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

You can deploy this model and start earning money today!

briziel/
distilbert-base-uncased-finetuned-squad

like

0

question-answering

generated_from_keras_callback

endpoints_compatible

Views

No views yet

briziel/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: 0.9786
Train End Logits Accuracy: 0.7287
Train Start Logits Accuracy: 0.6898
Validation Loss: 1.1064
Validation End Logits Accuracy: 0.6984
Validation Start Logits Accuracy: 0.6615
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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 11064, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_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
1.5081	0.6050	0.5681	1.1607	0.6881	0.6499	0
0.9786	0.7287	0.6898	1.1064	0.6984	0.6615	1

Framework versions

Transformers 4.30.2
TensorFlow 2.13.0
Datasets 2.13.1
Tokenizers 0.13.3