qa_model_upgraded – AI Model by mmiteva | AlphaNeural AI

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mmiteva/
qa_model_upgraded

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mmiteva/qa_model_upgraded

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: 0.3087
Train End Logits Accuracy: 0.8935
Train Start Logits Accuracy: 0.8884
Validation Loss: 1.1857
Validation End Logits Accuracy: 0.7448
Validation Start Logits Accuracy: 0.7335
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': 5e-05, 'decay_steps': 68700, '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
1.3498	0.6214	0.6049	1.0429	0.6891	0.6754	0
0.8373	0.7346	0.7236	0.9979	0.7117	0.6974	1
0.5988	0.8012	0.7931	0.9518	0.7267	0.7225	2
0.4309	0.8541	0.8465	1.0632	0.7417	0.7320	3
0.3087	0.8935	0.8884	1.1857	0.7448	0.7335	4

Framework versions

Transformers 4.25.1
TensorFlow 2.10.1
Datasets 2.7.1
Tokenizers 0.12.1