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nguyennghia0902/
electra-small-discriminator_5e-05_16

Name: electra-small-discriminator_5e-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_5e-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: 1.2106
Train End Logits Accuracy: 0.6868
Train Start Logits Accuracy: 0.6616
Validation Loss: 0.8171
Validation End Logits Accuracy: 0.7790
Validation Start Logits Accuracy: 0.7721
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': 5e-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
3.0718	0.3197	0.2882	2.3841	0.4358	0.4158	0
2.4050	0.4412	0.4100	1.9607	0.5286	0.5126	1
2.1054	0.5011	0.4714	1.6636	0.5884	0.5755	2
1.9002	0.5421	0.5122	1.4655	0.6276	0.6173	3
1.7347	0.5741	0.5496	1.2668	0.6755	0.6654	4
1.5852	0.6070	0.5807	1.1348	0.7053	0.6950	5
1.4627	0.6330	0.6039	1.0051	0.7336	0.7269	6
1.3557	0.6545	0.6285	0.9167	0.7577	0.7491	7
1.2715	0.6741	0.6457	0.8508	0.7708	0.7643	8
1.2106	0.6868	0.6616	0.8171	0.7790	0.7721	9

Framework versions

Transformers 4.39.3
TensorFlow 2.15.0
Datasets 2.18.0
Tokenizers 0.15.2