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# hsohn3/cchs-bert-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: 0.7195
- 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 |
|:----------:|:-----:|
| 3.8730     | 0     |
| 3.0562     | 1     |
| 3.0168     | 2     |
| 3.0032     | 3     |
| 2.9954     | 4     |
| 2.9951     | 5     |
| 2.9904     | 6     |
| 2.9765     | 7     |
| 2.9788     | 8     |
| 2.9692     | 9     |
| 2.9656     | 10    |
| 2.9761     | 11    |
| 2.9643     | 12    |
| 2.9393     | 13    |
| 2.9026     | 14    |
| 2.8685     | 15    |
| 2.8438     | 16    |
| 2.8279     | 17    |
| 2.8107     | 18    |
| 2.7896     | 19    |
| 2.7716     | 20    |
| 2.7458     | 21    |
| 2.7118     | 22    |
| 2.6519     | 23    |
| 2.5933     | 24    |
| 2.4702     | 25    |
| 2.2842     | 26    |
| 2.0712     | 27    |
| 1.8406     | 28    |
| 1.6374     | 29    |
| 1.4836     | 30    |
| 1.3824     | 31    |
| 1.3079     | 32    |
| 1.2538     | 33    |
| 1.2054     | 34    |
| 1.1700     | 35    |
| 1.1432     | 36    |
| 1.1122     | 37    |
| 1.0939     | 38    |
| 1.0645     | 39    |
| 1.0465     | 40    |
| 1.0248     | 41    |
| 1.0069     | 42    |
| 0.9902     | 43    |
| 0.9769     | 44    |
| 0.9510     | 45    |
| 0.9394     | 46    |
| 0.9316     | 47    |
| 0.9181     | 48    |
| 0.9090     | 49    |
| 0.9010     | 50    |
| 0.8934     | 51    |
| 0.8791     | 52    |
| 0.8759     | 53    |
| 0.8652     | 54    |
| 0.8566     | 55    |
| 0.8511     | 56    |
| 0.8414     | 57    |
| 0.8373     | 58    |
| 0.8302     | 59    |
| 0.8241     | 60    |
| 0.8246     | 61    |
| 0.8207     | 62    |
| 0.8110     | 63    |
| 0.8081     | 64    |
| 0.8010     | 65    |
| 0.7995     | 66    |
| 0.7965     | 67    |
| 0.7941     | 68    |
| 0.7849     | 69    |
| 0.7866     | 70    |
| 0.7874     | 71    |
| 0.7796     | 72    |
| 0.7742     | 73    |
| 0.7706     | 74    |
| 0.7687     | 75    |
| 0.7686     | 76    |
| 0.7663     | 77    |
| 0.7586     | 78    |
| 0.7554     | 79    |
| 0.7563     | 80    |
| 0.7541     | 81    |
| 0.7527     | 82    |
| 0.7482     | 83    |
| 0.7460     | 84    |
| 0.7436     | 85    |
| 0.7423     | 86    |
| 0.7422     | 87    |
| 0.7385     | 88    |
| 0.7367     | 89    |
| 0.7321     | 90    |
| 0.7320     | 91    |
| 0.7354     | 92    |
| 0.7271     | 93    |
| 0.7270     | 94    |
| 0.7210     | 95    |
| 0.7236     | 96    |
| 0.7263     | 97    |
| 0.7237     | 98    |
| 0.7195     | 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-bert-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-bert-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: 0.7195
* 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