metadata
license: apache-2.0
base_model: google/electra-small-discriminator
tags:
- generated_from_keras_callback
model-index:
- name: nguyennghia0902/electra-small-discriminator_1e-05_16
results: []
nguyennghia0902/electra-small-discriminator_1e-05_16
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: 2.3562
- Train End Logits Accuracy: 0.4505
- Train Start Logits Accuracy: 0.4144
- Validation Loss: 2.1406
- Validation End Logits Accuracy: 0.4888
- Validation Start Logits Accuracy: 0.4689
- Epoch: 7
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': 1e-05, 'decay_steps': 31270, '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 |
---|---|---|---|---|---|---|
3.6922 | 0.2260 | 0.1855 | 2.9570 | 0.3296 | 0.2955 | 0 |
2.9538 | 0.3373 | 0.2952 | 2.6908 | 0.3760 | 0.3455 | 1 |
2.7599 | 0.3690 | 0.3326 | 2.5323 | 0.4072 | 0.3820 | 2 |
2.6351 | 0.3920 | 0.3568 | 2.4256 | 0.4286 | 0.4008 | 3 |
2.5472 | 0.4089 | 0.3742 | 2.3283 | 0.4498 | 0.4264 | 4 |
2.4725 | 0.4221 | 0.3912 | 2.2602 | 0.4605 | 0.4399 | 5 |
2.4119 | 0.4369 | 0.4017 | 2.1953 | 0.4765 | 0.4559 | 6 |
2.3562 | 0.4505 | 0.4144 | 2.1406 | 0.4888 | 0.4689 | 7 |
Framework versions
- Transformers 4.39.3
- TensorFlow 2.15.0
- Datasets 2.18.0
- Tokenizers 0.15.2