991         | 50    |
| 5.6758     | 14.5258         | 51    |
| 5.7158     | 14.2373         | 52    |
| 5.7288     | 13.9851         | 53    |
| 5.9239     | 14.2297         | 54    |
| 5.6722     | 13.6866         | 55    |
| 5.8708     | 14.2755         | 56    |
| 5.7190     | 14.4764         | 57    |
| 5.7218     | 14.1861         | 58    |
| 5.7478     | 14.3363         | 59    |
| 5.7843     | 13.9645         | 60    |
| 5.6555     | 14.1351         | 61    |
| 5.6951     | 14.5155         | 62    |
| 5.6711     | 14.4671         | 63    |
| 5.7068     | 14.4064         | 64    |
| 5.7773     | 14.5143         | 65    |
| 5.7188     | 14.6878         | 66    |
| 5.7912     | 14.3496         | 67    |
| 5.9308     | 14.4187         | 68    |
| 5.8765     | 14.6648         | 69    |
| 5.7103     | 14.3686         | 70    |
| 5.6585     | 14.3171         | 71    |
| 5.8697     | 14.2778         | 72    |
| 5.6874     | 14.1511         | 73    |
| 5.7367     | 15.0222         | 74    |
| 5.8603     | 14.2226         | 75    |
| 5.8183     | 14.6257         | 76    |
| 5.7646     | 14.5472         | 77    |
| 5.7813     | 14.4560         | 78    |
| 5.6991     | 14.1486         | 79    |
| 5.7365     | 14.5998         | 80    |
| 5.7602     | 14.3595         | 81    |
| 5.7646     | 14.4916         | 82    |
| 5.6289     | 15.1076         | 83    |
| 5.8171     | 14.7216         | 84    |
| 5.7939     | 14.9316         | 85    |
| 5.8249     | 14.6632         | 86    |
| 5.6479     | 15.2074         | 87    |
| 5.7985     | 14.9238         | 88    |
| 5.7332     | 14.4504         | 89    |
| 5.7495     | 14.2924         | 90    |
| 5.7579     | 15.3362         | 91    |
| 5.7217     | 15.0819         | 92    |
| 5.6750     | 14.9618         | 93    |
| 5.8607     | 14.6850         | 94    |
| 5.6310     | 14.9199         | 95    |
| 5.7532     | 14.8353         | 96    |
| 5.6318     | 14.9707         | 97    |
| 5.6861     | 14.8903         | 98    |
| 5.7634     | 15.3237         | 99    |
| 5.7703     | 15.0675         | 100   |
| 5.7290     | 15.5422         | 101   |
| 5.8383     | 14.9575         | 102   |
| 5.7694     | 14.2810         | 103   |
| 5.6092     | 15.5547         | 104   |
| 5.7699     | 15.2309         | 105   |
| 5.8225     | 15.0764         | 106   |
| 5.8007     | 14.8694         | 107   |
| 5.7435     | 15.2683         | 108   |
| 5.7358     | 15.3533         | 109   |
| 5.8024     | 14.8301         | 110   |
| 5.8027     | 15.3505         | 111   |
| 5.8282     | 15.1353         | 112   |
| 5.6818     | 15.3525         | 113   |
| 5.8653     | 14.7720         | 114   |
| 5.7234     | 15.2079         | 115   |
| 5.8179     | 14.9355         | 116   |
| 5.6718     | 15.2269         | 117   |
| 5.8428     | 15.1447         | 118   |
| 5.6875     | 15.2709         | 119   |
| 5.7212     | 15.1541         | 120   |
| 5.8223     | 15.2145         | 121   |
| 5.7125     | 15.2783         | 122   |
| 5.7707     | 15.6087         | 123   |
| 5.7251     | 15.1095         | 124   |
| 5.6308     | 15.2443         | 125   |
| 5.7163     | 15.7562         | 126   |
| 5.7097     | 15.5930         | 127   |
| 5.6560     | 15.1742         | 128   |
| 5.9121     | 15.0983         | 129   |
| 5.5284     | 15.4298         | 130   |
| 5.7584     | 15.5905         | 131   |
| 5.8737     | 15.3326         | 132   |
| 5.7731     | 15.6967         | 133   |
| 5.6686     | 15.2850         | 134   |
| 5.7585     | 15.2779         | 135   |
| 5.7239     | 15.6021         | 136   |
| 5.7295     | 15.3237         | 137   |
| 5.7358     | 15.3199         | 138   |
| 5.8334     | 14.8834         | 139   |
| 5.6537     | 15.6226         | 140   |
| 5.6501     | 15.2466         | 141   |
| 5.7591     | 14.9815         | 142   |
| 5.7694     | 15.3828         | 143   |
| 5.7239     | 15.4082         | 144   |
