---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: Regression_xlnet_NOaug_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_NOaug_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: 0.1862
- Train Mae: 0.5631
- Train Mse: 0.4095
- Train R2-score: 0.8268
- Validation Loss: 0.1355
- Validation Mae: 0.5683
- Validation Mse: 0.3643
- Validation R2-score: 0.8811
- 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 |
|:----------:|:---------:|:---------:|:--------------:|:---------------:|:--------------:|:--------------:|:-------------------:|:-----:|
| 0.1966     | 0.5177    | 0.3647    | 0.3590         | 0.1412          | 0.6460         | 0.4895         | 0.8850              | 0     |
| 0.1804     | 0.5606    | 0.4181    | 0.8105         | 0.1540          | 0.6614         | 0.5259         | 0.8820              | 1     |
| 0.2037     | 0.5676    | 0.4319    | 0.6885         | 0.1399          | 0.6439         | 0.4849         | 0.8849              | 2     |
| 0.1833     | 0.5499    | 0.3954    | 0.8256         | 0.1804          | 0.6845         | 0.5879         | 0.8760              | 3     |
| 0.1627     | 0.5412    | 0.3866    | 0.8022         | 0.1661          | 0.6729         | 0.5558         | 0.8793              | 4     |
| 0.1822     | 0.5677    | 0.4178    | 0.7449         | 0.1327          | 0.6311         | 0.4580         | 0.8861              | 5     |
| 0.2117     | 0.5798    | 0.4520    | 0.5186         | 0.1282          | 0.6187         | 0.4345         | 0.8866              | 6     |
| 0.1843     | 0.5544    | 0.3998    | 0.5283         | 0.1272          | 0.6142         | 0.4265         | 0.8866              | 7     |
| 0.2074     | 0.5906    | 0.4639    | 0.6729         | 0.1269          | 0.6127         | 0.4239         | 0.8865              | 8     |
| 0.1756     | 0.5666    | 0.4032    | 0.8054         | 0.1272          | 0.5909         | 0.3908         | 0.8850              | 9     |
| 0.1706     | 0.5452    | 0.3948    | 0.7999         | 0.1282          | 0.5862         | 0.3845         | 0.8844              | 10    |
| 0.1727     | 0.5499    | 0.3928    | 0.8471         | 0.1453          | 0.6513         | 0.5021         | 0.8840              | 11    |
| 0.1688     | 0.5467    | 0.3884    | 0.3339         | 0.1777          | 0.6823         | 0.5817         | 0.8766              | 12    |
| 0.1625     | 0.5476    | 0.3918    | 0.5804         | 0.1483          | 0.6541         | 0.5098         | 0.8833              | 13    |
| 0.1862     | 0.5631    | 0.4095    | 0.8268         | 0.1355          | 0.5683         | 0.3643         | 0.8811              | 14    |


### Framework versions

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