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# hsohn3/cchs-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: 0.8009
- 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 |
|:----------:|:-----:|
| 3.8699     | 0     |
| 3.1667     | 1     |
| 3.1286     | 2     |
| 3.1169     | 3     |
| 3.1077     | 4     |
| 3.0989     | 5     |
| 3.0911     | 6     |
| 3.0896     | 7     |
| 3.0820     | 8     |
| 3.0856     | 9     |
| 3.0827     | 10    |
| 3.0800     | 11    |
| 3.0647     | 12    |
| 3.0396     | 13    |
| 3.0052     | 14    |
| 2.9879     | 15    |
| 2.9633     | 16    |
| 2.9449     | 17    |
| 2.9217     | 18    |
| 2.8921     | 19    |
| 2.8625     | 20    |
| 2.8153     | 21    |
| 2.7495     | 22    |
| 2.6202     | 23    |
| 2.3762     | 24    |
| 2.1064     | 25    |
| 1.8489     | 26    |
| 1.6556     | 27    |
| 1.5005     | 28    |
| 1.4110     | 29    |
| 1.3472     | 30    |
| 1.2896     | 31    |
| 1.2391     | 32    |
| 1.2001     | 33    |
| 1.1663     | 34    |
| 1.1418     | 35    |
| 1.1159     | 36    |
| 1.0987     | 37    |
| 1.0753     | 38    |
| 1.0608     | 39    |
| 1.0456     | 40    |
| 1.0381     | 41    |
| 1.0248     | 42    |
| 1.0127     | 43    |
| 0.9970     | 44    |
| 0.9958     | 45    |
| 0.9847     | 46    |
| 0.9789     | 47    |
| 0.9617     | 48    |
| 0.9575     | 49    |
| 0.9517     | 50    |
| 0.9442     | 51    |
| 0.9379     | 52    |
| 0.9350     | 53    |
| 0.9325     | 54    |
| 0.9235     | 55    |
| 0.9182     | 56    |
| 0.9139     | 57    |
| 0.9074     | 58    |
| 0.8984     | 59    |
| 0.8988     | 60    |
| 0.8958     | 61    |
| 0.8937     | 62    |
| 0.8853     | 63    |
| 0.8812     | 64    |
| 0.8758     | 65    |
| 0.8729     | 66    |
| 0.8732     | 67    |
| 0.8647     | 68    |
| 0.8634     | 69    |
| 0.8604     | 70    |
| 0.8577     | 71    |
| 0.8597     | 72    |
| 0.8508     | 73    |
| 0.8510     | 74    |
| 0.8450     | 75    |
| 0.8451     | 76    |
| 0.8398     | 77    |
| 0.8392     | 78    |
| 0.8345     | 79    |
| 0.8350     | 80    |
| 0.8329     | 81    |
| 0.8299     | 82    |
| 0.8257     | 83    |
| 0.8217     | 84    |
| 0.8192     | 85    |
| 0.8211     | 86    |
| 0.8208     | 87    |
| 0.8171     | 88    |
| 0.8166     | 89    |
| 0.8134     | 90    |
| 0.8124     | 91    |
| 0.8102     | 92    |
| 0.8133     | 93    |
| 0.8066     | 94    |
| 0.8023     | 95    |
| 0.8049     | 96    |
| 0.8024     | 97    |
| 0.7980     | 98    |
| 0.8009     | 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/cchs-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/cchs-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: 0.8009
* 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