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# hsohn3/mayo-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: 0.9559
- 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 |
|:----------:|:-----:|
| 4.1247     | 0     |
| 3.5129     | 1     |
| 3.4726     | 2     |
| 3.4483     | 3     |
| 3.4395     | 4     |
| 3.4301     | 5     |
| 3.4260     | 6     |
| 3.4131     | 7     |
| 3.3831     | 8     |
| 3.2925     | 9     |
| 3.2454     | 10    |
| 3.2092     | 11    |
| 3.1695     | 12    |
| 3.1346     | 13    |
| 3.0797     | 14    |
| 3.0154     | 15    |
| 2.9557     | 16    |
| 2.8814     | 17    |
| 2.7720     | 18    |
| 2.5472     | 19    |
| 2.3193     | 20    |
| 2.1005     | 21    |
| 1.9331     | 22    |
| 1.7971     | 23    |
| 1.6859     | 24    |
| 1.6062     | 25    |
| 1.5310     | 26    |
| 1.4706     | 27    |
| 1.4203     | 28    |
| 1.3681     | 29    |
| 1.3222     | 30    |
| 1.2939     | 31    |
| 1.2726     | 32    |
| 1.2494     | 33    |
| 1.2330     | 34    |
| 1.2161     | 35    |
| 1.1998     | 36    |
| 1.1874     | 37    |
| 1.1767     | 38    |
| 1.1641     | 39    |
| 1.1550     | 40    |
| 1.1407     | 41    |
| 1.1363     | 42    |
| 1.1272     | 43    |
| 1.1227     | 44    |
| 1.1163     | 45    |
| 1.1065     | 46    |
| 1.1008     | 47    |
| 1.0957     | 48    |
| 1.0837     | 49    |
| 1.0844     | 50    |
| 1.0778     | 51    |
| 1.0741     | 52    |
| 1.0693     | 53    |
| 1.0662     | 54    |
| 1.0608     | 55    |
| 1.0521     | 56    |
| 1.0526     | 57    |
| 1.0476     | 58    |
| 1.0454     | 59    |
| 1.0452     | 60    |
| 1.0348     | 61    |
| 1.0333     | 62    |
| 1.0342     | 63    |
| 1.0293     | 64    |
| 1.0249     | 65    |
| 1.0241     | 66    |
| 1.0194     | 67    |
| 1.0177     | 68    |
| 1.0102     | 69    |
| 1.0055     | 70    |
| 1.0052     | 71    |
| 1.0038     | 72    |
| 1.0005     | 73    |
| 0.9981     | 74    |
| 0.9991     | 75    |
| 0.9950     | 76    |
| 0.9928     | 77    |
| 0.9898     | 78    |
| 0.9906     | 79    |
| 0.9873     | 80    |
| 0.9849     | 81    |
| 0.9808     | 82    |
| 0.9804     | 83    |
| 0.9792     | 84    |
| 0.9789     | 85    |
| 0.9797     | 86    |
| 0.9741     | 87    |
| 0.9781     | 88    |
| 0.9678     | 89    |
| 0.9686     | 90    |
| 0.9651     | 91    |
| 0.9652     | 92    |
| 0.9613     | 93    |
| 0.9599     | 94    |
| 0.9566     | 95    |
| 0.9571     | 96    |
| 0.9577     | 97    |
| 0.9536     | 98    |
| 0.9559     | 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-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/mayo-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: 0.9559
* 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