metadata

license: apache-2.0
base_model: google/electra-small-discriminator
tags:
  - generated_from_keras_callback
model-index:
  - name: nguyennghia0902/electra-small-discriminator_0.0005_32_15e
    results: []

nguyennghia0902/electra-small-discriminator_0.0005_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: 0.5725
Train End Logits Accuracy: 0.8401
Train Start Logits Accuracy: 0.8151
Validation Loss: 0.2404
Validation End Logits Accuracy: 0.9316
Validation Start Logits Accuracy: 0.9222
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': 0.0005, '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
3.3489	0.2750	0.2432	2.6409	0.3858	0.3668	0
2.7567	0.3772	0.3444	2.3037	0.4607	0.4455	1
2.5118	0.4254	0.3927	2.0684	0.5046	0.4834	2
2.3234	0.4624	0.4283	1.8489	0.5461	0.5257	3
2.1433	0.4977	0.4608	1.6848	0.5907	0.5742	4
1.9832	0.5289	0.4980	1.4704	0.6378	0.6177	5
1.8204	0.5619	0.5290	1.2837	0.6769	0.6665	6
1.6387	0.5991	0.5696	1.0838	0.7217	0.7115	7
1.4657	0.6379	0.6048	0.9057	0.7589	0.7562	8
1.2902	0.6729	0.6458	0.7410	0.8034	0.7975	9
1.1103	0.7149	0.6867	0.5707	0.8407	0.8374	10
0.9500	0.7493	0.7214	0.4523	0.8761	0.8660	11
0.7931	0.7855	0.7606	0.3483	0.9018	0.8924	12
0.6702	0.8166	0.7889	0.2710	0.9236	0.9152	13
0.5725	0.8401	0.8151	0.2404	0.9316	0.9222	14

Framework versions

Transformers 4.39.3
TensorFlow 2.15.0
Datasets 2.18.0
Tokenizers 0.15.2