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# skillsBERT_v3_epoch200

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: 0.0256
- Validation Loss: 7.8012
- 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': 'AdamW', 'weight_decay': 0.004, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 5e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 6.8416     | 6.7417          | 0     |
| 6.2162     | 6.1436          | 1     |
| 5.3514     | 5.3111          | 2     |
| 4.5931     | 4.9790          | 3     |
| 4.0664     | 4.8477          | 4     |
| 3.6654     | 4.6776          | 5     |
| 3.3343     | 4.5758          | 6     |
| 3.0431     | 4.4659          | 7     |
| 2.7726     | 4.4337          | 8     |
| 2.5106     | 4.6514          | 9     |
| 2.2625     | 4.6512          | 10    |
| 2.0222     | 4.7317          | 11    |
| 1.7924     | 4.6184          | 12    |
| 1.5719     | 4.7085          | 13    |
| 1.3735     | 4.8741          | 14    |
| 1.1820     | 5.0078          | 15    |
| 1.0164     | 5.0224          | 16    |
| 0.8588     | 5.2085          | 17    |
| 0.7247     | 5.2827          | 18    |
| 0.6140     | 5.3904          | 19    |
| 0.5144     | 5.3287          | 20    |
| 0.4330     | 5.4909          | 21    |
| 0.3603     | 5.6482          | 22    |
| 0.3120     | 5.4950          | 23    |
| 0.2634     | 5.8465          | 24    |
| 0.2322     | 5.8744          | 25    |
| 0.2077     | 5.8122          | 26    |
| 0.1904     | 5.9466          | 27    |
| 0.1698     | 6.0747          | 28    |
| 0.1536     | 6.2833          | 29    |
| 0.1462     | 6.2201          | 30    |
| 0.1367     | 6.3858          | 31    |
| 0.1309     | 6.4264          | 32    |
| 0.1196     | 6.3880          | 33    |
| 0.1129     | 6.6965          | 34    |
| 0.1100     | 6.4638          | 35    |
| 0.1050     | 6.4099          | 36    |
| 0.0994     | 6.4061          | 37    |
| 0.0973     | 6.4458          | 38    |
| 0.0934     | 6.5099          | 39    |
| 0.0909     | 6.4002          | 40    |
| 0.0896     | 6.5372          | 41    |
| 0.0839     | 6.5808          | 42    |
| 0.0817     | 6.4682          | 43    |
| 0.0814     | 6.6921          | 44    |
| 0.0793     | 6.7584          | 45    |
| 0.0765     | 6.7847          | 46    |
| 0.0765     | 6.8182          | 47    |
| 0.0712     | 6.7281          | 48    |
| 0.0710     | 6.7083          | 49    |
| 0.0700     | 6.6643          | 50    |
| 0.0695     | 6.7186          | 51    |
| 0.0681     | 6.9158          | 52    |
| 0.0647     | 6.8065          | 53    |
| 0.0662     | 7.0515          | 54    |
| 0.0630     | 6.9353          | 55    |
| 0.0624     | 7.0418          | 56    |
| 0.0640     | 6.7393          | 57    |
| 0.0610     | 7.0111          | 58    |
| 0.0602     | 7.0310          | 59    |
| 0.0577     | 6.7995          | 60    |
| 0.0616     | 6.7364          | 61    |
| 0.0575     | 7.0542          | 62    |
| 0.0532     | 7.1219          | 63    |
| 0.0601     | 6.9904          | 64    |
| 0.0528     | 7.2782          | 65    |
| 0.0551     | 7.2465          | 66    |
| 0.0551     | 7.2380          | 67    |
| 0.0542     | 6.9920          | 68    |
| 0.0536     | 7.1704          | 69    |
| 0.0529     | 7.1467          | 70    |
| 0.0488     | 7.0684          | 71    |
| 0.0494     | 7.0333          | 72    |
| 0.0518     | 7.3027          | 73    |
| 0.0505     | 7.1332          | 74    |
| 0.0481     | 7.0856          | 75    |
| 0.0493     | 7.2170          | 76    |
| 0.0490     | 7.3652          | 77    |
| 0.0480     | 7.3370          | 78    |
| 0.0485     | 7.1336          | 79    |
| 0.0480     | 7.2017          | 80    |
| 0.0483     | 7.2421          | 81    |
| 0.0463     | 7.3675          | 82    |
| 0.0455     | 7.3847          | 83    |
| 0.0441     | 7.3112          | 84    |
| 0.0454     | 7.2941          | 85    |
| 0.0474     | 7.4086          | 86    |
| 0.0451     | 7.1806          | 87    |
| 0.0417     | 7.4458          | 88    |
| 0.0464     | 7.2912          | 89    |
| 0.0422     | 7.6368          | 90    |
| 0.0434     | 7.4060          | 91    |
| 0.0427     | 7.4733          | 92    |
| 0.0433     | 7.4114          | 93    |
| 0.0416     | 7.3643          | 94    |
| 0.0428     | 7.5354          | 95    |
| 0.0426     | 7.2827          | 96    |
| 0.0400     | 7.4285          | 97    |
| 0.0413     | 7.4499          | 98    |
| 0.0422     | 7.4816          | 99    |
| 0.0407     | 7.3491          | 100   |
| 0.0402     | 7.3784          | 101   |
| 0.0412     | 7.3845          | 102   |
| 0.0389     | 7.5468          | 103   |
| 0.0372     | 7.4723          | 104   |
| 0.0421     | 7.4283          | 105   |
| 0.0382     | 7.4074          | 106   |
| 0.0392     | 7.4365          | 107   |
| 0.0399     | 7.4375          | 108   |
| 0.0396     | 7.5146          | 109   |
| 0.0389     | 7.2877          | 110   |
| 0.0384     | 7.3907          | 111   |
| 0.0386     | 7.5558          | 112   |
| 0.0378     | 7.3746          | 113   |
| 0.0359     | 7.5122          | 114   |
| 0.0412     | 7.4631          | 115   |
| 0.0341     | 7.5950          | 116   |
| 0.0380     | 7.3713          | 117   |
| 0.0382     | 7.4232          | 118   |
| 0.0350     | 7.5180          | 119   |
| 0.0374     | 7.4993          | 120   |
| 0.0373     | 7.4308          | 121   |
| 0.0357     | 7.4511          | 122   |
| 0.0364     | 7.5254          | 123   |
| 0.0349     | 7.4326          | 124   |
| 0.0371     | 7.5467          | 125   |
| 0.0344     | 7.5324          | 126   |
| 0.0375     | 7.4660          | 127   |
| 0.0365     | 7.5816          | 128   |
| 0.0348     | 7.5425          | 129   |
| 0.0333     | 7.5655          | 130   |
| 0.0331     | 7.6466          | 131   |
| 0.0369     | 7.6142          | 132   |
| 0.0332     | 7.7292          | 133   |
| 0.0349     | 7.6649          | 134   |
| 0.0343     | 7.5255          | 135   |
| 0.0335     | 7.7736          | 136   |
| 0.0334     | 7.6680          | 137   |
| 0.0356     | 7.4846          | 138   |
| 0.0323     | 7.7691          | 139   |
| 0.0339     | 7.6986          | 140   |
| 0.0333     | 7.4287          | 141   |
| 0.0333     | 7.5534          | 142   |
| 0.0322     | 7.5383          | 143   |
| 0.0333     | 7.5212          | 144   |
| 0.0320     | 7.5945          | 145   |
| 0.0335     | 7.5932          | 146   |
| 0.0332     | 7.7700          | 147   |
| 0.0323     | 7.4798          | 148   |
| 0.0318     | 7.5804          | 149   |
| 0.0336     | 7.5721          | 150   |
| 0.0332     | 7.3627          | 151   |
| 0.0334     | 7.6093          | 152   |
| 0.0293     | 7.7731          | 153   |
| 0.0336     | 7.6722          | 154   |
| 0.0319     | 7.5856          | 155   |
| 0.0325     | 7.6355          | 156   |
| 0.0287     | 7.5941          | 157   |
| 0.0318     | 7.6476          | 158   |
| 0.0304     | 7.5365          | 159   |
| 0.0313     | 7.6429          | 160   |
| 0.0319     | 7.5318          | 161   |
| 0.0311     | 7.7468          | 162   |
| 0.0321     | 7.6332          | 163   |
| 0.0301     | 7.8412          | 164   |
| 0.0292     | 7.6819          | 165   |
| 0.0313     | 7.5544          | 166   |
| 0.0311     | 7.6667          | 167   |
| 0.0274     | 7.7875          | 168   |
| 0.0317     | 7.6632          | 169   |
| 0.0305     | 7.8710          | 170   |
| 0.0311     | 7.5799          | 171   |
| 0.0311     | 7.7357          | 172   |
| 0.0271     | 7.7491          | 173   |
| 0.0317     | 7.8025          | 174   |
| 0.0294     | 7.6856          | 175   |
| 0.0302     | 7.7687          | 176   |
| 0.0293     | 7.8676          | 177   |
| 0.0315     | 7.6371          | 178   |
| 0.0286     | 7.8114          | 179   |
| 0.0288     | 7.6690          | 180   |
| 0.0304     | 7.6712          | 181   |
| 0.0293     | 7.8668          | 182   |
| 0.0305     | 7.8221          | 183   |
| 0.0284     | 7.7506          | 184   |
| 0.0309     | 7.6629          | 185   |
| 0.0282     | 7.7157          | 186   |
| 0.0262     | 7.8241          | 187   |
| 0.0305     | 7.6471          | 188   |
| 0.0288     | 7.6409          | 189   |
| 0.0283     | 7.7386          | 190   |
| 0.0286     | 7.8070          | 191   |
| 0.0284     | 7.7921          | 192   |
| 0.0287     | 7.9042          | 193   |
| 0.0289     | 7.7297          | 194   |
| 0.0276     | 7.8584          | 195   |
| 0.0278     | 7.8580          | 196   |
| 0.0258     | 7.9323          | 197   |
| 0.0306     | 7.7566          | 198   |
| 0.0256     | 7.8012          | 199   |


