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# tf-tpu/roberta-base-epochs-100

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.0414
- Train Accuracy: 0.1136
- Validation Loss: 1.0103
- Validation Accuracy: 0.1144
- 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': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 0.0001, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0001, 'decay_steps': 55765, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 2935, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.001}
- training_precision: mixed_bfloat16

### Training results

| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 7.2121     | 0.0274         | 5.7188          | 0.0346              | 0     |
| 5.4335     | 0.0414         | 5.2266          | 0.0439              | 1     |
| 5.1579     | 0.0445         | 5.0625          | 0.0441              | 2     |
| 5.0231     | 0.0447         | 4.9453          | 0.0446              | 3     |
| 4.9323     | 0.0448         | 4.8633          | 0.0443              | 4     |
| 4.8672     | 0.0449         | 4.8789          | 0.0440              | 5     |
| 4.8200     | 0.0449         | 4.8164          | 0.0441              | 6     |
| 4.7841     | 0.0449         | 4.7734          | 0.0450              | 7     |
| 4.7546     | 0.0449         | 4.7539          | 0.0441              | 8     |
| 4.7288     | 0.0449         | 4.7305          | 0.0447              | 9     |
| 4.7084     | 0.0449         | 4.7422          | 0.0443              | 10    |
| 4.6884     | 0.0450         | 4.7148          | 0.0437              | 11    |
| 4.6764     | 0.0449         | 4.7070          | 0.0441              | 12    |
| 4.6637     | 0.0449         | 4.7227          | 0.0435              | 13    |
| 4.5963     | 0.0449         | 4.5195          | 0.0444              | 14    |
| 4.3462     | 0.0468         | 4.0742          | 0.0515              | 15    |
| 3.4139     | 0.0650         | 2.6348          | 0.0797              | 16    |
| 2.5336     | 0.0817         | 2.1816          | 0.0888              | 17    |
| 2.1859     | 0.0888         | 1.9648          | 0.0930              | 18    |
| 2.0043     | 0.0925         | 1.8154          | 0.0961              | 19    |
| 1.8887     | 0.0948         | 1.7129          | 0.0993              | 20    |
| 1.8058     | 0.0965         | 1.6729          | 0.0996              | 21    |
| 1.7402     | 0.0979         | 1.6191          | 0.1010              | 22    |
| 1.6861     | 0.0990         | 1.5693          | 0.1024              | 23    |
| 1.6327     | 0.1001         | 1.5273          | 0.1035              | 24    |
| 1.5906     | 0.1010         | 1.4766          | 0.1042              | 25    |
| 1.5545     | 0.1018         | 1.4561          | 0.1031              | 26    |
| 1.5231     | 0.1024         | 1.4365          | 0.1054              | 27    |
| 1.4957     | 0.1030         | 1.3975          | 0.1046              | 28    |
| 1.4700     | 0.1036         | 1.3789          | 0.1061              | 29    |
| 1.4466     | 0.1041         | 1.3262          | 0.1070              | 30    |
| 1.4253     | 0.1046         | 1.3223          | 0.1072              | 31    |
| 1.4059     | 0.1050         | 1.3096          | 0.1070              | 32    |
| 1.3873     | 0.1054         | 1.3164          | 0.1072              | 33    |
| 1.3703     | 0.1058         | 1.2861          | 0.1072              | 34    |
| 1.3550     | 0.1062         | 1.2705          | 0.1082              | 35    |
| 1.3398     | 0.1065         | 1.2578          | 0.1082              | 36    |
| 1.3260     | 0.1068         | 1.25            | 0.1096              | 37    |
| 1.3127     | 0.1071         | 1.2266          | 0.1102              | 38    |
| 1.2996     | 0.1074         | 1.2305          | 0.1098              | 39    |
| 1.2891     | 0.1077         | 1.2139          | 0.1088              | 40    |
| 1.2783     | 0.1079         | 1.2158          | 0.1093              | 41    |
| 1.2674     | 0.1081         | 1.1787          | 0.1114              | 42    |
| 1.2570     | 0.1084         | 1.1709          | 0.1107              | 43    |
| 1.2478     | 0.1086         | 1.1709          | 0.1104              | 44    |
| 1.2390     | 0.1088         | 1.1777          | 0.1101              | 45    |
| 1.2305     | 0.1090         | 1.1738          | 0.1111              | 46    |
| 1.2215     | 0.1092         | 1.1533          | 0.1112              | 47    |
| 1.2140     | 0.1094         | 1.1514          | 0.1117              | 48    |
| 1.2068     | 0.1096         | 1.1621          | 0.1119              | 49    |
| 1.1991     | 0.1097         | 1.1416          | 0.1108              | 50    |
| 1.1927     | 0.1099         | 1.1279          | 0.1113              | 51    |
| 1.1854     | 0.1101         | 1.1147          | 0.1123              | 52    |
| 1.1800     | 0.1102         | 1.125           | 0.1116              | 53    |
| 1.1727     | 0.1104         | 1.1167          | 0.1116              | 54    |
| 1.1679     | 0.1105         | 1.0884          | 0.1122              | 55    |
| 1.1613     | 0.1106         | 1.1084          | 0.1120              | 56    |
| 1.1563     | 0.1107         | 1.1035          | 0.1119              | 57    |
| 1.1517     | 0.1109         | 1.1035          | 0.1124              | 58    |
| 1.1454     | 0.1111         | 1.0718          | 0.1128              | 59    |
| 1.1403     | 0.1111         | 1.0874          | 0.1123              | 60    |
| 1.1360     | 0.1112         | 1.0742          | 0.1145              | 61    |
| 1.1318     | 0.1114         | 1.0811          | 0.1131              | 62    |
| 1.1277     | 0.1114         | 1.0723          | 0.1129              | 63    |
| 1.1226     | 0.1116         | 1.0640          | 0.1124              | 64    |
| 1.1186     | 0.1117         | 1.0840          | 0.1117              | 65    |
| 1.1144     | 0.1118         | 1.0522          | 0.1139              | 66    |
| 1.1111     | 0.1119         | 1.0557          | 0.1132              | 67    |
| 1.1069     | 0.1119         | 1.0718          | 0.1124              | 68    |
| 1.1038     | 0.1120         | 1.0376          | 0.1135              | 69    |
| 1.1007     | 0.1121         | 1.0537          | 0.1138              | 70    |
| 1.0975     | 0.1121         | 1.0503          | 0.1134              | 71    |
| 1.0941     | 0.1122         | 1.0317          | 0.1140              | 72    |
| 1.0902     | 0.1124         | 1.0439          | 0.1145              | 73    |
| 1.0881     | 0.1124         | 1.0352          | 0.1145              | 74    |
| 1.0839     | 0.1125         | 1.0449          | 0.1144              | 75    |
| 1.0821     | 0.1125         | 1.0229          | 0.1148              | 76    |
| 1.0791     | 0.1126         | 1.0244          | 0.1148              | 77    |
| 1.0764     | 0.1127         | 1.0366          | 0.1141              | 78    |
| 1.0741     | 0.1128         | 1.0308          | 0.1134              | 79    |
| 1.0716     | 0.1128         | 1.0400          | 0.1137              | 80    |
| 1.0688     | 0.1129         | 1.0225          | 0.1140              | 81    |
| 1.0664     | 0.1129         | 1.0269          | 0.1139              | 82    |
| 1.0643     | 0.1129         | 1.0156          | 0.1146              | 83    |
| 1.0629     | 0.1131         | 1.0127          | 0.1149              | 84    |
| 1.0602     | 0.1131         | 1.0420          | 0.1132              | 85    |
| 1.0580     | 0.1132         | 1.0205          | 0.1149              | 86    |
| 1.0568     | 0.1132         | 1.0024          | 0.1159              | 87    |
| 1.0547     | 0.1132         | 1.0210          | 0.1144              | 88    |
| 1.0536     | 0.1133         | 1.0176          | 0.1143              | 89    |
| 1.0522     | 0.1133         | 0.9951          | 0.1134              | 90    |
| 1.0505     | 0.1134         | 1.0283          | 0.1136              | 91    |
| 1.0484     | 0.1134         | 1.0063          | 0.1141              | 92    |
| 1.0482     | 0.1134         | 0.9917          | 0.1141              | 93    |
| 1.0463     | 0.1135         | 1.0244          | 0.1145              | 94    |
| 1.0458     | 0.1134         | 1.0220          | 0.1143              | 95    |
| 1.0448     | 0.1135         | 0.9785          | 0.1147              | 96    |
| 1.0435     | 0.1135         | 0.9771          | 0.1155              | 97    |
| 1.0433     | 0.1135         | 0.9946          | 0.1137              | 98    |
| 1.0414     | 0.1136         | 1.0103          | 0.1144              | 99    |


