Nighthawks/bert-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: 0.7768
• Train End Logits Accuracy: 0.7784
• Train Start Logits Accuracy: 0.7395
• Validation Loss: 1.1178
• Validation End Logits Accuracy: 0.7121
• Validation Start Logits Accuracy: 0.6721
• 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': 2e-05, 'decay_steps': 13278, '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
1.5823	0.5924	0.5516	1.1556	0.6903	0.6496	0
0.9907	0.7266	0.6861	1.0986	0.7079	0.6656	1
0.7768	0.7784	0.7395	1.1178	0.7121	0.6721	2

Framework versions

• Transformers 4.29.2
• TensorFlow 2.12.0
• Datasets 2.1.0
• Tokenizers 0.13.3