SlightlyFineTunedBertOnSquad

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horger89/SlightlyFineTunedBertOnSquad

This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• Train Loss: 6.5399
• Train End Logits Loss: 3.1546
• Train Start Logits Loss: 3.3853
• Train End Logits Accuracy: 0.3726
• Train Start Logits Accuracy: 0.3301
• Validation Loss: 6.1757
• Validation End Logits Loss: 3.0226
• Validation Start Logits Loss: 3.1531
• Validation End Logits Accuracy: 0.4046
• Validation Start Logits Accuracy: 0.3661
• 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 5250, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
• training_precision: float32

Training results

Framework versions

• Transformers 4.38.2
• TensorFlow 2.15.0
• Datasets 2.18.0
• Tokenizers 0.15.2