WaRKiD/bert-large-uncased-whole-word-masking-finetuned-intel-oneapi-llm-dataset

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bert-large-uncased-whole-word-masking-finetuned-intel-oneapi-llm-dataset

This model is a fine-tuned version of bert-large-uncased-whole-word-masking-finetuned-squad on an unknown dataset. It achieves the following results on the evaluation set:

• —Train Loss: 2.3381
• —Train End Logits Accuracy: 0.4801
• —Train Start Logits Accuracy: 0.4324
• —Validation Loss: 2.1970
• —Validation End Logits Accuracy: 0.5132
• —Validation Start Logits Accuracy: 0.4554
• —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', 'weightdecay': None, 'clipnorm': None, 'globalclipnorm': None, 'clipvalue': None, 'useema': False, 'emamomentum': 0.99, 'emaoverwritefrequency': None, 'jitcompile': False, 'islegacyoptimizer': False, 'learningrate': {'classname': 'PolynomialDecay', 'config': {'initiallearningrate': 3e-05, 'decaysteps': 8844, 'endlearningrate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta1': 0.9, 'beta2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
• —training_precision: float32

Training results

Framework versions

• —Transformers 4.34.0
• —TensorFlow 2.12.0
• —Datasets 2.14.5
• —Tokenizers 0.14.0