distilbert-uncased-finetuned-for-small_dataset – AI Model by HASAN55 | AlphaNeural AI

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HASAN55/
distilbert-uncased-finetuned-for-small_dataset

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HASAN55/distilbert-uncased-finetuned-for-small_dataset

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: 0.3166
Train End Logits Accuracy: 0.9034
Train Start Logits Accuracy: 0.8900
Validation Loss: 0.0
Validation End Logits Accuracy: 0.0
Validation Start Logits Accuracy: 0.0
Epoch: 2

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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 5673, '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, 'weight_decay_rate': 0.01}
training_precision: mixed_float16

Training results

Train Loss	Train End Logits Accuracy	Train Start Logits Accuracy	Validation Loss	Validation End Logits Accuracy	Validation Start Logits Accuracy	Epoch
0.3178	0.9047	0.8883	0.0	0.0	0.0	0
0.3164	0.9066	0.8879	0.0	0.0	0.0	1
0.3166	0.9034	0.8900	0.0	0.0	0.0	2

Framework versions

Transformers 4.28.1
TensorFlow 2.12.0
Datasets 2.11.0
Tokenizers 0.13.3