SHS/tokenization-practice

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SHS/tokenization-practice

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: 0.1203
• —Validation Loss: 0.2537
• —Train Precision: 0.5971
• —Train Recall: 0.4450
• —Train F1: 0.5099
• —Train Accuracy: 0.9475
• —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', 'learningrate': {'classname': 'PolynomialDecay', 'config': {'initiallearningrate': 2e-05, 'decaysteps': 636, 'endlearningrate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta1': 0.9, 'beta2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weightdecay_rate': 0.01}
• —training_precision: float32

Training results

Framework versions

• —Transformers 4.26.0
• —TensorFlow 2.11.0
• —Datasets 2.9.0
• —Tokenizers 0.13.2