distilbert-base-uncased-customized – AI Model by mmiteva | AlphaNeural AI

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mmiteva/
distilbert-base-uncased-customized

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mmiteva/distilbert-base-uncased-customized

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

Train Loss: 0.3257
Train End Logits Accuracy: 0.9017
Train Start Logits Accuracy: 0.8747
Validation Loss: 1.5040
Validation End Logits Accuracy: 0.6988
Validation Start Logits Accuracy: 0.6655
Epoch: 4

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', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 36885, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, '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
1.0773	0.7064	0.6669	1.1080	0.6973	0.6669	0
0.7660	0.7812	0.7433	1.1076	0.7093	0.6734	1
0.5586	0.8351	0.7988	1.2336	0.7039	0.6692	2
0.4165	0.8741	0.8434	1.3799	0.7034	0.6707	3
0.3257	0.9017	0.8747	1.5040	0.6988	0.6655	4

Framework versions

Transformers 4.25.0.dev0
TensorFlow 2.7.0
Datasets 2.6.1
Tokenizers 0.13.2