metadata

license: apache-2.0
base_model: google/electra-small-discriminator
tags:
  - generated_from_keras_callback
model-index:
  - name: textmining_proj02_electra
    results: []

textmining_proj02_electra

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: 2.0145
Train End Logits Accuracy: 0.5186
Train Start Logits Accuracy: 0.4898
Validation Loss: 1.7837
Validation End Logits Accuracy: 0.5643
Validation Start Logits Accuracy: 0.5514
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': 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
3.5498	0.2455	0.2154	2.7762	0.3625	0.3353	0
2.7913	0.3657	0.3291	2.5014	0.4105	0.3950	1
2.5710	0.4032	0.3725	2.2847	0.4635	0.4409	2
2.4125	0.4377	0.4076	2.1311	0.4904	0.4761	3
2.2918	0.4641	0.4348	2.0155	0.5161	0.5063	4
2.1978	0.4833	0.4512	1.9319	0.5343	0.5214	5
2.1306	0.4960	0.4664	1.8634	0.5505	0.5352	6
2.0801	0.5018	0.4755	1.8229	0.5568	0.5457	7
2.0404	0.5139	0.4837	1.7963	0.5611	0.5488	8
2.0145	0.5186	0.4898	1.7837	0.5643	0.5514	9

Framework versions

Transformers 4.39.3
TensorFlow 2.15.0
Datasets 2.18.0
Tokenizers 0.15.2