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roberta-base-finetuned-iso29148-promise-km-labels-nf-subclasses-cls

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roberta
generated_from_keras_callback
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metadata
license: mit
base_model: FacebookAI/roberta-base
tags:
  - generated_from_keras_callback
model-index:
  - name: >-
      kasrahabib/roberta-base-finetuned-iso29148-promise-km-labels-nf-subclasses-cls
    results: []

kasrahabib/roberta-base-finetuned-iso29148-promise-km-labels-nf-subclasses-cls

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

  • Train Loss: 0.0124
  • Validation Loss: 0.1623
  • Epoch: 29

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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1770, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
  • training_precision: float32

Training results

Train Loss Validation Loss Epoch
2.1848 1.7407 0
1.2611 0.8464 1
0.5377 0.4928 2
0.2843 0.2790 3
0.1531 0.3657 4
0.1186 0.1997 5
0.0646 0.1742 6
0.0498 0.1731 7
0.0417 0.1709 8
0.0351 0.1689 9
0.0309 0.1659 10
0.0270 0.1674 11
0.0255 0.1687 12
0.0229 0.1665 13
0.0210 0.1673 14
0.0193 0.1677 15
0.0185 0.1664 16
0.0168 0.1658 17
0.0162 0.1649 18
0.0156 0.1670 19
0.0150 0.1678 20
0.0144 0.1656 21
0.0141 0.1653 22
0.0138 0.1662 23
0.0132 0.1668 24
0.0127 0.1629 25
0.0125 0.1614 26
0.0123 0.1619 27
0.0122 0.1624 28
0.0124 0.1623 29

Framework versions

  • Transformers 4.42.3
  • TensorFlow 2.15.0
  • Datasets 2.19.1
  • Tokenizers 0.19.1