| 5.8641     | 14.8029         | 145   |
| 5.7668     | 15.4207         | 146   |
| 5.7180     | 15.8702         | 147   |
| 5.6461     | 15.7631         | 148   |
| 5.8629     | 15.2891         | 149   |
| 5.7973     | 15.9778         | 150   |
| 5.8458     | 15.4747         | 151   |
| 5.7720     | 15.9476         | 152   |
| 5.6491     | 15.2055         | 153   |
| 5.7801     | 15.3822         | 154   |
| 5.8175     | 15.7697         | 155   |
| 5.7536     | 15.2464         | 156   |
| 5.7925     | 15.4849         | 157   |
| 5.6012     | 15.5773         | 158   |
| 5.7623     | 15.7559         | 159   |
| 5.7078     | 15.7061         | 160   |
| 5.7834     | 15.5417         | 161   |
| 5.7058     | 15.3236         | 162   |
| 5.8079     | 15.1048         | 163   |
| 5.7757     | 15.2895         | 164   |
| 5.6822     | 15.9946         | 165   |
| 5.6205     | 15.8053         | 166   |
| 5.8778     | 15.9524         | 167   |
| 5.7211     | 15.5006         | 168   |
| 5.7499     | 15.7000         | 169   |
| 5.6561     | 16.1970         | 170   |
| 5.7077     | 15.7324         | 171   |
| 5.7177     | 15.8832         | 172   |
| 5.8901     | 15.2579         | 173   |
| 5.6842     | 16.1185         | 174   |
| 5.7424     | 15.8840         | 175   |
| 5.6889     | 15.5184         | 176   |
| 5.7339     | 15.9269         | 177   |
| 5.6635     | 15.8283         | 178   |
| 5.7331     | 16.0767         | 179   |
| 5.7096     | 15.7523         | 180   |
| 5.6715     | 16.0680         | 181   |
| 5.7703     | 15.6030         | 182   |
| 5.6772     | 15.6442         | 183   |
| 5.7933     | 15.6118         | 184   |
| 5.6788     | 15.5001         | 185   |
| 5.6985     | 15.4559         | 186   |
| 5.8450     | 15.5850         | 187   |
| 5.7437     | 15.9233         | 188   |
| 5.7502     | 15.8410         | 189   |
| 5.7081     | 16.0491         | 190   |
| 5.8119     | 15.3163         | 191   |
| 5.7426     | 15.7990         | 192   |
| 5.6422     | 15.9709         | 193   |
| 5.7431     | 15.3411         | 194   |
| 5.7894     | 15.5860         | 195   |
| 5.5432     | 16.2503         | 196   |
| 5.7073     | 16.0347         | 197   |
| 5.6637     | 16.2954         | 198   |
| 5.6892     | 15.9999         | 199   |


### Framework versions

- Transformers 4.27.0.dev0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2

{"tags": ["generated_from_keras_callback"], "model-index": [{"name": "pretrained-m-bert-200", "results": []}]}
transformers tf bert pretraining generated_from_keras_callback endpoints_compatible region:us
TAGS
#transformers #tf #bert #pretraining #generated_from_keras_callback #endpoints_compatible #region-us 

pretrained-m-bert-200
=====================


This model is a fine-tuned version of [](URL on an unknown dataset.
It achieves the following results on the evaluation set:


* Train Loss: 5.6892
* Validation Loss: 15.9999
* Epoch: 199


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', 'learning\_rate': 1e-04, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32


### Training results



### Framework versions


* Transformers 4.27.0.dev0
* TensorFlow 2.9.2
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': 1e-04, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* TensorFlow 2.9.2
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #tf #bert #pretraining #generated_from_keras_callback #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': 1e-04, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* TensorFlow 2.9.2
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #tf #bert #pretraining #generated_from_keras_callback #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': 1e-04, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32### Training results### Framework versions


* Transformers 4.27.0.dev0
* TensorFlow 2.9.2
* Datasets 2.9.0
* Tokenizers 0.13.2