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---
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: 0.2430
- 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
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