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# hsohn3/mayo-timebert-visit-uncased-wordlevel-block512-batch4-ep100

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.8536
- Epoch: 99

## 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': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32

### Training results

| Train Loss | Epoch |
|:----------:|:-----:|
| 3.9508     | 0     |
| 3.4063     | 1     |
| 3.3682     | 2     |
| 3.3468     | 3     |
| 3.3330     | 4     |
| 3.3308     | 5     |
| 3.3225     | 6     |
| 3.3106     | 7     |
| 3.2518     | 8     |
| 3.1859     | 9     |
| 3.1373     | 10    |
| 3.0923     | 11    |
| 3.0390     | 12    |
| 2.9560     | 13    |
| 2.8605     | 14    |
| 2.7564     | 15    |
| 2.4969     | 16    |
| 2.2044     | 17    |
| 1.9566     | 18    |
| 1.7686     | 19    |
| 1.5995     | 20    |
| 1.4932     | 21    |
| 1.4100     | 22    |
| 1.3538     | 23    |
| 1.2973     | 24    |
| 1.2610     | 25    |
| 1.2160     | 26    |
| 1.1916     | 27    |
| 1.1607     | 28    |
| 1.1468     | 29    |
| 1.1262     | 30    |
| 1.1123     | 31    |
| 1.0942     | 32    |
| 1.0816     | 33    |
| 1.0717     | 34    |
| 1.0575     | 35    |
| 1.0503     | 36    |
| 1.0411     | 37    |
| 1.0293     | 38    |
| 1.0229     | 39    |
| 1.0139     | 40    |
| 1.0081     | 41    |
| 1.0028     | 42    |
| 0.9967     | 43    |
| 0.9906     | 44    |
| 0.9834     | 45    |
| 0.9782     | 46    |
| 0.9766     | 47    |
| 0.9676     | 48    |
| 0.9618     | 49    |
| 0.9611     | 50    |
| 0.9553     | 51    |
| 0.9504     | 52    |
| 0.9483     | 53    |
| 0.9404     | 54    |
| 0.9423     | 55    |
| 0.9361     | 56    |
| 0.9327     | 57    |
| 0.9327     | 58    |
| 0.9263     | 59    |
| 0.9275     | 60    |
| 0.9218     | 61    |
| 0.9202     | 62    |
| 0.9158     | 63    |
| 0.9152     | 64    |
| 0.9091     | 65    |
| 0.9104     | 66    |
| 0.9094     | 67    |
| 0.9087     | 68    |
| 0.9034     | 69    |
| 0.9063     | 70    |
| 0.8984     | 71    |
| 0.8966     | 72    |
| 0.8953     | 73    |
| 0.8910     | 74    |
| 0.8913     | 75    |
| 0.8887     | 76    |
| 0.8868     | 77    |
| 0.8868     | 78    |
| 0.8815     | 79    |
| 0.8821     | 80    |
| 0.8791     | 81    |
| 0.8752     | 82    |
| 0.8731     | 83    |
| 0.8779     | 84    |
| 0.8727     | 85    |
| 0.8702     | 86    |
| 0.8712     | 87    |
| 0.8689     | 88    |
| 0.8646     | 89    |
| 0.8644     | 90    |
| 0.8608     | 91    |
| 0.8643     | 92    |
| 0.8602     | 93    |
| 0.8605     | 94    |
| 0.8568     | 95    |
| 0.8567     | 96    |
| 0.8557     | 97    |
| 0.8543     | 98    |
| 0.8536     | 99    |


### Framework versions

- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "hsohn3/mayo-timebert-visit-uncased-wordlevel-block512-batch4-ep100", "results": []}]}
transformers tf bert fill-mask generated_from_keras_callback license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

hsohn3/mayo-timebert-visit-uncased-wordlevel-block512-batch4-ep100
==================================================================


This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:


* Train Loss: 0.8536
* Epoch: 99


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': 'AdamWeightDecay', 'learning\_rate': 2e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: float32


### Training results



### Framework versions


* Transformers 4.20.1
* TensorFlow 2.8.2
* Datasets 2.3.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': 2e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.20.1
* TensorFlow 2.8.2
* Datasets 2.3.2
* Tokenizers 0.12.1
TAGS
#transformers #tf #bert #fill-mask #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': 'AdamWeightDecay', 'learning\_rate': 2e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.20.1
* TensorFlow 2.8.2
* Datasets 2.3.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #tf #bert #fill-mask #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': 'AdamWeightDecay', 'learning\_rate': 2e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: float32### Training results### Framework versions


* Transformers 4.20.1
* TensorFlow 2.8.2
* Datasets 2.3.2
* Tokenizers 0.12.1