Svetlana0303's picture
Upload TFXLNetForSequenceClassification
4704864
|
raw
history blame
3.83 kB
metadata
license: mit
tags:
  - generated_from_keras_callback
model-index:
  - name: Regression_xlnet_aug_CustomLoss
    results: []

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: 0.3652
  • 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