distilbert-base-uncased-finetuned-squad – AI Model by Ddaow | AlphaNeural AI

You can deploy this model and start earning money today!

Ddaow/
distilbert-base-uncased-finetuned-squad

like

0

question-answering

generated_from_keras_callback

endpoints_compatible

Views

No views yet

Ddaow/distilbert-base-uncased-finetuned-squad

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: 0.9692
Train End Logits Accuracy: 0.7314
Train Start Logits Accuracy: 0.6923
Validation Loss: 1.1071
Validation End Logits Accuracy: 0.7008
Validation Start Logits Accuracy: 0.6691
Epoch: 1

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': 11064, '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
1.5125	0.6064	0.5677	1.1969	0.6799	0.6471	0
0.9692	0.7314	0.6923	1.1071	0.7008	0.6691	1

Framework versions

Transformers 4.23.1
TensorFlow 2.9.2
Datasets 2.5.2
Tokenizers 0.13.1