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nguyennghia0902/
electra-small-discriminator_2e-05_16

Name: electra-small-discriminator_2e-05_16
Availability: InStock

transformers

electra

question-answering

generated_from_keras_callback

google/electra-small-discriminator

finetune

apache-2.0

endpoints_compatible

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nguyennghia0902/electra-small-discriminator_2e-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: 1.8204
Train End Logits Accuracy: 0.5584
Train Start Logits Accuracy: 0.5303
Validation Loss: 1.5543
Validation End Logits Accuracy: 0.6127
Validation Start Logits Accuracy: 0.5972
Epoch: 9

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': 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
3.3122	0.2821	0.2485	2.6648	0.3797	0.3553	0
2.6687	0.3873	0.3537	2.3167	0.4510	0.4297	1
2.4256	0.4356	0.4051	2.1007	0.4965	0.4787	2
2.2600	0.4678	0.4373	1.9512	0.5271	0.5112	3
2.1384	0.4927	0.4626	1.8342	0.5512	0.5353	4
2.0404	0.5101	0.4814	1.7279	0.5752	0.5598	5
1.9575	0.5275	0.4978	1.6628	0.5890	0.5718	6
1.8962	0.5405	0.5127	1.5958	0.6047	0.5877	7
1.8478	0.5503	0.5211	1.5622	0.6112	0.5964	8
1.8204	0.5584	0.5303	1.5543	0.6127	0.5972	9

Framework versions

Transformers 4.39.3
TensorFlow 2.15.0
Datasets 2.18.0
Tokenizers 0.15.2