bert-base-uncased-finetuned-squad-v2-epochs-2 – AI Model by alexandre-huynh | AlphaNeural AI

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alexandre-huynh/
bert-base-uncased-finetuned-squad-v2-epochs-2

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google-bert/bert-base-uncased

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alexandre-huynh/bert-base-uncased-finetuned-squad-v2-epochs-2

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

Train Loss: 1.2946
Train End Logits Accuracy: 0.6145
Train Start Logits Accuracy: 0.5950
Validation Loss: 1.0383
Validation End Logits Accuracy: 0.6707
Validation Start Logits Accuracy: 0.6631
Epoch: 0

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': 16468, '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

Train Loss	Train End Logits Accuracy	Train Start Logits Accuracy	Validation Loss	Validation End Logits Accuracy	Validation Start Logits Accuracy	Epoch
1.2946	0.6145	0.5950	1.0383	0.6707	0.6631	0

Framework versions

Transformers 4.38.2
TensorFlow 2.15.0
Datasets 2.18.0
Tokenizers 0.15.2