metadata

license: apache-2.0
base_model: google/electra-small-discriminator
tags:
  - generated_from_keras_callback
model-index:
  - name: nguyennghia0902/electra-small-discriminator_5e-05_32
    results: []

nguyennghia0902/electra-small-discriminator_5e-05_32

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: 1.4907
Train End Logits Accuracy: 0.6288
Train Start Logits Accuracy: 0.5993
Validation Loss: 1.1223
Validation End Logits Accuracy: 0.7084
Validation Start Logits Accuracy: 0.6998
Epoch: 8

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': 5e-05, 'decay_steps': 15630, '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
3.1881	0.3010	0.2689	2.5228	0.4049	0.3836	0
2.5007	0.4217	0.3882	2.0728	0.5036	0.4895	1
2.2043	0.4835	0.4496	1.8175	0.5578	0.5406	2
2.0197	0.5187	0.4879	1.6380	0.5947	0.5795	3
1.8749	0.5476	0.5157	1.4806	0.6256	0.6152	4
1.7536	0.5717	0.5421	1.3605	0.6524	0.6404	5
1.6484	0.5931	0.5631	1.2566	0.6796	0.6667	6
1.5646	0.6133	0.5839	1.1845	0.6976	0.6855	7
1.4907	0.6288	0.5993	1.1223	0.7084	0.6998	8

Framework versions

Transformers 4.39.3
TensorFlow 2.15.0
Datasets 2.18.0
Tokenizers 0.15.2