### Framework versions

- Transformers 4.27.0.dev0
- TensorFlow 2.9.1
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_keras_callback"], "mask_token": "[MASK]", "widget": [{"text": "Goal of my life is to [MASK]."}], "model-index": [{"name": "tf-tpu/roberta-base-epochs-100", "results": []}]}
transformers tf roberta fill-mask generated_from_keras_callback license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #tf #roberta #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us 

tf-tpu/roberta-base-epochs-100
==============================


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


* Train Loss: 1.0414
* Train Accuracy: 0.1136
* Validation Loss: 1.0103
* Validation Accuracy: 0.1144
* 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': {'class\_name': 'WarmUp', 'config': {'initial\_learning\_rate': 0.0001, 'decay\_schedule\_fn': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 0.0001, 'decay\_steps': 55765, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '**passive\_serialization**': True}, 'warmup\_steps': 2935, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.001}
* training\_precision: mixed\_bfloat16


### Training results



### Framework versions


* Transformers 4.27.0.dev0
* TensorFlow 2.9.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': {'class\_name': 'WarmUp', 'config': {'initial\_learning\_rate': 0.0001, 'decay\_schedule\_fn': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 0.0001, 'decay\_steps': 55765, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '**passive\_serialization**': True}, 'warmup\_steps': 2935, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.001}
* training\_precision: mixed\_bfloat16 ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* TensorFlow 2.9.1
* Tokenizers 0.13.2
TAGS
#transformers #tf #roberta #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': {'class\_name': 'WarmUp', 'config': {'initial\_learning\_rate': 0.0001, 'decay\_schedule\_fn': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 0.0001, 'decay\_steps': 55765, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '**passive\_serialization**': True}, 'warmup\_steps': 2935, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.001}
* training\_precision: mixed\_bfloat16 ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* TensorFlow 2.9.1
* Tokenizers 0.13.2
TAGS
#transformers #tf #roberta #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': {'class\_name': 'WarmUp', 'config': {'initial\_learning\_rate': 0.0001, 'decay\_schedule\_fn': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 0.0001, 'decay\_steps': 55765, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '**passive\_serialization**': True}, 'warmup\_steps': 2935, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.001}
* training\_precision: mixed\_bfloat16### Training results### Framework versions


* Transformers 4.27.0.dev0
* TensorFlow 2.9.1
* Tokenizers 0.13.2