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# Zemulax/masked-lm-tpu

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: 7.7770
- Train Accuracy: 0.0241
- Validation Loss: 7.7589
- Validation Accuracy: 0.0230
- Epoch: 98

## 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': 223250, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 11750, '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: float32

### Training results

| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 10.2868    | 0.0            | 10.2891         | 0.0                 | 0     |
| 10.2817    | 0.0000         | 10.2764         | 0.0                 | 1     |
| 10.2772    | 0.0000         | 10.2667         | 0.0000              | 2     |
| 10.2604    | 0.0000         | 10.2521         | 0.0                 | 3     |
| 10.2421    | 0.0000         | 10.2282         | 0.0000              | 4     |
| 10.2219    | 0.0            | 10.2010         | 0.0                 | 5     |
| 10.1957    | 0.0            | 10.1669         | 0.0                 | 6     |
| 10.1667    | 0.0000         | 10.1388         | 0.0000              | 7     |
| 10.1278    | 0.0000         | 10.0908         | 0.0000              | 8     |
| 10.0848    | 0.0000         | 10.0405         | 0.0001              | 9     |
| 10.0496    | 0.0002         | 9.9921          | 0.0007              | 10    |
| 9.9940     | 0.0010         | 9.9422          | 0.0039              | 11    |
| 9.9424     | 0.0035         | 9.8765          | 0.0110              | 12    |
| 9.8826     | 0.0092         | 9.8156          | 0.0182              | 13    |
| 9.8225     | 0.0155         | 9.7461          | 0.0209              | 14    |
| 9.7670     | 0.0201         | 9.6768          | 0.0222              | 15    |
| 9.7065     | 0.0219         | 9.6127          | 0.0222              | 16    |
| 9.6352     | 0.0227         | 9.5445          | 0.0220              | 17    |
| 9.5757     | 0.0226         | 9.4795          | 0.0219              | 18    |
| 9.4894     | 0.0232         | 9.3985          | 0.0222              | 19    |
| 9.4277     | 0.0234         | 9.3386          | 0.0222              | 20    |
| 9.3676     | 0.0229         | 9.2753          | 0.0220              | 21    |
| 9.2980     | 0.0229         | 9.2170          | 0.0219              | 22    |
| 9.2361     | 0.0233         | 9.1518          | 0.0219              | 23    |
| 9.1515     | 0.0236         | 9.0827          | 0.0223              | 24    |
| 9.1171     | 0.0228         | 9.0406          | 0.0218              | 25    |
| 9.0447     | 0.0234         | 8.9867          | 0.0218              | 26    |
| 9.0119     | 0.0229         | 8.9307          | 0.0221              | 27    |
| 8.9625     | 0.0229         | 8.8969          | 0.0221              | 28    |
| 8.9098     | 0.0230         | 8.8341          | 0.0223              | 29    |
| 8.8726     | 0.0227         | 8.8118          | 0.0220              | 30    |
| 8.8574     | 0.0223         | 8.7910          | 0.0219              | 31    |
| 8.7798     | 0.0231         | 8.7506          | 0.0221              | 32    |
| 8.7535     | 0.0231         | 8.7055          | 0.0222              | 33    |
| 8.7333     | 0.0228         | 8.6801          | 0.0223              | 34    |
| 8.6985     | 0.0231         | 8.6837          | 0.0220              | 35    |
| 8.6816     | 0.0229         | 8.6243          | 0.0223              | 36    |
| 8.6356     | 0.0228         | 8.6323          | 0.0217              | 37    |
| 8.6392     | 0.0225         | 8.5603          | 0.0225              | 38    |
| 8.5802     | 0.0233         | 8.5722          | 0.0219              | 39    |
| 8.5825     | 0.0228         | 8.5548          | 0.0220              | 40    |
| 8.5625     | 0.0228         | 8.5272          | 0.0220              | 41    |
| 8.5415     | 0.0228         | 8.5200          | 0.0222              | 42    |
| 8.5124     | 0.0230         | 8.4787          | 0.0222              | 43    |
| 8.4999     | 0.0229         | 8.4819          | 0.0218              | 44    |
| 8.4561     | 0.0235         | 8.4453          | 0.0221              | 45    |
| 8.4854     | 0.0223         | 8.4378          | 0.0220              | 46    |
| 8.4367     | 0.0229         | 8.4212          | 0.0222              | 47    |
| 8.4096     | 0.0232         | 8.4033          | 0.0221              | 48    |
| 8.4162     | 0.0228         | 8.3869          | 0.0221              | 49    |
| 8.4005     | 0.0229         | 8.3768          | 0.0218              | 50    |
| 8.3583     | 0.0235         | 8.3470          | 0.0224              | 51    |
| 8.3428     | 0.0235         | 8.3540          | 0.0221              | 52    |
| 8.3491     | 0.0231         | 8.3201          | 0.0225              | 53    |
| 8.3551     | 0.0231         | 8.3382          | 0.0221              | 54    |
