nguyennghia0902's picture
Training in progress epoch 14
b9ce205
metadata
license: apache-2.0
base_model: google/electra-small-discriminator
tags:
  - generated_from_keras_callback
model-index:
  - name: nguyennghia0902/electra-small-discriminator_0.0001_32_15e
    results: []

nguyennghia0902/electra-small-discriminator_0.0001_32_15e

This model is a fine-tuned version of google/electra-small-discriminator on an unknown dataset. It achieves the following results on the evaluation set:

  • Train Loss: 0.5958
  • Train End Logits Accuracy: 0.8298
  • Train Start Logits Accuracy: 0.8077
  • Validation Loss: 0.2565
  • Validation End Logits Accuracy: 0.9243
  • Validation Start Logits Accuracy: 0.9233
  • 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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0001, 'decay_steps': 23445, '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 Train End Logits Accuracy Train Start Logits Accuracy Validation Loss Validation End Logits Accuracy Validation Start Logits Accuracy Epoch
3.0253 0.3302 0.2968 2.2414 0.4704 0.4533 0
2.3162 0.4597 0.4260 1.8267 0.5511 0.5364 1
2.0285 0.5160 0.4813 1.5472 0.6109 0.5994 2
1.8125 0.5587 0.5287 1.2995 0.6688 0.6512 3
1.6192 0.5963 0.5677 1.0973 0.7105 0.7030 4
1.4482 0.6341 0.6066 0.8998 0.7637 0.7547 5
1.2931 0.6694 0.6423 0.7622 0.7920 0.7916 6
1.1518 0.6980 0.6741 0.6412 0.8260 0.8197 7
1.0351 0.7240 0.7025 0.5316 0.8518 0.8531 8
0.9269 0.7488 0.7270 0.4671 0.8701 0.8700 9
0.8354 0.7714 0.7489 0.3836 0.8910 0.8896 10
0.7520 0.7904 0.7699 0.3342 0.9048 0.9021 11
0.6869 0.8056 0.7848 0.2983 0.9134 0.9118 12
0.6320 0.8209 0.7994 0.2667 0.9223 0.9205 13
0.5958 0.8298 0.8077 0.2565 0.9243 0.9233 14

Framework versions

  • Transformers 4.39.3
  • TensorFlow 2.15.0
  • Datasets 2.18.0
  • Tokenizers 0.15.2