---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: Regression_xlnet_aug_CustomLoss
  results: []
---

<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->

# 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: 0.3652
- Train Mae: 0.5686
- Train Mse: 0.4521
- Train R2-score: 0.7022
- Validation Loss: 0.3538
- Validation Mae: 0.5478
- Validation Mse: 0.4335
- Validation R2-score: 0.7272
- 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 |
|:----------:|:---------:|:---------:|:--------------:|:---------------:|:--------------:|:--------------:|:-------------------:|:-----:|
| 0.3976     | 0.5843    | 0.4994    | 0.6170         | 0.3698          | 0.6232         | 0.4576         | 0.5391              | 0     |
| 0.3938     | 0.5830    | 0.4901    | 0.5860         | 0.4330          | 0.6806         | 0.5537         | 0.2841              | 1     |
| 0.3822     | 0.5739    | 0.4691    | -2.6800        | 0.3660          | 0.5198         | 0.4614         | 0.7579              | 2     |
| 0.3716     | 0.5723    | 0.4607    | -0.3420        | 0.3541          | 0.5461         | 0.4343         | 0.7295              | 3     |
| 0.3852     | 0.5794    | 0.4793    | 0.6654         | 0.3524          | 0.5654         | 0.4288         | 0.6982              | 4     |
| 0.3826     | 0.5782    | 0.4787    | 0.5114         | 0.3584          | 0.5991         | 0.4374         | 0.6172              | 5     |
| 0.3817     | 0.5746    | 0.4754    | 0.6431         | 0.3569          | 0.5945         | 0.4348         | 0.6304              | 6     |
| 0.3705     | 0.5689    | 0.4541    | 0.6679         | 0.3621          | 0.5210         | 0.4528         | 0.7540              | 7     |
| 0.3719     | 0.5654    | 0.4567    | 0.6807         | 0.3544          | 0.5446         | 0.4350         | 0.7315              | 8     |
| 0.3752     | 0.5698    | 0.4638    | 0.6324         | 0.3571          | 0.5343         | 0.4413         | 0.7433              | 9     |
| 0.3773     | 0.5714    | 0.4655    | 0.7523         | 0.3534          | 0.5787         | 0.4294         | 0.6701              | 10    |
| 0.3651     | 0.5615    | 0.4490    | 0.6223         | 0.3766          | 0.5372         | 0.4846         | 0.7600              | 11    |
| 0.3706     | 0.5649    | 0.4529    | 0.5998         | 0.3525          | 0.5683         | 0.4286         | 0.6924              | 12    |
| 0.3779     | 0.5687    | 0.4619    | 0.6900         | 0.3532          | 0.5771         | 0.4291         | 0.6737              | 13    |
| 0.3652     | 0.5686    | 0.4521    | 0.7022         | 0.3538          | 0.5478         | 0.4335         | 0.7272              | 14    |


### Framework versions

- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.11.0
- Tokenizers 0.13.3