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# Regression_xlnet_aug_CustomLoss

This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2430
- Train Mae: 0.5316
- Train Mse: 0.4353
- Train R2-score: 0.4207
- Validation Loss: 0.2455
- Validation Mae: 0.5751
- Validation Mse: 0.4288
- Validation R2-score: 0.6784
- Epoch: 14

## 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 1e-04, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Train Mae | Train Mse | Train R2-score | Validation Loss | Validation Mae | Validation Mse | Validation R2-score | Epoch |
|:----------:|:---------:|:---------:|:--------------:|:---------------:|:--------------:|:--------------:|:-------------------:|:-----:|
| 0.2950     | 0.5789    | 0.4896    | 0.6909         | 0.2512          | 0.5341         | 0.4801         | 0.7603              | 0     |
| 0.2659     | 0.5516    | 0.4538    | 0.7145         | 0.2828          | 0.5680         | 0.5282         | 0.7477              | 1     |
| 0.2656     | 0.5492    | 0.4587    | 0.6858         | 0.2337          | 0.5345         | 0.4412         | 0.7431              | 2     |
| 0.2563     | 0.5484    | 0.4490    | 0.7247         | 0.2413          | 0.5202         | 0.4619         | 0.7581              | 3     |
| 0.2589     | 0.5511    | 0.4542    | 0.6757         | 0.2411          | 0.5199         | 0.4615         | 0.7580              | 4     |
| 0.2537     | 0.5407    | 0.4437    | 0.7605         | 0.2359          | 0.5244         | 0.4495         | 0.7517              | 5     |
| 0.2494     | 0.5385    | 0.4399    | 0.7668         | 0.2510          | 0.5821         | 0.4301         | 0.6621              | 6     |
| 0.2495     | 0.5403    | 0.4424    | 0.7765         | 0.2360          | 0.5242         | 0.4496         | 0.7519              | 7     |
| 0.2501     | 0.5394    | 0.4383    | 0.5209         | 0.2349          | 0.5279         | 0.4464         | 0.7491              | 8     |
| 0.2446     | 0.5343    | 0.4346    | 0.7534         | 0.2366          | 0.5585         | 0.4298         | 0.7105              | 9     |
| 0.2439     | 0.5316    | 0.4323    | 0.7561         | 0.2543          | 0.5376         | 0.4853         | 0.7599              | 10    |
| 0.2415     | 0.5348    | 0.4330    | 0.7928         | 0.2341          | 0.5316         | 0.4434         | 0.7459              | 11    |
| 0.2408     | 0.5323    | 0.4289    | 0.7827         | 0.2346          | 0.5291         | 0.4454         | 0.7481              | 12    |
| 0.2499     | 0.5392    | 0.4410    | 0.6008         | 0.2364          | 0.5230         | 0.4508         | 0.7527              | 13    |
| 0.2430     | 0.5316    | 0.4353    | 0.4207         | 0.2455          | 0.5751         | 0.4288         | 0.6784              | 14    |


### Framework versions

- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Regression_xlnet_aug_CustomLoss", "results": []}]}
transformers pytorch tf tensorboard xlnet text-classification generated_from_keras_callback license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #tensorboard #xlnet #text-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us 

Regression\_xlnet\_aug\_CustomLoss
==================================


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


* Train Loss: 0.2430
* Train Mae: 0.5316
* Train Mse: 0.4353
* Train R2-score: 0.4207
* Validation Loss: 0.2455
* Validation Mae: 0.5751
* Validation Mse: 0.4288
* Validation R2-score: 0.6784
* Epoch: 14


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': 'Adam', 'weight\_decay': None, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': True, 'is\_legacy\_optimizer': False, 'learning\_rate': 1e-04, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32


### Training results



### Framework versions


* Transformers 4.28.1
* TensorFlow 2.12.0
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'weight\_decay': None, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': True, 'is\_legacy\_optimizer': False, 'learning\_rate': 1e-04, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.28.1
* TensorFlow 2.12.0
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tf #tensorboard #xlnet #text-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'weight\_decay': None, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': True, 'is\_legacy\_optimizer': False, 'learning\_rate': 1e-04, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.28.1
* TensorFlow 2.12.0
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tf #tensorboard #xlnet #text-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'weight\_decay': None, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': True, 'is\_legacy\_optimizer': False, 'learning\_rate': 1e-04, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32### Training results### Framework versions


* Transformers 4.28.1
* TensorFlow 2.12.0
* Datasets 2.12.0
* Tokenizers 0.13.3