### Framework versions

- Transformers 4.28.0.dev0
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2

{"tags": ["generated_from_keras_callback"], "model-index": [{"name": "skillsBERT_v3_epoch200", "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 

skillsBERT\_v3\_epoch200
========================


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


* Train Loss: 0.0256
* Validation Loss: 7.8012
* 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': 'AdamW', 'weight\_decay': 0.004, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': False, 'is\_legacy\_optimizer': False, 'learning\_rate': 5e-05, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32


### Training results



### Framework versions


* Transformers 4.28.0.dev0
* TensorFlow 2.11.0
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'AdamW', 'weight\_decay': 0.004, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': False, 'is\_legacy\_optimizer': False, 'learning\_rate': 5e-05, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* TensorFlow 2.11.0
* Datasets 2.10.1
* 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': 'AdamW', 'weight\_decay': 0.004, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': False, 'is\_legacy\_optimizer': False, 'learning\_rate': 5e-05, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* TensorFlow 2.11.0
* Datasets 2.10.1
* 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': 'AdamW', 'weight\_decay': 0.004, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': False, 'is\_legacy\_optimizer': False, 'learning\_rate': 5e-05, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32### Training results### Framework versions


* Transformers 4.28.0.dev0
* TensorFlow 2.11.0
* Datasets 2.10.1
* Tokenizers 0.13.2