| 8.3186     | 0.0231         | 8.3136          | 0.0219              | 55    |
| 8.3139     | 0.0226         | 8.2844          | 0.0222              | 56    |
| 8.3170     | 0.0229         | 8.2740          | 0.0221              | 57    |
| 8.2886     | 0.0231         | 8.2485          | 0.0223              | 58    |
| 8.2648     | 0.0233         | 8.2336          | 0.0223              | 59    |
| 8.2714     | 0.0225         | 8.2321          | 0.0221              | 60    |
| 8.2446     | 0.0233         | 8.2135          | 0.0223              | 61    |
| 8.2303     | 0.0230         | 8.1980          | 0.0223              | 62    |
| 8.2022     | 0.0237         | 8.1996          | 0.0222              | 63    |
| 8.2222     | 0.0227         | 8.1822          | 0.0222              | 64    |
| 8.1690     | 0.0236         | 8.2005          | 0.0220              | 65    |
| 8.1741     | 0.0233         | 8.1446          | 0.0226              | 66    |
| 8.1990     | 0.0224         | 8.1586          | 0.0219              | 67    |
| 8.1395     | 0.0236         | 8.1243          | 0.0225              | 68    |
| 8.1675     | 0.0229         | 8.1275          | 0.0222              | 69    |
| 8.1432     | 0.0229         | 8.1374          | 0.0217              | 70    |
| 8.1197     | 0.0234         | 8.1078          | 0.0221              | 71    |
| 8.1046     | 0.0232         | 8.0991          | 0.0221              | 72    |
| 8.1013     | 0.0231         | 8.0794          | 0.0222              | 73    |
| 8.0887     | 0.0228         | 8.0720          | 0.0221              | 74    |
| 8.0661     | 0.0233         | 8.0573          | 0.0222              | 75    |
| 8.0548     | 0.0231         | 8.0313          | 0.0226              | 76    |
| 8.0307     | 0.0235         | 8.0278          | 0.0222              | 77    |
| 8.0626     | 0.0226         | 8.0084          | 0.0224              | 78    |
| 8.0276     | 0.0229         | 8.0099          | 0.0221              | 79    |
| 8.0213     | 0.0231         | 7.9930          | 0.0222              | 80    |
| 7.9798     | 0.0237         | 7.9742          | 0.0224              | 81    |
| 8.0135     | 0.0226         | 7.9857          | 0.0218              | 82    |
| 7.9500     | 0.0235         | 7.9505          | 0.0223              | 83    |
| 7.9519     | 0.0234         | 7.9711          | 0.0217              | 84    |
| 7.9616     | 0.0228         | 7.9288          | 0.0223              | 85    |
| 7.9803     | 0.0225         | 7.8997          | 0.0226              | 86    |
| 7.9369     | 0.0227         | 7.9015          | 0.0225              | 87    |
| 7.9309     | 0.0229         | 7.9010          | 0.0224              | 88    |
| 7.9367     | 0.0226         | 7.8988          | 0.0220              | 89    |
| 7.8840     | 0.0230         | 7.8774          | 0.0216              | 90    |
| 7.8785     | 0.0233         | 7.8527          | 0.0225              | 91    |
| 7.8998     | 0.0226         | 7.8509          | 0.0219              | 92    |
| 7.8451     | 0.0232         | 7.8488          | 0.0221              | 93    |
| 7.8596     | 0.0231         | 7.8310          | 0.0222              | 94    |
| 7.8434     | 0.0231         | 7.8168          | 0.0229              | 95    |
| 7.7929     | 0.0238         | 7.7815          | 0.0233              | 96    |
| 7.8174     | 0.0236         | 7.7857          | 0.0232              | 97    |
| 7.7770     | 0.0241         | 7.7589          | 0.0230              | 98    |


### Framework versions

- Transformers 4.30.1
- TensorFlow 2.12.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Zemulax/masked-lm-tpu", "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 

Zemulax/masked-lm-tpu
=====================


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: 7.7770
* Train Accuracy: 0.0241
* Validation Loss: 7.7589
* Validation Accuracy: 0.0230
* Epoch: 98


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': 223250, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '**passive\_serialization**': True}, 'warmup\_steps': 11750, '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: float32


### Training results



### Framework versions


* Transformers 4.30.1
* TensorFlow 2.12.0
* Tokenizers 0.13.3



### 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': 223250, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '**passive\_serialization**': True}, 'warmup\_steps': 11750, '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: float32 ### Training results ### Framework versions


* Transformers 4.30.1
* TensorFlow 2.12.0
* Tokenizers 0.13.3
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': 223250, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '**passive\_serialization**': True}, 'warmup\_steps': 11750, '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: float32 ### Training results ### Framework versions


* Transformers 4.30.1
* TensorFlow 2.12.0
* Tokenizers 0.13.3